repo
stringclasses 32
values | instance_id
stringlengths 13
37
| base_commit
stringlengths 40
40
| patch
stringlengths 1
1.89M
| test_patch
stringclasses 1
value | problem_statement
stringlengths 304
69k
| hints_text
stringlengths 0
246k
| created_at
stringlengths 20
20
| version
stringclasses 1
value | FAIL_TO_PASS
stringclasses 1
value | PASS_TO_PASS
stringclasses 1
value | environment_setup_commit
stringclasses 1
value | traceback
stringlengths 64
23.4k
| __index_level_0__
int64 29
19k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DataDog/integrations-core | DataDog__integrations-core-446 | 0b9be7366a08b2fa1b83c036d823d8848762770f | diff --git a/postgres/check.py b/postgres/check.py
--- a/postgres/check.py
+++ b/postgres/check.py
@@ -651,14 +651,17 @@ def _get_custom_metrics(self, custom_metrics, key):
self.log.debug("Metric: {0}".format(m))
- for ref, (_, mtype) in m['metrics'].iteritems():
- cap_mtype = mtype.upper()
- if cap_mtype not in ('RATE', 'GAUGE', 'MONOTONIC'):
- raise CheckException("Collector method {0} is not known."
- " Known methods are RATE, GAUGE, MONOTONIC".format(cap_mtype))
-
- m['metrics'][ref][1] = getattr(PostgreSql, cap_mtype)
- self.log.debug("Method: %s" % (str(mtype)))
+ try:
+ for ref, (_, mtype) in m['metrics'].iteritems():
+ cap_mtype = mtype.upper()
+ if cap_mtype not in ('RATE', 'GAUGE', 'MONOTONIC'):
+ raise CheckException("Collector method {0} is not known."
+ " Known methods are RATE, GAUGE, MONOTONIC".format(cap_mtype))
+
+ m['metrics'][ref][1] = getattr(PostgreSql, cap_mtype)
+ self.log.debug("Method: %s" % (str(mtype)))
+ except Exception as e:
+ raise CheckException("Error processing custom metric '{}': {}".format(m, e))
self.custom_metrics[key] = custom_metrics
return custom_metrics
| [postgres] Improve config reading errors
I had this `postgres.yaml`:
```
init_config:
instances:
- host: pepepe
...
custom_metrics:
- query: SELECT %s FROM pg_locks WHERE granted = false;
metrics:
count(distinct pid): [postgresql.connections_locked]
descriptors: []
relation: false
```
with a few other hosts and custom metrics. When deploying this I got the following error:
```
2017-02-13 15:33:14 UTC | ERROR | dd.collector | checks.postgres(__init__.py:762) | Check 'postgres' instance #0 failed
Traceback (most recent call last):
File "/opt/datadog-agent/agent/checks/__init__.py", line 745, in run
self.check(copy.deepcopy(instance))
File "/opt/datadog-agent/agent/checks.d/postgres.py", line 606, in check
custom_metrics = self._get_custom_metrics(instance.get('custom_metrics', []), key)
File "/opt/datadog-agent/agent/checks.d/postgres.py", line 576, in _get_custom_metrics
for ref, (_, mtype) in m['metrics'].iteritems():
ValueError: need more than 1 value to unpack
```
This was caused by a missing metric type in the yaml above i.e. it should have been `[postgresql.connections_locked, GAUGE]`.
Because the error message is unclear and also doesn't point to the offending metric (remember I have other hosts and custom metrics), it took me a couple of hours to figure out the cause of this error.
Please consider improving the error messages around config reading.
| Thanks a lot for this report @mausch!
We can't validate the config in a consistent manner, which makes something like this tricky to make the error better. We will work on making this a lot better in the future.
However, what we can do in the very near future is make the documentation both online and in the config yaml itself a lot better. The documentation for the postgres check does not make it clear how to use the custom metrics very well, so better documentation will definitely help to assuage this issue!
Thanks again for your report, we really appreciate this and I will add this to our issue board.
> We can't validate the config in a consistent manner
Not sure what this means exactly, but generally speaking a good error message should give the user enough context so that they can readily fix it.
Better docs are great, but ultimately people will always make mistakes when defining complex config so you need good error messages.
In this particular case, it could be as easy as wrapping the iteration in `_get_custom_metrics` with a `try..except` and in the exception handler wrap the exception with another one that displays the metric being processed (e.g. `raise CheckException("Error processing custom metric: " + str(m)) from e`)
More generally, avoiding partial functions (like tuple unpacking in Python) makes it much easier to validate input and report errors correctly.
Adding to our queue, this would make the life of support engineers much easier, thanks for reporting and for the suggestions. | 2017-05-29T13:10:25Z | [] | [] |
Traceback (most recent call last):
File "/opt/datadog-agent/agent/checks/__init__.py", line 745, in run
self.check(copy.deepcopy(instance))
File "/opt/datadog-agent/agent/checks.d/postgres.py", line 606, in check
custom_metrics = self._get_custom_metrics(instance.get('custom_metrics', []), key)
File "/opt/datadog-agent/agent/checks.d/postgres.py", line 576, in _get_custom_metrics
for ref, (_, mtype) in m['metrics'].iteritems():
ValueError: need more than 1 value to unpack
| 29 |
|||
DataDog/integrations-core | DataDog__integrations-core-5659 | 3b850d826a2f245e9dcc8a1d87d5e2343123882e | diff --git a/datadog_checks_base/datadog_checks/base/checks/win/wmi/__init__.py b/datadog_checks_base/datadog_checks/base/checks/win/wmi/__init__.py
--- a/datadog_checks_base/datadog_checks/base/checks/win/wmi/__init__.py
+++ b/datadog_checks_base/datadog_checks/base/checks/win/wmi/__init__.py
@@ -114,14 +114,15 @@ def _get_tag_query_tag(self, sampler, wmi_obj, tag_query):
target_class, target_property, filters = self._format_tag_query(sampler, wmi_obj, tag_query)
# Create a specific sampler
- tag_query_sampler = WMISampler(self.log, target_class, [target_property], filters=filters, **sampler.connection)
+ with WMISampler(
+ self.log, target_class, [target_property], filters=filters, **sampler.connection
+ ) as tag_query_sampler:
+ tag_query_sampler.sample()
- tag_query_sampler.sample()
+ # Extract tag
+ self._raise_on_invalid_tag_query_result(tag_query_sampler, wmi_obj, tag_query)
- # Extract tag
- self._raise_on_invalid_tag_query_result(tag_query_sampler, wmi_obj, tag_query)
-
- link_value = str(tag_query_sampler[0][target_property]).lower()
+ link_value = str(tag_query_sampler[0][target_property]).lower()
tag = "{tag_name}:{tag_value}".format(tag_name=target_property.lower(), tag_value="_".join(link_value.split()))
@@ -235,14 +236,17 @@ def _get_instance_key(self, host, namespace, wmi_class, other=None):
return "{host}:{namespace}:{wmi_class}".format(host=host, namespace=namespace, wmi_class=wmi_class)
- def _get_wmi_sampler(self, instance_key, wmi_class, properties, tag_by="", **kwargs):
+ def _get_running_wmi_sampler(self, instance_key, wmi_class, properties, tag_by="", **kwargs):
"""
- Create and cache a WMISampler for the given (class, properties)
+ Return a running WMISampler for the given (class, properties).
+
+ If no matching WMISampler is running yet, start one and cache it.
"""
properties = list(properties) + [tag_by] if tag_by else list(properties)
if instance_key not in self.wmi_samplers:
wmi_sampler = WMISampler(self.log, wmi_class, properties, **kwargs)
+ wmi_sampler.start()
self.wmi_samplers[instance_key] = wmi_sampler
return self.wmi_samplers[instance_key]
diff --git a/datadog_checks_base/datadog_checks/base/checks/win/wmi/sampler.py b/datadog_checks_base/datadog_checks/base/checks/win/wmi/sampler.py
--- a/datadog_checks_base/datadog_checks/base/checks/win/wmi/sampler.py
+++ b/datadog_checks_base/datadog_checks/base/checks/win/wmi/sampler.py
@@ -105,6 +105,7 @@ def __init__(
# Sampling state
self._sampling = False
+ self._stopping = False
self.logger = logger
@@ -146,12 +147,35 @@ def __init__(
self._runSampleEvent = Event()
self._sampleCompleteEvent = Event()
- thread = Thread(target=self._query_sample_loop, name=class_name)
- thread.daemon = True
+ def start(self):
+ """
+ Start internal thread for sampling
+ """
+ thread = Thread(target=self._query_sample_loop, name=self.class_name)
+ thread.daemon = True # Python 2 does not support daemon as Thread constructor parameter
thread.start()
+ def stop(self):
+ """
+ Dispose of the internal thread
+ """
+ self._stopping = True
+ self._runSampleEvent.set()
+ self._sampleCompleteEvent.wait()
+
+ def __enter__(self):
+ self.start()
+ return self
+
+ def __exit__(self, type, value, traceback):
+ self.stop()
+
def _query_sample_loop(self):
try:
+ # Initialize COM for the current (dedicated) thread
+ # WARNING: any python COM object (locator, connection, etc) created in a thread
+ # shouldn't be used in other threads (can lead to memory/handle leaks if done
+ # without a deep knowledge of COM's threading model).
pythoncom.CoInitialize()
except Exception as e:
self.logger.info("exception in CoInitialize: %s", e)
@@ -159,6 +183,11 @@ def _query_sample_loop(self):
while True:
self._runSampleEvent.wait()
+ if self._stopping:
+ self.logger.debug("_query_sample_loop stopping")
+ self._sampleCompleteEvent.set()
+ return
+
self._runSampleEvent.clear()
if self.is_raw_perf_class and not self._previous_sample:
self._current_sample = self._query()
@@ -335,11 +364,6 @@ def get_connection(self):
self.username,
)
- # Initialize COM for the current thread
- # WARNING: any python COM object (locator, connection, etc) created in a thread
- # shouldn't be used in other threads (can lead to memory/handle leaks if done
- # without a deep knowledge of COM's threading model). Because of this and given
- # that we run each query in its own thread, we don't cache connections
additional_args = []
if self.provider != ProviderArchitecture.DEFAULT:
diff --git a/win32_event_log/datadog_checks/win32_event_log/win32_event_log.py b/win32_event_log/datadog_checks/win32_event_log/win32_event_log.py
--- a/win32_event_log/datadog_checks/win32_event_log/win32_event_log.py
+++ b/win32_event_log/datadog_checks/win32_event_log/win32_event_log.py
@@ -115,7 +115,7 @@ def check(self, instance):
filters.append(query)
- wmi_sampler = self._get_wmi_sampler(
+ wmi_sampler = self._get_running_wmi_sampler(
instance_key,
self.EVENT_CLASS,
event_properties,
diff --git a/wmi_check/datadog_checks/wmi_check/wmi_check.py b/wmi_check/datadog_checks/wmi_check/wmi_check.py
--- a/wmi_check/datadog_checks/wmi_check/wmi_check.py
+++ b/wmi_check/datadog_checks/wmi_check/wmi_check.py
@@ -52,7 +52,7 @@ def check(self, instance):
metric_name_and_type_by_property, properties = self._get_wmi_properties(instance_key, metrics, tag_queries)
- wmi_sampler = self._get_wmi_sampler(
+ wmi_sampler = self._get_running_wmi_sampler(
instance_key,
wmi_class,
properties,
| WMI integration throws Exception: SWbemLocator Not enough storage is available to process this command
```text
===============
Agent (v7.16.0)
===============
Status date: 2020-02-05 15:56:45.740020 GMT
Agent start: 2020-02-05 15:03:08.601503 GMT
Pid: 25188
Go Version: go1.12.9
Python Version: 3.7.4
Build arch: amd64
Host Info
=========
bootTime: 2020-01-30 09:06:55.000000 GMT
os: windows
platform: Windows Server 2016 Datacenter
platformFamily: Windows Server 2016 Datacenter
platformVersion: 10.0 Build 14393
procs: 255
uptime: 149h56m12s
wmi_check (1.6.0)
```
**Steps to reproduce the issue:**
The WMI Check integration is configured to capture metrics for multiple instances of a specific process and tag them using the command line, as below
```yaml
- class: Win32_PerfFormattedData_PerfProc_Process
metrics:
- - ThreadCount
- proc.threads.count
- gauge
- - VirtualBytes
- proc.mem.virtual
- gauge
- - PrivateBytes
- proc.mem.private
- gauge
- - WorkingSet
- proc.mem.workingset
- gauge
- - PageFaultsPerSec
- proc.mem.page_faults_per_sec
- gauge
- - PercentProcessorTime
- proc.cpu_pct
- gauge
- - IOReadBytesPerSec
- proc.io.bytes_read
- gauge
- - IOWriteBytesPerSec
- proc.io.bytes_written
- gauge
filters:
- Name: Calastone.Core.MessageAdapter.Console%
tag_by: Name
tag_queries:
- [IDProcess, Win32_Process, Handle, CommandLine]
```
There are 17 instances of the process running.
**Describe the results you received:**
- After a period of time (can be 40+ minutes) the following error starts to be logged
```
2020-02-04 16:31:29 GMT | CORE | WARN | (pkg/collector/python/datadog_agent.go:118 in LogMessage) | wmi_check:a7174f61bd7a5360 | (sampler.py:469) | Failed to execute WMI query (Select CommandLine from Win32_Process WHERE ( Handle = '8408' ))
Traceback (most recent call last):
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\datadog_checks\base\checks\win\wmi\sampler.py", line 464, in _query
raw_results = self.get_connection().ExecQuery(wql, "WQL", query_flags)
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\datadog_checks\base\checks\win\wmi\sampler.py", line 351, in get_connection
connection = locator.ConnectServer(self.host, self.namespace, self.username, self.password, *additional_args)
File "<COMObject WbemScripting.SWbemLocator>", line 5, in ConnectServer
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\win32com\client\dynamic.py", line 287, in _ApplyTypes_
result = self._oleobj_.InvokeTypes(*(dispid, LCID, wFlags, retType, argTypes) + args)
pywintypes.com_error: (-2147352567, 'Exception occurred.', (0, 'SWbemLocator', 'Not enough storage is available to process this command. ', None, 0, -2147024888), None)
2020-02-04 16:31:29 GMT | CORE | WARN | (pkg/collector/python/datadog_agent.go:118 in LogMessage) | wmi_check:a7174f61bd7a5360 | (__init__.py:88) | Failed to extract a tag from `tag_queries` parameter: no result was returned. wmi_object={'threadcount': 27.0, 'virtualbytes': 823386112.0, 'privatebytes': 304635904.0, 'workingset': 367628288.0, 'pagefaultspersec': 0.0, 'percentprocessortime': 0.0, 'ioreadbytespersec': 0.0, 'iowritebytespersec': 0.0, 'idprocess': 8408.0, 'name': 'Calastone.Core.MessageAdapter.Console#3'} - query=['IDProcess', 'Win32_Process', 'Handle', 'CommandLine']
2020-02-04 16:31:29 GMT | CORE | WARN | (pkg/collector/python/datadog_agent.go:118 in LogMessage) | wmi_check:a7174f61bd7a5360 | (sampler.py:469) | Failed to execute WMI query (Select CommandLine from Win32_Process WHERE ( Handle = '14836' ))
```
- The number of threads used by the agent process is observed to be rocketing (> 1700)
- The server becomes unresponsive
**Diagnosis:**
This issue didn't occur on the previous version of the agent we were using (6.7.0).
Looking at the source code suggests the problem was introduced as part of #3987
https://github.com/DataDog/integrations-core/blob/010ed622d62c9dd7de28d76f1191a4be5960a965/datadog_checks_base/datadog_checks/base/checks/win/wmi/__init__.py#L117 creates a WMISampler for EVERY tag query that needs to be run. With the new logic that creates a thread for each query that is never released!
**Solution:**
The follow hack fixes the problem. I'll put it into a PR.
Change `sampler.py`
```python
def _query_sample_loop(self):
...
while True:
self._runSampleEvent.wait()
if self._stopping:
return
def dispose(self):
"""
Dispose of the internal thread
"""
self._stopping = True
self._runSampleEvent.set()
```
Change `__init__.py`
```python
def _get_tag_query_tag(self, sampler, wmi_obj, tag_query):
...
tag = "{tag_name}:{tag_value}".format(tag_name=target_property.lower(), tag_value="_".join(link_value.split()))
tag_query_sampler.dispose()
```
There also looks to be scope to cache these WMISampler classes like the main metric samplers. Also the connection created in `get_connection` could be created in the sampler thread method since it is now bound to that thread
| 2020-02-06T12:16:14Z | [] | [] |
Traceback (most recent call last):
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\datadog_checks\base\checks\win\wmi\sampler.py", line 464, in _query
raw_results = self.get_connection().ExecQuery(wql, "WQL", query_flags)
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\datadog_checks\base\checks\win\wmi\sampler.py", line 351, in get_connection
connection = locator.ConnectServer(self.host, self.namespace, self.username, self.password, *additional_args)
File "<COMObject WbemScripting.SWbemLocator>", line 5, in ConnectServer
File "C:\Program Files\Datadog\Datadog Agent\embedded3\lib\site-packages\win32com\client\dynamic.py", line 287, in _ApplyTypes_
result = self._oleobj_.InvokeTypes(*(dispid, LCID, wFlags, retType, argTypes) + args)
pywintypes.com_error: (-2147352567, 'Exception occurred.', (0, 'SWbemLocator', 'Not enough storage is available to process this command. ', None, 0, -2147024888), None)
| 36 |
||||
DataDog/integrations-core | DataDog__integrations-core-9857 | 8006a053c108af2cf1988efe23db8f58df8262dc | diff --git a/mongo/datadog_checks/mongo/collectors/custom_queries.py b/mongo/datadog_checks/mongo/collectors/custom_queries.py
--- a/mongo/datadog_checks/mongo/collectors/custom_queries.py
+++ b/mongo/datadog_checks/mongo/collectors/custom_queries.py
@@ -56,8 +56,10 @@ def _collect_custom_metrics_for_query(self, api, raw_query):
mongo_query = deepcopy(raw_query.get('query'))
if not mongo_query: # no cov
raise ValueError("Custom query field `query` is required")
+ # The mongo command to run (find, aggregate, count...)
mongo_command = self._extract_command_from_mongo_query(mongo_query)
- collection_name = mongo_query[mongo_command]
+ # The value of the command, it is usually the collection name on which to run the query.
+ mongo_command_value = mongo_query[mongo_command]
del mongo_query[mongo_command]
if mongo_command not in ALLOWED_CUSTOM_QUERIES_COMMANDS:
raise ValueError("Custom query command must be of type {}".format(ALLOWED_CUSTOM_QUERIES_COMMANDS))
@@ -90,20 +92,26 @@ def _collect_custom_metrics_for_query(self, api, raw_query):
if field_type not in ALLOWED_CUSTOM_METRICS_TYPES + ['tag']:
raise ValueError('Field `type` must be one of {}'.format(ALLOWED_CUSTOM_METRICS_TYPES + ['tag']))
- tags = list(tags)
- tags.extend(raw_query.get('tags', []))
- tags.append('collection:{}'.format(collection_name))
-
try:
# This is where it is necessary to extract the command and its argument from the query to pass it as the
# first two params.
- result = db.command(mongo_command, collection_name, **mongo_query)
+ result = db.command(mongo_command, mongo_command_value, **mongo_query)
if result['ok'] == 0:
raise pymongo.errors.PyMongoError(result['errmsg'])
except pymongo.errors.PyMongoError:
self.log.error("Failed to run custom query for metric %s", metric_prefix)
raise
+ # `1` is Mongo default value for commands that are collection agnostics.
+ if str(mongo_command_value) == '1':
+ # https://github.com/mongodb/mongo-python-driver/blob/01e34cebdb9aac96c72ddb649e9b0040a0dfd3a0/pymongo/aggregation.py#L208
+ collection_name = '{}.{}'.format(db_name, mongo_command)
+ else:
+ collection_name = mongo_command_value
+
+ tags.append('collection:{}'.format(collection_name))
+ tags.extend(raw_query.get('tags', []))
+
if mongo_command == 'count':
# A count query simply returns a number, no need to iterate through it.
submit_method(metric_prefix, result['n'], tags)
| MongoDB: Collection-agnostic aggregations like $currentOp doesn't work
Agent 7.29.1, running on Ubuntu Linux 18.04.
**Steps to reproduce the issue:**
Add the following configuration to `/etc/datadog-agent/conf.d/mongo.d/conf.yaml` and restart the agent:
```
custom_queries:
- metric_prefix: mongodb.custom.queries_slower_than_60sec
run_on_secondary: true
query: { "aggregate": 1, "maxTimeMS": 1000, "pipeline": [ { "$currentOp": { "allUsers": true }}, { "$match": { "active": true, "secs_running": {"$gt": 60}}} ], "cursor": {}}
fields:
- field_name: secs_running
name: secs_running
type: gauge
- field_name: appName
name: app_name
type: tag
- field_name: ns
name: mongo_op_namespace
type: tag
```
**Describe the results you received:**
When Datadog attempts to run this command, it produces an error (found via `journalctl`):
```
Traceback (most recent call last):
2021-07-22 06:44:38 UTC | CORE | WARN | (pkg/collector/python/datadog_agent.go:122 in LogMessage) | mongo:375a6f2e54dabf11 | (custom_queries.py:153) | Errors while collecting custom metrics with prefix mongodb.custom.queries_slower_than_60sec
TypeError: name must be an instance of str
raise TypeError("name must be an instance "
File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/pymongo/collection.py", line 160, in __init__
pymongo.collection.Collection(db, collection_name), result['cursor'], None
File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/mongo/collectors/custom_queries.py", line 113, in _collect_custom_metrics_for_query
self._collect_custom_metrics_for_query(api, raw_query)
File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/mongo/collectors/custom_queries.py", line 150, in collect
```
**Describe the results you expected:**
I would like to be able to send information about slow queries to Datadog.
**Additional information you deem important (e.g. issue happens only occasionally):**
It seems like the problem here is that when using this syntax to run an admin aggregation like `$currentOp`, you have to specify `"aggregate": 1` in the query to indicate that there is no associated collection. However, the API that Datadog is calling in pymongo expects the collection name to always be a string. Unfortunately, `"aggregate": "1"` is not equivalent and will fail.
More details on the syntax: https://docs.mongodb.com/manual/reference/command/aggregate/
| Hey @atodd-circleci
Acknowledging the limitation, I'm able to reproduce.
I'm thinking we should be able to work around that by putting `$cmd.aggregate` instead of "1" as the collection name here: https://github.com/DataDog/integrations-core/blob/master/mongo/datadog_checks/mongo/collectors/custom_queries.py#L113 but I'd have to confirm that
@FlorianVeaux Thanks for taking a look so quickly. I manually edited `custom_queries.py` on my installation to replace `collection_name` with the literal `$cmd.aggregate`. It seems to have worked. When I start the agent, I see this in the log:
```
Exception: Custom query returned an empty result set.
raise Exception('Custom query returned an empty result set.')
File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/mongo/collectors/custom_queries.py", line 145, in _collect_custom_metrics_for_query
self._collect_custom_metrics_for_query(api, raw_query)
File "/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/mongo/collectors/custom_queries.py", line 150, in collect
Traceback (most recent call last):
2021-07-27 05:20:05 UTC | CORE | WARN | (pkg/collector/python/datadog_agent.go:122 in LogMessage) | mongo:<redacted> | (custom_queries.py:153) | Errors while collecting custom metrics with prefix mongodb.custom.queries_slower_than_60sec
```
I'm not expecting any results, so this is good. I can't really go around manually editing our installations this way, though, so I'm looking forward to a more permanent fix.
(I am a little concerned about having all of these exceptions in the system log, as well. I'll have to look at using [$count](https://docs.mongodb.com/manual/reference/operator/aggregation/count/) to always output a count instead of what I'm doing now). | 2021-08-05T15:17:59Z | [] | [] |
Traceback (most recent call last):
2021-07-22 06:44:38 UTC | CORE | WARN | (pkg/collector/python/datadog_agent.go:122 in LogMessage) | mongo:375a6f2e54dabf11 | (custom_queries.py:153) | Errors while collecting custom metrics with prefix mongodb.custom.queries_slower_than_60sec
TypeError: name must be an instance of str
| 58 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-1360 | ebd9fc9530242e1c9b5f3093dc62ceb4185735b0 | diff --git a/pytorch_lightning/loggers/wandb.py b/pytorch_lightning/loggers/wandb.py
--- a/pytorch_lightning/loggers/wandb.py
+++ b/pytorch_lightning/loggers/wandb.py
@@ -65,10 +65,11 @@ def __init__(self, name: Optional[str] = None, save_dir: Optional[str] = None,
def __getstate__(self):
state = self.__dict__.copy()
+ # args needed to reload correct experiment
+ state['_id'] = self._experiment.id if self._experiment is not None else None
+
# cannot be pickled
state['_experiment'] = None
- # args needed to reload correct experiment
- state['_id'] = self.experiment.id
return state
@property
@@ -87,7 +88,7 @@ def experiment(self) -> Run:
os.environ['WANDB_MODE'] = 'dryrun'
self._experiment = wandb.init(
name=self._name, dir=self._save_dir, project=self._project, anonymous=self._anonymous,
- id=self._id, resume='allow', tags=self._tags, entity=self._entity)
+ reinit=True, id=self._id, resume='allow', tags=self._tags, entity=self._entity)
# save checkpoints in wandb dir to upload on W&B servers
if self._log_model:
self.save_dir = self._experiment.dir
@@ -109,8 +110,11 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->
@property
def name(self) -> str:
- return self.experiment.project_name()
+ # don't create an experiment if we don't have one
+ name = self._experiment.project_name() if self._experiment else None
+ return name
@property
def version(self) -> str:
- return self.experiment.id
+ # don't create an experiment if we don't have one
+ return self._experiment.id if self._experiment else None
| WandbLogger cannot be used with 'ddp'
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
wandb modifies `init` such that a child process calling init returns None if the master process has called init. This seems to cause a bug with ddp, and results in rank zero having experiment = None, which crashes the program.
### To Reproduce
Can be reproduced with the basic MNIST gpu template, simply add a WandbLogger and pass 'ddp' as the distributed backend.
```
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 331, in ddp_train
self.run_pretrain_routine(model)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 757, in run_pretrain_routine
self.logger.log_hyperparams(ref_model.hparams)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/base.py", line 14, in wrapped_fn
fn(self, *args, **kwargs)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/wandb.py", line 79, in log_hyperparams
self.experiment.config.update(params)
AttributeError: 'NoneType' object has no attribute 'config'
```
This occurs with the latest wandb version and with pytorch-lightning 0.6.
| Hi! thanks for your contribution!, great first issue!
Some hacky solutions: calling `reinit=True` for wandb, adding or this terrible hack:
```python
def init_ddp_connection(self, *args, **kwargs):
super().init_ddp_connection(*args, **kwargs)
if torch.distributed.get_rank() == 0:
import wandb
wandb.run = None
```
These both seem to only kind-of work and result in multiple independent calls to wandb.init. I think the ideal solution is that the experiment is only ever initialized on rank zero. *However* doing this means that wandb *cannot* be initialized in the master thread at all.
Better than this probably requires some changes to the wandb API.
Following up slightly - my hacky solution doesn't really work. It's easy enough though to get the rank zero only solution to work. If this seems like a reasonable solution, let me know and I'll submit a PR.
well, have observed some issues with `wandb` earlier #906 could you check it?
Hmm, I think this is a slightly different issue (I'm running on Ubuntu so I don't think that's the issue). Pickling also works correctly.
This particular problem I think stems from this part of the `wandb.init(...)` code:
```python
def init(...):
...
# If a thread calls wandb.init() it will get the same Run object as
# the parent. If a child process with distinct memory space calls
# wandb.init(), it won't get an error, but it will get a result of
# None.
# This check ensures that a child process can safely call wandb.init()
# after a parent has (only the parent will create the Run object).
# This doesn't protect against the case where the parent doesn't call
# wandb.init but two children do.
if run or os.getenv(env.INITED):
return run
```
Child processes end up getting `None` for the wandb run object, which causes logging to fail. There are probably two reasonable and complementary solutions:
1. The main thread should avoid creating a wandb experiment unless absolutely necessary.
Right now, [this](https://github.com/PyTorchLightning/pytorch-lightning/blob/e586ed47674fd78b158322bb7b14d00aeb912abd/pytorch_lightning/loggers/wandb.py#L63-L69) is the only part of the logging code that the parent thread calls (I assume it's called when pickling):
```python
def __getstate__(self):
state = self.__dict__.copy()
# cannot be pickled
state['_experiment'] = None
# args needed to reload correct experiment
state['_id'] = self.experiment.id
return state
```
If this is changed to:
```python
def __getstate__(self):
state = self.__dict__.copy()
# args needed to reload correct experiment
if self._experiment is not None:
state['_id'] = self._experiment.id
else:
state['_id'] = None
# cannot be pickled
state['_experiment'] = None
return state
```
That will ensure that unless the user explicitly logs something or creates the wandb experiment first, then the main thread will not try to create an experiment. Since subsequent logging / saving code is wrapped by the `@rank_zero_only` decorator, this will generally solve the issue in the base case.
It's also possible that [these properties](https://github.com/PyTorchLightning/pytorch-lightning/blob/e586ed47674fd78b158322bb7b14d00aeb912abd/pytorch_lightning/loggers/wandb.py#L112-L118) are also called by master. Ideally they would be wrapped to not create the experiment unless it had been already created (i.e. experiment should only be created by a function that is wrapped with the `@rank_zero_only` decorator).
2. If the main thread *has* created an experiment, rank zero should be passed the re-init argument.
`wandb` does allow you to reinitialize the experiment. I tried to play around with this a little bit and got some errors, but in theory adding this:
```python
wandb.init(..., reinit=dist.is_available() and dist.is_initialized() and dist.get_rank() == 0)
```
should force a re-initialization when wandb is already initialzed for rank zero.
| 2020-04-03T13:32:07Z | [] | [] |
Traceback (most recent call last):
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 331, in ddp_train
self.run_pretrain_routine(model)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 757, in run_pretrain_routine
self.logger.log_hyperparams(ref_model.hparams)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/base.py", line 14, in wrapped_fn
fn(self, *args, **kwargs)
File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/wandb.py", line 79, in log_hyperparams
self.experiment.config.update(params)
AttributeError: 'NoneType' object has no attribute 'config'
| 104 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-1377 | b8ff9bc1d242a18f5e7147f34d63f43fcdd0e50a | diff --git a/pytorch_lightning/loggers/tensorboard.py b/pytorch_lightning/loggers/tensorboard.py
--- a/pytorch_lightning/loggers/tensorboard.py
+++ b/pytorch_lightning/loggers/tensorboard.py
@@ -9,6 +9,7 @@
from torch.utils.tensorboard import SummaryWriter
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
+from pytorch_lightning import _logger as log
class TensorBoardLogger(LightningLoggerBase):
@@ -163,6 +164,11 @@ def version(self) -> int:
def _get_next_version(self):
root_dir = os.path.join(self.save_dir, self.name)
+
+ if not os.path.isdir(root_dir):
+ log.warning('Missing logger folder: %s', root_dir)
+ return 0
+
existing_versions = []
for d in os.listdir(root_dir):
if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
| Tensorboard logger error: lightning_logs directory not exists in multi-node DDP on nodes with rank != 0
## 🐛 Bug
In multi-node DDP train mode on all nodes except rank 0 errors appears at the start of the training caused by accessing lightning_logs directory in tensorboard logger which is not exist at the moment.
### To Reproduce
Steps to reproduce the behavior:
1. setup multi-node cluster (without SLURM)
2. set environment variables on each node:
```
export MASTER_ADDR=<rank 0 node IP>
export MASTER_PORT=23456
export RANK=<node id>
export SLURM_NODEID=<node id>
export WORLD_SIZE=<world-size>
```
3. install dependencies:
```
pip install torch torchvision hydra-core pytorch-lightning
```
4. copy app.y and conf.yaml to each node
5. run script on each node
```
python app.py
```
6. see the error:
```
Exception:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 342, in ddp_train
self.run_pretrain_routine(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in run_pretrain_routine
self.configure_checkpoint_callback()
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_config.py", line 45, in configure_checkpoint_callback
f'version_{self.logger.version}',
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/loggers/tensorboard.py", line 161, in version
self._version = self._get_next_version()
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/loggers/tensorboard.py", line 167, in _get_next_version
for d in os.listdir(root_dir):
FileNotFoundError: [Errno 2] No such file or directory: '/home/ubuntu/pytorch-lightning-intro-guide/outputs/2020-04-04/15-53-26/lightning_logs'
```
#### Code sample
app.py:
```
import pathlib
import hydra
import pytorch_lightning as pl
import torch
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
class LitMNIST(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 256)
self.layer_3 = torch.nn.Linear(256, 10)
self.train_dataset = None
self.val_dataset = None
self.test_dataset = None
def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
def prepare_data(self):
# transform
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
# download
data_dir = pathlib.Path.home() / 'data'
mnist_train = datasets.MNIST(data_dir, train=True,
download=True, transform=transform)
mnist_test = datasets.MNIST(data_dir, train=False,
download=True, transform=transform)
# train/val split
mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
# assign to use in dataloaders
self.train_dataset = mnist_train
self.val_dataset = mnist_val
self.test_dataset = mnist_test
def train_dataloader(self):
return DataLoader(self.train_dataset, batch_size=64)
def val_dataloader(self):
return DataLoader(self.val_dataset, batch_size=64)
def test_dataloader(self):
return DataLoader(self.test_dataset, batch_size=64)
def configure_optimizers(self):
return Adam(self.parameters(), lr=1e-3)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
# add logging
logs = {'loss': loss}
return {'loss': loss, 'log': logs}
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return {'val_loss': loss}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack( # pylint: disable=no-member
[x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return {'val_loss': loss}
def test_epoch_end(self, outputs):
avg_loss = torch.stack( # pylint: disable=no-member
[x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def init_ddp_connection(self, proc_rank: int, world_size: int) -> None:
torch.distributed.init_process_group(
'nccl', rank=proc_rank, world_size=world_size)
@hydra.main(config_path='conf.yaml')
def main(conf: OmegaConf):
model = LitMNIST()
trainer = pl.Trainer(gpus=conf.gpus,
num_nodes=conf.num_nodes,
distributed_backend=conf.distributed_backend,
max_epochs=3)
trainer.fit(model)
if __name__ == '__main__':
main() # pylint: disable=no-value-for-parameter
```
conf.yaml:
```
gpus: 1
num_nodes: 2
distributed_backend: ddp
```
### Expected behavior
Train should go without error
### Environment
```
cuda:
GPU:
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
available: True
version: 10.1
packages:
numpy: 1.18.1
pyTorch_debug: False
pyTorch_version: 1.4.0
pytorch-lightning: 0.7.1
tensorboard: 2.2.0
tqdm: 4.45.0
system:
OS: Linux
architecture:
64bit
processor: x86_64
python: 3.6.10
version: #113-Ubuntu SMP Wed Jan 29 14:54:54 UTC 2020
```
### Additional context
<!-- Add any other context about the problem here. -->
| 2020-04-04T16:35:26Z | [] | [] |
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 342, in ddp_train
self.run_pretrain_routine(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in run_pretrain_routine
self.configure_checkpoint_callback()
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_config.py", line 45, in configure_checkpoint_callback
f'version_{self.logger.version}',
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/loggers/tensorboard.py", line 161, in version
self._version = self._get_next_version()
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.6/site-packages/pytorch_lightning/loggers/tensorboard.py", line 167, in _get_next_version
for d in os.listdir(root_dir):
FileNotFoundError: [Errno 2] No such file or directory: '/home/ubuntu/pytorch-lightning-intro-guide/outputs/2020-04-04/15-53-26/lightning_logs'
| 105 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-1385 | 4ed3027309fe1882554e9b7ffe33f1aa92c88106 | diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -363,15 +363,19 @@ def load_spawn_weights(self, original_model):
:param model:
:return:
"""
- # load weights saved in ddp
- path = os.path.join(self.default_save_path, '__temp_weight_ddp_end.ckpt')
- loaded_model = original_model.__class__.load_from_checkpoint(path)
- # copy loaded weights to old model
- original_model.load_state_dict(loaded_model.state_dict())
+ loaded_model = original_model
- # remove ddp weights
- os.remove(path)
+ if self.proc_rank == 0:
+ # load weights saved in ddp
+ path = os.path.join(self.default_save_path, '__temp_weight_ddp_end.ckpt')
+ loaded_model = original_model.__class__.load_from_checkpoint(path)
+
+ # copy loaded weights to old model
+ original_model.load_state_dict(loaded_model.state_dict())
+
+ # remove ddp weights
+ os.remove(path)
return loaded_model
| Trainer DDP should invoke load_spawn_weights() only in proc_rank == 0
## 🐛 Bug
Trainer DDP load_spawn_weights should happen only in proc_rank == 0 since only in this process (node) `save_spawn_weights` actually saves checkpoint
### To Reproduce
Steps to reproduce the behavior:
1. setup two-node cluster.
1. set SLURM_NODEID on each node: '0' on node 0 and '1' on node 1.
2. run the script `python app.py` on each node.
3. see stdout on the node 1:
```
Traceback (most recent call last):
File "app.py", line 166, in <module>
main_() # pylint: disable=no-value-for-parameter
File "app.py", line 162, in main_
trainer.fit(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 593, in fit
self.load_spawn_weights(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 368, in load_spawn_weights
loaded_model = original_model.__class__.load_from_checkpoint(path)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/core/lightning.py", line 1353, in load_from_checkpoint
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 525, in load
with _open_file_like(f, 'rb') as opened_file:
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 212, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 193, in __init__
super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/home/ubuntu/pytorch-lightning-intro-guide/__temp_weight_ddp_end.ckpt'
```
#### Code sample
app.py:
```
import pathlib
import pytorch_lightning as pl
import torch
from torch.nn import functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
class LitMNIST(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 256)
self.layer_3 = torch.nn.Linear(256, 10)
self.train_dataset = None
self.val_dataset = None
self.test_dataset = None
def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
def prepare_data(self):
# transform
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
# download
data_dir = pathlib.Path.home() / 'data'
mnist_train = datasets.MNIST(data_dir, train=True,
download=True, transform=transform)
mnist_test = datasets.MNIST(data_dir, train=False,
download=True, transform=transform)
# train/val split
mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
# assign to use in dataloaders
self.train_dataset = mnist_train
self.val_dataset = mnist_val
self.test_dataset = mnist_test
def train_dataloader(self):
return DataLoader(self.train_dataset, batch_size=64)
def val_dataloader(self):
return DataLoader(self.val_dataset, batch_size=64)
def test_dataloader(self):
return DataLoader(self.test_dataset, batch_size=64)
def configure_optimizers(self):
return Adam(self.parameters(), lr=1e-3)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
# add logging
logs = {'loss': loss}
return {'loss': loss, 'log': logs}
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return {'val_loss': loss}
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return {'val_loss': loss}
def test_epoch_end(self, outputs):
avg_loss = torch.stack( # pylint: disable=no-member
[x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def init_ddp_connection(self, proc_rank: int, world_size: int) -> None:
torch.distributed.init_process_group(
'nccl', rank=proc_rank, world_size=world_size)
def main():
model = LitMNIST()
gpus = 1
num_nodes = 2
trainer = pl.Trainer(gpus=gpus,
num_nodes=num_nodes,
distributed_backend='ddp',
max_epochs=3)
trainer.fit(model)
if __name__ == '__main__':
main()
```
### Expected behavior
All workers on all nodes should finish without errors.
### Environment
On each node:
```
cuda:
GPU:
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
Tesla K80
available: True
version: 10.1
packages:
numpy: 1.16.6
pyTorch_debug: False
pyTorch_version: 1.4.0
pytorch-lightning: 0.7.1
tensorboard: 2.2.0
tqdm: 4.44.1
system:
OS: Linux
architecture:
64bit
processor: x86_64
python: 3.7.7
version: #113-Ubuntu SMP Wed Jan 29 14:54:54 UTC 2020
```
### Additional context
<!-- Add any other context about the problem here. -->
| 2020-04-05T23:51:47Z | [] | [] |
Traceback (most recent call last):
File "app.py", line 166, in <module>
main_() # pylint: disable=no-value-for-parameter
File "app.py", line 162, in main_
trainer.fit(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 593, in fit
self.load_spawn_weights(model)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 368, in load_spawn_weights
loaded_model = original_model.__class__.load_from_checkpoint(path)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/pytorch_lightning/core/lightning.py", line 1353, in load_from_checkpoint
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 525, in load
with _open_file_like(f, 'rb') as opened_file:
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 212, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/home/ubuntu/anaconda3/envs/nightly_pt/lib/python3.7/site-packages/torch/serialization.py", line 193, in __init__
super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/home/ubuntu/pytorch-lightning-intro-guide/__temp_weight_ddp_end.ckpt'
| 107 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-1423 | 3f1e4b953f84ecdac7dada0c6b57d908efc9c3d3 | diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -566,7 +566,7 @@ def check_gpus_data_type(gpus):
:return: return unmodified gpus variable
"""
- if gpus is not None and type(gpus) not in (int, str, list):
+ if gpus is not None and (not isinstance(gpus, (int, str, list)) or isinstance(gpus, bool)):
raise MisconfigurationException("GPUs must be int, string or list of ints or None.")
| Use isinstance() instead of type() in trainer.distrib_parts.check_gpus_data_type
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
When instantiating a `Trainer` object, it makes sense to be able to pass a subclass of `list`.
Ideally, this would be something even more general like `collections.abc.Sequence`, but I'm not too familiar with Lightning's codebase and that change would have a greater likelihood of breaking things.
### To Reproduce
Instantiate a `Trainer` with the `gpus` parameter being a subclass of `list`.
#### Code sample
```python
>>> from pytorch_lightning import Trainer
>>> class MyList(list):
... pass
...
>>> gpus = MyList([0])
>>> t = Trainer(gpus=gpus)
```
This produces
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 366, in __init__
self.data_parallel_device_ids = parse_gpu_ids(self.gpus)
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 613, in parse_gpu_ids
check_gpus_data_type(gpus)
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 561, in check_gpus_data_type
raise MisconfigurationException("GPUs must be int, string or list of ints or None.")
pytorch_lightning.utilities.debugging.MisconfigurationException: GPUs must be int, string or list of ints or None.
```
### Expected behavior
`Trainer` is instantiated normally as it would had a list been passed.
### Environment
- PyTorch Version: 1.4.0
- PyTorch Lightning Version: 0.7.1
- OS: Ubuntu 19.10
- How you installed PyTorch: `pip`
- Python version: 3.7
### Potential Fix
In `pytorch_lightning/trainer/distrib_parts.py` check types using `isinstance()` instead of `type()`:
```python
def check_gpus_data_type(gpus):
# if gpus is not None and type(gpus) not in (int, str, list):
if gpus is not None and not isinstance(gpus, (int, str, list)):
raise MisconfigurationException("GPUs must be int, string or list of ints or None.")
```
I'll put in a PR if this change sounds good
| Hi! thanks for your contribution!, great first issue!
I do like this shift from `type` to an `isinstance` which extend accepted types also to child...
as always a good PR is always welcome
cc: @PyTorchLightning/core-contributors @jeremyjordan | 2020-04-09T09:44:35Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 366, in __init__
self.data_parallel_device_ids = parse_gpu_ids(self.gpus)
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 613, in parse_gpu_ids
check_gpus_data_type(gpus)
File "/opt/anaconda/miniconda3/envs/ai/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 561, in check_gpus_data_type
raise MisconfigurationException("GPUs must be int, string or list of ints or None.")
pytorch_lightning.utilities.debugging.MisconfigurationException: GPUs must be int, string or list of ints or None.
| 111 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-1513 | 9b31272cf0f3079a244944096b4a81eec20fe555 | diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -61,6 +61,7 @@ class TrainerDataLoadingMixin(ABC):
train_percent_check: float
val_percent_check: float
test_percent_check: float
+ replace_sampler_ddp: bool
@abstractmethod
def is_overriden(self, *args):
@@ -88,10 +89,8 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
# don't do anything if it's not a dataloader
if not isinstance(dataloader, DataLoader):
return dataloader
-
- need_dist_sampler = self.use_ddp or self.use_ddp2 or self.use_tpu
-
- if need_dist_sampler:
+ need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_tpu)
+ if self.replace_sampler_ddp and need_dist_sampler:
skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -127,6 +127,7 @@ def __init__(
benchmark: bool = False,
reload_dataloaders_every_epoch: bool = False,
auto_lr_find: Union[bool, str] = False,
+ replace_sampler_ddp: bool = True,
default_save_path=None, # backward compatible, todo: remove in v0.8.0
gradient_clip=None, # backward compatible, todo: remove in v0.8.0
nb_gpu_nodes=None, # backward compatible, todo: remove in v0.8.0
@@ -282,6 +283,9 @@ def __init__(
rate in self.hparams.lr | self.hparams.learning_rate in the lightning module.
To use a different key, set a string instead of True with the key name.
+ replace_sampler_ddp: Explicitly enables or disables sampler replacement.
+ If not specified this will toggled automatically ddp is used
+
benchmark: If true enables cudnn.benchmark.
terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the
@@ -362,6 +366,7 @@ def __init__(
self.reload_dataloaders_every_epoch = reload_dataloaders_every_epoch
self.auto_lr_find = auto_lr_find
+ self.replace_sampler_ddp = replace_sampler_ddp
self.truncated_bptt_steps = truncated_bptt_steps
self.resume_from_checkpoint = resume_from_checkpoint
| 0.7.3 breaks reusable dataloaders in DDP
## 🐛 Bug
0.7.3 breaks reusable dataloaders in DDP
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 345, in ddp_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 296, in train
self.reset_train_dataloader(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py", line 128, in reset_train_dataloader
self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py", line 112, in auto_add_sampler
dataloader = type(dataloader)(**dl_args)
File "../main/dataset.py", line 15, in __init__
super().__init__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'iterator'
```
#### Code sample
```
class _RepeatSampler(object):
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
yield from iter(self.sampler)
class FastDataLoader(torch.utils.data.dataloader.DataLoader):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
self.iterator = super().__iter__()
def __len__(self):
return len(self.batch_sampler.sampler)
def __iter__(self):
for i in range(len(self)):
yield next(self.iterator)
```
replace Dataloader with FastDataLoader in lightning
(this snippet is from https://github.com/pytorch/pytorch/issues/15849)
### Expected behavior
Dataloaders initialize correctly and are reused between train/val/epochs (works as expected in 0.7.1)
### Probable Cause
https://github.com/PyTorchLightning/pytorch-lightning/pull/1425
| ummm yeah. we should change the dataloader swap with swapping a dataloader init from the class or not swipe the dataloder at all but set the correct sampler.
@justusschock any ideas?
This is a mixture of #1425 and #1346
And I don't think we can prevent this when we want to set correct samplers also in subclasses of `DataLoader`. We use all public attributes for reinitialization.
The probably easiest fix for you, would be to change `self.iterator` to `self._iterator` to avoid passing this argument in reinit.
If we just change the sampler, this might yield unexpected behaviour. | 2020-04-17T07:59:07Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 345, in ddp_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 296, in train
self.reset_train_dataloader(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py", line 128, in reset_train_dataloader
self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py", line 112, in auto_add_sampler
dataloader = type(dataloader)(**dl_args)
File "../main/dataset.py", line 15, in __init__
super().__init__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'iterator'
| 128 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-1582 | 5ab5084f7b9e137c1e7769228aaed8da92eaad6e | diff --git a/pytorch_lightning/loggers/base.py b/pytorch_lightning/loggers/base.py
--- a/pytorch_lightning/loggers/base.py
+++ b/pytorch_lightning/loggers/base.py
@@ -280,6 +280,7 @@ class LoggerCollection(LightningLoggerBase):
Args:
logger_iterable: An iterable collection of loggers
"""
+
def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
super().__init__()
self._logger_iterable = logger_iterable
@@ -347,20 +348,28 @@ def merge_dicts(
Examples:
>>> import pprint
- >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1}
- >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1}
- >>> d3 = {'a': 1.1, 'v': 2.3}
+ >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}}
+ >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}}
+ >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}}
>>> dflt_func = min
- >>> agg_funcs = {'a': np.mean, 'v': max}
+ >>> agg_funcs = {'a': np.mean, 'v': max, 'd': {'d1': sum}}
>>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func))
- {'a': 1.3, 'b': 2.0, 'c': 1, 'v': 2.3}
+ {'a': 1.3,
+ 'b': 2.0,
+ 'c': 1,
+ 'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}},
+ 'v': 2.3}
"""
-
+ agg_key_funcs = agg_key_funcs or dict()
keys = list(functools.reduce(operator.or_, [set(d.keys()) for d in dicts]))
d_out = {}
for k in keys:
- fn = agg_key_funcs.get(k, default_func) if agg_key_funcs else default_func
- agg_val = fn([v for v in [d_in.get(k) for d_in in dicts] if v is not None])
- d_out[k] = agg_val
+ fn = agg_key_funcs.get(k)
+ values_to_agg = [v for v in [d_in.get(k) for d_in in dicts] if v is not None]
+
+ if isinstance(values_to_agg[0], dict):
+ d_out[k] = merge_dicts(values_to_agg, fn, default_func)
+ else:
+ d_out[k] = (fn or default_func)(values_to_agg)
return d_out
| After update from 0.5.x to 0.7.3 merge_dicts #1278 sometimes breaks training
## 🐛 Bug
After I updated from a quite old lightning version to the newest one, I sometimes get a TypeError from merge_dicts. I guess it's related to this MR #1278 . This Type error is deterministic, meaning it always occurs at the same global step during training. It somehow seems to be related to val_check_interval as well. For some data changing this value leads to no Error. But for other datasets this does not work. Also this only happens during training step, I suspect the training step after validating.
### To Reproduce
Steps to reproduce the behavior:
I have no Idea.
```
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 363, in train
self.run_training_epoch()
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 470, in run_training_epoch
self.log_metrics(batch_step_metrics, grad_norm_dic)
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/trainer/logging.py", line 74, in log_metrics
self.logger.agg_and_log_metrics(scalar_metrics, step=step)
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 128, in agg_and_log_metrics
agg_step, metrics_to_log = self._aggregate_metrics(metrics=metrics, step=step)
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 101, in _aggregate_metrics
agg_step, agg_mets = self._finalize_agg_metrics()
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 116, in _finalize_agg_metrics
agg_mets = merge_dicts(self._metrics_to_agg, self._agg_key_funcs, self._agg_default_func)
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 347, in merge_dicts
agg_val = fn([v for v in [d_in.get(k) for d_in in dicts] if v is not None])
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 3118, in mean
out=out, **kwargs)
File "/home/sebastian/.cache/pypoetry/virtualenvs/forgerydetection-iC5ox0X1-py3.7/lib/python3.7/site-packages/numpy/core/_methods.py", line 75, in _mean
ret = umr_sum(arr, axis, dtype, out, keepdims)
TypeError: unsupported operand type(s) for +: 'dict' and 'dict'
```
Sometimes its also 'dict' and 'int'
### Expected behavior
At least should not break training, but maybe a more verbose message what is wrong. Its quite hard for me to debug, as the structure of the logs I'm returning to lightning does not change.
### Environment
```
cuda:
GPU:
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
available: True
version: 10.1.243
packages:
numpy: 1.16.4
pyTorch_debug: False
pyTorch_version: 1.3.0
pytorch-lightning: 0.7.3
tensorboard: 2.2.0
tqdm: 4.45.0
system:
OS: Linux
architecture:
64bit
ELF
processor: x86_64
python: 3.7.7
version: #97~16.04.1-Ubuntu SMP Wed Apr 1 03:03:31 UTC 2020
```
### Additional context
Also for some reason some runs have an issue with multiprocessing, but it does not break the training:
```
Traceback (most recent call last):████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 8.76it/s]
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 277, in _run_finalizers
finalizer()
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 201, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 110, in _remove_temp_dir
rmtree(tempdir)
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/tmp/pymp-jcqai2xr'
```
| Did you passed any 'agg_key_funcs' to the logger class? If I understand the code correctly, by default np.mean is used to aggregate the dict values returned during training. Maybe numpy tries in the mean function to *add* (+ func) values which can't be summed up?
Can you maybe post the code snippets where you return the metrics to log in the lightning module and the initialization of the logger if you use one? If you don't use a logger, you can disable it by passing logger=False to the trainer (don't know if your previous version had logger on by default).
Hope I can help :)
Thanks for the quick reply!
No I'm not using any 'agg_key_funcs' that I know of.
> If I understand the code correctly, by default np.mean is used to aggregate the dict values returned during training.
This only happens when there is a step in time where two times stuff is logged, right? So my guess is that at some point that is the case that two logs have to be "unified" but this fails, because I'm using "dict in dicts". I need this tho, because I want to have i.e. loss train and val in the same graph.
I'm using the TestTubeLogger:
` logger = TestTubeLogger(save_dir=log_dir, name=name, description=description)
`
and just pass this to the Trainer.
The metric logging to lightning is a bit scattered:
1. train_step in model:
```
x, target = batch
pred = self.forward(x)
loss = self.loss(pred, target)
lightning_log = {"loss": loss}
with torch.no_grad():
train_acc = self.calculate_accuracy(pred, target)
tensorboard_log = {"loss": loss, "acc": train_acc}
return tensorboard_log, lightning_log
```
2. this is passed to a function that lets me add train and val to same graph:
```
def _construct_lightning_log(
self,
tensorboard_log: dict,
lightning_log: dict = None,
suffix: str = "train",
prefix: str = "metrics",
):
lightning_log = lightning_log or {}
fixed_log = {}
for metric, value in tensorboard_log.items():
if isinstance(value, dict):
fixed_log[f"{prefix}/{metric}"] = value
else:
fixed_log[f"{prefix}/{metric}"] = {suffix: value}
return {"log": fixed_log, **lightning_log}
```
Do you pass it after training_step or training_epoch_end? I think lightning collects your logs and tries to aggregate it to one value. I can't test it now. Maybe tomorrow.
But when I quickly type this into python interpreter:
```
>>> d={}
>>> np.mean([d,d])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<__array_function__ internals>", line 5, in mean
File "/usr/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 3334, in mean
return _methods._mean(a, axis=axis, dtype=dtype,
File "/usr/lib/python3.8/site-packages/numpy/core/_methods.py", line 151, in _mean
ret = umr_sum(arr, axis, dtype, out, keepdims)
TypeError: unsupported operand type(s) for +: 'dict' and 'dict'
```
Seems like getting your error.
Maybe print what you exactly return and when it crashes. When I have time tomorrow, I will also make some tests.
After training_step. I not have a training_epoch_end or training_end method defined.
> I think lightning collects your logs and tries to aggregate it to one value.
Yes I think so as well.
Ok I return something like this:
`{'metrics/aud_std': {'test': tensor(1.6337, device='cuda:0')},
'metrics/class_loss_diff': {'test': tensor(nan)},
'metrics/class_loss_val': {'0': tensor(nan), '1': tensor(91.5485)},
'metrics/loss': {'test': tensor(45.7742, device='cuda:0')},
'metrics/vid_std': {'test': tensor(1.6506, device='cuda:0')}}`
What do you mean by when it crashes exactly? I think when it crashes it's always the train step after an validation step (keep in mind I'm validation several times during one epoch). If I change the val_check_interval the error either disappears or happens at a different batch number.
Hello.
I think the problem is in your metrics type. Metrics must have the `Dict[str, float]` type. But in your case, the `metrics` is a nested dict. So, that's why values are failed to be aggregated.
Is it possible for you to flatten the dictionary?
@alexeykarnachev Hey! Ah yes that's what I thought. Do you know why the metrics dict is enforced to be of this type? In 0.5.x this was not an issue as far as I know.
I mean, yes I can flatten it but I want to have i.e. val/loss and train/loss in the same graph. It's basically this: https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_scalars
I know that here https://github.com/PyTorchLightning/pytorch-lightning/issues/1144#issuecomment-599089378 It was said that this should not be done, but for me this is essential.
Is there a way that I can overwrite the merge_dicts function? If so how would I do that?
@fellnerse Okay, I got your point, let's ask Borda's advice)
@Borda, what do you think? Is it possible to combine nested metrics dictionaries with metrics aggregation logic? At first sight, it doesn't look like a big problem. Maybe you can see any side effects of tracking aggregated metrics with nested dictionaries? If no, I can try to fix this issue
I ques it can be used, just need to care about the depth and the aggregation will be a bit complicated... | 2020-04-23T20:27:40Z | [] | [] |
Traceback (most recent call last):████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 8.76it/s]
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 277, in _run_finalizers
finalizer()
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 201, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/multiprocessing/util.py", line 110, in _remove_temp_dir
rmtree(tempdir)
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/shutil.py", line 498, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/home/sebastian/.pyenv/versions/3.7.7/lib/python3.7/shutil.py", line 496, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/tmp/pymp-jcqai2xr'
| 140 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-1589 | 79196246cfcc73391de1be71bfb27d4366daf75a | diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -461,10 +461,15 @@ def __transfer_data_to_device(self, batch, device, gpu_id=None):
# when tuple
if isinstance(batch, tuple):
- batch = list(batch)
- for i, x in enumerate(batch):
- batch[i] = self.__transfer_data_to_device(x, device, gpu_id)
- return tuple(batch)
+ # when namedtuple
+ if hasattr(batch, '_fields'):
+ elem_type = type(batch)
+ return elem_type(*(self.__transfer_data_to_device(x, device, gpu_id) for x in batch))
+ else:
+ batch = list(batch)
+ for i, x in enumerate(batch):
+ batch[i] = self.__transfer_data_to_device(x, device, gpu_id)
+ return tuple(batch)
# when dict
if isinstance(batch, dict):
| Named converted to regular tuples when sent to the gpu.
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
Named tuples returned from `Dataset` get converted to regular tuples when sent to the gpu.
This happens because `isinstance(instance_of_a_named_tuple, tuple)` evaluates to True in `distrib_parts.py`
https://github.com/PyTorchLightning/pytorch-lightning/blob/67d5f4dc392250d23bfeb11aba45e919a99ff1c0/pytorch_lightning/trainer/distrib_parts.py#L463
### To Reproduce
```python
import pytorch_lightning as pl
from collections import namedtuple
import torch
import numpy
NamedTupleDemoInput = namedtuple('DemoInput', ['x1', 'x2', 'y'])
class NamedTupleDemoDataset:
def __len__(self):
return 30000
def __getitem__(self, index):
x1 = numpy.random.uniform(0, 100)
x2 = numpy.random.uniform(0, 100)
y = 2*x1 + 3*x2 + numpy.random.normal(0, 0.05)
return NamedTupleDemoInput(x1, x2, y)
class WeightedSum(torch.nn.Module):
def __init__(self):
super(WeightedSum, self).__init__()
self.a = torch.nn.Parameter(torch.zeros(1))
self.b = torch.nn.Parameter(torch.zeros(1))
def forward(self, x1, x2):
return self.a * x1 + self.b * x2
class NamedTupleDemo(pl.LightningModule):
def __init__(self):
super(NamedTupleDemo, self).__init__()
self.model = WeightedSum()
def forward(self, x1, x2):
return self.model(x1, x2)
def train_dataloader(self):
return torch.utils.data.DataLoader(NamedTupleDemoDataset(), batch_size=128)
def training_step(self, batch, batch_index):
yhat = self.forward(batch.x1, batch.x2)
return {'loss': torch.nn.functional.mse_loss(batch.y, yhat)}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-2)
if __name__ == '__main__':
module = NamedTupleDemo()
pl.Trainer(max_epochs=20, gpus=1).fit(module)
print(f'a={float(module.model.a)} b={float(module.model.b)}')
```
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
```
Traceback (most recent call last):
File "demo.py", line 48, in <module>
pl.Trainer(max_epochs=20, gpus=1).fit(module)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 749, in fit
self.single_gpu_train(model)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 491, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 910, in run_pretrain_routine
self.train()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 384, in train
self.run_training_epoch()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 456, in run_training_epoch
_outputs = self.run_training_batch(batch, batch_idx)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 633, in run_training_batch
loss, batch_output = optimizer_closure()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 597, in optimizer_closure
output_dict = self.training_forward(split_batch, batch_idx, opt_idx, self.hiddens)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 770, in training_forward
output = self.model.training_step(*args)
File "demo.py", line 40, in training_step
yhat = self.forward(batch.x1, batch.x2)
AttributeError: 'tuple' object has no attribute 'x1'
```
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
### Expected behavior
Namedtuples returned from the dataset should be keep their original fields.
### Environment
* CUDA:
- GPU:
- GeForce RTX 2080 Ti
- available: True
- version: 10.2
* Packages:
- numpy: 1.18.3
- pyTorch_debug: False
- pyTorch_version: 1.5.0
- pytorch-lightning: 0.7.4rc5
- tensorboard: 2.2.1
- tqdm: 4.45.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor:
- python: 3.8.2
- version: #1 SMP PREEMPT Sun, 05 Apr 2020 05:13:14 +0000
<!-- Add any other context about the problem here. -->
| 2020-04-24T03:49:56Z | [] | [] |
Traceback (most recent call last):
File "demo.py", line 48, in <module>
pl.Trainer(max_epochs=20, gpus=1).fit(module)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 749, in fit
self.single_gpu_train(model)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 491, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 910, in run_pretrain_routine
self.train()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 384, in train
self.run_training_epoch()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 456, in run_training_epoch
_outputs = self.run_training_batch(batch, batch_idx)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 633, in run_training_batch
loss, batch_output = optimizer_closure()
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 597, in optimizer_closure
output_dict = self.training_forward(split_batch, batch_idx, opt_idx, self.hiddens)
File "/home/n/repos/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 770, in training_forward
output = self.model.training_step(*args)
File "demo.py", line 40, in training_step
yhat = self.forward(batch.x1, batch.x2)
AttributeError: 'tuple' object has no attribute 'x1'
| 141 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-2014 | 8b9b923ca8ad9fdb0ae22928de0029e7c2e7a782 | diff --git a/pl_examples/domain_templates/computer_vision_fine_tuning.py b/pl_examples/domain_templates/computer_vision_fine_tuning.py
--- a/pl_examples/domain_templates/computer_vision_fine_tuning.py
+++ b/pl_examples/domain_templates/computer_vision_fine_tuning.py
@@ -450,5 +450,4 @@ def get_args() -> argparse.Namespace:
if __name__ == '__main__':
-
main(get_args())
diff --git a/pl_examples/domain_templates/generative_adversarial_net.py b/pl_examples/domain_templates/generative_adversarial_net.py
--- a/pl_examples/domain_templates/generative_adversarial_net.py
+++ b/pl_examples/domain_templates/generative_adversarial_net.py
@@ -7,7 +7,7 @@
tensorboard --logdir default
"""
import os
-from argparse import ArgumentParser
+from argparse import ArgumentParser, Namespace
from collections import OrderedDict
import numpy as np
@@ -183,7 +183,7 @@ def on_epoch_end(self):
self.logger.experiment.add_image('generated_images', grid, self.current_epoch)
-def main(args):
+def main(args: Namespace) -> None:
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
diff --git a/pl_examples/domain_templates/imagenet.py b/pl_examples/domain_templates/imagenet.py
--- a/pl_examples/domain_templates/imagenet.py
+++ b/pl_examples/domain_templates/imagenet.py
@@ -1,7 +1,7 @@
"""
This example is largely adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
-import argparse
+from argparse import ArgumentParser, Namespace
import os
import random
from collections import OrderedDict
@@ -183,7 +183,7 @@ def val_dataloader(self):
@staticmethod
def add_model_specific_args(parent_parser): # pragma: no-cover
- parser = argparse.ArgumentParser(parents=[parent_parser])
+ parser = ArgumentParser(parents=[parent_parser])
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=MODEL_NAMES,
help='model architecture: ' +
' | '.join(MODEL_NAMES) +
@@ -210,7 +210,7 @@ def add_model_specific_args(parent_parser): # pragma: no-cover
def get_args():
- parent_parser = argparse.ArgumentParser(add_help=False)
+ parent_parser = ArgumentParser(add_help=False)
parent_parser.add_argument('--data-path', metavar='DIR', type=str,
help='path to dataset')
parent_parser.add_argument('--save-path', metavar='DIR', default=".", type=str,
@@ -228,20 +228,23 @@ def get_args():
return parser.parse_args()
-def main(hparams):
- model = ImageNetLightningModel(hparams)
- if hparams.seed is not None:
- random.seed(hparams.seed)
- torch.manual_seed(hparams.seed)
+def main(args: Namespace) -> None:
+ model = ImageNetLightningModel(**vars(args))
+
+ if args.seed is not None:
+ random.seed(args.seed)
+ torch.manual_seed(args.seed)
cudnn.deterministic = True
+
trainer = pl.Trainer(
- default_root_dir=hparams.save_path,
- gpus=hparams.gpus,
- max_epochs=hparams.epochs,
- distributed_backend=hparams.distributed_backend,
- precision=16 if hparams.use_16bit else 32,
+ default_root_dir=args.save_path,
+ gpus=args.gpus,
+ max_epochs=args.epochs,
+ distributed_backend=args.distributed_backend,
+ precision=16 if args.use_16bit else 32,
)
- if hparams.evaluate:
+
+ if args.evaluate:
trainer.run_evaluation()
else:
trainer.fit(model)
| Bug in GAN example
Bug in https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/domain_templates/generative_adversarial_net.py
When I run `python generative_adversarial_net.py `
I get
```
Traceback (most recent call last):
File "generative_adversarial_net.py", line 218, in <module>
main(hparams)
File "generative_adversarial_net.py", line 192, in main
model = GAN(hparams)
File "generative_adversarial_net.py", line 90, in __init__
self.generator = Generator(latent_dim=self.latent_dim, img_shape=mnist_shape)
File "generative_adversarial_net.py", line 39, in __init__
*block(latent_dim, 128, normalize=False),
File "generative_adversarial_net.py", line 32, in block
layers = [nn.Linear(in_feat, out_feat)]
File "/home/vladimir/anaconda3/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 72, in __init__
self.weight = Parameter(torch.Tensor(out_features, in_features))
TypeError: new(): argument 'size' must be tuple of ints, but found element of type Namespace at pos 2
```
| Replace with `model = GAN(**vars(hparams))` [here](https://github.com/PyTorchLightning/pytorch-lightning/blob/fdbbe968256f6c68a5dbb840a2004b77a618ef61/pl_examples/domain_templates/generative_adversarial_net.py#L192). Same bug in [imagenet script](https://github.com/PyTorchLightning/pytorch-lightning/blob/fdbbe968256f6c68a5dbb840a2004b77a618ef61/pl_examples/domain_templates/imagenet.py#L232) also.
@ternaus @rohitgr7 mind submitting a PR to fix? :) | 2020-05-30T12:26:09Z | [] | [] |
Traceback (most recent call last):
File "generative_adversarial_net.py", line 218, in <module>
main(hparams)
File "generative_adversarial_net.py", line 192, in main
model = GAN(hparams)
File "generative_adversarial_net.py", line 90, in __init__
self.generator = Generator(latent_dim=self.latent_dim, img_shape=mnist_shape)
File "generative_adversarial_net.py", line 39, in __init__
*block(latent_dim, 128, normalize=False),
File "generative_adversarial_net.py", line 32, in block
layers = [nn.Linear(in_feat, out_feat)]
File "/home/vladimir/anaconda3/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 72, in __init__
self.weight = Parameter(torch.Tensor(out_features, in_features))
TypeError: new(): argument 'size' must be tuple of ints, but found element of type Namespace at pos 2
| 177 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2115 | 0bd7780adc4d68007946cf380a6a24e1a08d99d1 | diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -139,6 +139,7 @@ def _get_distributed_sampler(self, dataloader):
else:
world_size = {
'ddp': self.num_nodes * self.num_processes,
+ 'ddp_spawn': self.num_nodes * self.num_processes,
'ddp2': self.num_nodes,
'ddp_cpu': self.num_processes * self.num_nodes
}
diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -221,7 +221,7 @@ def set_distributed_mode(self, distributed_backend):
elif self.num_gpus > 1:
self.use_dp = True
- elif distributed_backend == "ddp":
+ elif distributed_backend in ['ddp', 'ddp_spawn']:
if self.num_gpus == 0:
if self.num_nodes > 1 or self.num_processes > 1:
self.use_ddp = True # ddp_cpu
@@ -378,6 +378,7 @@ def spawn_ddp_children(self, model):
self.interactive_ddp_procs = []
for local_rank in range(1, self.num_processes):
+ print('launching local_rank', local_rank)
env_copy = os.environ.copy()
env_copy['LOCAL_RANK'] = f'{local_rank}'
@@ -394,7 +395,7 @@ def spawn_ddp_children(self, model):
local_rank = 0
self.ddp_train(local_rank, model, is_master=True)
- def ddp_train(self, process_idx, model, is_master=False):
+ def ddp_train(self, process_idx, model, is_master=False, proc_offset=0):
"""
Entry point into a DP thread
:param gpu_idx:
@@ -402,6 +403,9 @@ def ddp_train(self, process_idx, model, is_master=False):
:param cluster_obj:
:return:
"""
+ # offset the process id if requested
+ process_idx = process_idx + proc_offset
+
# show progressbar only on progress_rank 0
if (self.node_rank != 0 or process_idx != 0) and self.progress_bar_callback is not None:
self.progress_bar_callback.disable()
@@ -454,7 +458,7 @@ def ddp_train(self, process_idx, model, is_master=False):
self.reinit_scheduler_properties(self.optimizers, self.lr_schedulers)
# DDP2 uses all GPUs on the machine
- if self.distributed_backend == 'ddp':
+ if self.distributed_backend == 'ddp' or self.distributed_backend == 'ddp_spawn':
device_ids = [self.root_gpu]
elif self.use_ddp2:
device_ids = self.data_parallel_device_ids
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -246,7 +246,7 @@ def __init__(
Use `row_log_interval` instead. Will remove 0.9.0.
- distributed_backend: The distributed backend to use.
+ distributed_backend: The distributed backend to use (dp, ddp, ddp2, ddp_spawn)
use_amp:
.. warning:: .. deprecated:: 0.7.0
@@ -876,9 +876,16 @@ def fit(
self.ddp_train(task, model)
elif self.distributed_backend == 'cpu_ddp':
+ self.__set_random_port()
self.model = model
mp.spawn(self.ddp_train, nprocs=self.num_processes, args=(model,))
+ elif self.distributed_backend == 'ddp_spawn':
+ model.share_memory()
+
+ # spin up peers
+ mp.spawn(self.ddp_train, nprocs=self.num_processes, args=(model, ))
+
elif self.distributed_backend == 'ddp':
self.spawn_ddp_children(model)
| verify ddp and ddp_spawn implementation
CUDA error: an illegal memory access was encountered after updating to the latest stable packages
Can anyone help with this CUDA error: an illegal memory access was encountered ??
It runs fine for several iterations...
## 🐛 Bug
```
Traceback (most recent call last):
File "train_gpu.py", line 237, in <module>
main_local(hparam_trial)
File "train_gpu.py", line 141, in main_local
trainer.fit(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 859, in fit
self.single_gpu_train(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 503, in single_gpu_train
self.run_pretrain_routine(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1015, in run_pretrain_routine
self.train()
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 347, in train
self.run_training_epoch()
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 419, in run_training_epoch
_outputs = self.run_training_batch(batch, batch_idx)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 604, in run_training_batch
self.batch_loss_value.append(loss)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/supporters.py", line 44, in append
x = x.to(self.memory)
RuntimeError: CUDA error: an illegal memory access was encountered
```
### To Reproduce
### Environment
* CUDA:
- GPU:
- Quadro P6000
- available: True
- version: 10.2
* Packages:
- numpy: 1.18.1
- pyTorch_debug: False
- pyTorch_version: 1.5.0
- pytorch-lightning: 0.7.6
- tensorboard: 2.2.2
- tqdm: 4.46.1
* System:
- OS: Linux
- architecture:
- 64bit
-
- processor: x86_64
- python: 3.7.0
- version: #47~18.04.1-Ubuntu SMP Thu May 7 13:10:50 UTC 2020
| 2020-06-08T15:37:16Z | [] | [] |
Traceback (most recent call last):
File "train_gpu.py", line 237, in <module>
main_local(hparam_trial)
File "train_gpu.py", line 141, in main_local
trainer.fit(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 859, in fit
self.single_gpu_train(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 503, in single_gpu_train
self.run_pretrain_routine(model)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1015, in run_pretrain_routine
self.train()
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 347, in train
self.run_training_epoch()
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 419, in run_training_epoch
_outputs = self.run_training_batch(batch, batch_idx)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 604, in run_training_batch
self.batch_loss_value.append(loss)
File "/shared/storage/cs/staffstore/username/anaconda3/envs/sh1/lib/python3.7/site-packages/pytorch_lightning/trainer/supporters.py", line 44, in append
x = x.to(self.memory)
RuntimeError: CUDA error: an illegal memory access was encountered
| 188 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-2216 | e780072961562ab1d89bad871918fcc422ad0ac6 | diff --git a/pytorch_lightning/loggers/base.py b/pytorch_lightning/loggers/base.py
--- a/pytorch_lightning/loggers/base.py
+++ b/pytorch_lightning/loggers/base.py
@@ -3,13 +3,11 @@
import operator
from abc import ABC, abstractmethod
from argparse import Namespace
-from typing import Union, Optional, Dict, Iterable, Any, Callable, List, Sequence, Mapping, Tuple
+from typing import Union, Optional, Dict, Iterable, Any, Callable, List, Sequence, Mapping, Tuple, MutableMapping
import numpy as np
import torch
-from pytorch_lightning.utilities import rank_zero_only
-
class LightningLoggerBase(ABC):
"""
@@ -174,9 +172,9 @@ def _flatten_dict(params: Dict[str, Any], delimiter: str = '/') -> Dict[str, Any
def _dict_generator(input_dict, prefixes=None):
prefixes = prefixes[:] if prefixes else []
- if isinstance(input_dict, dict):
+ if isinstance(input_dict, MutableMapping):
for key, value in input_dict.items():
- if isinstance(value, (dict, Namespace)):
+ if isinstance(value, (MutableMapping, Namespace)):
value = vars(value) if isinstance(value, Namespace) else value
for d in _dict_generator(value, prefixes + [key]):
yield d
| Hydra MLFlow Clash
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
When using the MLFlow logger with Hydra, because the parameters passed to the LightningModule is a `DictConfig`, the condition in the `logger/base.py` is not met.
https://github.com/PyTorchLightning/pytorch-lightning/blob/8211256c46430e43e0c27e4f078c72085bb4ea34/pytorch_lightning/loggers/base.py#L177
### To Reproduce
Use Hydra and MLFlow together.
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
```python
Traceback (most recent call last):
File "/home/siavash/KroniKare/kwae2/kwae_ma/models/pl_train_segmentation_model.py", line 115, in <module>
main()
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/main.py", line 24, in decorated_main
strict=strict,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/_internal/utils.py", line 174, in run_hydra
overrides=args.overrides,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/_internal/hydra.py", line 86, in run
job_subdir_key=None,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/plugins/common/utils.py", line 109, in run_job
ret.return_value = task_function(task_cfg)
File "/home/siavash/KroniKare/kwae2/kwae_ma/models/pl_train_segmentation_model.py", line 111, in main
trainer.fit(wound_seg_pl)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 765, in fit
self.single_gpu_train(model)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 492, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 843, in run_pretrain_routine
self.logger.log_hyperparams(ref_model.hparams)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 275, in log_hyperparams
[logger.log_hyperparams(params) for logger in self._logger_iterable]
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 275, in <listcomp>
[logger.log_hyperparams(params) for logger in self._logger_iterable]
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py", line 10, in wrapped_fn
return fn(*args, **kwargs)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/mlflow.py", line 105, in log_hyperparams
self.experiment.log_param(self.run_id, k, v)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/tracking/client.py", line 206, in log_param
self._tracking_client.log_param(run_id, key, value)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 177, in log_param
_validate_param_name(key)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/utils/validation.py", line 120, in _validate_param_name
INVALID_PARAMETER_VALUE)
mlflow.exceptions.MlflowException: Invalid parameter name: ''. Names may be treated as files in certain cases, and must not resolve to other names when treated as such. This name would resolve to '.'
```
### Expected behavior
Check whether the instance if `dict` or `DictConfig` in the given line.
| Hi! thanks for your contribution!, great first issue!
> Check whether the instance if `dict` or `DictConfig` in the given line.
@ssakhavi that sounds reasonable solution, mind sending a PR - fix and its test? | 2020-06-17T03:24:11Z | [] | [] |
Traceback (most recent call last):
File "/home/siavash/KroniKare/kwae2/kwae_ma/models/pl_train_segmentation_model.py", line 115, in <module>
main()
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/main.py", line 24, in decorated_main
strict=strict,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/_internal/utils.py", line 174, in run_hydra
overrides=args.overrides,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/_internal/hydra.py", line 86, in run
job_subdir_key=None,
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/hydra/plugins/common/utils.py", line 109, in run_job
ret.return_value = task_function(task_cfg)
File "/home/siavash/KroniKare/kwae2/kwae_ma/models/pl_train_segmentation_model.py", line 111, in main
trainer.fit(wound_seg_pl)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 765, in fit
self.single_gpu_train(model)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 492, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 843, in run_pretrain_routine
self.logger.log_hyperparams(ref_model.hparams)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 275, in log_hyperparams
[logger.log_hyperparams(params) for logger in self._logger_iterable]
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/base.py", line 275, in <listcomp>
[logger.log_hyperparams(params) for logger in self._logger_iterable]
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py", line 10, in wrapped_fn
return fn(*args, **kwargs)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/pytorch_lightning/loggers/mlflow.py", line 105, in log_hyperparams
self.experiment.log_param(self.run_id, k, v)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/tracking/client.py", line 206, in log_param
self._tracking_client.log_param(run_id, key, value)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 177, in log_param
_validate_param_name(key)
File "/home/siavash/anaconda3/envs/kwae-ma/lib/python3.7/site-packages/mlflow/utils/validation.py", line 120, in _validate_param_name
INVALID_PARAMETER_VALUE)
mlflow.exceptions.MlflowException: Invalid parameter name: ''. Names may be treated as files in certain cases, and must not resolve to other names when treated as such. This name would resolve to '.'
| 201 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2255 | b5a2f1ec4463064394dc6d977ffd246aa11158af | diff --git a/pl_examples/basic_examples/gpu_template.py b/pl_examples/basic_examples/gpu_template.py
--- a/pl_examples/basic_examples/gpu_template.py
+++ b/pl_examples/basic_examples/gpu_template.py
@@ -23,7 +23,7 @@ def main(hparams):
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
- model = LightningTemplateModel(hparams)
+ model = LightningTemplateModel(**vars(hparams))
# ------------------------
# 2 INIT TRAINER
@@ -61,7 +61,7 @@ def main(hparams):
'--distributed_backend',
type=str,
default='dp',
- help='supports three options dp, ddp, ddp2'
+ help='supports four options dp, ddp, ddp2, ddp_spawn'
)
parent_parser.add_argument(
'--use_16bit',
| CPU/GPU Template
## 🐛 Bug
The GPU or CPU template do not run currently on master after changes including the setup hook.
```
python -m pl_examples.basic_examples.gpu_template --gpus 4 --distributed_backend ddp
python -m pl_examples.basic_examples.cpu_template
```
CPU Template Error:
```
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/basic_examples/cpu_template.py", line 53, in <module>
main(args)
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/basic_examples/cpu_template.py", line 34, in main
trainer.fit(model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 952, in fit
self.run_pretrain_routine(model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 1063, in run_pretrain_routine
self.reset_val_dataloader(ref_model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 331, in reset_val_dataloader
self._reset_eval_dataloader(model, 'val')
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 253, in _reset_eval_dataloader
dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 352, in request_dataloader
dataloader = dataloader_fx()
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/models/lightning_template.py", line 158, in val_dataloader
return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=4)
File "/home/anthony/.cache/pypoetry/virtualenvs/robotics-zp-60jGk-py3.6/lib/python3.6/site-packages/torch/nn/modules/module.py", line 594, in __getattr__
type(self).__name__, name))
AttributeError: 'LightningTemplateModel' object has no attribute 'mnist_test'
```
GPU Template Error:
```
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/models/lightning_template.py", line 64, in __init__
self.c_d1_drop = nn.Dropout(self.drop_prob)
File "/home/anthony/.cache/pypoetry/virtualenvs/robotics-zp-60jGk-py3.6/lib/python3.6/site-packages/torch/nn/modules/dropout.py", line 10, in __init__
if p < 0 or p > 1:
TypeError: '<' not supported between instances of 'Namespace' and 'int'
```
### Environment
* CUDA:
- GPU:
- GeForce RTX 2080 Ti
- GeForce RTX 2080 Ti
- GeForce RTX 2080 Ti
- GeForce RTX 2080 Ti
- available: True
- version: 10.2
* Packages:
- numpy: 1.18.4
- pyTorch_debug: False
- pyTorch_version: 1.5.0
- pytorch-lightning: 0.8.0
- tensorboard: 2.2.1
- tqdm: 4.46.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.6.8
- version: #44~18.04.2-Ubuntu SMP Thu Apr 23 14:27:18 UTC 2020
| try again?
> try again?
it is in master now... :( | 2020-06-19T02:43:10Z | [] | [] |
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/basic_examples/cpu_template.py", line 53, in <module>
main(args)
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/basic_examples/cpu_template.py", line 34, in main
trainer.fit(model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 952, in fit
self.run_pretrain_routine(model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 1063, in run_pretrain_routine
self.reset_val_dataloader(ref_model)
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 331, in reset_val_dataloader
self._reset_eval_dataloader(model, 'val')
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 253, in _reset_eval_dataloader
dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))
File "/home/anthony/Downloads/pytorch-lightning/pytorch_lightning/trainer/data_loading.py", line 352, in request_dataloader
dataloader = dataloader_fx()
File "/home/anthony/Downloads/pytorch-lightning/pl_examples/models/lightning_template.py", line 158, in val_dataloader
return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=4)
File "/home/anthony/.cache/pypoetry/virtualenvs/robotics-zp-60jGk-py3.6/lib/python3.6/site-packages/torch/nn/modules/module.py", line 594, in __getattr__
type(self).__name__, name))
AttributeError: 'LightningTemplateModel' object has no attribute 'mnist_test'
| 209 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2293 | 3256fe4e5a405db1ab00d4cf4d48cbbfc7730959 | diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -52,6 +52,8 @@ def _has_len(dataloader: DataLoader) -> bool:
return True
except TypeError:
return False
+ except NotImplementedError: # e.g. raised by torchtext if a batch_size_fn is used
+ return False
class TrainerDataLoadingMixin(ABC):
| _has_len does not handle NotImplementedError (raised by torchtext)
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
When using torchtext.data.Iterator with a batch_size_fn function the __len__ function raises a NotImplementedError which is not caught by _has_len function.
A bug-fix is **very simple** by just returning False if a NotImplementedError is raised. This is unlikely to have any negative side effects since it corresponds with what _hads_len is expected to do. The fix allowed me to train my model using torch text.
I plan to submit a pull request with the fix above.
There are no additional dependencies required; however this problem occurred when using torchtext.
Example stack trace:
```
Traceback (most recent call last):
File "/Users/thomas/scm/OakDataPrep/oakSkipThoughtTrainer.py", line 18, in <module>
trainer.fit(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 952, in fit
self.run_pretrain_routine(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1091, in run_pretrain_routine
self.train()
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 334, in train
self.reset_train_dataloader(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py", line 201, in reset_train_dataloader
if not _has_len(self.train_dataloader):
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py", line 49, in _has_len
if len(dataloader) == 0:
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/torchtext/data/iterator.py", line 136, in __len__
raise NotImplementedError
NotImplementedError
```
### To Reproduce
Sorry I currently don't have a minimal example. The issue will always occur when torchtext.data.Iterator gets a batch_size_fn passed in. If the fix is not convincing I can take the time and construct a code example. Hope this is not necessary.
#### Code sample
I created my own Iterator for a Skip-Thought model, that dynamically batches sentences together. This might be unnecessary complex, or even not really useful however it revealed that issue described above when using torchtext. For context here is a code excerpt that creates the issue:
```
import torchtext
...
global max_src_in_batch, max_tgt_in_batch
def batch_size_fn(new, count, sofar):
"Keep augmenting batch and calculate total number of tokens + padding."
global max_src_in_batch, max_tgt_in_batch
if count == 1:
max_src_in_batch = 0
max_tgt_in_batch = 0
max_src_in_batch = max(max_src_in_batch, len(new.current))
max_tgt_in_batch = max(max_tgt_in_batch, len(new.next) + 2)
src_elements = count * max_src_in_batch
tgt_elements = count * max_tgt_in_batch
return max(src_elements, tgt_elements)
class MyIterator(torchtext.data.Iterator):
def create_batches(self):
if self.train:
def pool(d, random_shuffler):
for p in data.batch(d, self.batch_size * 100):
p_batch = data.batch(
sorted(p, key=self.sort_key),
self.batch_size, self.batch_size_fn)
for b in random_shuffler(list(p_batch)):
yield b
self.batches = pool(self.data(), self.random_shuffler)
else:
self.batches = []
for b in data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
...
class SkipThoughts(pl.LightningModule):
...
@pl.data_loader
def train_dataloader(self):
train_iter = MyIterator(self.my_train_dataloader, batch_size=self.batch_size, repeat=False,
sort_key=lambda x:
data.interleave_keys(len(x.current),
data.interleave_keys(len(x.prev),
len(x.next))),
batch_size_fn=batch_size_fn, train=True,
shuffle=True)
return train_iter
```
But this happens whenever a batch_size_fn is used in torchtext. Because it is unknown how many batches the data set will have torchtext __len__ method returns a NotImplementedError. See code snipped below:
```
def __len__(self):
if self.batch_size_fn is not None:
raise NotImplementedError
return math.ceil(len(self.dataset) / self.batch_size)
```
### Expected behavior
The function _has_len tests if len can is available and then returns True, otherwise False. It shoudl return False if NotImplementedError is raised.
### Environment
/Users/thomas/virtualenv/Python3/PyTorch/env/bin/python /Users/thomas/scm/OakDataPrep/collect_env_details.py
* CUDA:
- GPU:
- available: False
- version: None
* Packages:
- numpy: 1.18.2
- pyTorch_debug: False
- pyTorch_version: 1.5.0
- pytorch-lightning: 0.8.0
- tensorboard: 2.2.0
- tqdm: 4.45.0
* System:
- OS: Darwin
- architecture:
- 64bit
-
- processor: i386
- python: 3.7.7
- version: Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
Process finished with exit code 0
### Additional context
Issue occur with Pytorch-Lighning 0.8 and Torchtext 0.6
<!-- Add any other context about the problem here. -->
| 2020-06-19T23:57:59Z | [] | [] |
Traceback (most recent call last):
File "/Users/thomas/scm/OakDataPrep/oakSkipThoughtTrainer.py", line 18, in <module>
trainer.fit(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 952, in fit
self.run_pretrain_routine(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1091, in run_pretrain_routine
self.train()
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 334, in train
self.reset_train_dataloader(model)
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py", line 201, in reset_train_dataloader
if not _has_len(self.train_dataloader):
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py", line 49, in _has_len
if len(dataloader) == 0:
File "/Users/thomas/virtualenv/Python3/PyTorch/env/lib/python3.7/site-packages/torchtext/data/iterator.py", line 136, in __len__
raise NotImplementedError
NotImplementedError
| 213 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-2356 | 220bb6db57e7181e857a128e245ce242b6cf429f | diff --git a/pytorch_lightning/trainer/optimizers.py b/pytorch_lightning/trainer/optimizers.py
--- a/pytorch_lightning/trainer/optimizers.py
+++ b/pytorch_lightning/trainer/optimizers.py
@@ -111,15 +111,25 @@ def configure_schedulers(self, schedulers: list):
def reinit_scheduler_properties(self, optimizers: list, schedulers: list):
# Reinitialize optimizer.step properties added by schedulers
for scheduler in schedulers:
+ scheduler = scheduler['scheduler']
+
for optimizer in optimizers:
- scheduler = scheduler['scheduler']
# check that we dont mix users optimizers and schedulers
if scheduler.optimizer == optimizer:
# Find the mro belonging to the base lr scheduler class
for i, mro in enumerate(scheduler.__class__.__mro__):
- if mro == optim.lr_scheduler._LRScheduler:
+ if (
+ mro == optim.lr_scheduler._LRScheduler
+ or mro == optim.lr_scheduler.ReduceLROnPlateau
+ ):
idx = i
- scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
+ state = scheduler.state_dict()
+ else:
+ state = None
+
+ scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
+ if state is not None:
+ scheduler.load_state_dict(state)
class _MockOptimizer(Optimizer):
| Trainer(precision=16) fails with optim.lr_scheduler.ReduceLROnPlateau
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
### To Reproduce
Steps to reproduce the behavior:
1. Create a `pl.LightningModule` that returns your optimizer along with a `optim.lr_scheduler.ReduceLROnPlateau` scheduler from `configure_optimizers`
2. Create a `pl.Trainer` wit `precision=16`
3. Run your training (i.e., `trainer.fit(model)`)
4. See error
```console
Traceback (most recent call last):
File "main.py", line 65, in <module>
main()
File "main.py", line 61, in main
trainer.fit(model)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 889, in fit
self.dp_train(model)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 223, in dp_train
self.reinit_scheduler_properties(optimizers, self.lr_schedulers)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/optimizers.py", line 122, in reinit_scheduler_properties
scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
UnboundLocalError: local variable 'idx' referenced before assignment
```
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
<!-- #### Code sample -->
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
<!-- ### Expected behavior -->
<!-- A clear and concise description of what you expected to happen. -->
<!-- ### Environment
Please copy and paste the output from our
[environment collection script](https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py)
(or fill out the checklist below manually).
You can get the script and run it with:
```
wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
# For security purposes, please check the contents of collect_env_details.py before running it.
python collect_env_details.py
```
- PyTorch Version (1.5):
- OS (Linux):
### Additional context
-->
<!-- Add any other context about the problem here. -->
The error occurs in `pytorch-lightning/pytorch_lightning/trainer/optimizers.py", line 122`.
```python
def reinit_scheduler_properties(self, optimizers: list, schedulers: list):
# Reinitialize optimizer.step properties added by schedulers
for scheduler in schedulers:
for optimizer in optimizers:
scheduler = scheduler['scheduler']
# check that we dont mix users optimizers and schedulers
if scheduler.optimizer == optimizer:
# Find the mro belonging to the base lr scheduler class
for i, mro in enumerate(scheduler.__class__.__mro__):
if mro == optim.lr_scheduler._LRScheduler:
idx = i
scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
```
The `idx` local variable is unassigned because `optim.lr_scheduler.ReduceLROnPlateau` is not a subclass of `optim.lr_scheduler._LRScheduler`.
I could work around the error by adding a specific check for `optim.lr_scheduler.ReduceLROnPlateau` but I'm not sure if this is a good solution.
```python
def reinit_scheduler_properties(self, optimizers: list, schedulers: list):
# Reinitialize optimizer.step properties added by schedulers
for scheduler in schedulers:
for optimizer in optimizers:
scheduler = scheduler['scheduler']
# check that we dont mix users optimizers and schedulers
if scheduler.optimizer == optimizer:
# Find the mro belonging to the base lr scheduler class
for i, mro in enumerate(scheduler.__class__.__mro__):
if mro == optim.lr_scheduler._LRScheduler:
idx = i
elif mro == optim.lr_scheduler.ReduceLROnPlateau:
idx = i
scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
```
### Related issue in PyTorch:
ReduceLROnPlateau parent class is not _LRScheduler #21981
https://github.com/pytorch/pytorch/issues/21981
| Hi! thanks for your contribution!, great first issue!
@naokishibuya good catch. It seems like a problem that should be solved upstream in pytorch, but for now we can solve this locally. Would you be up for a PR?
When I tried this fix, it solved the error but unfortunately `ReduceLROnPlateau` stopped working for me (i.e. there was no indication of the LR decreasing with `verbose=True` or on TensorBoard). If I switched back to `precision=32`, it works normally again
I think that the fix is actually working, however only calling `__init__(scheduler, optimizer)` will reset all other arguments (patience, mode, ect) to default values for the `ReduceLrOnPlauteau` scheduler. A solution to this is to copy over these properties:
```
__init__(scheduler, optimizer, patience=scheduler.patience,mode=scheduler.mode,...)
```
Again I think this is a bit hacky, and a proper solution upstream in pytorch is better.
I think this does the trick for me:
```python
def reinit_scheduler_properties(self, optimizers: list, schedulers: list):
# Reinitialize optimizer.step properties added by schedulers
for scheduler in schedulers:
for optimizer in optimizers:
scheduler = scheduler["scheduler"]
# check that we dont mix users optimizers and schedulers
if scheduler.optimizer == optimizer:
# Find the mro belonging to the base lr scheduler class
for i, mro in enumerate(scheduler.__class__.__mro__):
if (
mro == optim.lr_scheduler._LRScheduler
or mro == optim.lr_scheduler.ReduceLROnPlateau
):
idx = i
state = scheduler.state_dict()
else:
state = None
scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
if state is not None:
scheduler.load_state_dict(state)
```
Happy to open a PR if it looks ok to you guys | 2020-06-25T02:42:06Z | [] | [] |
Traceback (most recent call last):
File "main.py", line 65, in <module>
main()
File "main.py", line 61, in main
trainer.fit(model)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 889, in fit
self.dp_train(model)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 223, in dp_train
self.reinit_scheduler_properties(optimizers, self.lr_schedulers)
File "/workspace/pytorch-lightning/pytorch_lightning/trainer/optimizers.py", line 122, in reinit_scheduler_properties
scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer)
UnboundLocalError: local variable 'idx' referenced before assignment
| 219 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2358 | a5f45787eabddfec4559983f8e6ba1c8317f62f1 | diff --git a/pl_examples/basic_examples/gpu_template.py b/pl_examples/basic_examples/gpu_template.py
--- a/pl_examples/basic_examples/gpu_template.py
+++ b/pl_examples/basic_examples/gpu_template.py
@@ -61,7 +61,8 @@ def main(hparams):
'--distributed_backend',
type=str,
default='dp',
- help='supports four options dp, ddp, ddp2, ddp_spawn'
+ help='supports four options dp, ddp, ddp2, ddp_spawn, ...',
+ choices=['dp', 'ddp', 'ddp2', 'ddp_spawn', 'ddp_cpu'],
)
parent_parser.add_argument(
'--use_16bit',
diff --git a/pytorch_lightning/core/saving.py b/pytorch_lightning/core/saving.py
--- a/pytorch_lightning/core/saving.py
+++ b/pytorch_lightning/core/saving.py
@@ -279,7 +279,7 @@ def load_hparams_from_tags_csv(tags_csv: str) -> Dict[str, Any]:
"""Load hparams from a file.
>>> hparams = Namespace(batch_size=32, learning_rate=0.001, data_root='./any/path/here')
- >>> path_csv = './testing-hparams.csv'
+ >>> path_csv = os.path.join('.', 'testing-hparams.csv')
>>> save_hparams_to_tags_csv(path_csv, hparams)
>>> hparams_new = load_hparams_from_tags_csv(path_csv)
>>> vars(hparams) == hparams_new
@@ -304,7 +304,7 @@ def save_hparams_to_tags_csv(tags_csv: str, hparams: Union[dict, Namespace]) ->
if isinstance(hparams, Namespace):
hparams = vars(hparams)
- with open(tags_csv, 'w') as fp:
+ with open(tags_csv, 'w', newline='') as fp:
fieldnames = ['key', 'value']
writer = csv.DictWriter(fp, fieldnames=fieldnames)
writer.writerow({'key': 'key', 'value': 'value'})
diff --git a/pytorch_lightning/metrics/converters.py b/pytorch_lightning/metrics/converters.py
--- a/pytorch_lightning/metrics/converters.py
+++ b/pytorch_lightning/metrics/converters.py
@@ -10,8 +10,16 @@
import numpy as np
import torch
from torch.utils.data._utils.collate import np_str_obj_array_pattern
-
from pytorch_lightning.utilities.apply_func import apply_to_collection
+from pytorch_lightning.utilities import rank_zero_warn
+
+try:
+ from torch.distributed import ReduceOp
+except ImportError:
+ class ReduceOp:
+ SUM = None
+
+ rank_zero_warn('Unsupported `ReduceOp` for distributed computing.')
def _apply_to_inputs(func_to_apply: Callable, *dec_args, **dec_kwargs) -> Callable:
@@ -217,7 +225,7 @@ def _tensor_collection_metric_conversion(func_to_decorate: Callable) -> Callable
def _sync_ddp_if_available(result: Union[torch.Tensor],
group: Optional[Any] = None,
- reduce_op: Optional[torch.distributed.ReduceOp] = None,
+ reduce_op: Optional[ReduceOp] = None,
) -> torch.Tensor:
"""
Function to reduce the tensors from several ddp processes to one master process
@@ -247,7 +255,7 @@ def _sync_ddp_if_available(result: Union[torch.Tensor],
def sync_ddp(group: Optional[Any] = None,
- reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
+ reduce_op: Optional[ReduceOp] = None) -> Callable:
"""
This decorator syncs a functions outputs across different processes for DDP.
@@ -269,7 +277,7 @@ def decorator_fn(func_to_decorate):
def numpy_metric(group: Optional[Any] = None,
- reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
+ reduce_op: Optional[ReduceOp] = None) -> Callable:
"""
This decorator shall be used on all function metrics working on numpy arrays.
It handles the argument conversion and DDP reduction for metrics working on numpy.
@@ -292,7 +300,7 @@ def decorator_fn(func_to_decorate):
def tensor_metric(group: Optional[Any] = None,
- reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
+ reduce_op: Optional[ReduceOp] = None) -> Callable:
"""
This decorator shall be used on all function metrics working on tensors.
It handles the argument conversion and DDP reduction for metrics working on tensors.
@@ -314,7 +322,7 @@ def decorator_fn(func_to_decorate):
def tensor_collection_metric(group: Optional[Any] = None,
- reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
+ reduce_op: Optional[ReduceOp] = None) -> Callable:
"""
This decorator shall be used on all function metrics working on tensors and returning collections
that cannot be converted to tensors.
diff --git a/pytorch_lightning/metrics/sklearns.py b/pytorch_lightning/metrics/sklearns.py
--- a/pytorch_lightning/metrics/sklearns.py
+++ b/pytorch_lightning/metrics/sklearns.py
@@ -5,6 +5,18 @@
from pytorch_lightning import _logger as lightning_logger
from pytorch_lightning.metrics.metric import NumpyMetric
+from pytorch_lightning.utilities import rank_zero_warn
+
+try:
+ from torch.distributed import ReduceOp, group
+except ImportError:
+ class ReduceOp:
+ SUM = None
+
+ class group:
+ WORLD = None
+
+ rank_zero_warn('Unsupported `ReduceOp` for distributed computing.')
class SklearnMetric(NumpyMetric):
@@ -20,8 +32,8 @@ class SklearnMetric(NumpyMetric):
def __init__(
self,
metric_name: str,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
**kwargs,
):
"""
@@ -82,8 +94,8 @@ class Accuracy(SklearnMetric):
def __init__(
self,
normalize: bool = True,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -136,8 +148,8 @@ class AUC(SklearnMetric):
"""
def __init__(
self,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -174,8 +186,8 @@ class AveragePrecision(SklearnMetric):
def __init__(
self,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -240,8 +252,8 @@ class ConfusionMatrix(SklearnMetric):
"""
def __init__(
self, labels: Optional[Sequence] = None,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -304,8 +316,8 @@ def __init__(
self, labels: Optional[Sequence] = None,
pos_label: Union[str, int] = 1,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -397,8 +409,8 @@ def __init__(
labels: Optional[Sequence] = None,
pos_label: Union[str, int] = 1,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -488,8 +500,8 @@ def __init__(
labels: Optional[Sequence] = None,
pos_label: Union[str, int] = 1,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -576,8 +588,8 @@ def __init__(
labels: Optional[Sequence] = None,
pos_label: Union[str, int] = 1,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -663,8 +675,8 @@ class PrecisionRecallCurve(SklearnMetric):
def __init__(
self,
pos_label: Union[str, int] = 1,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -737,8 +749,8 @@ class ROC(SklearnMetric):
def __init__(
self,
pos_label: Union[str, int] = 1,
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
@@ -795,8 +807,8 @@ class AUROC(SklearnMetric):
def __init__(
self,
average: Optional[str] = 'macro',
- reduce_group: Any = torch.distributed.group.WORLD,
- reduce_op: Any = torch.distributed.ReduceOp.SUM,
+ reduce_group: Any = group.WORLD,
+ reduce_op: Any = ReduceOp.SUM,
):
"""
Args:
diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -35,7 +35,7 @@
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -139,7 +139,7 @@ def train_fx(trial_hparams, cluster_manager, _):
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -38,7 +38,7 @@
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
--- a/pytorch_lightning/trainer/evaluation_loop.py
+++ b/pytorch_lightning/trainer/evaluation_loop.py
@@ -144,7 +144,7 @@
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -52,7 +52,7 @@
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
@@ -255,7 +255,7 @@ def __init__(
Use `row_log_interval` instead. Will remove 0.9.0.
- distributed_backend: The distributed backend to use (dp, ddp, ddp2, ddp_spawn)
+ distributed_backend: The distributed backend to use (dp, ddp, ddp2, ddp_spawn, ddp_cpu)
use_amp:
.. warning:: .. deprecated:: 0.7.0
@@ -885,7 +885,7 @@ def fit(
task = int(os.environ['LOCAL_RANK'])
self.ddp_train(task, model)
- elif self.distributed_backend == 'cpu_ddp':
+ elif self.distributed_backend == 'ddp_cpu':
self.set_random_port()
self.model = model
mp.spawn(self.ddp_train, nprocs=self.num_processes, args=(model,))
diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -114,7 +114,7 @@
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -183,7 +183,7 @@ def training_step(self, batch, batch_idx):
try:
import horovod.torch as hvd
-except ImportError:
+except (ModuleNotFoundError, ImportError):
HOROVOD_AVAILABLE = False
else:
HOROVOD_AVAILABLE = True
diff --git a/pytorch_lightning/utilities/cloud_io.py b/pytorch_lightning/utilities/cloud_io.py
--- a/pytorch_lightning/utilities/cloud_io.py
+++ b/pytorch_lightning/utilities/cloud_io.py
@@ -5,8 +5,7 @@
def load(path_or_url: str, map_location=None):
- parsed = urlparse(path_or_url)
- if parsed.scheme == '' or Path(path_or_url).is_file():
- # no scheme or local file
+ if urlparse(path_or_url).scheme == '' or Path(path_or_url).drive: # no scheme or with a drive letter
return torch.load(path_or_url, map_location=map_location)
- return torch.hub.load_state_dict_from_url(path_or_url, map_location=map_location)
+ else:
+ return torch.hub.load_state_dict_from_url(path_or_url, map_location=map_location)
| accuracy metric dosen't support windows
## 🐛 Bug
Pytorch Metric.Accuracy uses `ReduceOp` from 'torch.distribution' but torch.distributrion doesn't support `windows`
- https://github.com/pytorch/pytorch/blob/cf8a9b50cacb1702f5855859c657a5358976437b/torch/distributed/__init__.py#L10 : `torch.distributed is available on Linux and MacOS.`
### To Reproduce
Use Metric.Accuracy in Windows environment
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
#### Code sample
- I use code sample from `https://github.com/PyTorchLightning/pytorch-lightning/issues/2355`
### Expected behavior
add check OS in `metric.accuracy` and use condition for import different module
```
try:
return platform.linux_distribution()
except:
return "N/A"
```
or warning to windows user, they can't use `metric.accuracy`
### Environment
```
* CUDA:
- GPU:
- GeForce RTX 2080 Ti
- GeForce GTX 1080 Ti
- available: True
- version: 10.1
* Packages:
- numpy: 1.18.1
- pyTorch_debug: False
- pyTorch_version: 1.5.1
- pytorch-lightning: 0.8.1
- tensorboard: 2.2.1
- tqdm: 4.46.0
* System:
- OS: Windows
- architecture:
- 64bit
- WindowsPE
- processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
- python: 3.6.10
- version: 10.0.18362
```
### Additional context
```
Traceback (most recent call last):
File "test.py", line 11, in <module>
from pytorch_lightning.metrics.functional import accuracy
File "C:\Users\dcho\Anaconda3\envs\torch_py36\lib\site-packages\pytorch_lightning\metrics\__init__.py", line 1, in <module>
from pytorch_lightning.metrics.converters import numpy_metric, tensor_metric
File "C:\Users\dcho\Anaconda3\envs\torch_py36\lib\site-packages\pytorch_lightning\metrics\converters.py", line 220, in <module>
reduce_op: Optional[torch.distributed.ReduceOp] = None,
AttributeError: module 'torch.distributed' has no attribute 'ReduceOp'
```
<!-- Add any other context about the problem here. -->
Always thanks for developing & maintaining the cool framework
| 2020-06-25T07:51:08Z | [] | [] |
Traceback (most recent call last):
File "test.py", line 11, in <module>
from pytorch_lightning.metrics.functional import accuracy
File "C:\Users\dcho\Anaconda3\envs\torch_py36\lib\site-packages\pytorch_lightning\metrics\__init__.py", line 1, in <module>
from pytorch_lightning.metrics.converters import numpy_metric, tensor_metric
File "C:\Users\dcho\Anaconda3\envs\torch_py36\lib\site-packages\pytorch_lightning\metrics\converters.py", line 220, in <module>
reduce_op: Optional[torch.distributed.ReduceOp] = None,
AttributeError: module 'torch.distributed' has no attribute 'ReduceOp'
| 220 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-2360 | f2710bb500be017d48ccc6cf596bbed6cc9bdad5 | diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -1193,7 +1193,8 @@ def test(
self.teardown('test')
if self.is_function_implemented('teardown'):
- self.model.teardown('test')
+ model_ref = self.get_model()
+ model_ref.teardown('test')
def check_model_configuration(self, model: LightningModule):
r"""
| AttributeError: 'LightningDataParallel' object has no attribute 'teardown'
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
### To Reproduce
Steps to reproduce the behavior:
trainer = pytorch_lightning.Trainer(
gpus=2,
distributed_backend='dp'
)
model = BaseModel.load_from_checkpoint(...)
trainer.test(model)
Traceback (most recent call last):
File "run_kitti.py", line 351, in <module>
trainer.test(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1198, in test
self.model.teardown('test')
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 594, in __getattr__
type(self).__name__, name))
AttributeError: 'LightningDataParallel' object has no attribute 'teardown'
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
#### Code sample
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
### Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
### Environment
* CUDA:
- GPU:
- GeForce GTX 1080 Ti
- GeForce GTX 1080 Ti
- available: True
- version: 10.1
* Packages:
- numpy: 1.18.1
- pyTorch_debug: False
- pyTorch_version: 1.5.1
- pytorch-lightning: 0.8.1
- tensorboard: 2.2.2
- tqdm: 4.46.0
* System:
- OS: Linux
- architecture:
- 64bit
-
- processor: x86_64
- python: 3.7.7
- version: #53~18.04.1-Ubuntu SMP Thu Jun 4 14:58:26 UTC 2020
### Additional context
<!-- Add any other context about the problem here. -->
If I'm not missing something, this AttributeError is a bug on your side.
| Hi! thanks for your contribution!, great first issue!
+1 on this issue.
Also confirm this issue. | 2020-06-25T14:11:42Z | [] | [] |
Traceback (most recent call last):
File "run_kitti.py", line 351, in <module>
trainer.test(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1198, in test
self.model.teardown('test')
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 594, in __getattr__
type(self).__name__, name))
AttributeError: 'LightningDataParallel' object has no attribute 'teardown'
| 221 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2428 | a75398530c3447ecf13f043a1bc817929b90fd65 | diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -776,6 +776,7 @@ def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
# PROCESS THE RESULT
# ----------------------------
# format and reduce outputs accordingly
+ training_step_output_for_epoch_end = training_step_output
training_step_output = self.process_output(training_step_output, train=True)
# TODO: temporary part of structured results PR
@@ -788,7 +789,7 @@ def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
)
# if the user decides to finally reduce things in epoch_end, save raw output without graphs
- training_step_output_for_epoch_end = recursive_detach(training_step_output)
+ training_step_output_for_epoch_end = recursive_detach(training_step_output_for_epoch_end)
# accumulate loss
# (if accumulate_grad_batches = 1 no effect)
| training_epoch_end's outputs doesn't have 'loss' key
pytorch-lightning: build from master
```
Traceback (most recent call last):
File "main.py", line 140, in <module>
main(hparams)
File "main.py", line 72, in main
trainer.fit(model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 881, in fit
self.ddp_train(task, model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 539, in ddp_train
self.run_pretrain_routine(model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1091, in run_pretrain_routine
self.train()
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 376, in train
self.run_training_epoch()
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 510, in run_training_epoch
self.run_training_epoch_end(epoch_output)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 535, in run_training_epoch_end
epoch_output = model.training_epoch_end(epoch_output)
File "/mnt/lustre/maxiao1/PVM/models/baseline.py", line 335, in training_epoch_end
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
File "/mnt/lustre/maxiao1/PVM/models/baseline.py", line 335, in <listcomp>
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
KeyError: 'loss'
```
This is my code:
```
def training_step(self, batch, batch_idx):
...
return {'loss': loss, "train_acc": acc}
def training_epoch_end(self, outputs):
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
avg_acc = torch.stack([x['train_acc'] for x in outputs]).mean()
logs = {'loss': avg_loss, 'train_acc': avg_acc}
progress_bar = {'train_loss': avg_loss, 'train_acc': avg_acc}
results = {
'log': logs,
'progress_bar': progress_bar
}
return results
```
| Try: `avg_loss = torch.stack([x['batch_loss'] for x in outputs]).mean()`
Thanks， it works
but 'train_acc' key doesn't exist, neither do `batch_train_acc`. How to access other keys returned in training_step?
As of now in lightning you can access them using `x['callback_metrics']['loss']` and `x['callback_metrics']['train_acc']`, but I think it should be handled in a similar way we do this with `validation_epoch_end` and `test_epoch_end`.
Hi! One hint: for me it works with "loss" under windows but not under ubuntu.
Weird!! Why is this think platform dependent?? :thinking:
@Pet222 , are u sure that versions on ubuntu and windows are same?
Hey @williamFalcon is this intended behaviour? I was surprised to see this breaking change being introduced with no warning.
If it is intended, why not have consistent behaviour over `validation_epoch_end` and `test_epoch_end`.
If it is not intended, as it seems due to the "bug fix" tag, are you working on it or should I make a PR for this?
what is the behavior? that the "loss" key is not in training_epoch_end? If so, that's a bug because it should be there
@williamFalcon , on the latest version, the `loss` key was changed to the `batch_loss`. I think it was changed [here](https://github.com/PyTorchLightning/pytorch-lightning/commit/0f073819d3e0df8db7602eab489b1bad0fc0949c#diff-c45bd21c331565cbe62aaa12fa43aa0aR717)
Yes, the fact that you need to access it through 'callback metrics'.
Got it!
On Tue, 30 Jun 2020 at 12:44, William Falcon <notifications@github.com>
wrote:
> what is the behavior? that the "loss" key is not in training_epoch_end? If
> so, that's a bug because it should be there
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <https://github.com/PyTorchLightning/pytorch-lightning/issues/2372#issuecomment-651740702>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ABKWP6XTUJDTEDJ2NZQ3RKTRZHFY5ANCNFSM4OJKX4KQ>
> .
>
--
Best Regards,
Miguel Vera
+351 915 198 452
miguel.coimbra.vera@protonmail.com
Github/Captainvera <http://www.github.com/captainvera>
@captainvera would love a PR :) | 2020-06-30T13:23:18Z | [] | [] |
Traceback (most recent call last):
File "main.py", line 140, in <module>
main(hparams)
File "main.py", line 72, in main
trainer.fit(model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 881, in fit
self.ddp_train(task, model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 539, in ddp_train
self.run_pretrain_routine(model)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1091, in run_pretrain_routine
self.train()
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 376, in train
self.run_training_epoch()
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 510, in run_training_epoch
self.run_training_epoch_end(epoch_output)
File "/mnt/lustre/maxiao1/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 535, in run_training_epoch_end
epoch_output = model.training_epoch_end(epoch_output)
File "/mnt/lustre/maxiao1/PVM/models/baseline.py", line 335, in training_epoch_end
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
File "/mnt/lustre/maxiao1/PVM/models/baseline.py", line 335, in <listcomp>
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
KeyError: 'loss'
| 230 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2433 | d4a02e3bd8471946c606fef7512ce44d42f07d3a | diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -802,9 +802,22 @@ def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
if self.precision == 16 and not self.on_tpu:
closure_loss = model_ref.amp_scale_loss(closure_loss, optimizer, opt_idx)
+ # enter amp context
+ if not NATIVE_AMP_AVALAIBLE:
+ context = closure_loss
+ closure_loss = closure_loss.__enter__()
+
# do backward pass
model_ref.backward(self, closure_loss, optimizer, opt_idx)
+ # exit amp context
+ if self.precision == 16 and not NATIVE_AMP_AVALAIBLE:
+ a, b, c = None, None, None
+ error = context.__exit__(a, b, c)
+ if error:
+ rank_zero_warn(a, b, c)
+ raise Exception('apex unscale error')
+
# once backward has been applied, release graph
closure_loss = closure_loss.detach()
training_step_output.batch_loss = training_step_output.batch_loss.detach()
| 0.8.2 calls backward on '_GeneratorContextManager'
## 🐛 Bug
0.8.2 calls backward on '_GeneratorContextManager' and crashes training.
0.8.1 works correctly. my `training_step` returns `{'loss':loss, 'log':{'learn_rate':self.lr}}`
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 538, in ddp_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1100, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 370, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 452, in run_training_epoch
batch_output = self.run_training_batch(batch, batch_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 630, in run_training_batch
self.hiddens
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 804, in optimizer_closure
model_ref.backward(self, closure_loss, optimizer, opt_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/hooks.py", line 189, in backward
loss.backward()
AttributeError: '_GeneratorContextManager' object has no attribute 'backward'
```
### Expected behavior
backward is called on the loss and training runs correctly
| did you override optimizer step?
could you try master? we just pushed a fix to a typo we had
Can confirm this happens on 0.8.3
ok. Can you post a colab example that replicates this?
@Anjum48 @s-rog
colab please
@williamFalcon my optimizer step was untouched, I can't run more testing atm but I'll get to it as soon as I can
@williamFalcon Hi I also encountered this, with normal Adam optimizer. I don't have a colab to replicate this atm but from what I saw earlier, this can be replicated with any setting as long as the Trainer is set to precision=16 when using Apex. Under this condition, the following lines from training_loop.py and hooks.py will run:
`if self.precision == 16 and not self.on_tpu
closure_loss = model_ref.amp_scale_loss(closure_loss, optimizer, opt_idx) `
`scaled_loss = amp.scale_loss(unscaled_loss, optimizer)`
will cause the closure_loss be a _GeneratorContextManager object. Which then cannot have a **backward()** method.
It seems under the current design, pytorch lighting's **scale_loss** function can only be used as a context?
@williamFalcon Here's a colab example (my first time using colab so let me know if you have issues seeing it) https://colab.research.google.com/drive/1G08jVDpx-T-5HE2c89RLJdq4u67mM2-o?usp=sharing
I suspect the issue lies with Apex AMP as suggested above by @aeryen
ummm. I think this is an apex issue. I can't replicate it with 16-bit native.
@aeryen min share a minimal example to reproduce?
hi sorry for the delay: https://colab.research.google.com/drive/1rjaRRwgBTm4CKPfe9po_WSxnKqY4jDRv?usp=sharing
I agree this is an apex issue, i.e. only occur when NATIVE_AMP_AVALAIBLE is false in the hooks.py | 2020-06-30T18:33:09Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 538, in ddp_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1100, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 370, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 452, in run_training_epoch
batch_output = self.run_training_batch(batch, batch_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 630, in run_training_batch
self.hiddens
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 804, in optimizer_closure
model_ref.backward(self, closure_loss, optimizer, opt_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/hooks.py", line 189, in backward
loss.backward()
AttributeError: '_GeneratorContextManager' object has no attribute 'backward'
| 231 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2565 | e1bc208f66891e22f0139619a1be5c06235a0f34 | diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -189,6 +189,7 @@ class TrainerDDPMixin(ABC):
num_nodes: int
node_rank: int
tpu_cores: int
+ testing: bool
@property
@abstractmethod
@@ -555,15 +556,35 @@ def ddp_train(self, process_idx, q, model, is_master=False, proc_offset=0):
# continue training routine
results = self.run_pretrain_routine(model)
+ # persist info in ddp_spawn
+ self.__transfer_ddp_spawn_state_on_fit_end(model, q, results)
+
# clean up memory
torch.cuda.empty_cache()
+ if self.global_rank == 0 and self.distributed_backend not in ['ddp_spawn', 'ddp_cpu']:
+ return results
+
+ def __transfer_ddp_spawn_state_on_fit_end(self, model, q, results):
+ if not self.distributed_backend in ['ddp_spawn', 'ddp_cpu']:
+ return
+
+ # track the best model path
+ best_model_path = None
+ if self.checkpoint_callback is not None:
+ best_model_path = self.checkpoint_callback.best_model_path
+
if self.global_rank == 0 and q is not None:
- q.put(self.checkpoint_callback.best_model_path)
+ rank_zero_warn('cleaning up ddp environment...')
+ q.put(best_model_path)
q.put(results)
- if self.global_rank == 0 and self.distributed_backend != 'ddp_spawn':
- return results
+ # save the last weights
+ last_path = None
+ if not self.testing:
+ last_path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')
+ torch.save(model.state_dict(), last_path)
+ q.put(last_path)
def save_spawn_weights(self, model):
"""
@@ -574,6 +595,7 @@ def save_spawn_weights(self, model):
if self.is_global_zero:
path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')
self.save_checkpoint(path)
+ return path
def load_spawn_weights(self, original_model):
"""
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -35,7 +35,7 @@
from pytorch_lightning.utilities import rank_zero_warn, parsing, rank_zero_info, rank_zero_only
import warnings
-# warnings to ignore
+# warnings to ignore in trainer
warnings.filterwarnings('ignore', message='torch.distributed.reduce_op is deprecated, '
'please use torch.distributed.ReduceOp instead')
@@ -1063,9 +1063,14 @@ def __run_ddp_spawn(self, model, nprocs):
# restore main state with best weights
best_path = q.get()
results = q.get()
- if best_path is not None and len(best_path) > 0:
- self.checkpoint_callback.best_model_path = best_path
- model.load_from_checkpoint(best_path)
+ last_path = q.get()
+
+ # transfer back the best path to the trainer
+ self.checkpoint_callback.best_model_path = best_path
+
+ # load last weights
+ if last_path is not None and not self.testing:
+ torch.load(last_path, map_location=lambda storage, loc: storage)
self.model = model
return results
| Can't use None (anymore) in checkpoint_callback
## 🐛 Bug
using None in checkpoint_callback now errors out
```
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 562, in ddp_train
q.put(self.checkpoint_callback.best_model_path)
AttributeError: 'NoneType' object has no attribute 'best_model_path'
```
### To Reproduce
`trainer = Trainer(checkpoint_callback=None)`
Ran into this issue from upgrading to masters, was using masters from a few commits ago before
Edit: `False` casuses the same error as well
| 2020-07-09T10:46:34Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 562, in ddp_train
q.put(self.checkpoint_callback.best_model_path)
AttributeError: 'NoneType' object has no attribute 'best_model_path'
| 250 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-2572 | c197b74289997fa11cd372b51adb637f3e3846ec | diff --git a/pytorch_lightning/core/memory.py b/pytorch_lightning/core/memory.py
--- a/pytorch_lightning/core/memory.py
+++ b/pytorch_lightning/core/memory.py
@@ -209,7 +209,7 @@ def _forward_example_input(self) -> None:
input_ = model.example_input_array
input_ = model.transfer_batch_to_device(input_, model.device)
- if trainer is not None and trainer.use_amp:
+ if trainer is not None and trainer.use_amp and not trainer.use_tpu:
if NATIVE_AMP_AVALAIBLE:
model.forward = torch.cuda.amp.autocast()(model.forward)
diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -240,14 +240,14 @@ def dp_train(self, model):
# hack forward to do autocast for the user
model_autocast_original_forward = model.forward
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
# wrap the user's forward in autocast and give it back at the end
model.forward = torch.cuda.amp.autocast()(model.forward)
# TODO: remove with dropping NVIDIA AMP support
# check for this bug (amp + dp + !01 doesn't work)
# https://github.com/NVIDIA/apex/issues/227
- if self.use_dp and self.use_amp and not NATIVE_AMP_AVALAIBLE:
+ if self.use_dp and self.use_amp and not NATIVE_AMP_AVALAIBLE and not self.use_tpu:
if self.amp_level == 'O2':
raise MisconfigurationException(
f'Amp level {self.amp_level} with DataParallel is not supported.'
diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
--- a/pytorch_lightning/trainer/evaluation_loop.py
+++ b/pytorch_lightning/trainer/evaluation_loop.py
@@ -286,7 +286,7 @@ def _evaluate(
# -----------------
# RUN EVALUATION STEP
# -----------------
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
with torch.cuda.amp.autocast():
output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
else:
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -1118,7 +1118,7 @@ def run_pretrain_routine(self, model: LightningModule):
self.copy_trainer_model_properties(ref_model)
# init amp. Must be done here instead of __init__ to allow ddp to work
- if NATIVE_AMP_AVALAIBLE and self.precision == 16:
+ if NATIVE_AMP_AVALAIBLE and self.precision == 16 and not self.use_tpu:
self.scaler = torch.cuda.amp.GradScaler()
# log hyper-parameters
@@ -1300,6 +1300,11 @@ def __test_using_best_weights(self, ckpt_path, test_dataloaders):
if ckpt_path == 'best':
ckpt_path = self.checkpoint_callback.best_model_path
+ if len(ckpt_path) == 0:
+ rank_zero_warn(f'.test() found no path for the best weights, {ckpt_path}. Please '
+ f'specify a path for a checkpoint .test(ckpt_path=PATH)')
+ return {}
+
ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt['state_dict'])
diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -358,7 +358,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
checkpoint['lr_schedulers'] = lr_schedulers
# save native amp scaling
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
# add the module_arguments and state_dict from the model
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -702,7 +702,7 @@ def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer):
# ------------------
# CLIP GRADS
# ------------------
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
self.scaler.unscale_(optimizer)
self.clip_gradients()
@@ -750,7 +750,7 @@ def call_optimizer_step(self, optimizer, opt_idx, batch_idx, split_batch):
using_native_amp=native_amp)
# in native 16-bit we need to update scaler after optimizer step
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
self.scaler.update()
# model hook
@@ -767,7 +767,7 @@ def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
# FORWARD
# ---------------------------
with self.profiler.profile('model_forward'):
- if self.use_amp and NATIVE_AMP_AVALAIBLE:
+ if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
with torch.cuda.amp.autocast():
training_step_output = self.training_forward(split_batch, batch_idx,
opt_idx, hiddens)
@@ -817,7 +817,7 @@ def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
model_ref.backward(self, closure_loss, optimizer, opt_idx)
# exit amp context
- if self.precision == 16 and not NATIVE_AMP_AVALAIBLE:
+ if self.precision == 16 and not NATIVE_AMP_AVALAIBLE and not self.on_tpu:
a, b, c = None, None, None
error = context.__exit__(a, b, c)
if error:
| TPU fp16 requires apex installed
<!--
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
When I tried to use precision=16 on TPU, pytorch-lightning is trying to find amp, which is unnecessary.
The backtrace is
```
GPU available: False, used: False
TPU available: True, using: 8 TPU cores
Traceback (most recent call last):
File "bert_ner/light/fp16_debug.py", line 16, in <module>
trainer = pl.Trainer(tpu_cores=8, precision=16)
File "/anaconda3/envs/torch-xla-1.5/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 607, in __init__
self.init_amp()
File "/anaconda3/envs/torch-xla-1.5/lib/python3.6/site-packages/pytorch_lightning/trainer/auto_mix_precision.py", line 27, in init_amp
"You set `use_amp=True` but do not have apex installed."
ModuleNotFoundError: You set `use_amp=True` but do not have apex installed.Install apex first using this guide and rerun with use_amp=True:https://github.com/NVIDIA/apex#linux his run will NOT use 16 bit precision
```
### To Reproduce
Steps to reproduce the behavior:
build a whatever Trainer in TPU and use fp16
#### Code sample
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
```
import pytorch_lightning as pl
trainer = pl.Trainer(tpu_cores=8, precision=16)
```
### Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
Should have nothing error.
### Environment
- PyTorch Version (e.g., 1.5.0):
- OS (e.g., Linux): Linux
- How you installed PyTorch (`conda`, `pip`, source): conda
- Build command you used (if compiling from source):
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- Any other relevant information: actually I directly use pytorch-xla-1.5 docker on Google Cloud
### Additional context
<!-- Add any other context about the problem here. -->
| Hi! thanks for your contribution!, great first issue!
If you want to do 16 bit precision training, you either need to have the nightly version of pytorch install or have apex installed. Based on the traceback I guess that you do not have any of them.
I could get this working using nightly version of pytorch:
```
pl.Trainer(precision=16, tpu_cores=8)
>>>GPU available: False, used: False
>>>TPU available: True, using: 8 TPU cores
>>>Using native 16bit precision.
```
> If you want to do 16 bit precision training, you either need to have the nightly version of pytorch install or have apex installed. Based on the traceback I guess that you do not have any of them.
> I could get this working using nightly version of pytorch:
>
> ```
> pl.Trainer(precision=16, tpu_cores=8)
> >>>GPU available: False, used: False
> >>>TPU available: True, using: 8 TPU cores
> >>>Using native 16bit precision.
> ```
Thanks for the quick reply. But [the document](https://pytorch-lightning.readthedocs.io/en/latest/apex.html) does not point out that I must have nightly version of pytorch installed or have apex installed when training on TPU with fp16. Maybe it's better to revise that part of document?
Yes, I agree that from the documentation it would look like it is only a requirement for gpu training. I guess that the specific requirement for TPU is to have pytorch version 1.6 or higher. | 2020-07-10T01:17:22Z | [] | [] |
Traceback (most recent call last):
File "bert_ner/light/fp16_debug.py", line 16, in <module>
trainer = pl.Trainer(tpu_cores=8, precision=16)
File "/anaconda3/envs/torch-xla-1.5/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 607, in __init__
self.init_amp()
File "/anaconda3/envs/torch-xla-1.5/lib/python3.6/site-packages/pytorch_lightning/trainer/auto_mix_precision.py", line 27, in init_amp
"You set `use_amp=True` but do not have apex installed."
ModuleNotFoundError: You set `use_amp=True` but do not have apex installed.Install apex first using this guide and rerun with use_amp=True:https://github.com/NVIDIA/apex#linux his run will NOT use 16 bit precision
| 252 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-275 | 222d7d2d5d01771e63449bdcfcdaf7dd8ed1ecec | diff --git a/pytorch_lightning/root_module/decorators.py b/pytorch_lightning/root_module/decorators.py
--- a/pytorch_lightning/root_module/decorators.py
+++ b/pytorch_lightning/root_module/decorators.py
@@ -10,13 +10,18 @@ def data_loader(fn):
attr_name = '_lazy_' + fn.__name__
- @property
- def _data_loader(self):
+ def _get_data_loader(self):
try:
value = getattr(self, attr_name)
except AttributeError:
try:
value = fn(self) # Lazy evaluation, done only once.
+ if (
+ value is not None and
+ not isinstance(value, list) and
+ fn.__name__ in['test_dataloader', 'val_dataloader']
+ ):
+ value = [value]
except AttributeError as e:
# Guard against AttributeError suppression. (Issue #142)
traceback.print_exc()
@@ -25,4 +30,4 @@ def _data_loader(self):
setattr(self, attr_name, value) # Memoize evaluation.
return value
- return _data_loader
+ return _get_data_loader
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -24,6 +24,7 @@
import pdb
from pytorch_lightning.trainer import ignored_warnings
+
try:
from apex import amp
APEX_AVAILABLE = True
@@ -141,9 +142,9 @@ def __init__(self,
self.nb_val_batches = 0
self.nb_training_batches = 0
self.nb_test_batches = 0
- self.train_dataloader = None
- self.test_dataloader = None
- self.val_dataloader = None
+ self.get_train_dataloader = None
+ self.get_test_dataloaders = None
+ self.get_val_dataloaders = None
# training state
self.model = None
@@ -441,19 +442,21 @@ def training_tqdm_dict(self):
def __layout_bookeeping(self):
# determine number of training batches
- self.nb_training_batches = len(self.train_dataloader)
+ self.nb_training_batches = len(self.get_train_dataloader())
self.nb_training_batches = int(self.nb_training_batches * self.train_percent_check)
# determine number of validation batches
# val datasets could be none, 1 or 2+
- if self.val_dataloader is not None:
- self.nb_val_batches = sum(len(dataloader) for dataloader in self.val_dataloader)
+ if self.get_val_dataloaders() is not None:
+ self.nb_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())
self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
self.nb_val_batches = max(1, self.nb_val_batches)
# determine number of test batches
- if self.test_dataloader is not None:
- self.nb_test_batches = sum(len(dataloader) for dataloader in self.test_dataloader)
+ if self.get_test_dataloaders() is not None:
+ self.nb_test_batches = sum(
+ len(dataloader) for dataloader in self.get_test_dataloaders()
+ )
self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
self.nb_test_batches = max(1, self.nb_test_batches)
@@ -472,10 +475,10 @@ def __evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=Fal
# make dataloader_idx arg in validation_step optional
args = [batch, batch_idx]
- if test and len(self.test_dataloader) > 1:
+ if test and len(self.get_test_dataloaders()) > 1:
args.append(dataloader_idx)
- elif not test and len(self.val_dataloader) > 1:
+ elif not test and len(self.get_val_dataloaders()) > 1:
args.append(dataloader_idx)
# handle DP, DDP forward
@@ -520,9 +523,9 @@ def evaluate(self, model, dataloaders, max_batches, test=False):
outputs = []
# run training
- for dataloader_idx, dl in enumerate(dataloaders):
+ for dataloader_idx, dataloader in enumerate(dataloaders):
dl_outputs = []
- for batch_idx, batch in enumerate(dl):
+ for batch_idx, batch in enumerate(dataloader):
if batch is None: # pragma: no cover
continue
@@ -570,21 +573,11 @@ def get_dataloaders(self, model):
:param model:
:return:
"""
+ self.get_train_dataloader = model.train_dataloader
+ self.get_test_dataloaders = model.test_dataloader
+ self.get_val_dataloaders = model.val_dataloader
- self.train_dataloader = model.train_dataloader
- self.test_dataloader = model.test_dataloader
- self.val_dataloader = model.val_dataloader
-
- # handle returning an actual dataloader instead of a list of loaders
- have_test_loaders = self.test_dataloader is not None
- if have_test_loaders and not isinstance(self.test_dataloader, list):
- self.test_dataloader = [self.test_dataloader]
-
- have_val_loaders = self.val_dataloader is not None
- if have_val_loaders and not isinstance(self.val_dataloader, list):
- self.val_dataloader = [self.val_dataloader]
-
- if self.use_ddp and not isinstance(self.train_dataloader.sampler, DistributedSampler):
+ if self.use_ddp and not isinstance(self.get_train_dataloader().sampler, DistributedSampler):
msg = """
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
@@ -603,8 +596,8 @@ def get_dataloaders(self, model):
"""
warnings.warn(msg)
- if self.use_ddp and self.val_dataloader is not None:
- for dataloader in self.val_dataloader:
+ if self.use_ddp and self.get_val_dataloaders is not None:
+ for dataloader in self.get_val_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your val_dataloader(s) don't use DistributedSampler.
@@ -626,8 +619,8 @@ def get_dataloaders(self, model):
warnings.warn(msg)
break
- if self.use_ddp and self.test_dataloader is not None:
- for dataloader in self.test_dataloader:
+ if self.use_ddp and self.get_test_dataloaders is not None:
+ for dataloader in self.get_test_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your test_dataloader(s) don't use DistributedSampler.
@@ -912,12 +905,12 @@ def __run_pretrain_routine(self, model):
# run tiny validation (if validation defined)
# to make sure program won't crash during val
ref_model.on_sanity_check_start()
- if self.val_dataloader is not None and self.nb_sanity_val_steps > 0:
+ if self.get_val_dataloaders() is not None and self.nb_sanity_val_steps > 0:
# reset progress_bar limit for sanity check
if self.show_progress_bar:
self.progress_bar.reset(self.nb_sanity_val_steps)
- self.evaluate(model, self.val_dataloader, self.nb_sanity_val_steps, self.testing)
+ self.evaluate(model, self.get_val_dataloaders(), self.nb_sanity_val_steps, self.testing)
# ---------------------------
# CORE TRAINING LOOP
@@ -928,8 +921,8 @@ def __train(self):
# run all epochs
for epoch_nb in range(self.current_epoch, self.max_nb_epochs):
# set seed for distributed sampler (enables shuffling for each epoch)
- if self.use_ddp and hasattr(self.train_dataloader.sampler, 'set_epoch'):
- self.train_dataloader.sampler.set_epoch(epoch_nb)
+ if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):
+ self.get_train_dataloader().sampler.set_epoch(epoch_nb)
# get model
model = self.__get_model()
@@ -974,7 +967,7 @@ def run_training_epoch(self):
model.on_epoch_start()
# run epoch
- for batch_nb, batch in enumerate(self.train_dataloader):
+ for batch_nb, batch in enumerate(self.get_train_dataloader()):
self.batch_nb = batch_nb
self.global_step += 1
@@ -1272,12 +1265,12 @@ def __run_evaluation(self, test=False):
model.on_pre_performance_check()
# select dataloaders
- dataloaders = self.val_dataloader
+ dataloaders = self.get_val_dataloaders()
max_batches = self.nb_val_batches
# calculate max batches to use
if test:
- dataloaders = self.test_dataloader
+ dataloaders = self.get_test_dataloaders()
max_batches = self.nb_test_batches
# cap max batches to 1 when using fast_dev_run
| JIT support
**Initial issue description below**
JIT support requires several changes in the `Trainer` and `LightningModule`:
- [x] No use of python properties like the current dataloader implementation
- Possible solution: Use getters like implemented in #275
- Other possibility: Handle dataloading completely in trainer. The user is able to modify the dataloaders e.g. every epoch using hooks/callbacks
- [ ] The trainer cannot set PLModule's class members afterwards like `self.model.trainer = self`
- This is because after converting `my_pl_module = torch.jit.script(my_pl_module)`, the module has the class `ScriptModule`. Adding members only adds the members to the `ScriptModule` not to the underlying `LightningModule`.
- Solution could be: Implement setter in `LightningModule`. These methods will be transfered to the `ScriptModule`
- [ ] Saving and restoring might need some changes, too. One could conditionally check the class of the provided module in the trainer for use of `torch.jit.save` in trainer_io
- [ ] JIT is currently not compatible with distributed training (see pytorch issue [#15421](https://github.com/pytorch/pytorch/issues/15421))
**Is your feature request related to a problem? Please describe.**
The current implementation of `pl.LightningModule` does not support pytorch's JIT. This is due to the use of python properties for the dataloaders, which is currently not supported in JIT ([see here](https://github.com/pytorch/pytorch/issues/23958)).
example trace:
```py
$ python train.py
VISIBLE GPUS:
Traceback (most recent call last):
File "train.py", line 69, in <module>
train(config, data_dir)
File "train.py", line 37, in train
trainer.fit(pl_module)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 694, in fit
self.__run_pretrain_routine(model)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 879, in __run_pretrain_routine
self.get_dataloaders(ref_model)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 574, in get_dataloaders
self.train_dataloader = model.train_dataloader
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/torch/jit/__init__.py", line 1563, in __getattr__
return super(ScriptModule, self).__getattr__(attr)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/torch/nn/modules/module.py", line 589, in __getattr__
type(self).__name__, name))
AttributeError: 'ScriptModule' object has no attribute 'train_dataloader'
```
where pl_module has:
```py
class PlModule(pl.LightningModule):
# ....
@pl.data_loader
def train_dataloader(self):
return self.__dataloader("train")
```
**Describe the solution you'd like**
```py
torch.jit.script(pl_module)
```
**Describe alternatives you've considered**
A workaround might be defining a separate Module handling all nn.Module stuff and transforming only this part into a jit script module.
A solution might be to define explicit getters like `get_train_dataloader(self)` instead of using properties.
| Good point. Hadn't tested lightning with JIT. I think the choice of property vs getter made sense for the first design of the framework. However, recent refactors have made those differences irrelevant.
I don't see any reason why we shouldn't move to a getter instead of the property. Do you want to submit a PR for this?
Yes I can do that | 2019-10-01T11:06:59Z | [] | [] |
Traceback (most recent call last):
File "train.py", line 69, in <module>
train(config, data_dir)
File "train.py", line 37, in train
trainer.fit(pl_module)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 694, in fit
self.__run_pretrain_routine(model)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 879, in __run_pretrain_routine
self.get_dataloaders(ref_model)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 574, in get_dataloaders
self.train_dataloader = model.train_dataloader
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/torch/jit/__init__.py", line 1563, in __getattr__
return super(ScriptModule, self).__getattr__(attr)
File "/home/schroeter/.virtualenvs/pytorch-1.2/lib/python3.7/site-packages/torch/nn/modules/module.py", line 589, in __getattr__
type(self).__name__, name))
AttributeError: 'ScriptModule' object has no attribute 'train_dataloader'
| 270 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2832 | ad0f1194aa2fa8cc82c915e49aca3a1149901709 | diff --git a/pytorch_lightning/accelerator_backends/ddp_spawn_backend.py b/pytorch_lightning/accelerator_backends/ddp_spawn_backend.py
--- a/pytorch_lightning/accelerator_backends/ddp_spawn_backend.py
+++ b/pytorch_lightning/accelerator_backends/ddp_spawn_backend.py
@@ -49,7 +49,8 @@ def teardown(self, model):
last_path = self.mp_queue.get()
# transfer back the best path to the trainer
- self.trainer.checkpoint_callback.best_model_path = best_path
+ if self.trainer.checkpoint_callback:
+ self.trainer.checkpoint_callback.best_model_path = best_path
# todo, pass also bets score
# load last weights
| Can't use None (anymore) in checkpoint_callback
## 🐛 Bug
using None in checkpoint_callback now errors out
```
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 562, in ddp_train
q.put(self.checkpoint_callback.best_model_path)
AttributeError: 'NoneType' object has no attribute 'best_model_path'
```
### To Reproduce
`trainer = Trainer(checkpoint_callback=None)`
Ran into this issue from upgrading to masters, was using masters from a few commits ago before
Edit: `False` casuses the same error as well
| @williamFalcon I saw that this issue was mentioned and supposedly fixed in the merge, but I just tested with master and I'm still getting the same error | 2020-08-05T06:45:37Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 562, in ddp_train
q.put(self.checkpoint_callback.best_model_path)
AttributeError: 'NoneType' object has no attribute 'best_model_path'
| 285 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2874 | 664258c825a68ac46c8305cb09350a7be0ae8d1c | diff --git a/pytorch_lightning/utilities/__init__.py b/pytorch_lightning/utilities/__init__.py
--- a/pytorch_lightning/utilities/__init__.py
+++ b/pytorch_lightning/utilities/__init__.py
@@ -5,7 +5,7 @@
from pytorch_lightning.utilities.apply_func import move_data_to_device
from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn, rank_zero_info
-from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict
+from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict, is_picklable
try:
from apex import amp
diff --git a/pytorch_lightning/utilities/parsing.py b/pytorch_lightning/utilities/parsing.py
--- a/pytorch_lightning/utilities/parsing.py
+++ b/pytorch_lightning/utilities/parsing.py
@@ -1,7 +1,10 @@
import inspect
+import pickle
from argparse import Namespace
from typing import Dict
+from pytorch_lightning.utilities import rank_zero_warn
+
def str_to_bool(val):
"""Convert a string representation of truth to true (1) or false (0).
@@ -25,26 +28,28 @@ def str_to_bool(val):
raise ValueError(f'invalid truth value {val}')
+def is_picklable(obj: object) -> bool:
+ """Tests if an object can be pickled"""
+
+ try:
+ pickle.dumps(obj)
+ return True
+ except pickle.PicklingError:
+ return False
+
+
def clean_namespace(hparams):
- """Removes all functions from hparams so we can pickle."""
+ """Removes all unpicklable entries from hparams"""
+ hparams_dict = hparams
if isinstance(hparams, Namespace):
- del_attrs = []
- for k in hparams.__dict__:
- if callable(getattr(hparams, k)):
- del_attrs.append(k)
-
- for k in del_attrs:
- delattr(hparams, k)
-
- elif isinstance(hparams, dict):
- del_attrs = []
- for k, v in hparams.items():
- if callable(v):
- del_attrs.append(k)
-
- for k in del_attrs:
- del hparams[k]
+ hparams_dict = hparams.__dict__
+
+ del_attrs = [k for k, v in hparams_dict.items() if not is_picklable(v)]
+
+ for k in del_attrs:
+ rank_zero_warn(f"attribute '{k}' removed from hparams because it cannot be pickled", UserWarning)
+ del hparams_dict[k]
def get_init_args(frame) -> dict:
| self.hparam silently removes params that are not serializable
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
Following the approach found under [hyperparameters in the docs](https://pytorch-lightning.readthedocs.io/en/latest/hyperparameters.html#lightningmodule-hyperparameters), step 3, I passed a dict with parameters to my `pl.LightningModule`.
In the `__init__` printing `self.hparams` shows all contents I passed.
However, in the function `def configure_optimizers(self)`, some hparams are gone.
This might be related to that not all param values are YAML serializable, and therefore automatically removed? Because the 2 removed params are `"criterion": torch.nn.BCELoss()` and `"optimizer": partial(optim.Adam, lr=0.001)`.
### To Reproduce
Steps to reproduce the behavior:
1. Run the following script:
```python
from functools import partial
import torch
import torch.optim as optim
from torch.utils.data import Dataset
import pytorch_lightning as pl
from pytorch_lightning import Trainer
# partial to give all params, except the data
hparams = {
"criterion": torch.nn.BCELoss(), # F.cross_entropy(), # loss function
"optimizer": partial(optim.Adam, lr=0.001), # (lr=0.001),
# "learning_rate": 0.001,
"filters": 64,
"layers": 2
}
class EmptyDataset(Dataset):
def __init__(self, transform=None):
pass
def __len__(self):
return 32
def __getitem__(self, idx):
return {"input": np.array([1]), "output": "nothing"}
class LitLake(pl.LightningModule):
def __init__(self, hparams: dict, transforms: dict = None):
super().__init__()
self.hparams = hparams
print("self.hparams\n", self.hparams)
def forward(self, x):
pass
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
# forward pass
x, y = batch
y_hat = self(x)
loss = self.hparams["criterion"](y_hat, y)
tensorboard_logs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboard_logs}
def configure_optimizers(self):
print("self.hparams\n", self.hparams)
optimizer = self.hparams["optimizer"](self.parameters())
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
return [optimizer], [scheduler]
def train_dataloader(self):
return DataLoader(EmptyDataset(), batch_size=4, num_workers=1)
model = LitLake(hparams=hparams)
# most basic trainer, uses good defaults
trainer = Trainer() # gpus=1, num_nodes=1
trainer.fit(model) # KeyError: 'optimizer'
```
2. See error
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
<details><summary>Script output (CLICK ME)</summary>
<p>
```python
self.hparams
"criterion": BCELoss()
"filters": 64
"layers": 2
"optimizer": functools.partial(<class 'torch.optim.adam.Adam'>, lr=0.001)
GPU available: True, used: False
TPU available: False, using: 0 TPU cores
self.hparams
"filters": 64
"layers": 2
Traceback (most recent call last):
File "lightning_hparams_bug.py", line 61, in <module>
trainer.fit(model) # KeyError: 'optimizer'
File "/home/*user*/anaconda3/envs/onseilake/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 965, in fit
self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
File "/home/*user*/anaconda3/envs/onseilake/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py", line 18, in init_optimizers
optim_conf = model.configure_optimizers()
File "lightning_hparams_bug.py", line 51, in configure_optimizers
optimizer = self.hparams["optimizer"](self.parameters())
KeyError: 'optimizer'
```
</p>
</details>
#### Code sample
<!-- Ideally attach a minimal code sample to reproduce the decried issue.
Minimal means having the shortest code but still preserving the bug. -->
### Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
Either:
1. `self.hparams` keeping the non-serializable parameters (will give problems with loading?)
2. Throw an error explaining why those param values are not acceptable and how to approach it, instead of silently removing them.
### Environment
- PyTorch Version (e.g., 1.0): 1.5.0
- OS (e.g., Linux): Ubuntu 18.04
- How you installed PyTorch (`conda`, `pip`, source): `conda`
- Build command you used (if compiling from source): -
- Python version: 3.7.6
- CUDA/cuDNN version: 10.2
- GPU models and configuration: 1x GeForce GTX 1080 Ti
- Any other relevant information: -
### Additional context
<!-- Add any other context about the problem here. -->
Bug reproduced by @Borda
| Happens to me too. I'm also waiting for this fix, since i really want to use the new feature to put anything in hparams.
@dscarmo you can take it over and send a PR :] or I ll check it tomorrow...
Need to add warning about this. | 2020-08-07T23:59:21Z | [] | [] |
Traceback (most recent call last):
File "lightning_hparams_bug.py", line 61, in <module>
trainer.fit(model) # KeyError: 'optimizer'
File "/home/*user*/anaconda3/envs/onseilake/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 965, in fit
self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
File "/home/*user*/anaconda3/envs/onseilake/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py", line 18, in init_optimizers
optim_conf = model.configure_optimizers()
File "lightning_hparams_bug.py", line 51, in configure_optimizers
optimizer = self.hparams["optimizer"](self.parameters())
KeyError: 'optimizer'
| 290 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-2911 | f9d88f8088bbc27341f9d19c4aaf27259d22e072 | diff --git a/pytorch_lightning/core/saving.py b/pytorch_lightning/core/saving.py
--- a/pytorch_lightning/core/saving.py
+++ b/pytorch_lightning/core/saving.py
@@ -167,8 +167,9 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *cls_args, **cls_kwargs):
cls_kwargs = {k: v for k, v in cls_kwargs.items() if k in cls_init_args_name}
# prevent passing positional arguments if class does not accept any
- if len(cls_spec.args) <= 1 and not cls_spec.kwonlyargs:
+ if len(cls_spec.args) <= 1 and not cls_spec.varargs and not cls_spec.kwonlyargs:
cls_args, cls_kwargs = [], {}
+
model = cls(*cls_args, **cls_kwargs)
# load the state_dict on the model automatically
model.load_state_dict(checkpoint['state_dict'])
| load_from_checkpoint: TypeError: __init__() missing 1 required positional argument
## ❓ Questions and Help
#### What is your question?
load_from_checkpoint: TypeError: __init__() missing 1 required positional argument
I have read the issues before, but the things different is **my `LightningModule` is inherited from my self-defined `LightningModule`.**
How to solve this problem or what is the best practice better suited to my needs?
#### Code
To reproduce the error:
```python
import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms
import pytorch_lightning as pl
from pytorch_lightning import Trainer
from argparse import Namespace
class _LitModel(pl.LightningModule):
def __init__(self, hparams):
super().__init__()
if isinstance(hparams, dict):
hparams = Namespace(**hparams)
self.hparams = hparams
self.l1 = torch.nn.Linear(28 * 28, hparams.classes)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1)))
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
tensorboard_logs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return {'val_loss': loss}
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
return {'val_loss': avg_loss}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.001)
class LitModel(_LitModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--classes', type=int, default=10)
parser.add_argument('--checkpoint', type=str, default=None)
hparams = parser.parse_args()
mnist_train = MNIST(os.getcwd(), train=True, download=True,
transform=transforms.ToTensor())
mnist_train = DataLoader(mnist_train, num_workers=1)
mnist_val = MNIST(os.getcwd(), train=False, download=False,
transform=transforms.ToTensor())
mnist_val = DataLoader(mnist_val, num_workers=1)
# A bit weird here. I just want to show `load_from_checkpoint` will fail.
if hparams.checkpoint is None:
model = LitModel(hparams)
else:
model = LitModel.load_from_checkpoint(hparams.checkpoint)
trainer = Trainer(max_epochs=2, limit_train_batches=2,
limit_val_batches=2, progress_bar_refresh_rate=0)
trainer.fit(model, mnist_train, mnist_val)
```
#### Error msg
```
Traceback (most recent call last):
File "main.py", line 64, in <module>
model = LitModel.load_from_checkpoint(hparams.checkpoint)
File "/home/siahuat0727/.local/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 138, in load_from_checkpoint
model = cls._load_model_state(checkpoint, *args, **kwargs)
File "/home/siahuat0727/.local/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 174, in _load_model_state
model = cls(*cls_args, **cls_kwargs)
File "main.py", line 46, in __init__
super().__init__(*args, **kwargs)
TypeError: __init__() missing 1 required positional argument: 'hparams'
```
#### How to run to get the error
```bash
$ python3 main.py
$ python3 main.py --checkpoint lightning_logs/version_0/checkpoints/epoch\=1.ckpt
```
#### What's your environment?
- OS: Linux
- Packaging: pip
- Version 0.9.0rc12
| Did you try to call `self.save_hyperparameters()` in _LitModel?
Because it looks like hparams were not saved to checkpoint.
@awaelchli
Hihi, the result is the same.
It works if I directly use `_LitModel` instead of `LitModel`. So I think that's sth about inheritance.
https://pytorch-lightning.readthedocs.io/en/latest/hyperparameters.html
> Anything assigned to self.hparams will also be saved automatically. | 2020-08-11T08:17:15Z | [] | [] |
Traceback (most recent call last):
File "main.py", line 64, in <module>
model = LitModel.load_from_checkpoint(hparams.checkpoint)
File "/home/siahuat0727/.local/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 138, in load_from_checkpoint
model = cls._load_model_state(checkpoint, *args, **kwargs)
File "/home/siahuat0727/.local/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 174, in _load_model_state
model = cls(*cls_args, **cls_kwargs)
File "main.py", line 46, in __init__
super().__init__(*args, **kwargs)
TypeError: __init__() missing 1 required positional argument: 'hparams'
| 297 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-3045 | 9031dc3b817d46dc9b36007cce1360cfcf99939f | diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -354,7 +354,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
checkpoint['lr_schedulers'] = lr_schedulers
# save native amp scaling
- if self.amp_backend == AMPType.NATIVE and not self.use_tpu:
+ if self.amp_backend == AMPType.NATIVE and not self.use_tpu and self.scaler is not None:
checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
elif self.amp_backend == AMPType.APEX:
checkpoint['amp_scaling_state'] = amp.state_dict()
| auto_lr_finder crashes when using 16-bit precision with pytorch-nightly and torchvision-nightly
I heard that the nightly version of pytorch has native support for 16-bit training and wanted to give it a try since I'm trying to train some recent models on a GTX 1080. FYI, I'm using `pytorch-lightning=0.85.0`.
I've installed the following version of the two libraries:
* torch: https://download.pytorch.org/whl/nightly/cu102/torch-1.7.0.dev20200720-cp37-cp37m-linux_x86_64.whl
* torch-vision: https://download.pytorch.org/whl/nightly/cu102/torchvision-0.8.0.dev20200720-cp37-cp37m-linux_x86_64.whl
I've also setup the `Trainer` as follows:
```python
trainer = Trainer(
gpus=1,
max_epochs=hparams.epochs,
auto_lr_find=True,
progress_bar_refresh_rate=0,
accumulate_grad_batches=10,
# overfit_batches=5,
amp_level="O2",
precision=16,
logger=logger,
checkpoint_callback=checkpoint_callback,
)
```
I'm training a resnext101_32x8d_wsl model using the weights provided by Facebook in `pytorch-hub`.
```
Running command:
python pipe/train_cnn.py
/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:25: UserWarning: Checkpoint directory /home/gianluca/git/kaggle/siim-isic-melanoma-classification/models exists and is not empty with save_top_k != 0.All files in this directory will be deleted when
a checkpoint is saved!
warnings.warn(*args, **kwargs)
Using cache found in /home/gianluca/.cache/torch/hub/facebookresearch_WSL-Images_master
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
CUDA_VISIBLE_DEVICES: [0]
Using native 16bit precision.
Traceback (most recent call last):
File "pipe/train_cnn.py", line 237, in <module>
main(create_submission=True)
File "pipe/train_cnn.py", line 48, in main
preds, weight_fpath = train(fold_number=fold_number, folds=folds)
File "pipe/train_cnn.py", line 120, in train
trainer.fit(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 956, in fit
self._run_lr_finder_internally(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/lr_finder.py", line 58, in _run_lr_finder_internally
lr_finder = self.lr_find(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/lr_finder.py", line 180, in lr_find
self.save_checkpoint(str(save_path))
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py", line 268, in save_checkpoint
checkpoint = self.dump_checkpoint(weights_only)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py", line 362, in dump_checkpoint
checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
AttributeError: 'NoneType' object has no attribute 'state_dict'
ERROR: failed to reproduce 'train_cnn.dvc': stage: 'train_cnn.dvc' cmd 'python pipe/train_cnn.py' failed
```
- PyTorch Version (e.g., 1.0): torch-1.7.0.dev20200720
- OS (e.g., Linux): Ubuntu 18.04
- How you installed PyTorch (`conda`, `pip`, source): poetry
- Build command you used (if compiling from source):
- Python version: 3.7.0
- CUDA/cuDNN version: 10.2
- GPU models and configuration: 1 x GTX 1080
- Any other relevant information:
### Additional context
Since `torch^1.6.0` has native support to 16-bit training, I did not install NVidia APEX. The whole reason of using a nightly version of pytorch was to avoid to install APEX since I wasn't able to figure out how to install it with `poetry`.
| Hi! thanks for your contribution!, great first issue!
After a few rapid experiments, the issue seems to be related to using the `auto_lr_finder`. In fact, disabling it fixes the issue.
Ran into same issue, the error is clearer when you call lr_find directly:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-003731b0ec57> in <module>
55 # trainer.scaler = torch.cuda.amp.GradScaler()
56
---> 57 lrf = trainer.lr_find(model=net, train_dataloader=trn_dl, early_stop_threshold=10.)
58
~/anaconda3/envs/dl/lib/python3.7/site-packages/pytorch_lightning/trainer/lr_finder.py in lr_find(self, model, train_dataloader, val_dataloaders, min_lr, max_lr, num_training, mode, early_stop_threshold, num_accumulation_steps)
178
179 # Dump model checkpoint
--> 180 self.save_checkpoint(str(save_path))
181
182 # Configure optimizer and scheduler
~/anaconda3/envs/dl/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py in save_checkpoint(self, filepath, weights_only)
266
267 def save_checkpoint(self, filepath, weights_only: bool = False):
--> 268 checkpoint = self.dump_checkpoint(weights_only)
269
270 if self.is_global_zero:
~/anaconda3/envs/dl/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py in dump_checkpoint(self, weights_only)
360 # save native amp scaling
361 if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
--> 362 checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
363
364 # add the module_arguments and state_dict from the model
AttributeError: 'NoneType' object has no attribute 'state_dict'
```
trainer.scaler is initialized to None, and then set to torch.cuda.amp.GradScaler() [here](https://github.com/PyTorchLightning/pytorch-lightning/blob/bc833fbf5271171136824286346b04a7f1bdd0de/pytorch_lightning/trainer/trainer.py#L1104). Meanwhile lr_find wants to checkpoint the state of the scaler at some point before this happens.
Quick fix: just set the value of trainer.scaler after trainer init and before lr_find. This doesn't work if you want to use auto_lr_find option.
```
trainer = pl.Trainer(gpus=1,
max_epochs=20,
precision=16)
trainer.scaler = torch.cuda.amp.GradScaler()
lrf = trainer.lr_find(model=net, train_dataloader=trn_dl)
```
Real fix: ensure that given trainer args, the scaler is initialized to non-nil before it's needed elsewhere, needs contributors to weigh in on how.
see also https://github.com/PyTorchLightning/pytorch-lightning/issues/2642
seems to be duplicate to #1827 | 2020-08-19T02:08:12Z | [] | [] |
Traceback (most recent call last):
File "pipe/train_cnn.py", line 237, in <module>
main(create_submission=True)
File "pipe/train_cnn.py", line 48, in main
preds, weight_fpath = train(fold_number=fold_number, folds=folds)
File "pipe/train_cnn.py", line 120, in train
trainer.fit(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 956, in fit
self._run_lr_finder_internally(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/lr_finder.py", line 58, in _run_lr_finder_internally
lr_finder = self.lr_find(model)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/lr_finder.py", line 180, in lr_find
self.save_checkpoint(str(save_path))
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py", line 268, in save_checkpoint
checkpoint = self.dump_checkpoint(weights_only)
File "/home/gianluca/git/kaggle/siim-isic-melanoma-classification/.venv/lib/python3.7/site-packages/pytorch_lightning/trainer/training_io.py", line 362, in dump_checkpoint
checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
AttributeError: 'NoneType' object has no attribute 'state_dict'
| 318 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-3229 | f7dac3ff6c1b807734437188c66c226d490853f6 | diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -476,7 +476,8 @@ def run_training_batch(self, batch, batch_idx, dataloader_idx):
self.accumulated_loss.append(opt_closure_result.loss)
# track all the outputs across all steps
- batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
+ batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0
+ batch_outputs[batch_opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
# ------------------------------
# BACKWARD PASS
| Trainer crashed when optimizer frequency is defined.
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
### To Reproduce
Steps to reproduce the behavior:
Run the following code:
https://gist.github.com/24hours/ec67de5384bb05e28544d580ae424639
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
```
Traceback (most recent call last):
File "pl_bug.py", line 40, in <module>
trainer.fit(mnist_model, train_loader)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
result = fn(self, *args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1073, in fit
results = self.accelerator_backend.train(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/gpu_backend.py", line 51, in train
results = self.trainer.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1239, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 394, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 491, in run_training_epoch
batch_output = self.run_training_batch(batch, batch_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 883, in run_training_batch
batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
```
#### Code sample
```python
def configure_optimizers(self):
optimizer_G = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)
optimizer_D = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)
return [
{'optimizer': optimizer_D, 'frequency': 5},
{'optimizer': optimizer_G, 'frequency': 1}
]
```
the culprit is 'frequency' : 5, removing the line will allow trainer to run smoothly.
https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html?highlight=optimizers#configure-optimizers
The definition is correct according to this documentation.
### Expected behavior
Model should train without crash.
The code work in 0.8.5 environment.
### Environment
* CUDA:
- GPU:
- GeForce GTX TITAN X
- available: True
- version: 11.0
* Packages:
- numpy: 1.18.5
- pyTorch_debug: False
- pyTorch_version: 1.6.0a0+9907a3e
- pytorch-lightning: 0.9.0
- tensorboard: 2.2.0
- tqdm: 4.48.2
* System:
- OS: Linux
- architecture:
- 64bit
-
- processor: x86_64
- python: 3.6.10
- version: #110-Ubuntu SMP Tue Jun 23 02:39:32 UTC 2020
| Hi! thanks for your contribution!, great first issue!
this is the case with multiple optimizers, you need to spec them.. so you would prefer having default 1 if freq is not specified?
the exception occur because trainer_loop.py incorrect count number of optimizer if `frequency` is defined. Since this configuration work in version 0.8.5, this look like regression error.
Unless of course if the configuration is unsupported in version 0.9.0
> so you would prefer having default 1 if freq is not specified?
That is a totally different case I think. If the frequency is not specified we run `train_step` for both optimizers but if it is specified to 1 for both then in such case it will run 1st batch for opt_1, 2nd for opt_2, 3rd for opt_1, 4th for opt_2...
A simple fix here can be:
https://github.com/PyTorchLightning/pytorch-lightning/blob/a7705c8677b9e2b5105e26a38db9d1b650182576/pytorch_lightning/trainer/training_loop.py#L872
```python
if len(batch_outputs) == 1: # when frequencies are defined
batch_outputs[0].append(opt_closure_result.training_step_output_for_epoch_end)
else: # no frequencies
batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
```
ok, can someone write a test and submit a PR for this? show the test failing on master first.
@awaelchli or @rohitgr7 ? | 2020-08-27T18:51:21Z | [] | [] |
Traceback (most recent call last):
File "pl_bug.py", line 40, in <module>
trainer.fit(mnist_model, train_loader)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
result = fn(self, *args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1073, in fit
results = self.accelerator_backend.train(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/gpu_backend.py", line 51, in train
results = self.trainer.run_pretrain_routine(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1239, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 394, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 491, in run_training_epoch
batch_output = self.run_training_batch(batch, batch_idx)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 883, in run_training_batch
batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
```
#### Code sample
```python
def configure_optimizers(self):
optimizer_G = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)
optimizer_D = torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-5)
return [
{'optimizer': optimizer_D, 'frequency': 5},
{'optimizer': optimizer_G, 'frequency': 1}
]
```
the culprit is 'frequency' : 5, removing the line will allow trainer to run smoothly.
| 336 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-3404 | ff5f099cb759c9e77f363732ab20c9ec9e380f9b | diff --git a/pytorch_lightning/accelerators/horovod_backend.py b/pytorch_lightning/accelerators/horovod_backend.py
--- a/pytorch_lightning/accelerators/horovod_backend.py
+++ b/pytorch_lightning/accelerators/horovod_backend.py
@@ -72,11 +72,6 @@ def setup(self, model):
if isinstance(scheduler, _LRScheduler):
scheduler.base_lrs = [lr * hvd.size() for lr in scheduler.base_lrs]
- if self.trainer.amp_backend:
- model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
- self.trainer.optimizers = optimizers
- self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
-
# Horovod: broadcast parameters & optimizer state to ensure consistent initialization
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
for optimizer in self.trainer.optimizers:
@@ -92,6 +87,11 @@ def filter_named_parameters(model, optimizer):
for optimizer in self.trainer.optimizers
]
+ if self.trainer.amp_backend == AMPType.APEX:
+ model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
+ self.trainer.optimizers = optimizers
+ self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
+
# Update logger rank info from Horovod to avoid race conditions from different ranks
# creating directories / writing files in the same locations.
self.trainer.global_rank = hvd.rank()
| Horovod with native 16 precision not working
<!--
### Common bugs:
1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).
2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)
-->
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
### To Reproduce
Steps to reproduce the behavior:
1. using precision=16 with distributed_backend=horovod
```
Traceback (most recent call last):
File "/workspace/main_lightning.py", line 500, in <module>
main(hyperparams)
File "/workspace/main_lightning.py", line 492, in main
trainer.fit(model)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
result = fn(self, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 1068, in fit
results = self.horovod_train(model)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/distrib_parts.py", line 213, in horovod_train
model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/core/lightning.py", line 954, in configure_apex
model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)
```
#### Code sample
```
trainer = Trainer(
precision=16,
gpus=1,
distributed_backend="horovod")
```
### Environment
- PyTorch Version: 1.6.0+cu101
- How you installed PyTorch: pip
| mind have look @tgaddair 🐰
Absolutely, let me take a look today and get back to you, @mutasem-mattar. | 2020-09-08T21:34:31Z | [] | [] |
Traceback (most recent call last):
File "/workspace/main_lightning.py", line 500, in <module>
main(hyperparams)
File "/workspace/main_lightning.py", line 492, in main
trainer.fit(model)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
result = fn(self, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 1068, in fit
results = self.horovod_train(model)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/distrib_parts.py", line 213, in horovod_train
model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/core/lightning.py", line 954, in configure_apex
model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)
```
#### Code sample
```
trainer = Trainer(
| 355 |
|||
Lightning-AI/lightning | Lightning-AI__lightning-453 | 37729f0a17995e847fa8693f0fe694f8dd0b259b | diff --git a/pytorch_lightning/root_module/memory.py b/pytorch_lightning/root_module/memory.py
--- a/pytorch_lightning/root_module/memory.py
+++ b/pytorch_lightning/root_module/memory.py
@@ -3,6 +3,7 @@
'''
import gc
+import os
import subprocess
import numpy as np
@@ -198,19 +199,10 @@ def get_memory_profile(mode):
memory_map = get_gpu_memory_map()
if mode == 'min_max':
- min_mem = 1000000
- min_k = None
- max_mem = 0
- max_k = None
- for k, v in memory_map:
- if v > max_mem:
- max_mem = v
- max_k = k
- if v < min_mem:
- min_mem = v
- min_k = k
-
- memory_map = {min_k: min_mem, max_k: max_mem}
+ min_index, min_memory = min(memory_map.items(), key=lambda item: item[1])
+ max_index, max_memory = max(memory_map.items(), key=lambda item: item[1])
+
+ memory_map = {min_index: min_memory, max_index: max_memory}
return memory_map
@@ -224,17 +216,18 @@ def get_gpu_memory_map():
Keys are device ids as integers.
Values are memory usage as integers in MB.
"""
- result = subprocess.check_output(
+ result = subprocess.run(
[
- 'nvidia-smi', '--query-gpu=memory.used',
- '--format=csv,nounits,noheader'
- ], encoding='utf-8')
+ 'nvidia-smi',
+ '--query-gpu=memory.used',
+ '--format=csv,nounits,noheader',
+ ],
+ encoding='utf-8',
+ capture_output=True,
+ check=True)
# Convert lines into a dictionary
- gpu_memory = [int(x) for x in result.strip().split('\n')]
- gpu_memory_map = {}
- for k, v in zip(range(len(gpu_memory)), gpu_memory):
- k = f'gpu_{k}'
- gpu_memory_map[k] = v
+ gpu_memory = [int(x) for x in result.stdout.strip().split(os.linesep)]
+ gpu_memory_map = {f'gpu_{index}': memory for index, memory in enumerate(gpu_memory)}
return gpu_memory_map
| min_max log_gpu_memory option bug
**Describe the bug**
Setting `log_gpu_memory='min_max'` in `Trainer` leads to the following bug.
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 347, in fit
self.single_gpu_train(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/dp_mixin.py", line 79, in single_gpu_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 467, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 60, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 126, in run_training_epoch
self.log_metrics(batch_step_metrics, grad_norm_dic)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/logging_mixin.py", line 20, in log_metrics
mem_map = memory.get_memory_profile(self.log_gpu_memory)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/root_module/memory.py", line 205, in get_memory_profile
for k, v in memory_map:
ValueError: too many values to unpack (expected 2)
```
**To Reproduce**
On current master, execute the following.
```
trainer = Trainer(
...
log_gpu_memory='min_max',
...
)
trainer.fit(model)
```
**Expected behavior**
Log the min/max utilization of gpu memory, as `min_max` option is documented.
**Desktop (please complete the following information):**
- OS: Ubuntu 18.04
- Version: Current master
I am working on this issue. Will submit a PR soon.
| 2019-11-03T14:35:56Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 347, in fit
self.single_gpu_train(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/dp_mixin.py", line 79, in single_gpu_train
self.run_pretrain_routine(model)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 467, in run_pretrain_routine
self.train()
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 60, in train
self.run_training_epoch()
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 126, in run_training_epoch
self.log_metrics(batch_step_metrics, grad_norm_dic)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/logging_mixin.py", line 20, in log_metrics
mem_map = memory.get_memory_profile(self.log_gpu_memory)
File "/opt/conda/lib/python3.7/site-packages/pytorch_lightning/root_module/memory.py", line 205, in get_memory_profile
for k, v in memory_map:
ValueError: too many values to unpack (expected 2)
| 370 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-499 | d1b6b011c3403c6ca1c27c66ec6a613cdad0955f | diff --git a/pytorch_lightning/utilities/arg_parse.py b/pytorch_lightning/utilities/arg_parse.py
--- a/pytorch_lightning/utilities/arg_parse.py
+++ b/pytorch_lightning/utilities/arg_parse.py
@@ -81,7 +81,7 @@ def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None
parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true',
help='false removes the progress bar')
parser.add_argument('--overfit', default=-1, type=float,
- help='% of dataset to use with this option. float, or -1 for none')
+ help='%% of dataset to use with this option. float, or -1 for none')
# debug args
if rand_seed is not None:
| Escaping % in add_default_args
**Describe the bug**
In utilities/arg_parse.py, a percentage symbol is not escaped and would cause an error when printing help information.
```python
parser.add_argument('--overfit', default=-1, type=float,
help='% of dataset to use with this option. float, or -1 for none')
```
**To Reproduce**
Steps to reproduce the behavior:
```
import os
import random
import sys
from pytorch_lightning.utilities.arg_parse import add_default_args
from test_tube import HyperOptArgumentParser, Experiment
if __name__ == "__main__":
root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=True)
add_default_args(parent_parser, root_dir)
hyperparams = parent_parser.parse_args()
```
Execute the file with `--help`
```
python temp.py --help
```
Throws an error:
```
WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.
Traceback (most recent call last):
File "/Users/chenghaomou/Code/ai2/temp.py", line 11, in <module>
hyperparams = parent_parser.parse_args()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/site-packages/test_tube/argparse_hopt.py", line 238, in parse_args
results = self.__parse_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/site-packages/test_tube/argparse_hopt.py", line 157, in __parse_args
args, argv = self.parse_known_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1782, in parse_known_args
namespace, args = self._parse_known_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1988, in _parse_known_args
start_index = consume_optional(start_index)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1928, in consume_optional
take_action(action, args, option_string)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1856, in take_action
action(self, namespace, argument_values, option_string)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1038, in __call__
parser.print_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 2475, in print_help
self._print_message(self.format_help(), file)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 2459, in format_help
return formatter.format_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 284, in format_help
help = self._root_section.format_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in format_help
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in <listcomp>
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in format_help
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in <listcomp>
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 525, in _format_action
help_text = self._expand_help(action)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 615, in _expand_help
return self._get_help_string(action) % params
TypeError: %o format: an integer is required, not dict
```
**Expected behavior**
Escape the percentage sign and help can be printed.
**Desktop (please complete the following information):**
- OS: macOS 10.15
- Browser Chrome
- Version 78.0.3904.87
**Additional context**
Add any other context about the problem here.
| 2019-11-12T18:39:31Z | [] | [] |
Traceback (most recent call last):
File "/Users/chenghaomou/Code/ai2/temp.py", line 11, in <module>
hyperparams = parent_parser.parse_args()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/site-packages/test_tube/argparse_hopt.py", line 238, in parse_args
results = self.__parse_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/site-packages/test_tube/argparse_hopt.py", line 157, in __parse_args
args, argv = self.parse_known_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1782, in parse_known_args
namespace, args = self._parse_known_args(args, namespace)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1988, in _parse_known_args
start_index = consume_optional(start_index)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1928, in consume_optional
take_action(action, args, option_string)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1856, in take_action
action(self, namespace, argument_values, option_string)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 1038, in __call__
parser.print_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 2475, in print_help
self._print_message(self.format_help(), file)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 2459, in format_help
return formatter.format_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 284, in format_help
help = self._root_section.format_help()
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in format_help
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in <listcomp>
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in format_help
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 215, in <listcomp>
item_help = join([func(*args) for func, args in self.items])
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 525, in _format_action
help_text = self._expand_help(action)
File "/Users/chenghaomou/Anaconda/envs/Elisa/lib/python3.7/argparse.py", line 615, in _expand_help
return self._get_help_string(action) % params
TypeError: %o format: an integer is required, not dict
| 374 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-575 | 89ececb32ba0cfd810737cb90b2285a27332f5d4 | diff --git a/pytorch_lightning/callbacks/pt_callbacks.py b/pytorch_lightning/callbacks/pt_callbacks.py
--- a/pytorch_lightning/callbacks/pt_callbacks.py
+++ b/pytorch_lightning/callbacks/pt_callbacks.py
@@ -312,16 +312,16 @@ def on_epoch_end(self, epoch, logs=None):
self.best = max(self.best_k_models.values())
if self.verbose > 0:
logging.info(
- f'\nEpoch {epoch:05d}: {self.monitor} reached',
- f'{current:0.5f} (best {self.best:0.5f}), saving model to',
- f'{filepath} as top {self.save_top_k}')
+ f'\nEpoch {epoch:05d}: {self.monitor} reached'
+ f' {current:0.5f} (best {self.best:0.5f}), saving model to'
+ f' {filepath} as top {self.save_top_k}')
self._save_model(filepath)
else:
if self.verbose > 0:
logging.info(
- f'\nEpoch {epoch:05d}: {self.monitor}',
- f'was not in top {self.save_top_k}')
+ f'\nEpoch {epoch:05d}: {self.monitor}'
+ f' was not in top {self.save_top_k}')
else:
if self.verbose > 0:
| Error in `logging` call `pt_callbacks.py`
Stack trace:
```
....
[00:04<00:00, 1.83s/batch, batch_nb=1, loss=0.478, v_nb=13--- Logging error ---
Traceback (most recent call last):
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 994, in emit
msg = self.format(record)
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 840, in format
return fmt.format(record)
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 577, in format
record.message = record.getMessage()
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 338, in getMessage
msg = msg % self.args
TypeError: not all arguments converted during string formatting
Call stack:
File "edge_regressor_lightning.py", line 172, in <module>
cli()
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/core.py", line 764, in __call__
return self.main(*args, **kwargs)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/core.py", line 717, in main
rv = self.invoke(ctx)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/core.py", line 1137, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/core.py", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/core.py", line 555, in invoke
return callback(*args, **kwargs)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/click/decorators.py", line 17, in new_func
return f(get_current_context(), *args, **kwargs)
File "edge_regressor_lightning.py", line 166, in train
trainer.fit(model)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 366, in fit
self.run_pretrain_routine(model)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 474, in run_pretrain_routine
self.train()
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 211, in train
self.run_training_epoch()
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 265, in run_training_epoch
self.run_evaluation(test=self.testing)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop_mixin.py", line 286, in run_evaluation
logs=self.callback_metrics)
File "/Users/kdang/.pyenv/versions/id_detection_ssd/lib/python3.6/site-packages/pytorch_lightning/callbacks/pt_callbacks.py", line 317, in on_epoch_end
f'{filepath} as top {self.save_top_k}')
Message: '\nEpoch 00010: val_loss reached'
```
This is due to problem errors on these lines
```
if self.verbose > 0:
logging.info(
f'\nEpoch {epoch:05d}: {self.monitor} reached ',
f'{current:0.5f} (best {self.best:0.5f}), saving model to ',
f'{filepath} as top {self.save_top_k}')
self._save_model(filepath)
else:
if self.verbose > 0:
logging.info(
f'\nEpoch {epoch:05d}: {self.monitor} ',
f'was not in top {self.save_top_k}')
```
The fix is simple. just need to change `,` to `+` for string concatenation
| 2019-12-03T09:19:11Z | [] | [] |
Traceback (most recent call last):
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 994, in emit
msg = self.format(record)
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 840, in format
return fmt.format(record)
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 577, in format
record.message = record.getMessage()
File "/Users/kdang/.pyenv/versions/3.6.7/lib/python3.6/logging/__init__.py", line 338, in getMessage
msg = msg % self.args
TypeError: not all arguments converted during string formatting
| 384 |
||||
Lightning-AI/lightning | Lightning-AI__lightning-701 | bc67689068a0db11adaf10b32a41bcd33b8ae88e | diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -151,7 +151,7 @@ def training_step(self, batch, batch_idx):
"""
-
+import copy
import inspect
from abc import ABC, abstractmethod
import warnings
@@ -586,7 +586,7 @@ def training_forward(self, batch, batch_idx, opt_idx, hiddens):
gpu_id = 0
if isinstance(self.data_parallel_device_ids, list):
gpu_id = self.data_parallel_device_ids[0]
- batch = self.transfer_batch_to_gpu(batch.copy(), gpu_id)
+ batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)
args[0] = batch
output = self.model.training_step(*args)
| batch may not have the copy method
## 🐛 Bug
In this commit: https://github.com/PyTorchLightning/pytorch-lightning/commit/48b797fdb046bab73fc04ef6d6780f05d3623485
The training `batch` is copied before `transfer_batch_to_gpu `, but a batch may not have the `copy` method. Thus, the following error will be raised in some cases (e.g., the batch is a tuple ):
```
Traceback (most recent call last):
File "scripts/msmacro.py", line 113, in <module>
main()
File "scripts/msmacro.py", line 109, in main
trainer.fit(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 405, in fit
self.single_gpu_train(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 441, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 539, in run_pretrain_routine
self.train()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 332, in train
self.run_training_epoch()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 385, in run_training_epoch
output = self.run_training_batch(batch, batch_idx)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 504, in run_training_batch
loss = optimizer_closure()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 473, in optimizer_closure
split_batch, batch_idx, opt_idx, self.hiddens)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 589, in training_forward
batch = self.transfer_batch_to_gpu(batch.copy(), gpu_id)
AttributeError: 'tuple' object has no attribute 'copy'
```
| 2020-01-17T11:51:57Z | [] | [] |
Traceback (most recent call last):
File "scripts/msmacro.py", line 113, in <module>
main()
File "scripts/msmacro.py", line 109, in main
trainer.fit(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 405, in fit
self.single_gpu_train(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py", line 441, in single_gpu_train
self.run_pretrain_routine(model)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/trainer.py", line 539, in run_pretrain_routine
self.train()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 332, in train
self.run_training_epoch()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 385, in run_training_epoch
output = self.run_training_batch(batch, batch_idx)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 504, in run_training_batch
loss = optimizer_closure()
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 473, in optimizer_closure
split_batch, batch_idx, opt_idx, self.hiddens)
File "/home/zhaohao/Documents/pytorch-lightning/pytorch_lightning/trainer/training_loop.py", line 589, in training_forward
batch = self.transfer_batch_to_gpu(batch.copy(), gpu_id)
AttributeError: 'tuple' object has no attribute 'copy'
| 398 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-1165 | ce13ac978c14fd6df79a66be501f5c83f245c0f6 | diff --git a/src/prefect/engine/signals.py b/src/prefect/engine/signals.py
--- a/src/prefect/engine/signals.py
+++ b/src/prefect/engine/signals.py
@@ -23,8 +23,9 @@ class PrefectStateSignal(PrefectError):
def __init__(self, message: str = None, *args, **kwargs): # type: ignore
super().__init__(message) # type: ignore
+ kwargs.setdefault("result", self)
self.state = self._state_cls( # type: ignore
- result=self, message=message, *args, **kwargs
+ message=message, *args, **kwargs
)
| Cannot raise a skip signal with a result
I am filing an issue by suggestion of @cicdw after a conversation on gitter.
I came up with the following use case: a task that raises a skip signal with a result because its logic has detected that there is no work to do and the result is already calculated somewhere. I could just return it, but it would be useful for me to know that the _heavy_ part of the task did not actually execute.
An example of the use case would be:
```python
from prefect import task, Flow
from prefect.engine import signals
@task
def test_skipped():
raise signals.SKIP('skipping', result=5)
f = Flow("test", tasks=[test_skipped])
flow_state = f.run()
```
which fails because of how the `PrefectStateSignal` constructor handles its initialization:
```
Traceback (most recent call last):
File ".../prefect/engine/signals.py", line 27, in __init__
result=self, message=message, *args, **kwargs
TypeError: type object got multiple values for keyword argument 'result'
```
Chris suggested the following workaround, which works correctly, but still pointed out that the case above should work.
```python
from prefect import task, Flow
from prefect.engine.runner import ENDRUN
from prefect.engine.state import Skipped
@task
def test_skipped():
skip = Skipped("skipping", result=5)
raise ENDRUN(state=skip)
f = Flow("test", tasks=[test_skipped])
flow_state = f.run()
flow_state.result[test_skipped].result # 5
```
| 2019-06-21T23:17:24Z | [] | [] |
Traceback (most recent call last):
File ".../prefect/engine/signals.py", line 27, in __init__
result=self, message=message, *args, **kwargs
TypeError: type object got multiple values for keyword argument 'result'
| 465 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-1704 | 39131bbce118029957cc3137c7f5483b14a9e65e | diff --git a/src/prefect/environments/storage/docker.py b/src/prefect/environments/storage/docker.py
--- a/src/prefect/environments/storage/docker.py
+++ b/src/prefect/environments/storage/docker.py
@@ -102,17 +102,20 @@ def __init__(
else:
# create an image from python:*-slim directly
self.base_image = "python:{}-slim".format(python_version)
- self.extra_commands.extend(
- [
- "apt update && apt install -y gcc git && rm -rf /var/lib/apt/lists/*",
- "pip install git+https://github.com/PrefectHQ/prefect.git@{}#egg=prefect[kubernetes]".format(
- self.prefect_version
- ),
- ]
+ self.extra_commands.append(
+ "apt update && apt install -y gcc git && rm -rf /var/lib/apt/lists/*",
)
else:
self.base_image = base_image
+ # we should always try to install prefect, unless it is already installed. We can't determine this until
+ # image build time.
+ self.extra_commands.append(
+ "pip show prefect || pip install git+https://github.com/PrefectHQ/prefect.git@{}#egg=prefect[kubernetes]".format(
+ self.prefect_version
+ ),
+ )
+
not_absolute = [
file_path for file_path in self.files if not os.path.isabs(file_path)
]
| Cloudpickle error when base_image isn't specified
## Description
If you remove the base_image kwarg from storage and attempt to deploy a flow, a Cloudpickle error is triggered
Traceback (most recent call last):
File "/root/.prefect/healthcheck.py", line 12, in <module>
import cloudpickle
ModuleNotFoundError: No module named 'cloudpickle'
## Expected Behavior
No errors and flow deploys as expected (note: using kwarg base_image="prefecthq/prefect:0.7.0-3.7" allowed me to deploy my flow successfully)
## Reproduction
Remove base_image kwarg from storage and deploy flow
## Environment
Running Core 0.7.0
| @cicdw @wagoodman Do you think this could be due to cached layers? I haven't encountered this yet and I often don't provide a base image.
@nanseay could you include what version of Python you are running?
Yep, Python 3.7.3
We looked at this offline and it seems like it's an issue with assumptions on the Docker storage. If you provide that your `base_image` is `python:3.7` and you specify a `prefect_version` of `0.7.0` it won't install that version of prefect (no matter what you provide). This is due to the assumption that if a base image is provided the docker storage does not perform the extra prefect installation.
https://github.com/PrefectHQ/prefect/blob/master/src/prefect/environments/storage/docker.py#L94
After more discussion, we think the best way forward is to always attempt to install the prefect package unless it is already installed. For example, regardless of the `base_image` or `prefect_version` there is an expectation that prefect will be installed, thus we should always attempt to install it. However, we should check for existing installations (via `pip show prefect`) and do not reinstall if it already exists.
Additionally, it would be good to check if the user specified a `prefect_version` that the final installed version matches what the user provided. This will be a safety measure for when a user brings a specific image that already has prefect installed, and additionally provides a `prefect_version` (which mismatches the version from the base image)... we want the flow deploy to fail since there is an unexpected version in use. | 2019-11-05T15:01:30Z | [] | [] |
Traceback (most recent call last):
File "/root/.prefect/healthcheck.py", line 12, in <module>
import cloudpickle
ModuleNotFoundError: No module named 'cloudpickle'
| 538 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-1782 | d91c5ebf3f7d6a11bdb895125efe203e8ba34bab | diff --git a/src/prefect/core/flow.py b/src/prefect/core/flow.py
--- a/src/prefect/core/flow.py
+++ b/src/prefect/core/flow.py
@@ -1086,13 +1086,21 @@ def get_color(task: Task, map_index: int = None) -> str:
name = "{} <map>".format(t.name) if is_mapped else t.name
if is_mapped and flow_state:
assert isinstance(flow_state.result, dict)
- for map_index, _ in enumerate(flow_state.result[t].map_states):
+ if flow_state.result[t].is_mapped():
+ for map_index, _ in enumerate(flow_state.result[t].map_states):
+ kwargs = dict(
+ color=get_color(t, map_index=map_index),
+ style="filled",
+ colorscheme="svg",
+ )
+ graph.node(
+ str(id(t)) + str(map_index), name, shape=shape, **kwargs
+ )
+ else:
kwargs = dict(
- color=get_color(t, map_index=map_index),
- style="filled",
- colorscheme="svg",
+ color=get_color(t), style="filled", colorscheme="svg",
)
- graph.node(str(id(t)) + str(map_index), name, shape=shape, **kwargs)
+ graph.node(str(id(t)), name, shape=shape, **kwargs)
else:
kwargs = (
{}
@@ -1108,15 +1116,22 @@ def get_color(task: Task, map_index: int = None) -> str:
or any(edge.mapped for edge in self.edges_to(e.downstream_task))
) and flow_state:
assert isinstance(flow_state.result, dict)
- for map_index, _ in enumerate(
- flow_state.result[e.downstream_task].map_states
- ):
- upstream_id = str(id(e.upstream_task))
- if any(edge.mapped for edge in self.edges_to(e.upstream_task)):
- upstream_id += str(map_index)
+ down_state = flow_state.result[e.downstream_task]
+ if down_state.is_mapped():
+ for map_index, _ in enumerate(down_state.map_states):
+ upstream_id = str(id(e.upstream_task))
+ if any(edge.mapped for edge in self.edges_to(e.upstream_task)):
+ upstream_id += str(map_index)
+ graph.edge(
+ upstream_id,
+ str(id(e.downstream_task)) + str(map_index),
+ e.key,
+ style=style,
+ )
+ else:
graph.edge(
- upstream_id,
- str(id(e.downstream_task)) + str(map_index),
+ str(id(e.upstream_task)),
+ str(id(e.downstream_task)),
e.key,
style=style,
)
| Flow state visualization fails if a mapped task is skipped
## Description
When a flow contains a task which maps over a collection and this task is skipped, the visualization of the flow state fails with the following error message:
> AttributeError: 'Skipped' object has no attribute 'map_states'
The flow itself executes successfully.
## Expectation
Mapped tasks should be visualised without error as a grey box. This was the case in earlier versions of prefect.
## Reproduction
A slightly modified version of the ETL flow from the documentation:
```python
from prefect import task, Flow, Parameter
from prefect.tasks.control_flow.conditional import ifelse
@task
def extract():
"""Get a list of data"""
return [1, 2, 3]
@task
def transform(data):
"""Multiply the input by 10"""
return [i * 10 for i in data]
@task
def load(data):
"""Print the data to indicate it was received"""
print("Here's your data: {}".format(data))
with Flow('ETL') as flow:
do_load = Parameter('do_load')
e = extract()
t = transform(e)
l = load.map(t)
ifelse(do_load, l, None)
state = flow.run(do_load=False)
flow.visualize(flow_state=state)
```
Both changing `do_load` to `True` or removing the `map` on the load task will lead to a successful visualization being produced.
Output:
```
[2019-11-21 09:41:33,824] INFO - prefect.FlowRunner | Beginning Flow run for 'ETL'
[2019-11-21 09:41:33,826] INFO - prefect.FlowRunner | Starting flow run.
[2019-11-21 09:41:33,831] INFO - prefect.TaskRunner | Task 'extract': Starting task run...
[2019-11-21 09:41:33,834] INFO - prefect.TaskRunner | Task 'extract': finished task run for task with final state: 'Success'
[2019-11-21 09:41:33,839] INFO - prefect.TaskRunner | Task 'do_load': Starting task run...
[2019-11-21 09:41:33,841] INFO - prefect.TaskRunner | Task 'do_load': finished task run for task with final state: 'Success'
[2019-11-21 09:41:33,848] INFO - prefect.TaskRunner | Task 'CompareValue: "False"': Starting task run...
[2019-11-21 09:41:33,850] INFO - prefect.TaskRunner | Task 'CompareValue: "False"': finished task run for task with final state: 'Success'
[2019-11-21 09:41:33,856] INFO - prefect.TaskRunner | Task 'CompareValue: "True"': Starting task run...
[2019-11-21 09:41:33,858] INFO - prefect.TaskRunner | Task 'CompareValue: "True"': finished task run for task with final state: 'Skipped'
[2019-11-21 09:41:33,863] INFO - prefect.TaskRunner | Task 'transform': Starting task run...
[2019-11-21 09:41:33,865] INFO - prefect.TaskRunner | Task 'transform': finished task run for task with final state: 'Success'
[2019-11-21 09:41:33,870] INFO - prefect.TaskRunner | Task 'load': Starting task run...
[2019-11-21 09:41:33,872] INFO - prefect.TaskRunner | Task 'load': finished task run for task with final state: 'Skipped'
[2019-11-21 09:41:33,874] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
Traceback (most recent call last):
File "min_example.py", line 28, in <module>
flow.visualize(flow_state=state)
File "/Users/jtherhaa/miniconda3/envs/prefect/lib/python3.6/site-packages/prefect/core/flow.py", line 1089, in visualize
for map_index, _ in enumerate(flow_state.result[t].map_states):
AttributeError: 'Skipped' object has no attribute 'map_states'
```
## Environment
prefect 0.7.2 on macOS Mojave 10.14.6
| 2019-11-30T00:47:11Z | [] | [] |
Traceback (most recent call last):
File "min_example.py", line 28, in <module>
flow.visualize(flow_state=state)
File "/Users/jtherhaa/miniconda3/envs/prefect/lib/python3.6/site-packages/prefect/core/flow.py", line 1089, in visualize
for map_index, _ in enumerate(flow_state.result[t].map_states):
AttributeError: 'Skipped' object has no attribute 'map_states'
| 546 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-1862 | 43fb417c3020dc0e91b4c5d34b0ce6c52492214b | diff --git a/src/prefect/engine/cloud/task_runner.py b/src/prefect/engine/cloud/task_runner.py
--- a/src/prefect/engine/cloud/task_runner.py
+++ b/src/prefect/engine/cloud/task_runner.py
@@ -3,13 +3,14 @@
import _thread
import time
import warnings
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Iterable, Optional, Tuple
import pendulum
import prefect
from prefect.client import Client
from prefect.core import Edge, Task
+from prefect.utilities.executors import tail_recursive
from prefect.engine.cloud.utilities import prepare_state_for_cloud
from prefect.engine.result import NoResult, Result
from prefect.engine.result_handlers import ResultHandler
@@ -249,6 +250,7 @@ def check_task_is_cached(self, state: State, inputs: Dict[str, Result]) -> State
return state
+ @tail_recursive
def run(
self,
state: State = None,
diff --git a/src/prefect/engine/task_runner.py b/src/prefect/engine/task_runner.py
--- a/src/prefect/engine/task_runner.py
+++ b/src/prefect/engine/task_runner.py
@@ -46,7 +46,11 @@
TimedOut,
TriggerFailed,
)
-from prefect.utilities.executors import run_with_heartbeat
+from prefect.utilities.executors import (
+ run_with_heartbeat,
+ tail_recursive,
+ RecursiveCall,
+)
if TYPE_CHECKING:
from prefect.engine.result_handlers import ResultHandler
@@ -177,6 +181,7 @@ def initialize_run( # type: ignore
return TaskRunnerInitializeResult(state=state, context=context)
+ @tail_recursive
def run(
self,
state: State = None,
@@ -310,6 +315,8 @@ def run(
if exc.state.is_pending() or exc.state.is_failed():
exc.state.cached_inputs = task_inputs or {} # type: ignore
state = exc.state
+ except RecursiveCall as exc:
+ raise exc
except Exception as exc:
msg = "Task '{name}': unexpected error while running task: {exc}".format(
@@ -1028,7 +1035,9 @@ def check_task_is_looping(
)
context.update(task_run_version=prefect.context.get("task_run_version"))
new_state = Pending(message=msg)
- return self.run(
+ raise RecursiveCall(
+ self.run,
+ self,
new_state,
upstream_states=upstream_states,
context=context,
diff --git a/src/prefect/utilities/executors.py b/src/prefect/utilities/executors.py
--- a/src/prefect/utilities/executors.py
+++ b/src/prefect/utilities/executors.py
@@ -286,3 +286,56 @@ def run_with_ctx(*args: Any, _ctx_dict: dict, **kwargs: Any) -> Any:
return fut.result(timeout=timeout)
except FutureTimeout:
raise TimeoutError("Execution timed out.")
+
+
+class RecursiveCall(Exception):
+ def __init__(self, func: Callable, *args: Any, **kwargs: Any):
+ self.func = func
+ self.args = args
+ self.kwargs = kwargs
+
+
+def tail_recursive(func: Callable) -> Callable:
+ """
+ Helper function to facilitate tail recursion of the wrapped function.
+
+ This allows for recursion with unlimited depth since a stack is not allocated for
+ each "nested" call. Note: instead of calling the target function in question, a
+ `RecursiveCall` exception must be raised instead.
+
+ Args:
+ - fn (callable): the function to execute
+
+ Returns:
+ - the result of `f(*args, **kwargs)`
+
+ Raises:
+ - RecursionError: if a recursive "call" (raised exception) is made with a function that is
+ not decorated with `tail_recursive` decorator.
+ """
+
+ @wraps(func)
+ def wrapper(*args: Any, **kwargs: Any) -> Any:
+ while True:
+ try:
+ return func(*args, **kwargs)
+ except RecursiveCall as exc:
+ try:
+ call_func = getattr(exc.func, "__wrapped_func__")
+ except AttributeError:
+ raise RecursionError(
+ "function has not been wrapped to provide tail recursion (func={})".format(
+ exc.func
+ )
+ )
+
+ # there may be multiple nested recursive calls, we should only respond to calls for the
+ # wrapped function explicitly, otherwise allow the call to continue to propagate
+ if call_func != func:
+ raise exc
+ args = exc.args
+ kwargs = exc.kwargs
+ continue
+
+ setattr(wrapper, "__wrapped_func__", func)
+ return wrapper
| LOOPing a Task depends on the recursion limit for Python
## Description
*A clear description of the bug*
Right now, Looping a task relies on recursion. This can cause the user to experience the following error: `RecursionError: maximum recursion depth exceeded in comparison`. Unless the user updates the system recursion limit for python (which may not be a great idea) the maximum number of times their task can loop is capped.
## Expected Behavior
*What did you expect to happen instead?*
I expected looping to support more than a limited loop count.
## Reproduction
*A minimal example that exhibits the behavior.*
```python
import prefect
from prefect import task, Flow
from prefect.engine.signals import LOOP
@task
def example():
loop_payload = prefect.context.get("task_loop_result", 0)
if loop_payload < 4000:
loop_payload += 1
raise LOOP(result=loop_payload)
return loop_payload
with Flow("Example") as flow:
result = example()
```
I run into the following error:
```bash
In [16]: flow.run()
[2019-12-10 23:16:25,571] INFO - prefect.FlowRunner | Beginning Flow run for 'Example'
[2019-12-10 23:16:25,575] INFO - prefect.FlowRunner | Starting flow run.
[2019-12-10 23:16:25,583] INFO - prefect.TaskRunner | Task 'example': Starting task run...
[2019-12-10 23:16:29,646] ERROR - prefect.TaskRunner | Task 'example': unexpected error while running task: RecursionError('maximum recursion depth exceeded in comparison')
Traceback (most recent call last):
File "/Users/dylanhughes/dev/prefect/src/prefect/engine/task_runner.py", line 229, in run
with prefect.context(context):
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/context.py", line 111, in __call__
previous_context = self.copy()
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/collections.py", line 107, in copy
return type(self)(self.__dict__.copy())
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/context.py", line 78, in __init__
super().__init__(*args, **kwargs)
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/collections.py", line 62, in __init__
super().update(init_dict)
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/_collections_abc.py", line 839, in update
if isinstance(other, Mapping):
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/abc.py", line 139, in __instancecheck__
return _abc_instancecheck(cls, instance)
RecursionError: maximum recursion depth exceeded in comparison
[2019-12-10 23:16:29,750] INFO - prefect.TaskRunner | Task 'example': finished task run for task with final state: 'Failed'
[2019-12-10 23:16:29,753] INFO - prefect.TaskRunner | Task 'example': finished task run for task with final state: 'Failed'
[2019-12-10 23:16:29,753] INFO - prefect.FlowRunner | Flow run FAILED: some reference tasks failed.
Out[16]: <Failed: "Some reference tasks failed.">
```
## Environment
*Any additional information about your environment*
Standard python environment (python 3.7 docker container)
| Specific task run for reference https://cloud.prefect.io/prefect-qa2/task-run/fa0d004f-df37-40c4-a2b7-a71318ce0724 | 2019-12-17T15:21:34Z | [] | [] |
Traceback (most recent call last):
File "/Users/dylanhughes/dev/prefect/src/prefect/engine/task_runner.py", line 229, in run
with prefect.context(context):
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/context.py", line 111, in __call__
previous_context = self.copy()
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/collections.py", line 107, in copy
return type(self)(self.__dict__.copy())
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/context.py", line 78, in __init__
super().__init__(*args, **kwargs)
File "/Users/dylanhughes/dev/prefect/src/prefect/utilities/collections.py", line 62, in __init__
super().update(init_dict)
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/_collections_abc.py", line 839, in update
if isinstance(other, Mapping):
File "/Users/dylanhughes/miniconda3/envs/product_flows/lib/python3.7/abc.py", line 139, in __instancecheck__
return _abc_instancecheck(cls, instance)
RecursionError: maximum recursion depth exceeded in comparison
| 554 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-1991 | 4aa4808648dd8c49d4a2aa35417fcc277f1e5d56 | diff --git a/src/prefect/tasks/control_flow/conditional.py b/src/prefect/tasks/control_flow/conditional.py
--- a/src/prefect/tasks/control_flow/conditional.py
+++ b/src/prefect/tasks/control_flow/conditional.py
@@ -110,7 +110,11 @@ def ifelse(condition: Task, true_task: Task, false_task: Task) -> None:
- false_task (Task): a task that will be executed if the condition is False
"""
- switch(condition=condition, cases={True: true_task, False: false_task})
+ @prefect.task
+ def as_bool(x):
+ return bool(x)
+
+ switch(condition=as_bool(condition), cases={True: true_task, False: false_task})
def merge(*tasks: Task) -> Task:
| ifelse checks for True/False rather than truthy/falsy values
## Description
`prefect.tasks.control_flow.conditional.ifelse` should check for truthy/falsy values, but (relying on `switch`) checks for exact equality to `True` or `False`.
## Expected Behavior
[From the docs](https://docs.prefect.io/core/task_library/control_flow.html#if-else):
> If the condition evaluates True(ish), the true_task will run. If it evaluates False(ish), the false_task will run.
`ifelse` should run the `true_branch` for any value that evaluates to `True`: non-empty strings, dicts and lists, ints not equal to 0...
## Reproduction
```python
from prefect import Flow, task
from prefect.tasks.control_flow.conditional import ifelse, merge
@task
def run_if_truthy():
return 'a'
@task
def run_if_falsy():
return 'b'
@task
def return_truthy_value():
# non-empty strings are truthy
assert('c')
return 'c'
with Flow('test-flow') as flow:
branch_truthy = run_if_truthy()
branch_falsy = run_if_falsy()
ifelse(return_truthy_value(), branch_truthy, branch_falsy)
merged_result = merge(branch_truthy, branch_falsy)
result = flow.run()
assert(not result.result.get(merged_result).is_skipped())
assert(result.result.get(merged_result)._result.value == 'a')
```
Output:
```
[2020-02-03 16:38:57,428] INFO - prefect.FlowRunner | Beginning Flow run for 'test-flow'
[2020-02-03 16:38:57,431] INFO - prefect.FlowRunner | Starting flow run.
[2020-02-03 16:38:57,441] INFO - prefect.TaskRunner | Task 'return_truthy_value': Starting task run...
[2020-02-03 16:38:57,445] INFO - prefect.TaskRunner | Task 'return_truthy_value': finished task run for task with final state: 'Success'
[2020-02-03 16:38:57,455] INFO - prefect.TaskRunner | Task 'CompareValue: "True"': Starting task run...
[2020-02-03 16:38:57,460] INFO - prefect.TaskRunner | Task 'CompareValue: "True"': finished task run for task with final state: 'Skipped'
[2020-02-03 16:38:57,470] INFO - prefect.TaskRunner | Task 'run_if_truthy': Starting task run...
[2020-02-03 16:38:57,474] INFO - prefect.TaskRunner | Task 'run_if_truthy': finished task run for task with final state: 'Skipped'
[2020-02-03 16:38:57,483] INFO - prefect.TaskRunner | Task 'CompareValue: "False"': Starting task run...
[2020-02-03 16:38:57,488] INFO - prefect.TaskRunner | Task 'CompareValue: "False"': finished task run for task with final state: 'Skipped'
[2020-02-03 16:38:57,497] INFO - prefect.TaskRunner | Task 'run_if_falsy': Starting task run...
[2020-02-03 16:38:57,501] INFO - prefect.TaskRunner | Task 'run_if_falsy': finished task run for task with final state: 'Skipped'
[2020-02-03 16:38:57,510] INFO - prefect.TaskRunner | Task 'Merge': Starting task run...
[2020-02-03 16:38:57,514] INFO - prefect.TaskRunner | Task 'Merge': finished task run for task with final state: 'Skipped'
[2020-02-03 16:38:57,516] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
Traceback (most recent call last):
File "/tmp/test-flow.py", line 25, in <module>
assert(not result.result.get(merged_result).is_skipped())
AssertionError
```
## Environment
Prefect 0.9.2, Python 3.6, on Linux x64.
| 2020-02-07T02:01:29Z | [] | [] |
Traceback (most recent call last):
File "/tmp/test-flow.py", line 25, in <module>
assert(not result.result.get(merged_result).is_skipped())
AssertionError
| 573 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-2047 | e3e540e2bb18e3454016d06c698e086770b6ad36 | diff --git a/src/prefect/engine/executors/dask.py b/src/prefect/engine/executors/dask.py
--- a/src/prefect/engine/executors/dask.py
+++ b/src/prefect/engine/executors/dask.py
@@ -33,8 +33,10 @@ class DaskExecutor(Executor):
Defaults to `False`.
- debug (bool, optional): whether to operate in debug mode; `debug=True`
will produce many additional dask logs. Defaults to the `debug` value in your Prefect configuration
- - **kwargs (dict, optional): additional kwargs to be passed to the
- `dask.distributed.Client` upon initialization (e.g., `n_workers`)
+ - **kwargs (dict, optional): additional kwargs to be passed to the [`dask.distributed.Client`](https://distributed.dask.org/en/latest/api.html#client) upon
+ initialization (e.g., `n_workers`, `security`, etc.), which will also pass any unmatched kwargs down to child objects such as
+ [`distributed.deploy.local.LocalCluster`](https://docs.dask.org/en/latest/setup/single-distributed.html#distributed.deploy.local.LocalCluster).
+ Please see the Dask docs to see all of the options that child objects will respond to.
"""
def __init__(
| DaskExecutor.address doesn't work with Dask Gateway proxy
## Description
Hello! I'm attempting to run a Prefect flow with DaskExecutor connected to a Dask cluster that was created using [Dask Gateway.](https://github.com/dask/dask-gateway) This raises an SSL error, however it could have something to do with my DG implementation. DG is relatively new, so I'm wondering if it has been tested with Prefect?
Thanks!
## Expected Behavior
*What did you expect to happen instead?* Flow to run as it normally would with a DaskExecutor.
## Reproduction
```
from dask_gateway import Gateway
from prefect.engine.executors import DaskExecutor
from prefect import task, Flow
import datetime
import random
from time import sleep
@task
def inc(x):
sleep(random.random() / 10)
return x + 1
with Flow("dask-example") as flow:
incs = inc.map(x=range(100))
gateway = Gateway()
cluster = gateway.new_cluster()
cluster.scale(4)
# Example scheduler address from DG: 'gateway://dask-scheduler-proxy.<fqdn>:443/<hash from dg>'
executor = DaskExecutor(address=cluster.scheduler_address)
flow.run(executor=executor)
```
Error:
```
[2020-01-28 19:17:47,571] INFO - prefect.FlowRunner | Beginning Flow run for 'dask-example'
[2020-01-28 19:17:47,574] INFO - prefect.FlowRunner | Starting flow run.
[2020-01-28 19:17:47,578] ERROR - prefect.FlowRunner | Unexpected error: TypeError('Gateway expects a `ssl_context` argument of type ssl.SSLContext, instead got None')
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/flow_runner.py", line 400, in get_flow_run_state
with executor.start():
File "/opt/conda/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/executors/dask.py", line 75, in start
with Client(self.address, **self.kwargs) as client:
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 728, in __init__
self.start(timeout=timeout)
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 893, in start
sync(self.loop, self._start, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/distributed/utils.py", line 335, in sync
raise exc.with_traceback(tb)
File "/opt/conda/lib/python3.7/site-packages/distributed/utils.py", line 319, in f
result[0] = yield future
File "/opt/conda/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
value = future.result()
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 986, in _start
await self._ensure_connected(timeout=timeout)
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 1043, in _ensure_connected
connection_args=self.connection_args,
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 218, in connect
quiet_exceptions=EnvironmentError,
File "/opt/conda/lib/python3.7/site-packages/dask_gateway/comm.py", line 41, in connect
"ssl.SSLContext, instead got %s" % ctx
TypeError: Gateway expects a `ssl_context` argument of type ssl.SSLContext, instead got None
[2020-01-28 19:17:47,584] ERROR - prefect.Flow: dask-example | Unexpected error occured in FlowRunner: TypeError('Gateway expects a `ssl_context` argument of type ssl.SSLContext, instead got None')
<Failed: "Unexpected error: TypeError('Gateway expects a `ssl_context` argument of type ssl.SSLContext, instead got None')">
```
## Environment
Dask cluster running on Kubernetes managed with Dask Gateway.
| Successful connection and execution of `prefect` flow by passing in the `cluster.security` attribute as a `kwarg`:
```
executor = DaskExecutor(address=cluster.scheduler_address, security=cluster.security)
flow.run(executor=executor)
[2020-01-28 21:10:04,687] INFO - prefect.FlowRunner | Beginning Flow run for 'dask-example'
[2020-01-28 21:10:04,690] INFO - prefect.FlowRunner | Starting flow run.
[2020-01-28 21:10:07,820] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
<Success: "All reference tasks succeeded.">
```
Great! @cicdw You may want to have someone add a note to the docs on how to do this. | 2020-02-18T21:33:05Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/flow_runner.py", line 400, in get_flow_run_state
with executor.start():
File "/opt/conda/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/executors/dask.py", line 75, in start
with Client(self.address, **self.kwargs) as client:
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 728, in __init__
self.start(timeout=timeout)
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 893, in start
sync(self.loop, self._start, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/distributed/utils.py", line 335, in sync
raise exc.with_traceback(tb)
File "/opt/conda/lib/python3.7/site-packages/distributed/utils.py", line 319, in f
result[0] = yield future
File "/opt/conda/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
value = future.result()
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 986, in _start
await self._ensure_connected(timeout=timeout)
File "/opt/conda/lib/python3.7/site-packages/distributed/client.py", line 1043, in _ensure_connected
connection_args=self.connection_args,
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 218, in connect
quiet_exceptions=EnvironmentError,
File "/opt/conda/lib/python3.7/site-packages/dask_gateway/comm.py", line 41, in connect
"ssl.SSLContext, instead got %s" % ctx
TypeError: Gateway expects a `ssl_context` argument of type ssl.SSLContext, instead got None
| 580 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2136 | 974625cfcb6bbd317afa36f320f6fe0575bdba54 | diff --git a/src/prefect/engine/result_handlers/s3_result_handler.py b/src/prefect/engine/result_handlers/s3_result_handler.py
--- a/src/prefect/engine/result_handlers/s3_result_handler.py
+++ b/src/prefect/engine/result_handlers/s3_result_handler.py
@@ -7,6 +7,7 @@
import cloudpickle
import pendulum
+import prefect
from prefect.client import Secret
from prefect.engine.result_handlers import ResultHandler
@@ -54,7 +55,10 @@ def initialize_client(self) -> None:
aws_access_key = aws_credentials["ACCESS_KEY"]
aws_secret_access_key = aws_credentials["SECRET_ACCESS_KEY"]
- s3_client = boto3.client(
+ # use a new boto session when initializing in case we are in a new thread
+ # see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/resources.html?#multithreading-multiprocessing
+ session = boto3.session.Session()
+ s3_client = session.client(
"s3",
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_access_key,
@@ -63,8 +67,13 @@ def initialize_client(self) -> None:
@property
def client(self) -> "boto3.client":
- if not hasattr(self, "_client"):
+ """
+ Initializes a client if we believe we are in a new thread.
+ We consider ourselves in a new thread if we haven't stored a client yet in the current context.
+ """
+ if not prefect.context.get("boto3client"):
self.initialize_client()
+ prefect.context["boto3client"] = self._client
return self._client
@client.setter
| Flow S3ResultHandler Fails for Dask Worker with nthreads > 1
## Description
Specifying S3ResultHandler for a Flow running on Dask worker(s) with nthreads > 1 fails with: `KeyError: 'credential_provider'`, likely due to a race condition in using the global boto3 session (boto3.client) between threads.
## Expected Behavior
In a multithreaded environment, boto3 recommends creating a session per thread rather than sharing the default boto3 session, i.e. boto3.client. See boto3 documentation at: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/resources.html?highlight=multithreading#multithreading-multiprocessing
This thread in Prefect's Community Slack describes using this session-per-thread approach to successfully fix a similar issue when using boto3 in Prefect tasks: https://prefect-community.slack.com/archives/CM28LL405/p1581434710167100
## Reproduction
A Flow with tasks that can run in parallel (e.g. mapped tasks or different Flow branches) and where the Flow-level result_handler is set to S3ResultHandler should reproduce this behavior.
Full stack trace:
```
February 29th 2020 at 8:09:43am | prefect.CloudTaskRunner
ERROR
Failed to set task state with error: KeyError('credential_provider')
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/cloud/task_runner.py", line 117, in call_runner_target_handlers
cloud_state = prepare_state_for_cloud(new_state)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/cloud/utilities.py", line 21, in prepare_state_for_cloud
res.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result.py", line 93, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 103, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 67, in client
self.initialize_client()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 60, in initialize_client
aws_secret_access_key=aws_secret_access_key,
File "/opt/conda/lib/python3.7/site-packages/boto3/__init__.py", line 91, in client
return _get_default_session().client(*args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/boto3/session.py", line 263, in client
aws_session_token=aws_session_token, config=config)
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 823, in create_client
credentials = self.get_credentials()
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 428, in get_credentials
'credential_provider').load_credentials()
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 923, in get_component
del self._deferred[name]
KeyError: 'credential_provider'
```
## Environment
We create a long-running Dask cluster where our Dask workers are started with --nprocs 1 --nthreads 3.
(Thanks to @JLouSRM for identifying this issue and capturing log evidence!)
| Very interesting - thanks for the issue! For reference, [this is the boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/resources.html?highlight=multithreading#multithreading-multiprocessing) referenced in that Slack thread, and Adam said:
> By first creating a session and then creating the client from the session, each thread has a different session.
As an aside, this could also motivate introducing a new [S3FS](https://github.com/dask/s3fs) Result Handler (similar to the discussion in https://github.com/PrefectHQ/prefect/issues/1475). | 2020-03-09T22:03:19Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/cloud/task_runner.py", line 117, in call_runner_target_handlers
cloud_state = prepare_state_for_cloud(new_state)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/cloud/utilities.py", line 21, in prepare_state_for_cloud
res.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result.py", line 93, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 103, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 67, in client
self.initialize_client()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 60, in initialize_client
aws_secret_access_key=aws_secret_access_key,
File "/opt/conda/lib/python3.7/site-packages/boto3/__init__.py", line 91, in client
return _get_default_session().client(*args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/boto3/session.py", line 263, in client
aws_session_token=aws_session_token, config=config)
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 823, in create_client
credentials = self.get_credentials()
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 428, in get_credentials
'credential_provider').load_credentials()
File "/opt/conda/lib/python3.7/site-packages/botocore/session.py", line 923, in get_component
del self._deferred[name]
KeyError: 'credential_provider'
| 591 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2205 | f3717b1a5b3625efe919c1c0c9f1a8e938b3b39d | diff --git a/src/prefect/engine/result_handlers/s3_result_handler.py b/src/prefect/engine/result_handlers/s3_result_handler.py
--- a/src/prefect/engine/result_handlers/s3_result_handler.py
+++ b/src/prefect/engine/result_handlers/s3_result_handler.py
@@ -36,6 +36,7 @@ class S3ResultHandler(ResultHandler):
def __init__(self, bucket: str, aws_credentials_secret: str = None) -> None:
self.bucket = bucket
self.aws_credentials_secret = aws_credentials_secret
+ self._client = None
super().__init__()
def initialize_client(self) -> None:
@@ -71,9 +72,10 @@ def client(self) -> "boto3.client":
Initializes a client if we believe we are in a new thread.
We consider ourselves in a new thread if we haven't stored a client yet in the current context.
"""
- if not prefect.context.get("boto3client"):
+ if not prefect.context.get("boto3client") or not self._client:
self.initialize_client()
prefect.context["boto3client"] = self._client
+
return self._client
@client.setter
| S3ResultHandler fails during Cloud Flow Run
## Description
Running on `0.9.8` we've observed the S3ResultHandler failing with `AttributeError: 'S3ResultHandler' object has no attribute '_client'`. This was also reported by another user in the Prefect Community Slack. (See thread: https://prefect-community.slack.com/archives/CL09KU1K7/p1584980231422300)
For now we are working around this by disabling checkpointing in all of our tasks, but this will obviously prevent tasks from being able to retry, etc.
## Expected Behavior
S3ResultHandler should correctly checkpoint tasks to store outputs in an S3 bucket.
## Reproduction
The following is a stub Flow that recreates the issue:
```
import prefect
from prefect import Flow
from prefect.tasks.shell import ShellTask
from prefect.environments.storage import S3
from prefect.engine.result_handlers import S3ResultHandler
env = <an environment, we use our own secure Dask environment for TLS by extending RemoteEnvironment>
BUCKET = "<redacted>"
s3_storage = S3(bucket=BUCKET)
rh = S3ResultHandler(BUCKET)
shell_task = ShellTask(name="shell", return_all=True)
with Flow("RecreateS3ResultHandlerIssue", environment=env, storage=s3_storage, result_handler=rh) as flow:
final_cmd = ["ls","cd;ls"]
embulk_results = shelltask.map(command=final_cmd)
s3_storage.build()
flow.register(project_name="Test Project 1")
# Now run Flow from Cloud either via UI or API
```
The Flow run will fail with this full stack trace:
```
Unexpected error: AttributeError("'S3ResultHandler' object has no attribute '_client'")
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 925, in get_task_run_state
state._result.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result.py", line 121, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 112, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 77, in client
return self._client
AttributeError: 'S3ResultHandler' object has no attribute '_client'
```
## Environment
We use a long-running Dask cluster on k8s via AWS EKS.
(Hat tip to @JLouSRM for identifying this issue.)
| Hi! I will take a look to reproduce this evening, I changed the S3ResultHandler in 0.9.8 re: trying to thread safe the boto3 client, so I'm the one who probably broke it.
Looking at it I think there's a chance the `client` property is retrieved without `self._client` being set prior. https://github.com/PrefectHQ/prefect/blob/master/src/prefect/engine/result_handlers/s3_result_handler.py#L77
Possibly defining `self._client = None` in the `__init__` would do the trick | 2020-03-27T19:06:20Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 925, in get_task_run_state
state._result.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result.py", line 121, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 112, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 77, in client
return self._client
AttributeError: 'S3ResultHandler' object has no attribute '_client'
| 606 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2233 | bdf152392320be34c77bb9886a2cf876e52f5f93 | diff --git a/src/prefect/engine/result_handlers/s3_result_handler.py b/src/prefect/engine/result_handlers/s3_result_handler.py
--- a/src/prefect/engine/result_handlers/s3_result_handler.py
+++ b/src/prefect/engine/result_handlers/s3_result_handler.py
@@ -72,7 +72,7 @@ def client(self) -> "boto3.client":
Initializes a client if we believe we are in a new thread.
We consider ourselves in a new thread if we haven't stored a client yet in the current context.
"""
- if not prefect.context.get("boto3client") or not self._client:
+ if not prefect.context.get("boto3client") or not getattr(self, "_client", None):
self.initialize_client()
prefect.context["boto3client"] = self._client
| S3ResultHandler still failing on 0.10.0
## Description
We're still seeing https://github.com/PrefectHQ/prefect/issues/2204 after upgrading to `0.10.0`. We do see the code change from https://github.com/PrefectHQ/prefect/pull/2205 in the stack trace:
```
Unexpected error: AttributeError("'S3ResultHandler' object has no attribute '_client'")
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 925, in get_task_run_state
state._result.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result/base.py", line 127, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 114, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 75, in client
if not prefect.context.get("boto3client") or not self._client:
AttributeError: 'S3ResultHandler' object has no attribute '_client'
```
Just eyeballing it, maybe hasattr() will fix it, i.e. `if not prefect.context.get("boto3client") or not hasattr(self, "_client"):` will fix it.
## Expected Behavior
S3ResultHandler should checkpoint task outputs.
## Reproduction
See previous issue.
## Environment
We're running the Flow via Cloud with S3 storage.
| 2020-03-31T16:56:46Z | [] | [] |
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 925, in get_task_run_state
state._result.store_safe_value()
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result/base.py", line 127, in store_safe_value
value = self.result_handler.write(self.value)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 114, in write
self.client.upload_fileobj(stream, Bucket=self.bucket, Key=uri)
File "/opt/conda/lib/python3.7/site-packages/prefect/engine/result_handlers/s3_result_handler.py", line 75, in client
if not prefect.context.get("boto3client") or not self._client:
AttributeError: 'S3ResultHandler' object has no attribute '_client'
| 610 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-2337 | 1babcb7f38d1ff5a8e7eeec06a2ae7bbe7eeb89b | diff --git a/src/prefect/cli/auth.py b/src/prefect/cli/auth.py
--- a/src/prefect/cli/auth.py
+++ b/src/prefect/cli/auth.py
@@ -182,17 +182,17 @@ def switch_tenants(id, slug):
@auth.command(hidden=True)
@click.option("--name", "-n", required=True, help="A token name.", hidden=True)
-@click.option("--role", "-r", required=True, help="A token role.", hidden=True)
-def create_token(name, role):
+@click.option("--scope", "-s", required=True, help="A token scopre.", hidden=True)
+def create_token(name, scope):
"""
Create a Prefect Cloud API token.
- For more info on API tokens visit https://docs.prefect.io/cloud/concepts/api.html
+ For more info on API tokens visit https://docs.prefect.io/orchestration/concepts/api.html
\b
Options:
--name, -n TEXT A name to give the generated token
- --role, -r TEXT A role for the token
+ --scope, -r TEXT A scope for the token
"""
check_override_auth_token()
@@ -204,7 +204,7 @@ def create_token(name, role):
"create_api_token(input: $input)": {"token"}
}
},
- variables=dict(input=dict(name=name, role=role)),
+ variables=dict(input=dict(name=name, scope=scope)),
)
if not output.get("data", None):
| HTTPError attempting to retrieve a runner token from the CLI
## Description
Attempting to follow the docs, I tried to create a runner token from the command line after successfully logging in with a user token. I was greeted with an HTTPError (400)
```
(default) prefect auth login -t $(cat token)
Login successful!
(default) prefect auth create-token -n my-runner-token -r RUNNER
Traceback (most recent call last):
File "/opt/continuum/anaconda/envs/default/bin/prefect", line 6, in <module>
exit(cli())
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/cli/auth.py", line 201, in create_token
output = client.graphql(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 212, in graphql
result = self.post(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 171, in post
response = self._request(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 314, in _request
response.raise_for_status()
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/requests/models.py", line 941, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.prefect.io/graphql/alpha
```
## Expected Behavior
I expected to get a token
## Reproduction
```
conda create -n default -c defaults -c conda-forge prefect
source activate prefect
prefect auth login -t <TOKEN>
prefect auth create-token -n my-runner-token -r RUNNER
```
## Environment
Conda environment:
```
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: linux-64
_libgcc_mutex=0.1=main
appdirs=1.4.3=pyh91ea838_0
asn1crypto=1.3.0=py38_0
blas=1.0=mkl
bokeh=2.0.1=py38_0
ca-certificates=2020.1.1=0
certifi=2020.4.5.1=py38_0
cffi=1.14.0=py38h2e261b9_0
chardet=3.0.4=py38_1003
click=7.1.1=py_0
cloudpickle=1.2.2=py_0
croniter=0.3.30=py_0
cryptography=2.8=py38h1ba5d50_0
cytoolz=0.10.1=py38h7b6447c_0
dask=2.14.0=py_0
dask-core=2.14.0=py_0
distributed=2.14.0=py38_0
docker-py=4.2.0=py38_0
docker-pycreds=0.4.0=py_0
freetype=2.9.1=h8a8886c_1
fsspec=0.7.1=py_0
heapdict=1.0.1=py_0
idna=2.9=py_1
intel-openmp=2020.0=166
jinja2=2.11.1=py_0
jpeg=9b=h024ee3a_2
ld_impl_linux-64=2.33.1=h53a641e_7
libedit=3.1.20181209=hc058e9b_0
libffi=3.2.1=hd88cf55_4
libgcc-ng=9.1.0=hdf63c60_0
libgfortran-ng=7.3.0=hdf63c60_0
libpng=1.6.37=hbc83047_0
libstdcxx-ng=9.1.0=hdf63c60_0
libtiff=4.1.0=h2733197_0
locket=0.2.0=py38_1
markupsafe=1.1.1=py38h7b6447c_0
marshmallow=3.5.1=py_0
marshmallow-oneofschema=2.0.1=py_0
mkl=2020.0=166
mkl-service=2.3.0=py38he904b0f_0
mkl_fft=1.0.15=py38ha843d7b_0
mkl_random=1.1.0=py38h962f231_0
msgpack-python=1.0.0=py38hfd86e86_1
mypy_extensions=0.4.3=py38_0
ncurses=6.2=he6710b0_0
numpy=1.18.1=py38h4f9e942_0
numpy-base=1.18.1=py38hde5b4d6_1
olefile=0.46=py_0
openssl=1.1.1f=h7b6447c_0
packaging=20.3=py_0
pandas=1.0.3=py38h0573a6f_0
partd=1.1.0=py_0
pendulum=2.1.0=py38_1
pillow=7.0.0=py38hb39fc2d_0
pip=20.0.2=py38_1
prefect=0.10.2=py_0
psutil=5.7.0=py38h7b6447c_0
pycparser=2.20=py_0
pyopenssl=19.1.0=py38_0
pyparsing=2.4.6=py_0
pysocks=1.7.1=py38_0
python=3.8.2=hcf32534_0
python-box=4.2.2=py_0
python-dateutil=2.8.1=py_0
python-slugify=3.0.4=py_0
pytz=2019.3=py_0
pytzdata=2019.3=py_0
pyyaml=5.3.1=py38h7b6447c_0
readline=8.0=h7b6447c_0
requests=2.23.0=py38_0
ruamel.yaml=0.16.5=py38h7b6447c_1
ruamel.yaml.clib=0.2.0=py38h7b6447c_0
setuptools=46.1.3=py38_0
six=1.14.0=py38_0
sortedcontainers=2.1.0=py38_0
sqlite=3.31.1=h7b6447c_0
tabulate=0.8.3=py38_0
tblib=1.6.0=py_0
text-unidecode=1.3=py_0
tk=8.6.8=hbc83047_0
toml=0.10.0=pyh91ea838_0
toolz=0.10.0=py_0
tornado=6.0.4=py38h7b6447c_1
typing_extensions=3.7.4.1=py38_0
unidecode=1.1.1=py_0
urllib3=1.25.8=py38_0
websocket-client=0.56.0=py38_0
wheel=0.34.2=py38_0
xz=5.2.4=h14c3975_4
yaml=0.1.7=had09818_2
zict=2.0.0=py_0
zlib=1.2.11=h7b6447c_3
zstd=1.3.7=h0b5b093_0
```
Prefect diagnostics:
```
{
"config_overrides": {},
"env_vars": [],
"system_information": {
"platform": "Linux-3.10.0-957.21.3.el7.x86_64-x86_64-with-glibc2.10",
"prefect_version": "0.10.2",
"python_version": "3.8.2"
}
}
```
| I should point out that a runner token retrieved from the UI works fine.
Thank for opening @mcg1969! I am able to reproduce, looks to be a change in the input to the mutation that this command needs to be adjusted for. Will make the change | 2020-04-15T16:08:04Z | [] | [] |
Traceback (most recent call last):
File "/opt/continuum/anaconda/envs/default/bin/prefect", line 6, in <module>
exit(cli())
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/cli/auth.py", line 201, in create_token
output = client.graphql(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 212, in graphql
result = self.post(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 171, in post
response = self._request(
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/prefect/client/client.py", line 314, in _request
response.raise_for_status()
File "/opt/continuum/anaconda/envs/default/lib/python3.8/site-packages/requests/models.py", line 941, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.prefect.io/graphql/alpha
| 621 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2502 | 58126ed79fa90c0a3d682e2074c9c96b0887cfbd | diff --git a/src/prefect/engine/task_runner.py b/src/prefect/engine/task_runner.py
--- a/src/prefect/engine/task_runner.py
+++ b/src/prefect/engine/task_runner.py
@@ -637,9 +637,11 @@ def check_task_is_cached(self, state: State, inputs: Dict[str, Result]) -> State
state = Pending("Cache was invalid; ready to run.")
if self.task.cache_for is not None:
- candidate_states = prefect.context.caches.get(
- self.task.cache_key or self.task.name, []
- )
+ candidate_states = []
+ if prefect.context.get("caches"):
+ candidate_states = prefect.context.caches.get(
+ self.task.cache_key or self.task.name, []
+ )
sanitized_inputs = {key: res.value for key, res in inputs.items()}
for candidate in candidate_states:
if self.task.cache_validator(
| FlowRunner manages context cache wrongly
## Description
AttributeError and different behavior of output caching between Flow.run and FlowRunner.run.
## Expected Behavior
No AttributeError, same behavior and cached result reuse of FlowRunner.run after Flow.run.
## Reproduction
```python
from datetime import timedelta
import random
from prefect import task, Flow
from prefect.engine import FlowRunner
from prefect.engine.cache_validators import duration_only
@task(cache_for=timedelta(seconds=10),
cache_validator=duration_only)
def rand_inc(r, x):
rand = random.randint(0, r)
print("RAND", rand)
return rand + x
with Flow("Cache") as f:
a1 = rand_inc(10, 0)
# this fails:
runner = FlowRunner(f)
state1 = runner.run()
# this would pass:
#f.run()
```
Traceback:
```
Traceback (most recent call last):
File "/Users/dafcok/miniconda3/lib/python3.6/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "~/miniconda3/lib/python3.6/site-packages/prefect/engine/task_runner.py", line 631, in check_task_is_cached
candidate_states = prefect.context.caches.get(
AttributeError: 'Context' object has no attribute 'caches'
```
Moreover, if you `f.run()` once before `runner.run()`, cache validation always logs `Task 'rand_inc': can't use cache because it is now invalid`.
## Environment
```
{
"config_overrides": {},
"env_vars": [],
"system_information": {
"platform": "Darwin-19.0.0-x86_64-i386-64bit",
"prefect_version": "0.10.4",
"python_version": "3.6.8"
}
}
```
| Looking for some thoughts on this. What's happening here is the `flow.run` function sets a global `caches` block in context (among other things)
https://github.com/PrefectHQ/prefect/blob/6d141372cf89064d24bbafad582a9db26be0cbd5/src/prefect/core/flow.py#L1024-L1025
And then is uses flow runners to run that flow where each flow runner would have access to that `context.caches`. Now running the flow directly from the flow runner takes it a level deeper, below that context block. This means that if the flow runner is updated to make this `context.caches` in a similar way to the `flow.run` then the cache will not persist across runs since the flow runner is responsible for only a single run. We could add this in there with the knowledge that it will proceed without error but in calling something like
```
runner = FlowRunner(f)
state1 = runner.run()
state2 = runner.run()
```
the second run will not use the cache from the first run.
I think one design goal could be that FlowRunner.run() is guaranteed to not modify global state, caches or otherwise. Is that already the case? If yes, passing `context=my_dict` could explicitly modify `my_dict` which can be used for other runs.
In the long run, I'm not sure if in-memory caching should become obsolete after #2394 | 2020-05-06T15:46:49Z | [] | [] |
Traceback (most recent call last):
File "/Users/dafcok/miniconda3/lib/python3.6/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "~/miniconda3/lib/python3.6/site-packages/prefect/engine/task_runner.py", line 631, in check_task_is_cached
candidate_states = prefect.context.caches.get(
AttributeError: 'Context' object has no attribute 'caches'
| 651 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2570 | d3305a7dd590ad1dee4bb85a18ddbefadfa7531c | diff --git a/src/prefect/agent/agent.py b/src/prefect/agent/agent.py
--- a/src/prefect/agent/agent.py
+++ b/src/prefect/agent/agent.py
@@ -98,9 +98,11 @@ def __init__(
no_cloud_logs: bool = False,
) -> None:
self.name = name or config.cloud.agent.get("name", "agent")
- self.labels = list(
- labels or ast.literal_eval(config.cloud.agent.get("labels", "[]"))
- )
+
+ self.labels = labels or config.cloud.agent.get("labels", [])
+ # quick hack in case config has not been evaluated to a list yet
+ if isinstance(self.labels, str):
+ self.labels = ast.literal_eval(self.labels)
self.env_vars = env_vars or config.cloud.agent.get("env_vars", dict())
self.max_polls = max_polls
self.log_to_cloud = False if no_cloud_logs else True
@@ -166,7 +168,7 @@ def _register_agent(self) -> str:
- The agent ID as a string
"""
agent_id = self.client.register_agent(
- agent_type=type(self).__name__, name=self.name, labels=self.labels
+ agent_type=type(self).__name__, name=self.name, labels=self.labels # type: ignore
)
self.logger.debug(f"Agent ID: {agent_id}")
| Kubernetes Agent Failing to Parse Labels (version 0.11.0)
## Description
*A clear description of the bug*
It appears the kubernetes agent in version 0.11.0 is failing to read labels (possibly tied to https://github.com/PrefectHQ/prefect/pull/2558) with the following error:
```
(dw_kube) dylanhughes@Dylans-MacBook-Pro-Prefect ~> kubectl get po 1
NAME READY STATUS RESTARTS AGE
prefect-agent-74d7947c44-dsxxw 1/2 CrashLoopBackOff 5 5m14s
(dw_kube) dylanhughes@Dylans-MacBook-Pro-Prefect ~> kubectl logs prefect-agent-74d7947c44-dsxxw agent 1
Traceback (most recent call last):
File "/usr/local/bin/prefect", line 8, in <module>
sys.exit(cli())
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/prefect/cli/agent.py", line 278, in start
agent_address=agent_address,
File "/usr/local/lib/python3.6/site-packages/prefect/agent/kubernetes/agent.py", line 67, in __init__
no_cloud_logs=no_cloud_logs,
File "/usr/local/lib/python3.6/site-packages/prefect/agent/agent.py", line 102, in __init__
labels or ast.literal_eval(config.cloud.agent.get("labels", "[]"))
File "/usr/local/lib/python3.6/ast.py", line 85, in literal_eval
return _convert(node_or_string)
File "/usr/local/lib/python3.6/ast.py", line 84, in _convert
raise ValueError('malformed node or string: ' + repr(node))
ValueError: malformed node or string: <BoxList: ['prefect-data-warehouse']>
(dw_kube) dylanhughes@Dylans-MacBook-Pro-Prefect ~> prefect version
0.11.0
```
## Reproduction
*A minimal example that exhibits the behavior.*
While running version 0.11.0 run:
```
prefect agent install kubernetes -t TOKEN --label prefect-data-warehouse --rbac --resource-manager | kubectl apply -f -
```
The deployment will come up but the pod will die with the above error.
## Environment
*Any additional information about your environment*
Not sure it's relevant given above error
*Optionally run `prefect diagnostics` from the command line and paste the information here*
| Looks like there's a double `literal_eval` happening. Looking into it | 2020-05-15T15:22:11Z | [] | [] |
Traceback (most recent call last):
File "/usr/local/bin/prefect", line 8, in <module>
sys.exit(cli())
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/prefect/cli/agent.py", line 278, in start
agent_address=agent_address,
File "/usr/local/lib/python3.6/site-packages/prefect/agent/kubernetes/agent.py", line 67, in __init__
no_cloud_logs=no_cloud_logs,
File "/usr/local/lib/python3.6/site-packages/prefect/agent/agent.py", line 102, in __init__
labels or ast.literal_eval(config.cloud.agent.get("labels", "[]"))
File "/usr/local/lib/python3.6/ast.py", line 85, in literal_eval
return _convert(node_or_string)
File "/usr/local/lib/python3.6/ast.py", line 84, in _convert
raise ValueError('malformed node or string: ' + repr(node))
ValueError: malformed node or string: <BoxList: ['prefect-data-warehouse']>
| 662 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2594 | 2032c445521f223bc8569715fcb35f0b339a8210 | diff --git a/src/prefect/engine/results/s3_result.py b/src/prefect/engine/results/s3_result.py
--- a/src/prefect/engine/results/s3_result.py
+++ b/src/prefect/engine/results/s3_result.py
@@ -167,7 +167,7 @@ def exists(self, location: str, **kwargs: Any) -> bool:
Bucket=self.bucket, Key=location.format(**kwargs)
).load()
except botocore.exceptions.ClientError as exc:
- if exc.response["Error"]["Code"] == "404":
+ if exc.response["Error"]["Code"] == "NoSuchKey":
return False
raise
except Exception as exc:
| S3Result with target raises error
## Archived from the [Prefect Public Slack Community](https://join.slack.com/t/prefect-public/shared_invite/enQtNzE5OTU3OTQwNzc1LTQ5M2FkZmQzZjI0ODg1ZTBmOTc0ZjVjYWFjMWExZDAyYzBmYjVmMTE1NTQ1Y2IxZTllOTc4MmI3NzYxMDlhYWU)
**livni.itay**: Hi - I am working with `S3Result` and receiving a
```
botocore.errorfactory.NoSuchKey: An error occurred (NoSuchKey) when calling the GetObject operation: The specified key does not exist
```
Which upon further research - it can be anything including a permission error. (I tried different buckets with settings) The credentials are stored as AWS_CREDENTIALS in prefect cloud. With the config.toml set to use cloud secrets
```
[cloud]
use_local_secrets = false
```
Switching back to `result_handler` argument with `S3Result` subclass *did work,* . And combining `result handler` with `target` does not. Is there something different in the way that credentials are handled between `result` and `result_handler`?
The new prefect is really nice :slightly_smiling_face:
**chris**: Hi itay - could you share the code you used to initialize the `result_handler` and the `result`?
**livni.itay**: `tsx_imb_res = S3Result(bucket="tsx-moc-bcp")`
**livni.itay**:
```
@task(
max_retries=3,
retry_delay=timedelta(seconds=1), # In production this will be change to 20 minutes
result_handler=tsx_imb_res,
target="{task_name}-{today}",
state_handlers=[imb_handler, error_handler]
)
```
**livni.itay**: Works with `target` commented out
**chris**: Ah! The `result_handler` kwarg is now deprecated, so you should instead try:
```
...
result=tsx_imb_res,
...
```
**livni.itay**: Right that does not work
**livni.itay**: That is the problem
**chris**: ahhh interesting! OK so this might actually be a bug with our `exists` logic on the `S3Result` type. Would you mind sharing this example code + the traceback you’re seeing? Sorry about that!
**livni.itay**: Actually it looks like I am not using `target` right?
```
[2020-05-16 21:31:37] INFO - prefect.FlowRunner | Beginning Flow run for 'Our first flow'
[2020-05-16 21:31:37] INFO - prefect.FlowRunner | Starting flow run.
[2020-05-16 21:31:37] INFO - prefect.TaskRunner | Task 'tsx_url': Starting task run...
[2020-05-16 21:31:37] INFO - prefect.TaskRunner | Task 'tsx_url': finished task run for task with final state: 'Success'
[2020-05-16 21:31:37] INFO - prefect.TaskRunner | Task 'get_tsx_moc_imb': Starting task run...
[2020-05-16 21:31:38] ERROR - prefect.TaskRunner | Unexpected error: NoSuchKey('An error occurred (NoSuchKey) when calling the GetObject operation: The specified key does not exist.')
Traceback (most recent call last):
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 651, in check_target
if result.exists(target, **prefect.context):
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/results/s3_result.py", line 167, in exists
Bucket=self.bucket, Key=location.format(**kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/botocore/client.py", line 626, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.errorfactory.NoSuchKey: An error occurred (NoSuchKey) when calling the GetObject operation: The specified key does not exist.
[2020-05-16 21:31:38] INFO - prefect.TaskRunner | Task 'get_tsx_moc_imb': finished task run for task with final state: 'Skipped'
[2020-05-16 21:31:38] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
```
**livni.itay**: `target="{task_name}-{today}",`
**chris**: your code looks alright to me actually, including your `target` specification; I think this exception catching logic here is flawed: <https://github.com/PrefectHQ/prefect/blob/master/src/prefect/engine/results/s3_result.py#L169>
**chris**: it’s possible this was tested on a different version of `boto3` or something, we’ll need to investigate a little deeper
**livni.itay**: Cool. Let me know if you need anything more.
**chris**: I’ll use our bot to open the issue and we can track progress there
**chris**: <@ULVA73B9P> archive “S3Result with target raises error”
Original thread can be found [here](https://prefect-community.slack.com/archives/CL09KU1K7/p1589663959085200?thread_ts=1589663959.085200&cid=CL09KU1K7).
| Opening because this is still an active issue
From the thread, relevant package versions are:
```
botocore: 1.15.32
boto3: 1.12.32
```
Oh interesting, looks like we'll want to check something like:
```python
if ex.response['Error']['Code'] == 'NoSuchKey':
return False
```
or
```python
except client.exceptions.NoSuchKey
```
boto3 has minimal documentation on this | 2020-05-18T13:27:48Z | [] | [] |
Traceback (most recent call last):
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 651, in check_target
if result.exists(target, **prefect.context):
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/results/s3_result.py", line 167, in exists
Bucket=self.bucket, Key=location.format(**kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/botocore/client.py", line 316, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/ilivni/miniconda3/envs/py37moc/lib/python3.7/site-packages/botocore/client.py", line 626, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.errorfactory.NoSuchKey: An error occurred (NoSuchKey) when calling the GetObject operation: The specified key does not exist.
| 664 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2608 | 254cbf7f80b2612447e32bd95184f2e9656513fc | diff --git a/src/prefect/core/flow.py b/src/prefect/core/flow.py
--- a/src/prefect/core/flow.py
+++ b/src/prefect/core/flow.py
@@ -469,9 +469,12 @@ def add_task(self, task: Task) -> Task:
self.tasks.add(task)
self._cache.clear()
- case = prefect.context.get("case", None)
- if case is not None:
- case.add_task(task, self)
+ # Parameters must be root tasks
+ # All other new tasks should be added to the current case (if any)
+ if not isinstance(task, Parameter):
+ case = prefect.context.get("case", None)
+ if case is not None:
+ case.add_task(task, self)
return task
| Parameter must be bound before case context
## Current behavior 0.11.0
`case.__enter__` mimics python if blocks, yet assumes that parameters are bound to the flow outside its context. That can cause some head-scratching for users.
```python
from prefect import task, Parameter, Flow
from prefect.tasks.control_flow import merge, case
with Flow("test maybe first param use") as f:
x = Parameter("x")
p = Parameter("p")
with case(p > 10, True):
y = x-p
y = merge(y, p)
state = f.run(parameters=dict(x=0, p=11))
assert state.result[y].result == -11
state = f.run(parameters=dict(x=0, p=9))
assert state.result[y].result == 9
```
**Errors** with:
```
Traceback (most recent call last):
File "pref_case_bind.py", line 8, in <module>
y = x-p
File "~/miniconda3/lib/python3.6/site-packages/prefect/tasks/control_flow/case.py", line 100, in __exit__
child.set_upstream(cond, flow=self._flow)
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/task.py", line 591, in set_upstream
self.set_dependencies(flow=flow, upstream_tasks=[task], mapped=mapped)
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/task.py", line 566, in set_dependencies
mapped=mapped,
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/flow.py", line 811, in set_dependencies
mapped=is_mapped,
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/flow.py", line 482, in add_edge
"Parameters must be root tasks and can not have upstream dependencies."
ValueError: Parameters must be root tasks and can not have upstream dependencies.
```
**Passes** if: `x = Parameter("x")()`
## Proposed behavior
If `child` is a `Parameter` we would `.bind(self_flow)` between L#99 and L#100. I cannot think of any unintended side-effects, because use outside of a case context also binds yet unbound parameters (please confirm though).
## Example
See above
| 2020-05-19T18:27:00Z | [] | [] |
Traceback (most recent call last):
File "pref_case_bind.py", line 8, in <module>
y = x-p
File "~/miniconda3/lib/python3.6/site-packages/prefect/tasks/control_flow/case.py", line 100, in __exit__
child.set_upstream(cond, flow=self._flow)
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/task.py", line 591, in set_upstream
self.set_dependencies(flow=flow, upstream_tasks=[task], mapped=mapped)
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/task.py", line 566, in set_dependencies
mapped=mapped,
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/flow.py", line 811, in set_dependencies
mapped=is_mapped,
File "~/miniconda3/lib/python3.6/site-packages/prefect/core/flow.py", line 482, in add_edge
"Parameters must be root tasks and can not have upstream dependencies."
ValueError: Parameters must be root tasks and can not have upstream dependencies.
| 667 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-2686 | 4cc0606a0219bfe8b33bbb50507a9f3e3b581823 | diff --git a/src/prefect/utilities/gcp.py b/src/prefect/utilities/gcp.py
--- a/src/prefect/utilities/gcp.py
+++ b/src/prefect/utilities/gcp.py
@@ -3,7 +3,6 @@
"""
import prefect
-from google.cloud import bigquery, storage
from google.oauth2.service_account import Credentials
@@ -47,6 +46,8 @@ def get_storage_client(credentials: dict = None, project: str = None):
Returns:
- Client: an initialized and authenticated Google Client
"""
+ from google.cloud import storage
+
return get_google_client(storage, credentials=credentials, project=project)
@@ -63,4 +64,6 @@ def get_bigquery_client(credentials: dict = None, project: str = None):
Returns:
- Client: an initialized and authenticated Google Client
"""
+ from google.cloud import bigquery
+
return get_google_client(bigquery, credentials=credentials, project=project)
| Google Imports are Tied Together
## Description
*A clear description of the bug*
I’m using the new `GCSResult` and I’m getting an import error when I don’t also specify `google-cloud-bigquery` as a dependency since they’re imports occur in the same file, I think?
```
Unexpected error: ImportError("cannot import name 'bigquery' from 'google.cloud' (unknown location)")
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 986, in get_task_run_state
result = self.result.write(value, filename="output", **prefect.context)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/results/gcs_result.py", line 73, in write
self.gcs_bucket.blob(new.location).upload_from_string(binary_data)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/results/gcs_result.py", line 35, in gcs_bucket
from prefect.utilities.gcp import get_storage_client
File "/usr/local/lib/python3.7/site-packages/prefect/utilities/gcp.py", line 6, in <module>
from google.cloud import bigquery, storage
ImportError: cannot import name 'bigquery' from 'google.cloud' (unknown location)
```
https://cloud.prefect.io/prefect/flow-run/6704aa4e-ba9b-40ed-a4f8-386920839a8e?logId=75b1fc01-0ee8-4061-ab8b-5481e6123a79
On a cool note, changing to `python_dependencies=["prefect[google]"]` did work 🎉
## Expected Behavior
*What did you expect to happen instead?*
I'd like to be able to specify one import in insolation (in this case `google-cloud-storage`)
## Reproduction
*A minimal example that exhibits the behavior.*
```
from prefect import task, Flow
from prefect.tasks.notifications.slack_task import SlackTask
from prefect.schedules import CronSchedule
from prefect.environments.storage import Docker
from prefect.engine.results import GCSResult
import pendulum
import datetime
@task(name="Get Week Message", max_retries=5, retry_delay=datetime.timedelta(seconds=5))
def get_week_message():
prefects_birthday = pendulum.date(2018, 1, 17)
current_week = prefects_birthday.diff(pendulum.now()).in_weeks()
return f"Hello, Jeremiah! It is week {current_week}."
send_message = SlackTask(
name="Slack Jeremiah",
max_retries=5,
retry_delay=datetime.timedelta(seconds=5),
webhook_secret="SLACK_WEBHOOK",
)
schedule = CronSchedule(cron="50 11 * * MON", start_date=pendulum.now(tz="US/Eastern"))
storage = Docker(
base_image="prefecthq/prefect:latest-python3.7",
registry_url=URL,
python_dependencies=["google-cloud-storage"],
files={
FILE_LOCATION: FILENAME
},
env_vars={"GOOGLE_APPLICATION_CREDENTIALS": FILENAME},
)
gcs_result = GCSResult(bucket="what_week_is_it_results")
with Flow(
name="What Week is It?", schedule=schedule, storage=storage, result=gcs_result
) as flow:
week_message = get_week_message()
result = send_message(message=week_message)
```
## Environment
*Any additional information about your environment*
*Optionally run `prefect diagnostics` from the command line and paste the information here*
```
{
"config_overrides": {
"cloud": {
"auth_token": true,
"use_local_secrets": true
},
"context": {
"secrets": false
},
"home_dir": true
},
"env_vars": [],
"system_information": {
"platform": "Darwin-19.4.0-x86_64-i386-64bit",
"prefect_version": "0.11.2",
"python_version": "3.7.7"
}
}
```
| 2020-06-01T03:14:15Z | [] | [] |
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 986, in get_task_run_state
result = self.result.write(value, filename="output", **prefect.context)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/results/gcs_result.py", line 73, in write
self.gcs_bucket.blob(new.location).upload_from_string(binary_data)
File "/usr/local/lib/python3.7/site-packages/prefect/engine/results/gcs_result.py", line 35, in gcs_bucket
from prefect.utilities.gcp import get_storage_client
File "/usr/local/lib/python3.7/site-packages/prefect/utilities/gcp.py", line 6, in <module>
from google.cloud import bigquery, storage
ImportError: cannot import name 'bigquery' from 'google.cloud' (unknown location)
| 682 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-2868 | 960f15e9f59fcbd43a3f61199907f4970a3230e9 | diff --git a/src/prefect/core/flow.py b/src/prefect/core/flow.py
--- a/src/prefect/core/flow.py
+++ b/src/prefect/core/flow.py
@@ -6,6 +6,7 @@
import os
import tempfile
import time
+import uuid
import warnings
from contextlib import contextmanager
from pathlib import Path
@@ -943,7 +944,14 @@ def _run(
# run this flow indefinitely, so long as its schedule has future dates
while True:
- flow_run_context.update(scheduled_start_time=next_run_time)
+ # add relevant context keys
+ # many of these are intended to ensure local runs behave similarly as runs against a backend
+ flow_run_context.update(
+ scheduled_start_time=next_run_time,
+ flow_id=self.name,
+ flow_run_id=str(uuid.uuid4()),
+ flow_run_name=str(uuid.uuid4()),
+ )
if flow_state.is_scheduled():
next_run_time = flow_state.start_time
@@ -960,12 +968,17 @@ def _run(
# begin a single flow run
while not flow_state.is_finished():
runner = runner_cls(flow=self)
+ task_ctxts = kwargs.pop("task_contexts", {}).copy()
+ for t in self.tasks:
+ task_ctxts.setdefault(t, dict())
+ task_ctxts[t].update(task_run_id=str(uuid.uuid4()))
flow_state = runner.run(
parameters=parameters,
return_tasks=self.tasks,
state=flow_state,
task_states=flow_state.result,
context=flow_run_context,
+ task_contexts=task_ctxts,
**kwargs,
)
diff --git a/src/prefect/engine/flow_runner.py b/src/prefect/engine/flow_runner.py
--- a/src/prefect/engine/flow_runner.py
+++ b/src/prefect/engine/flow_runner.py
@@ -173,8 +173,11 @@ def initialize_run( # type: ignore
for task in self.flow.tasks:
task_contexts.setdefault(task, {}).update(
- task_name=task.name, task_slug=task.slug
+ task_name=task.name,
+ task_slug=self.flow.slugs[task],
+ task_id=self.flow.slugs[task],
)
+
state, context = super().initialize_run(state=state, context=context)
return FlowRunnerInitializeResult(
state=state,
diff --git a/src/prefect/engine/task_runner.py b/src/prefect/engine/task_runner.py
--- a/src/prefect/engine/task_runner.py
+++ b/src/prefect/engine/task_runner.py
@@ -165,7 +165,6 @@ def initialize_run( # type: ignore
task_run_count=run_count,
task_name=self.task.name,
task_tags=self.task.tags,
- task_slug=self.task.slug,
)
context.setdefault("checkpointing", config.flows.checkpointing)
| Key error when using task_run_id in a target template
## Description
Similar to one of the issues in #2640 where `filename` throws a key error when used in a template, so does `task_run_id`.
```
(py37moc) :~/prefect_guide$ /miniconda3/envs/py37moc/bin/python /home/ilivni/prefect_guide/tst_map.py
0.11.3
[2020-05-27 18:56:17] INFO - prefect.FlowRunner | Beginning Flow run for 'blah'
[2020-05-27 18:56:17] INFO - prefect.FlowRunner | Starting flow run.
[2020-05-27 18:56:17] INFO - prefect.TaskRunner | Task 'return_list': Starting task run...
[2020-05-27 18:56:17] ERROR - prefect.TaskRunner | Unexpected error: KeyError('task_run_id')
Traceback (most recent call last):
File "/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 660, in check_target
if result.exists(target, **prefect.context):
File "//miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/results/local_result.py", line 123, in exists
return os.path.exists(os.path.join(self.dir, location.format(**kwargs)))
KeyError: 'task_run_id'
```
## Reproduction
```
import prefect
print(prefect.__version__)
from prefect import task, Flow
from prefect.engine.results import LocalResult
lcl_res = LocalResult(dir="~/prefect_guide/results/{flow_name}")
@task(target="{task_name}-{task_run_id}",
)
def return_list():
return [1, 2, 3]
@task(target="{task_name}/{map_index}.prefect")
def mapped_task(x):
return x + 1
with Flow("blah", result=lcl_res) as flow:
mapped_task.map(return_list)
st = flow.run()
flow.visualize(flow_state=st)
```
## Environment
*Any additional information about your environment*
*Optionally run `prefect diagnostics` from the command line and paste the information here*
| Hey @gryBox having a task run ID requires a use of a backend—either core's server or Cloud. Since each task run ID corresponds to a unique entry in the database. So I would expect some behavior like this to happen. I do think we _could_ raise a more informative error though.
@joshmeek I think there's an opportunity here for us to polish the templating interface; things like `task_run_id` don't exist in Core-alone, but I can imagine people doing local development with Cloud-specific context vars like this and it'd be nice if we can find a way to support that. Same issue with the `{filename}` template with mapping.
Hi - Currently not exposing the `{filename}` in core results in an error loop if trying to deploy with docker.
1. Test locally (no `{filename}`) -> Good
2. Deploy with docker storage -> Error need `{filename}`
3. Add `{filename}` -> KeyError
4. Rinse lather repeat
@cicdw on the perspective of 'finding a way to support [Cloud-specific context vars in Core only]', do we think making some context that is ephmeral and meaningless (like a random UUID that won't be stored anywhere in the example of `task_run_id`) is anywhere towards the right direction?
Alternatively we were also talking about catching KeyErrors and raising our own exception that redirects people to the context docs as a way to deal with all cloud-only context.
> making some context that is ephmeral and meaningless (like a random UUID...)
Yea I think that would work! To ensure the ID is somewhat meaningful and constant across the full lifecycle of a flow run we could randomly generate them all for each task somewhere in this region: https://github.com/PrefectHQ/prefect/blob/master/src/prefect/core/flow.py#L904-L919
The only edge case here is for mapped tasks - when running against a backend, we generate a unique task run id for each child (happens here: https://github.com/PrefectHQ/prefect/blob/master/src/prefect/core/flow.py#L904-L919) so we'd want to consider what to do when running locally in this situation.
> Alternatively we were also talking about catching KeyErrors and raising our own exception that redirects people to the context docs as a way to deal with all cloud-only context.
Yea, I def think catching `KeyError`s and pointing to docs is a great idea; however, I do think it's important to ensure some amount of consistency between local runs -> cloud runs, so as a goal we should try to have context key parity where it makes sense.
Was this fixed by #2717, or is there still more to do here?
@jcrist I think there's still an opportunity here to ensure Cloud <-> Core populate the exact same context keys. Here you can find a list of the keys that Cloud populates that Core alone does not: https://docs.prefect.io/api/latest/utilities/context.html. I'm 90% that most of these can be handled with auto-generated UUIDs somewhere around here: https://github.com/PrefectHQ/prefect/blob/master/src/prefect/core/flow.py#L903 | 2020-06-25T21:06:18Z | [] | [] |
Traceback (most recent call last):
File "/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/runner.py", line 48, in inner
new_state = method(self, state, *args, **kwargs)
File "/miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/task_runner.py", line 660, in check_target
if result.exists(target, **prefect.context):
File "//miniconda3/envs/py37moc/lib/python3.7/site-packages/prefect/engine/results/local_result.py", line 123, in exists
return os.path.exists(os.path.join(self.dir, location.format(**kwargs)))
KeyError: 'task_run_id'
| 702 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-2877 | 71d40d7a4d3f97d0ca562b80b9033646e1a2c9ae | diff --git a/src/prefect/agent/fargate/agent.py b/src/prefect/agent/fargate/agent.py
--- a/src/prefect/agent/fargate/agent.py
+++ b/src/prefect/agent/fargate/agent.py
@@ -370,7 +370,18 @@ def _parse_kwargs(self, user_kwargs: dict, check_envars: bool = False) -> tuple:
self.logger.debug("{} = {}".format(key, item))
container_definitions_kwargs = {}
- for key, item in user_kwargs.get("containerDefinitions", [{}])[0].items():
+ container_defs = user_kwargs.get("containerDefinitions", [{}])
+ try:
+ container_defs = literal_eval(container_defs)
+ except (ValueError, SyntaxError):
+ pass
+
+ if len(container_defs) != 1:
+ raise ValueError(
+ "Fargate agent only accepts configuration for a single container definition."
+ )
+
+ for key, item in container_defs[0].items():
if key in container_definitions_kwarg_list:
try:
# Parse kwarg if needed
| Fargate container definitions argument parsing failing (CLI)
## Description
It seems the string data input to be used by the Fargate Agent is never cast as a dict:
```
prefect agent start fargate --containerDefinitions="[{'logConfiguration': 'options': {'awslogs-group': 'something', 'awslogs-stream-prefix': 'prefect-flow-runs', 'awslogs-create-group': 'true'}}]"
```
```
Traceback (most recent call last):
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/bin/prefect", line 10, in <module>
sys.exit(cli())
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/cli/agent.py", line 268, in start
from_qualified_name(retrieved_agent)(
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/agent/fargate/agent.py", line 142, in __init__
) = self._parse_kwargs(kwargs, True)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/agent/fargate/agent.py", line 359, in _parse_kwargs
for key, item in user_kwargs.get("containerDefinitions", [{}])[0].items():
AttributeError: 'str' object has no attribute 'items'
```
## Expected Behavior
<!-- What did you expect to happen instead? -->
It should behave [as the docs describe](https://docs.prefect.io/orchestration/agents/fargate.html#prefect-cli-using-kwargs) and use the above for the fargate task container definition, example in the docs:
```
prefect agent start fargate cpu=256 memory=512 networkConfiguration="{'awsvpcConfiguration': {'assignPublicIp': 'ENABLED', 'subnets': ['my_subnet_id'], 'securityGroups': []}}"
```
## Environment
```
poetry run prefect diagnostics
{
"config_overrides": {
"server": {
"telemetry": {
"enabled": true
}
}
},
"env_vars": [
"PREFECT__CLOUD__AUTH_TOKEN"
],
"system_information": {
"platform": "macOS-10.15.5-x86_64-i386-64bit",
"prefect_version": "0.12.1",
"python_version": "3.8.2"
}
}
```
(I deactivated telemetry in the config, need to look into this too)
| 2020-06-26T14:39:43Z | [] | [] |
Traceback (most recent call last):
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/bin/prefect", line 10, in <module>
sys.exit(cli())
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/cli/agent.py", line 268, in start
from_qualified_name(retrieved_agent)(
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/agent/fargate/agent.py", line 142, in __init__
) = self._parse_kwargs(kwargs, True)
File "/Users/nbatalha/Library/Caches/pypoetry/virtualenvs/pipelines-P6yz7rn1-py3.8/lib/python3.8/site-packages/prefect/agent/fargate/agent.py", line 359, in _parse_kwargs
for key, item in user_kwargs.get("containerDefinitions", [{}])[0].items():
AttributeError: 'str' object has no attribute 'items'
| 704 |
||||
PrefectHQ/prefect | PrefectHQ__prefect-3085 | f0a2056af0bcac28cca3554862dc0f2041b88b02 | diff --git a/src/prefect/engine/task_runner.py b/src/prefect/engine/task_runner.py
--- a/src/prefect/engine/task_runner.py
+++ b/src/prefect/engine/task_runner.py
@@ -159,7 +159,12 @@ def initialize_run( # type: ignore
task_name=self.task.name,
task_tags=self.task.tags,
)
- context.setdefault("checkpointing", config.flows.checkpointing)
+ # Use the config stored in context if possible (should always be present)
+ try:
+ checkpointing = context["config"]["flows"]["checkpointing"]
+ except KeyError:
+ checkpointing = config.flows.checkpointing
+ context.setdefault("checkpointing", checkpointing)
map_index = context.get("map_index", None)
if isinstance(map_index, int) and context.get("task_full_name"):
| DaskExecutor doesn't write results
## Description
DaskExecutor doesn't write LocalResults with JSONSerializer.
In our tests, also other results and with a cluster aren't written. For the Repro, I picked the simplest case.
## Expected Behavior
DaskExecutor writes LocalResults. If workers need extra treatment to set their global checkpointing config, I'd consider it an enhancement if configs percolate.
## Reproduction
```python
import json
import os
from tempfile import TemporaryDirectory
from typing import Optional
from prefect import Flow, task
from prefect.engine.executors import Executor, DaskExecutor
from prefect.engine.results import LocalResult
from prefect.engine.serializers import JSONSerializer
from prefect.utilities.configuration import set_temporary_config
from prefect.utilities.debug import raise_on_exception
def test(e: Optional[Executor]):
with TemporaryDirectory() as tmpdir:
flow_result = LocalResult(tmpdir, serializer=JSONSerializer(),
location="{task_name}.json")
with Flow("write_result", result=flow_result) as f:
_terminal = task(lambda: 42, checkpoint=True, name="magic")()
with set_temporary_config({"flows.checkpointing": True}), \
raise_on_exception():
f.run(executor=e)
files = os.listdir(tmpdir)
assert files == ["magic.json"], files
with open(os.path.join(tmpdir, files[0]), "rb") as file:
val = json.load(file)
assert val==42
if __name__ == "__main__":
print("Local")
test(None)
print("DaskExecutor")
test(DaskExecutor())
```
*Output:*
```bash
Local
[2020-08-01 09:48:05] INFO - prefect.FlowRunner | Beginning Flow run for 'write_result'
[2020-08-01 09:48:05] INFO - prefect.FlowRunner | Starting flow run.
[2020-08-01 09:48:05] INFO - prefect.TaskRunner | Task 'magic': Starting task run...
[2020-08-01 09:48:05] INFO - prefect.TaskRunner | Task 'magic': finished task run for task with final state: 'Success'
[2020-08-01 09:48:05] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
DaskExecutor
[2020-08-01 09:48:05] INFO - prefect.FlowRunner | Beginning Flow run for 'write_result'
[2020-08-01 09:48:05] INFO - prefect.FlowRunner | Starting flow run.
[2020-08-01 09:48:07] INFO - prefect.TaskRunner | Task 'magic': Starting task run...
[2020-08-01 09:48:07] INFO - prefect.TaskRunner | Task 'magic': finished task run for task with final state: 'Success'
[2020-08-01 09:48:08] INFO - prefect.FlowRunner | Flow run SUCCESS: all reference tasks succeeded
Traceback (most recent call last):
File "repro_local_write.py", line 37, in <module>
test(DaskExecutor())
File "repro_local_write.py", line 26, in test
assert files == ["magic.json"], files
AssertionError: []
```
## Environment
```json
{
"config_overrides": {},
"env_vars": [],
"system_information": {
"platform": "Darwin-19.5.0-x86_64-i386-64bit",
"prefect_version": "0.12.5",
"python_version": "3.6.8"
}
}
```
`dask==2.21.0`
| Hi @ahirner I don't think setting temporary config like that will do what you expect due to the workers being created outside of that context manager. Having checkpointing set in your global config.toml (or through an env var) should do the trick:
```
[flows]
checkpointing = true
```
Global configuration options work, thanks! I didn't expect clients to have only partial control over checkpointing. | 2020-08-04T15:38:51Z | [] | [] |
Traceback (most recent call last):
File "repro_local_write.py", line 37, in <module>
test(DaskExecutor())
File "repro_local_write.py", line 26, in test
assert files == ["magic.json"], files
AssertionError: []
| 723 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-473 | f5d5a4349fefcac65d856f615040ae2306bcfb22 | diff --git a/src/prefect/core/flow.py b/src/prefect/core/flow.py
--- a/src/prefect/core/flow.py
+++ b/src/prefect/core/flow.py
@@ -858,7 +858,12 @@ def run(
Returns:
- State of the flow after it is run resulting from it's return tasks
"""
- runner = prefect.engine.flow_runner.FlowRunner(flow=self) # type: ignore
+ if prefect.config.get("prefect_cloud", False) is True:
+ runner_cls = prefect.engine.cloud_runners.CloudFlowRunner # type: ignore
+ else:
+ runner_cls = prefect.engine.flow_runner.FlowRunner # type: ignore
+
+ runner = runner_cls(flow=self)
parameters = parameters or {}
unknown_params = [
p for p in parameters if p not in self.parameters(names_only=True)
diff --git a/src/prefect/engine/__init__.py b/src/prefect/engine/__init__.py
--- a/src/prefect/engine/__init__.py
+++ b/src/prefect/engine/__init__.py
@@ -3,5 +3,6 @@
import prefect.engine.executors
import prefect.engine.state
import prefect.engine.signals
+import prefect.engine.cloud_runners
from prefect.engine.flow_runner import FlowRunner
from prefect.engine.task_runner import TaskRunner
diff --git a/src/prefect/engine/cloud_runners.py b/src/prefect/engine/cloud_runners.py
new file mode 100644
--- /dev/null
+++ b/src/prefect/engine/cloud_runners.py
@@ -0,0 +1,285 @@
+# Licensed under LICENSE.md; also available at https://www.prefect.io/licenses/alpha-eula
+
+import warnings
+from typing import Any, Callable, Dict, Iterable, Optional, Tuple
+
+import prefect
+from prefect import config
+from prefect.client import Client
+from prefect.client.result_handlers import ResultHandler
+from prefect.core import Flow, Task
+from prefect.engine import signals
+from prefect.engine.runner import ENDRUN
+from prefect.engine.state import Failed, State
+from prefect.engine.flow_runner import FlowRunner
+from prefect.engine.task_runner import TaskRunner
+
+
+class CloudTaskRunner(TaskRunner):
+ """
+ TaskRunners handle the execution of Tasks and determine the State of a Task
+ before, during and after the Task is run.
+
+ In particular, through the TaskRunner you can specify the states of any upstream dependencies,
+ any inputs required for this Task to run, and what state the Task should be initialized with.
+
+ Args:
+ - task (Task): the Task to be run / executed
+ - result_handler (ResultHandler, optional): the handler to use for
+ retrieving and storing state results during execution
+ - state_handlers (Iterable[Callable], optional): A list of state change handlers
+ that will be called whenever the task changes state, providing an
+ opportunity to inspect or modify the new state. The handler
+ will be passed the task runner instance, the old (prior) state, and the new
+ (current) state, with the following signature:
+
+ ```
+ state_handler(
+ task_runner: TaskRunner,
+ old_state: State,
+ new_state: State) -> State
+ ```
+
+ If multiple functions are passed, then the `new_state` argument will be the
+ result of the previous handler.
+ """
+
+ def __init__(
+ self,
+ task: Task,
+ result_handler: ResultHandler = None,
+ state_handlers: Iterable[Callable] = None,
+ ) -> None:
+ self.task = task
+ self.client = Client()
+ self.result_handler = result_handler
+ super().__init__(
+ task=task, result_handler=result_handler, state_handlers=state_handlers
+ )
+
+ def _heartbeat(self) -> None:
+ try:
+ task_run_id = self.task_run_id
+ self.client.update_task_run_heartbeat(task_run_id)
+ except:
+ warnings.warn("Heartbeat failed for Task '{}'".format(self.task.name))
+
+ def call_runner_target_handlers(self, old_state: State, new_state: State) -> State:
+ """
+ A special state handler that the TaskRunner uses to call its task's state handlers.
+ This method is called as part of the base Runner's `handle_state_change()` method.
+
+ Args:
+ - old_state (State): the old (previous) state
+ - new_state (State): the new (current) state
+
+ Returns:
+ - State: the new state
+ """
+ new_state = super().call_runner_target_handlers(
+ old_state=old_state, new_state=new_state
+ )
+
+ task_run_id = prefect.context.get("task_run_id")
+ version = prefect.context.get("task_run_version")
+
+ try:
+ res = self.client.set_task_run_state(
+ task_run_id=task_run_id,
+ version=version,
+ state=new_state,
+ cache_for=self.task.cache_for,
+ result_handler=self.result_handler,
+ )
+ except Exception as exc:
+ raise ENDRUN(state=new_state)
+
+ prefect.context.update(task_run_version=version + 1) # type: ignore
+
+ return new_state
+
+ def initialize_run(
+ self, state: Optional[State], context: Dict[str, Any]
+ ) -> Tuple[State, Dict[str, Any]]:
+ """
+ Initializes the Task run by initializing state and context appropriately.
+
+ Args:
+ - state (State): the proposed initial state of the flow run; can be `None`
+ - context (dict): the context to be updated with relevant information
+
+ Returns:
+ - tuple: a tuple of the updated state and context objects
+ """
+ flow_run_id = context.get("flow_run_id", None)
+ try:
+ task_run_info = self.client.get_task_run_info(
+ flow_run_id,
+ context.get("task_id", ""),
+ map_index=context.get("map_index", None),
+ result_handler=self.result_handler,
+ )
+ except Exception as exc:
+ if state is None:
+ state = Failed(
+ message="Could not retrieve state from Prefect Cloud", result=exc
+ )
+ raise ENDRUN(state=state)
+
+ # if state is set, keep it; otherwise load from db
+ state = state or task_run_info.state # type: ignore
+ context.update(
+ task_run_version=task_run_info.version, # type: ignore
+ task_run_id=task_run_info.id, # type: ignore
+ )
+ self.task_run_id = task_run_info.id # type: ignore
+
+ # update inputs, prioritizing kwarg-provided inputs
+ if hasattr(state, "cached_inputs") and isinstance(
+ state.cached_inputs, dict # type: ignore
+ ):
+ inputs = state.cached_inputs # type: ignore
+ inputs.update(context.get("inputs", {}))
+ context.update(inputs=inputs)
+
+ context.update(task_name=self.task.name)
+ return super().initialize_run(state=state, context=context)
+
+
+class CloudFlowRunner(FlowRunner):
+ """
+ FlowRunners handle the execution of Flows and determine the State of a Flow
+ before, during and after the Flow is run.
+
+ In particular, through the FlowRunner you can specify which tasks should be
+ the first tasks to run, which tasks should be returned after the Flow is finished,
+ and what states each task should be initialized with.
+
+ Args:
+ - flow (Flow): the `Flow` to be run
+ - task_runner_cls (TaskRunner, optional): The class used for running
+ individual Tasks. Defaults to [TaskRunner](task_runner.html)
+ - state_handlers (Iterable[Callable], optional): A list of state change handlers
+ that will be called whenever the flow changes state, providing an
+ opportunity to inspect or modify the new state. The handler
+ will be passed the flow runner instance, the old (prior) state, and the new
+ (current) state, with the following signature:
+
+ ```
+ state_handler(
+ flow_runner: FlowRunner,
+ old_state: State,
+ new_state: State) -> State
+ ```
+
+ If multiple functions are passed, then the `new_state` argument will be the
+ result of the previous handler.
+
+ Note: new FlowRunners are initialized within the call to `Flow.run()` and in general,
+ this is the endpoint through which FlowRunners will be interacted with most frequently.
+
+ Example:
+ ```python
+ @task
+ def say_hello():
+ print('hello')
+
+ with Flow() as f:
+ say_hello()
+
+ fr = FlowRunner(flow=f)
+ flow_state = fr.run()
+ ```
+ """
+
+ def __init__(
+ self,
+ flow: Flow,
+ task_runner_cls: type = None,
+ state_handlers: Iterable[Callable] = None,
+ ) -> None:
+ self.flow = flow
+ self.task_runner_cls = task_runner_cls or CloudTaskRunner
+ self.client = Client()
+ super().__init__(
+ flow=flow,
+ task_runner_cls=self.task_runner_cls,
+ state_handlers=state_handlers,
+ )
+
+ def _heartbeat(self) -> None:
+ try:
+ flow_run_id = prefect.context.get("flow_run_id")
+ self.client.update_flow_run_heartbeat(flow_run_id)
+ except:
+ warnings.warn("Heartbeat failed for Flow '{}'".format(self.flow.name))
+
+ def call_runner_target_handlers(self, old_state: State, new_state: State) -> State:
+ """
+ A special state handler that the FlowRunner uses to call its flow's state handlers.
+ This method is called as part of the base Runner's `handle_state_change()` method.
+
+ Args:
+ - old_state (State): the old (previous) state
+ - new_state (State): the new (current) state
+
+ Returns:
+ - State: the new state
+ """
+ new_state = super().call_runner_target_handlers(
+ old_state=old_state, new_state=new_state
+ )
+
+ flow_run_id = prefect.context.get("flow_run_id", None)
+ version = prefect.context.get("flow_run_version")
+
+ try:
+ res = self.client.set_flow_run_state(
+ flow_run_id=flow_run_id,
+ version=version,
+ state=new_state,
+ result_handler=self.flow.result_handler,
+ )
+ except Exception as exc:
+ raise ENDRUN(state=new_state)
+
+ prefect.context.update(flow_run_version=version + 1) # type: ignore
+
+ return new_state
+
+ def initialize_run(
+ self, state: Optional[State], context: Dict[str, Any]
+ ) -> Tuple[State, Dict[str, Any]]:
+ """
+ Initializes the Flow run by initializing state and context appropriately.
+
+ Args:
+ - state (State): the proposed initial state of the flow run; can be `None`
+ - context (dict): the context to be updated with relevant information
+
+ Returns:
+ - tuple: a tuple of the updated state and context objects
+ """
+
+ try:
+ flow_run_info = self.client.get_flow_run_info(
+ flow_run_id=prefect.context.get("flow_run_id", ""),
+ result_handler=self.flow.result_handler,
+ )
+ except Exception as exc:
+ if state is None:
+ state = Failed(
+ message="Could not retrieve state from Prefect Cloud", result=exc
+ )
+ raise ENDRUN(state=state)
+
+ context.update(flow_run_version=flow_run_info.version) # type: ignore
+ # if state is set, keep it; otherwise load from db
+ state = state or flow_run_info.state # type: ignore
+
+ # update parameters, prioritizing kwarg-provided params
+ parameters = flow_run_info.parameters or {} # type: ignore
+ parameters.update(context.get("parameters", {}))
+ context.update(parameters=parameters)
+
+ return super().initialize_run(state=state, context=context)
diff --git a/src/prefect/engine/flow_runner.py b/src/prefect/engine/flow_runner.py
--- a/src/prefect/engine/flow_runner.py
+++ b/src/prefect/engine/flow_runner.py
@@ -6,7 +6,6 @@
import prefect
from prefect import config
-from prefect.client import Client
from prefect.core import Edge, Flow, Task
from prefect.engine import signals
from prefect.engine.executors import DEFAULT_EXECUTOR
@@ -79,13 +78,8 @@ def __init__(
):
self.flow = flow
self.task_runner_cls = task_runner_cls or TaskRunner
- self.client = Client()
super().__init__(state_handlers=state_handlers)
- def _heartbeat(self) -> None:
- flow_run_id = prefect.context.get("flow_run_id")
- self.client.update_flow_run_heartbeat(flow_run_id)
-
def call_runner_target_handlers(self, old_state: State, new_state: State) -> State:
"""
A special state handler that the FlowRunner uses to call its flow's state handlers.
@@ -101,51 +95,8 @@ def call_runner_target_handlers(self, old_state: State, new_state: State) -> Sta
for handler in self.flow.state_handlers:
new_state = handler(self.flow, old_state, new_state)
- # Set state if in prefect cloud
- if config.get("prefect_cloud", None):
- flow_run_id = prefect.context.get("flow_run_id", None)
- version = prefect.context.get("flow_run_version")
-
- res = self.client.set_flow_run_state(
- flow_run_id=flow_run_id,
- version=version,
- state=new_state,
- result_handler=self.flow.result_handler,
- )
- prefect.context.update(flow_run_version=res.version) # type: ignore
-
return new_state
- def initialize_run(
- self, state: Optional[State], context: Dict[str, Any]
- ) -> Tuple[State, Dict[str, Any]]:
- """
- Initializes the Flow run by initializing state and context appropriately.
-
- Args:
- - state (State): the proposed initial state of the flow run; can be `None`
- - context (dict): the context to be updated with relevant information
-
- Returns:
- - tuple: a tuple of the updated state and context objects
- """
-
- if config.get("prefect_cloud", None):
- flow_run_info = self.client.get_flow_run_info(
- flow_run_id=prefect.context.get("flow_run_id", ""),
- result_handler=self.flow.result_handler,
- )
- context.update(flow_run_version=flow_run_info.version) # type: ignore
- # if state is set, keep it; otherwise load from db
- state = state or flow_run_info.state # type: ignore
-
- ## update parameters, prioritizing kwarg-provided params
- parameters = flow_run_info.parameters or {} # type: ignore
- parameters.update(context.get("parameters", {}))
- context.update(parameters=parameters)
-
- return super().initialize_run(state=state, context=context)
-
def run(
self,
state: State = None,
@@ -193,7 +144,13 @@ def run(
parameters = parameters or {}
context.update(parameters=parameters, flow_name=self.flow.name)
- state, context = self.initialize_run(state, context)
+
+ # if run fails to initialize, end the run
+ try:
+ state, context = self.initialize_run(state, context)
+ except ENDRUN as exc:
+ state = exc.state
+ return state
if return_tasks.difference(self.flow.tasks):
raise ValueError("Some tasks in return_tasks were not found in the flow.")
diff --git a/src/prefect/engine/runner.py b/src/prefect/engine/runner.py
--- a/src/prefect/engine/runner.py
+++ b/src/prefect/engine/runner.py
@@ -71,6 +71,9 @@ def __init__(self, state_handlers: Iterable[Callable] = None):
self.state_handlers = state_handlers or []
self.logger = logging.get_logger(type(self).__name__)
+ def _heartbeat(self) -> None:
+ pass
+
def initialize_run(
self, state: Optional[State], context: Dict[str, Any]
) -> Tuple[State, Dict[str, Any]]:
@@ -122,7 +125,8 @@ def handle_state_change(self, old_state: State, new_state: State) -> State:
Raises:
- PAUSE: if raised by a handler
- - ENDRUN(Failed()): if any of the handlers fail
+ - ENDRUN: if raised by a handler
+ - ENDRUN(Failed()): if any of the handlers fail unexpectedly
"""
raise_on_exception = prefect.context.get("raise_on_exception", False)
@@ -135,8 +139,8 @@ def handle_state_change(self, old_state: State, new_state: State) -> State:
for handler in self.state_handlers:
new_state = handler(self, old_state, new_state)
- # raise pauses
- except signals.PAUSE:
+ # raise pauses and ENDRUNs
+ except (signals.PAUSE, ENDRUN):
raise
# trap signals
diff --git a/src/prefect/engine/task_runner.py b/src/prefect/engine/task_runner.py
--- a/src/prefect/engine/task_runner.py
+++ b/src/prefect/engine/task_runner.py
@@ -19,7 +19,6 @@
import prefect
from prefect import config
-from prefect.client import Client
from prefect.client.result_handlers import ResultHandler
from prefect.core import Edge, Task
from prefect.engine import signals
@@ -80,14 +79,9 @@ def __init__(
state_handlers: Iterable[Callable] = None,
):
self.task = task
- self.client = Client()
self.result_handler = result_handler
super().__init__(state_handlers=state_handlers)
- def _heartbeat(self) -> None:
- task_run_id = self.task_run_id
- self.client.update_task_run_heartbeat(task_run_id)
-
def call_runner_target_handlers(self, old_state: State, new_state: State) -> State:
"""
A special state handler that the TaskRunner uses to call its task's state handlers.
@@ -103,20 +97,6 @@ def call_runner_target_handlers(self, old_state: State, new_state: State) -> Sta
for handler in self.task.state_handlers:
new_state = handler(self.task, old_state, new_state)
- # Set state if in prefect cloud
- if config.get("prefect_cloud", None):
- task_run_id = prefect.context.get("task_run_id")
- version = prefect.context.get("task_run_version")
-
- res = self.client.set_task_run_state(
- task_run_id=task_run_id,
- version=version,
- state=new_state,
- cache_for=self.task.cache_for,
- result_handler=self.result_handler,
- )
- prefect.context.update(task_run_version=res.version) # type: ignore
-
return new_state
def initialize_run(
@@ -132,23 +112,6 @@ def initialize_run(
Returns:
- tuple: a tuple of the updated state and context objects
"""
- if config.get("prefect_cloud", None):
- flow_run_id = context.get("flow_run_id", None)
- task_run_info = self.client.get_task_run_info(
- flow_run_id,
- context.get("task_id", ""),
- map_index=context.get("map_index", None),
- result_handler=self.result_handler,
- )
-
- # if state is set, keep it; otherwise load from db
- state = state or task_run_info.state # type: ignore
- context.update(
- task_run_version=task_run_info.version, # type: ignore
- task_run_id=task_run_info.id, # type: ignore
- )
- self.task_run_id = task_run_info.id # type: ignore
-
context.update(task_name=self.task.name)
return super().initialize_run(state=state, context=context)
@@ -198,8 +161,15 @@ def run(
context = context or {}
executor = executor or DEFAULT_EXECUTOR
- context.update(map_index=map_index)
- state, context = self.initialize_run(state, context)
+ context.update(inputs=inputs, map_index=map_index)
+
+ # if run fails to initialize, end the run
+ try:
+ state, context = self.initialize_run(state, context)
+ inputs = context.get("inputs") or {}
+ except ENDRUN as exc:
+ state = exc.state
+ return state
# construct task inputs
task_inputs = {} # type: Dict[str, Any]
diff --git a/src/prefect/utilities/executors.py b/src/prefect/utilities/executors.py
--- a/src/prefect/utilities/executors.py
+++ b/src/prefect/utilities/executors.py
@@ -29,18 +29,19 @@ def inner(
self: "prefect.engine.runner.Runner", *args: Any, **kwargs: Any
) -> "prefect.engine.state.State":
try:
- if prefect.config.get("prefect_cloud", None):
- timer = threading.Timer(
- prefect.config.cloud.heartbeat_interval, self._heartbeat
- )
+ timer = threading.Timer(
+ prefect.config.cloud.heartbeat_interval, self._heartbeat
+ )
+ try:
self._heartbeat()
- timer.start()
+ except:
+ pass
+ timer.start()
return runner_method(self, *args, **kwargs)
except Exception as exc:
raise exc
finally:
- if prefect.config.get("prefect_cloud", None):
- timer.cancel()
+ timer.cancel()
return inner
| Create separate CloudFlowRunner and CloudTaskRunner classes to encapsulate logic
We're starting to get to a point of lots of conditionals (`if prefect_cloud:`) and repetitive logic; I think these could be standalone classes set as default by a config.
State handler exception raised on client error
Output of error:
`"Failed(\"Exception raised while calling state handlers.\")\r\n"`
Traceback:
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/usr/local/lib/python3.6/site-packages/prefect/environments.py", line 204, in run
return runner.run(**(runner_kwargs or {}))
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 229, in run
raise exc
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 210, in run
state = self.set_flow_to_running(state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/runner.py", line 60, in inner
return self.handle_state_change(old_state=state, new_state=new_state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/runner.py", line 112, in handle_state_change
new_state = self.call_runner_target_handlers(old_state, new_state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 109, in call_runner_target_handlers
result_handler=self.flow.result_handler,
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 368, in set_flow_run_state
parse_graphql(mutation), state=json.dumps(serialized_state)
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 108, in graphql
server=self.graphql_server,
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 83, in post
response = self._request(method="POST", path=path, params=params, server=server)
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 170, in _request
return request_fn()
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 164, in request_fn
response.raise_for_status()
File "/usr/local/lib/python3.6/site-packages/requests/models.py", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: client_url_here
```
cc @cicdw
| Following conversation with @cicdw:
- CloudTaskRunner needs to query for all upstream states immediately before using them (to ensure they aren't stale)
- CloudFlowRunner needs to query for all reference/terminal states immediately before using them (to ensure they aren't stale)
| 2019-01-01T02:42:37Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/usr/local/lib/python3.6/site-packages/prefect/environments.py", line 204, in run
return runner.run(**(runner_kwargs or {}))
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 229, in run
raise exc
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 210, in run
state = self.set_flow_to_running(state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/runner.py", line 60, in inner
return self.handle_state_change(old_state=state, new_state=new_state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/runner.py", line 112, in handle_state_change
new_state = self.call_runner_target_handlers(old_state, new_state)
File "/usr/local/lib/python3.6/site-packages/prefect/engine/flow_runner.py", line 109, in call_runner_target_handlers
result_handler=self.flow.result_handler,
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 368, in set_flow_run_state
parse_graphql(mutation), state=json.dumps(serialized_state)
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 108, in graphql
server=self.graphql_server,
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 83, in post
response = self._request(method="POST", path=path, params=params, server=server)
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 170, in _request
return request_fn()
File "/usr/local/lib/python3.6/site-packages/prefect/client/client.py", line 164, in request_fn
response.raise_for_status()
File "/usr/local/lib/python3.6/site-packages/requests/models.py", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: client_url_here
| 756 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-863 | 9291bef70c4dcf1b3491813174268d3454786802 | diff --git a/src/prefect/core/flow.py b/src/prefect/core/flow.py
--- a/src/prefect/core/flow.py
+++ b/src/prefect/core/flow.py
@@ -3,6 +3,7 @@
import functools
import inspect
import json
+import os
import tempfile
import time
import uuid
@@ -1105,8 +1106,12 @@ def get_color(task: Task, map_index: int = None) -> str:
except Exception:
pass
- with tempfile.NamedTemporaryFile() as tmp:
- graph.render(tmp.name, view=True)
+ with tempfile.NamedTemporaryFile(delete=False) as tmp:
+ tmp.close()
+ try:
+ graph.render(tmp.name, view=True)
+ finally:
+ os.unlink(tmp.name)
return graph
| flow.visualize() fails on windows
A simple flow.visualize() command fails when running on Windows. Looks like it's due to the way that windows creates tempfiles (see this thread: https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file)
Traceback (most recent call last):
File "C:\Dev\source\python\prefecttesting\dostuff.py", line 34, in <module>
flow.visualize()
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\prefect\core\flow.py", line 1106, in visualize
graph.render(tmp.name, view=True)
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\graphviz\files.py", line 183, in render
filepath = self.save(filename, directory)
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\graphviz\files.py", line 155, in save
with io.open(filepath, 'w', encoding=self.encoding) as fd:
PermissionError: [Errno 13] Permission denied: 'c:\\dev\\temp\\tmpn3cczj0a'
| Thanks for the issue and the helpful pointer @mblye !
It looks like `graphviz` is attempting to open the temporary file a _second_ time here: https://github.com/PrefectHQ/prefect/blob/master/src/prefect/core/flow.py#L1105-L1106 and Windows doesn't like that.
It should be possible to work around this by creating a temporary _directory_ and then using an arbitrary filename in the call to `graph.render` 👍 | 2019-03-27T19:22:59Z | [] | [] |
Traceback (most recent call last):
File "C:\Dev\source\python\prefecttesting\dostuff.py", line 34, in <module>
flow.visualize()
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\prefect\core\flow.py", line 1106, in visualize
graph.render(tmp.name, view=True)
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\graphviz\files.py", line 183, in render
filepath = self.save(filename, directory)
File "C:\Dev\source\python\prefecttesting\venv\lib\site-packages\graphviz\files.py", line 155, in save
with io.open(filepath, 'w', encoding=self.encoding) as fd:
PermissionError: [Errno 13] Permission denied: 'c:\\dev\\temp\\tmpn3cczj0a'
| 824 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-973 | 4d09b0832f675847def55050bb1c7f0e651e93ef | diff --git a/src/prefect/environments/storage/docker.py b/src/prefect/environments/storage/docker.py
--- a/src/prefect/environments/storage/docker.py
+++ b/src/prefect/environments/storage/docker.py
@@ -7,6 +7,7 @@
import tempfile
import textwrap
import uuid
+from slugify import slugify
from typing import Any, Callable, Dict, Iterable, List
import docker
@@ -125,7 +126,7 @@ def add_flow(self, flow: "prefect.core.flow.Flow") -> str:
flow.name
)
)
- flow_path = "/root/.prefect/{}.prefect".format(flow.name.replace(" ", ""))
+ flow_path = "/root/.prefect/{}.prefect".format(slugify(flow.name))
self.flows[flow.name] = flow_path
self._flows[flow.name] = flow # needed prior to build
return flow_path
@@ -289,11 +290,12 @@ def create_dockerfile_object(self, directory: str = None) -> None:
# Write all flows to file and load into the image
copy_flows = ""
for flow_name, flow_location in self.flows.items():
- flow_path = os.path.join(directory, "{}.flow".format(flow_name))
+ clean_name = slugify(flow_name)
+ flow_path = os.path.join(directory, "{}.flow".format(clean_name))
with open(flow_path, "wb") as f:
cloudpickle.dump(self._flows[flow_name], f)
copy_flows += "COPY {source} {dest}\n".format(
- source="{}.flow".format(flow_name), dest=flow_location
+ source="{}.flow".format(clean_name), dest=flow_location
)
# Write a healthcheck script into the image
| Flow storage copying fails with spaces in flow.name
```
Step 1/9 : FROM python:3.6
---> 2bb3204ab1d1
Step 2/9 : RUN pip install pip --upgrade
---> Using cache
---> 4d8b101e2933
Step 3/9 : RUN pip install wheel
---> Using cache
---> 7389d69ba240
Step 4/9 : RUN mkdir /root/.prefect/
---> Using cache
---> 10619fcde458
Step 5/9 : COPY Test Flow.flow /root/.prefect/TestFlow.prefect
Traceback (most recent call last):
File "managed_agent_testing.py", line 15, in <module>
f.deploy(project_name="Test Project")
File "/Users/josh/Desktop/code/prefect/src/prefect/core/flow.py", line 1361, in deploy
set_schedule_inactive=set_schedule_inactive,
File "/Users/josh/Desktop/code/prefect/src/prefect/client/client.py", line 357, in deploy
serializedFlow=flow.serialize(build=build),
File "/Users/josh/Desktop/code/prefect/src/prefect/core/flow.py", line 1116, in serialize
storage = self.storage.build() # type: Optional[Storage]
File "/Users/josh/Desktop/code/prefect/src/prefect/environments/storage/docker.py", line 170, in build
image_name, image_tag = self.build_image(push=push)
File "/Users/josh/Desktop/code/prefect/src/prefect/environments/storage/docker.py", line 219, in build_image
"Your flow failed to deserialize in the container; please ensure that all necessary files and dependencies have been included."
prefect.utilities.exceptions.SerializationError: Your flow failed to deserialize in the container; please ensure that all necessary files and dependencies have been included.
```
Code to reproduce:
```
from prefect import task, Flow
from prefect.environments.storage import Docker
@task
def my_task():
print("ASDF")
with Flow(
"Test Flow", storage=Docker(...)
) as f:
t1 = my_task()
f.deploy(project_name="Test Project")
```
`COPY Test Flow.flow /root/.prefect/TestFlow.prefect` the whitespace between Test and Flow.flow is the issue. We need to sanitize this by replacing it with a special character
| Since we already require `slugify`, we could use that as a quick off-the-shelf solution:
```python
import slugify
slugify.slugify("Test Flow")
## 'test-flow'
```
Check emojis too... | 2019-04-24T00:00:55Z | [] | [] |
Traceback (most recent call last):
File "managed_agent_testing.py", line 15, in <module>
f.deploy(project_name="Test Project")
File "/Users/josh/Desktop/code/prefect/src/prefect/core/flow.py", line 1361, in deploy
set_schedule_inactive=set_schedule_inactive,
File "/Users/josh/Desktop/code/prefect/src/prefect/client/client.py", line 357, in deploy
serializedFlow=flow.serialize(build=build),
File "/Users/josh/Desktop/code/prefect/src/prefect/core/flow.py", line 1116, in serialize
storage = self.storage.build() # type: Optional[Storage]
File "/Users/josh/Desktop/code/prefect/src/prefect/environments/storage/docker.py", line 170, in build
image_name, image_tag = self.build_image(push=push)
File "/Users/josh/Desktop/code/prefect/src/prefect/environments/storage/docker.py", line 219, in build_image
"Your flow failed to deserialize in the container; please ensure that all necessary files and dependencies have been included."
prefect.utilities.exceptions.SerializationError: Your flow failed to deserialize in the container; please ensure that all necessary files and dependencies have been included.
| 844 |
|||
PrefectHQ/prefect | PrefectHQ__prefect-978 | d5317a9bcdf1cd73e10f0f5b6948260ceb84cfc4 | diff --git a/src/prefect/environments/execution/cloud/environment.py b/src/prefect/environments/execution/cloud/environment.py
--- a/src/prefect/environments/execution/cloud/environment.py
+++ b/src/prefect/environments/execution/cloud/environment.py
@@ -5,6 +5,7 @@
from os import path
from typing import Any, List
+import cloudpickle
import docker
import yaml
@@ -94,14 +95,13 @@ def run_flow(self) -> None:
cluster.adapt(minimum=1, maximum=1)
# Load serialized flow from file and run it with a DaskExecutor
- schema = prefect.serialization.flow.FlowSchema()
with open(
prefect.context.get(
"flow_file_path", "/root/.prefect/flow_env.prefect"
),
- "r",
+ "rb",
) as f:
- flow = schema.load(json.load(f))
+ flow = cloudpickle.load(f)
executor = DaskExecutor(address=cluster.scheduler_address)
FlowRunner(flow=flow).run(executor=executor)
| Decoding error when running a stored flow
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/local/lib/python3.6/site-packages/prefect/environments/execution/cloud/environment.py", line 104, in run_flow
flow = schema.load(json.load(f))
File "/usr/local/lib/python3.6/json/__init__.py", line 296, in load
return loads(fp.read(),
File "/usr/local/lib/python3.6/codecs.py", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
```
Still investigating the issue
| 2019-04-24T12:54:06Z | [] | [] |
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/local/lib/python3.6/site-packages/prefect/environments/execution/cloud/environment.py", line 104, in run_flow
flow = schema.load(json.load(f))
File "/usr/local/lib/python3.6/json/__init__.py", line 296, in load
return loads(fp.read(),
File "/usr/local/lib/python3.6/codecs.py", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
| 845 |
||||
Qiskit/qiskit | Qiskit__qiskit-10126 | 574da7ee5cfb58cf3d7eb5ef726d15166c5e247a | diff --git a/qiskit/quantum_info/synthesis/qsd.py b/qiskit/quantum_info/synthesis/qsd.py
--- a/qiskit/quantum_info/synthesis/qsd.py
+++ b/qiskit/quantum_info/synthesis/qsd.py
@@ -81,7 +81,7 @@ def qs_decomposition(
circ = decomposer_1q(mat)
elif dim == 4:
if decomposer_2q is None:
- if opt_a2:
+ if opt_a2 and _depth > 0:
from qiskit.extensions.unitary import UnitaryGate # pylint: disable=cyclic-import
def decomp_2q(mat):
@@ -118,7 +118,7 @@ def decomp_2q(mat):
right_circ = _demultiplex(u1, u2, opt_a1=opt_a1, opt_a2=opt_a2, _depth=_depth)
circ.append(right_circ.to_instruction(), qr)
- if opt_a2 and _depth == 0:
+ if opt_a2 and _depth == 0 and dim > 4:
return _apply_a2(circ)
return circ
@@ -236,6 +236,8 @@ def _apply_a2(circ):
for i, instruction in enumerate(ccirc.data):
if instruction.operation.name == "qsd2q":
ind2q.append(i)
+ if not ind2q:
+ return ccirc
# rolling over diagonals
ind2 = None # lint
for ind1, ind2 in zip(ind2q[0:-1:], ind2q[1::]):
| Quantum shannon decomposition failing for some inputs
### Environment
- **Qiskit Terra version**: 0.42.1
- **Python version**: 3.10.8
### What is happening?
qs_decomposition in [qsd.py](https://github.com/Qiskit/qiskit-terra/blob/main/qiskit/quantum_info/synthesis/qsd.py) throws an error for some examples, seemingly due to a bug in the code
### How can we reproduce the issue?
```
from qiskit.quantum_info.synthesis.qsd import qs_decomposition
qs_decomposition(np.array([[0,1],[1,0]]))
```
Output:
```
Traceback (most recent call last):
Cell In[14], line 3
qs_decomposition(np.array([[0,1],[1,0]]))
File /opt/conda/lib/python3.10/site-packages/qiskit/quantum_info/synthesis/qsd.py:122 in qs_decomposition
return _apply_a2(circ)
File /opt/conda/lib/python3.10/site-packages/qiskit/quantum_info/synthesis/qsd.py:253 in _apply_a2
qc3 = two_qubit_decompose.two_qubit_cnot_decompose(mat2)
UnboundLocalError: local variable 'mat2' referenced before assignment
Use %tb to get the full traceback.
```
Alternatively,
```
qs_decomposition(qiskit.quantum_info.random_unitary(4).to_matrix())
```
Giving the same error
### What should happen?
We should still be able to produce a decomposed circuit from these examples
### Any suggestions?
This seems to occur when line 233 of qsd.py in the function ‘_apply_a2()’ does not transpile to include any of the ‘qsd2q’ gate type for an instance.
To fix this add something such as the following before the loop on line 242:
```
if not ind2q:
return ccirc
```
Additionally should add a test for this kind of case to the [test](https://github.com/Qiskit/qiskit-terra/blob/main/test/python/quantum_info/test_synthesis.py)
| I can correct this with the above indicated as long as the reasoning follows
Yeah, I believe your reasoning is correct here, thanks - I don't think there's anything to do if there's nothing to decompose. @ewinston can check me, though, and I'll assign him to the PR if you're able to make it. Let us know if not, though, and one of us will.
The `_apply_a2` function actually wasn't supposed to by applied for `dim == 2`. I can submit a pr for that fix. Did you ever notice this for dimension > 2? | 2023-05-17T15:30:11Z | [] | [] |
Traceback (most recent call last):
Cell In[14], line 3
qs_decomposition(np.array([[0,1],[1,0]]))
File /opt/conda/lib/python3.10/site-packages/qiskit/quantum_info/synthesis/qsd.py:122 in qs_decomposition
return _apply_a2(circ)
File /opt/conda/lib/python3.10/site-packages/qiskit/quantum_info/synthesis/qsd.py:253 in _apply_a2
qc3 = two_qubit_decompose.two_qubit_cnot_decompose(mat2)
UnboundLocalError: local variable 'mat2' referenced before assignment
| 860 |
|||
Qiskit/qiskit | Qiskit__qiskit-1020 | 017e4566bbc91cdecc9181ef0dc46e8656d2e3ac | diff --git a/qiskit/unroll/_dagunroller.py b/qiskit/unroll/_dagunroller.py
--- a/qiskit/unroll/_dagunroller.py
+++ b/qiskit/unroll/_dagunroller.py
@@ -78,11 +78,12 @@ def expand_gates(self, basis=None):
gatedefs.append(Gate(children))
# Walk through the DAG and examine each node
builtins = ["U", "CX", "measure", "reset", "barrier"]
+ simulator_builtins = ['snapshot', 'save', 'load', 'noise']
topological_sorted_list = list(nx.topological_sort(self.dag_circuit.multi_graph))
for node in topological_sorted_list:
current_node = self.dag_circuit.multi_graph.node[node]
if current_node["type"] == "op" and \
- current_node["name"] not in builtins + basis and \
+ current_node["name"] not in builtins + basis + simulator_builtins and \
not self.dag_circuit.gates[current_node["name"]]["opaque"]:
subcircuit, wires = self._build_subcircuit(gatedefs,
basis,
| Using simulator instructions crashes the latex drawer
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**: latest master
- **Python version**: 3.7
- **Operating system**: linux
### What is the current behavior?
Attempting to use the latex drawer to render a circuit with simulator instructions stack traces in the dagunroller. For example:
```
Traceback (most recent call last):
File "test_qiskit.py", line 67, in <module>
visualization.generate_latex_source(qc, filename='out.tex')
File "/tmp/qiskit/qiskit-terra/qiskit/tools/visualization/_circuit_visualization.py", line 354, in generate_latex_source
json_circuit = transpile(dag_circuit, basis_gates=basis, format='json')
File "/tmp/qiskit/qiskit-terra/qiskit/transpiler/_transpiler.py", line 346, in transpile
dag = dag_unroller.expand_gates()
File "/tmp/qiskit/qiskit-terra/qiskit/unroll/_dagunroller.py", line 86, in expand_gates
not self.dag_circuit.gates[current_node["name"]]["opaque"]:
KeyError: 'snapshot'
```
It looks like it's trying to treat the snapshot instruction as a gate (which it's not) and that's causing things to crash.
### Steps to reproduce the problem
I've been running:
```
import qiskit.extensions.simulator
from qiskit import *
from qiskit.tools import visualization
q = QuantumRegister(2)
c = ClassicalRegister(1)
qc = QuantumCircuit(q, c)
qc.x(q[0])
qc.snapshot(slot=3)
qc.x(q[1])
qc.h(q[0])
qc.barrier()
qc.measure(q[0], c[0])
visualization.generate_latex_source(qc, filename='out.tex')
```
Also replacing snapshot() with save(), load(), and noise()
### What is the expected behavior?
This should draw a circuit (the barriers won't be drawn for the simulator instructions, that's what I was working on adding when I encountered this) and not stack trace.
### Suggested solutions
Fix the crash.
| 2018-10-03T18:28:06Z | [] | [] |
Traceback (most recent call last):
File "test_qiskit.py", line 67, in <module>
visualization.generate_latex_source(qc, filename='out.tex')
File "/tmp/qiskit/qiskit-terra/qiskit/tools/visualization/_circuit_visualization.py", line 354, in generate_latex_source
json_circuit = transpile(dag_circuit, basis_gates=basis, format='json')
File "/tmp/qiskit/qiskit-terra/qiskit/transpiler/_transpiler.py", line 346, in transpile
dag = dag_unroller.expand_gates()
File "/tmp/qiskit/qiskit-terra/qiskit/unroll/_dagunroller.py", line 86, in expand_gates
not self.dag_circuit.gates[current_node["name"]]["opaque"]:
KeyError: 'snapshot'
| 869 |
||||
Qiskit/qiskit | Qiskit__qiskit-10495 | 42a0ee84df3e15834b174bdb0295142b987b261f | diff --git a/qiskit/circuit/commutation_checker.py b/qiskit/circuit/commutation_checker.py
--- a/qiskit/circuit/commutation_checker.py
+++ b/qiskit/circuit/commutation_checker.py
@@ -65,10 +65,20 @@ def _hashable_parameters(self, params):
return ("fallback", str(params))
def commute(
- self, op1: Operation, qargs1: List, cargs1: List, op2: Operation, qargs2: List, cargs2: List
- ):
+ self,
+ op1: Operation,
+ qargs1: List,
+ cargs1: List,
+ op2: Operation,
+ qargs2: List,
+ cargs2: List,
+ max_num_qubits: int = 3,
+ ) -> bool:
"""
- Checks if two Operations commute.
+ Checks if two Operations commute. The return value of `True` means that the operations
+ truly commute, and the return value of `False` means that either the operations do not
+ commute or that the commutation check was skipped (for example, when the operations
+ have conditions or have too many qubits).
Args:
op1: first operation.
@@ -77,10 +87,14 @@ def commute(
op2: second operation.
qargs2: second operation's qubits.
cargs2: second operation's clbits.
+ max_num_qubits: the maximum number of qubits to consider, the check may be skipped if
+ the number of qubits for either operation exceeds this amount.
Returns:
bool: whether two operations commute.
"""
+ # pylint: disable=too-many-return-statements
+
# We don't support commutation of conditional gates for now due to bugs in
# CommutativeCancellation. See gh-8553.
if (
@@ -105,6 +119,10 @@ def commute(
if not (intersection_q or intersection_c):
return True
+ # Skip the check if the number of qubits for either operation is too large
+ if len(qargs1) > max_num_qubits or len(qargs2) > max_num_qubits:
+ return False
+
# These lines are adapted from commutation_analysis, which is more restrictive than the
# check from dag_dependency when considering nodes with "_directive". It would be nice to
# think which optimizations from dag_dependency can indeed be used.
| `commutation analysis` lead to `numpy.core._exceptions._ArrayMemoryError` with large `mct`
### Environment
- **Qiskit Terra version**: 0.43.1 meta package, terra 0.24.1
- **Python version**: 3.10
- **Operating system**: docker continuumio/miniconda3
### What is happening?
When transpiling (level 2) a circuit with a large multi-cx gate (see doc: [here](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.mct.html)) with a large numebr of qubits (e.g. `28`) the `commutation analysis` pass crashes.
### How can we reproduce the issue?
Run this python script:
```python
from qiskit import QuantumCircuit, transpile
qc = QuantumCircuit(29)
qc.mct(list(range(28)), qc.num_qubits - 1)
transpile(qc, optimization_level=2)
```
Produces this output and error:
```bash
Traceback (most recent call last):
File "myfile.py", line 7, in <module> transpile(qc, optimization_level=2)
File "...qiskit/compiler/transpiler.py", line 380, in transpile _serial_transpile_circuit(
File "...qiskit/compiler/transpiler.py", line 462, in _serial_transpile_circuit result = pass_manager.run(circuit, callback=callback, output_name=outpu
t_name) File "...qiskit/transpiler/passmanager.py", line 537, in run return super().run(circuits, output_name, callback)
File "...qiskit/transpiler/passmanager.py", line 231, in run return self._run_single_circuit(circuits, output_name, callback)
File "...qiskit/transpiler/passmanager.py", line 292, in _run_single_circuit result = running_passmanager.run(circuit, output_name=output_name, call
back=callback) File "...qiskit/transpiler/runningpassmanager.py", line 125, in run dag = self._do_pass(pass_, dag, passset.options)
File "...qiskit/transpiler/runningpassmanager.py", line 169, in _do_pass dag = self._do_pass(required_pass, dag, options)
File "...qiskit/transpiler/runningpassmanager.py", line 173, in _do_pass dag = self._run_this_pass(pass_, dag)
File "...qiskit/transpiler/runningpassmanager.py", line 227, in _run_this_pass pass_.run(FencedDAGCircuit(dag))
File "...qiskit/transpiler/passes/optimization/commutation_analysis.py", line 75, in run does_commute = self.comm_checker.commute(
File "...qiskit/circuit/commutation_checker.py", line 136, in commute operator_2 = Operator(op2, input_dims=(2,) * len(qarg2), output_dims=(2
,) * len(qarg2)) File "...qiskit/quantum_info/operators/operator.py", line 85, in __init__ self._data = self._init_instruction(data).data
File "...qiskit/quantum_info/operators/operator.py", line 610, in _init_instruction op = Operator(np.eye(dimension))
File "...numpy/lib/twodim_base.py", line 215, in eye m = zeros((N, M), dtype=dtype, order=order)
numpy.core._exceptions._ArrayMemoryError: Unable to allocate 2.00 EiB for a
n array with shape (536870912, 536870912) and data type float64
```
### What should happen?
I would expect the optimizer to skip the pass if too large to optimize and leave it unoptimized.
### Any suggestions?
I would skip the optimization pass when the number of qubits is too large (precise threshold to be determined based on ram memory of the current machine).
| 2023-07-25T09:22:38Z | [] | [] |
Traceback (most recent call last):
File "myfile.py", line 7, in <module> transpile(qc, optimization_level=2)
File "...qiskit/compiler/transpiler.py", line 380, in transpile _serial_transpile_circuit(
File "...qiskit/compiler/transpiler.py", line 462, in _serial_transpile_circuit result = pass_manager.run(circuit, callback=callback, output_name=outpu
t_name) File "...qiskit/transpiler/passmanager.py", line 537, in run return super().run(circuits, output_name, callback)
| 913 |
||||
Qiskit/qiskit | Qiskit__qiskit-10521 | c8552f6b51a36aa432b21b0d31b88e212a104ca7 | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -2180,7 +2180,7 @@ def copy(self, name: str | None = None) -> "QuantumCircuit":
"""Copy the circuit.
Args:
- name (str): name to be given to the copied circuit. If None, then the name stays the same
+ name (str): name to be given to the copied circuit. If None, then the name stays the same.
Returns:
QuantumCircuit: a deepcopy of the current circuit, with the specified name
@@ -2222,6 +2222,10 @@ def copy_empty_like(self, name: str | None = None) -> "QuantumCircuit":
Returns:
QuantumCircuit: An empty copy of self.
"""
+ if not (name is None or isinstance(name, str)):
+ raise TypeError(
+ f"invalid name for a circuit: '{name}'. The name must be a string or 'None'."
+ )
cpy = copy.copy(self)
# copy registers correctly, in copy.copy they are only copied via reference
cpy.qregs = self.qregs.copy()
| `QuantumCircuit.copy(name=?)` allows for any object to be used as name
### Environment
- **Qiskit Terra version**: 0.43.1 meta package, terra 0.24.1
- **Python version**: 3.10
- **Operating system**: docker continuumio/miniconda3
### What is happening?
Creating a circuit via copy allows to give an invalid name (not a string), that is then checked only when the circuit is transpiled.
In this case the `copy()` api raises no error when passing a circuit as name (see API: [copy](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.copy.html))
### How can we reproduce the issue?
Run this python script:
```python
from qiskit import QuantumCircuit
from qiskit.compiler import transpile
qc_output = QuantumCircuit(2, 2)
input_circuit = QuantumCircuit(2, 2)
input_circuit.measure([0, 1], [0, 1])
qc_output = qc_output.compose(input_circuit, qubits=range(2))
final_circuit = qc_output.copy(qc_output)
final_circuit.measure([0, 1], [0, 1])
print(final_circuit.draw())
print("Circuit name: ", final_circuit.name)
transpile(final_circuit, optimization_level=0)
```
Produces this output and error:
```bash
┌─┐ ┌─┐
q_0: ┤M├───┤M├───
└╥┘┌─┐└╥┘┌─┐
q_1: ─╫─┤M├─╫─┤M├
║ └╥┘ ║ └╥┘
c: 2/═╩══╩══╩══╩═
0 1 0 1
Circuit name: ┌─┐
q_0: ┤M├───
└╥┘┌─┐
q_1: ─╫─┤M├
║ └╥┘
c: 2/═╩══╩═
0 1
Traceback (most recent call last):
File "myfile.py", line 13, in <module> transpile(final_circuit, optimization_level=0)
File ".../qiskit/compiler/transpiler.py", line 380, in transpile _serial_transpile_circuit(
File ".../qiskit/compiler/transpiler.py", line 462, in _serial_transpile_circuit result = pass_manager.run(circuit, callback=callback, output_name=outpu
t_name) File ".../qiskit/transpiler/passmanager.py", line 537, in run return super().run(circuits, output_name, callback)
File ".../qiskit/transpiler/passmanager.py", line 231, in run return self._run_single_circuit(circuits, output_name, callback)
File ".../qiskit/transpiler/passmanager.py", line 292, in _run_single_circuit result = running_passmanager.run(circuit, output_name=output_name, call
back=callback) File ".../qiskit/transpiler/runningpassmanager.py", line 127, in run circuit = dag_to_circuit(dag, copy_operations=False)
File ".../qiskit/converters/dag_to_circuit.py", line 58, in dag_to_circuit circuit = QuantumCircuit(
File ".../qiskit/circuit/quantumcircuit.py", line 260, in __init__ raise CircuitError(
qiskit.circuit.exceptions.CircuitError: 'The circuit name should be a strin
g (or None to auto-generate a name).'
```
### What should happen?
I would expect the `copy` method to check the name of the circuit, and raise an error if it is not a string, whereas now it is allowed without any check (see [here](https://github.com/Qiskit/qiskit-terra/blob/802a735ebea547d0d96339c2de4a10f04b0ab8a6/qiskit/circuit/quantumcircuit.py#L2231)).
Even this is allowed:
```python
...
class AnyObject(object):
def __init__(self, field):
self.field = field
new_circuit = qc_output.copy(name=AnyObject("new_circuit"))
print(new_circuit.name)
```
Outputting:
```
...
<__main__.AnyObject object at 0x7f7c98809ab0>
```
### Any suggestions?
I think the `copy` method should check the name of the circuit, and raise an error if it is not a string, so that the error is more precise on the line of the `copy()` call rather than the `transpile()`.
| This is somewhat a regular part of Python programming; the language doesn't enforce type checking. You'll _always_ be able to put badly typed objects into Python classes, since the language fundamentally doesn't have access control.
That said, in this particular case, we _do_ do the manual check in `QuantumCircuit.__init__`, it's not very costly to do, and doesn't have greater performance implications, so it's reasonable that we could do the check in `QuantumCircuit.copy` as well.
@jakelishman @MattePalte
I would like to solve this issue and contribute to Qiskit. Can you please assign me?
Thank you
running tox -elint-incr on my repository gives the following error:
`ERROR: sympy is imported via qiskit.circuit.quantumcircuit`
But sympy is not being imported. **Why is this error showing up?**
And by the way, I have fixed this issue and am in the testing phase. Will make a pull request once the above error gets solved.
EDIT - I think it is showing up because qiskit/circuit/parameter.py is importing sympy
@Abhiraj-Shrotriya
For various reasons, Sympy is optionally imported. Tox flags that as an error, but this is not the case in the CI tests. If this is the only error you are facing, you should pass the CI tests.
Updating my branch to the latest version of Qiskit:main breaks my Pull Request. The CI tests ( which of course take time) need to rerun. Some of these re-tests fail which passed in the original run.
Will not updating my branch affect merging process?
You don't need to update your branch to `main` unless there are merge conflicts. We'll handle that automatically during the final merge window. | 2023-07-27T19:56:41Z | [] | [] |
Traceback (most recent call last):
File "myfile.py", line 13, in <module> transpile(final_circuit, optimization_level=0)
File ".../qiskit/compiler/transpiler.py", line 380, in transpile _serial_transpile_circuit(
File ".../qiskit/compiler/transpiler.py", line 462, in _serial_transpile_circuit result = pass_manager.run(circuit, callback=callback, output_name=outpu
t_name) File ".../qiskit/transpiler/passmanager.py", line 537, in run return super().run(circuits, output_name, callback)
| 916 |
|||
Qiskit/qiskit | Qiskit__qiskit-10537 | 4722c50a59157a5be638c8b30a2b77a0b127e4ae | diff --git a/qiskit/qpy/binary_io/circuits.py b/qiskit/qpy/binary_io/circuits.py
--- a/qiskit/qpy/binary_io/circuits.py
+++ b/qiskit/qpy/binary_io/circuits.py
@@ -970,10 +970,13 @@ def write_circuit(file_obj, circuit, metadata_serializer=None):
new_custom_operations = list(custom_operations.keys())
while new_custom_operations:
operations_to_serialize = new_custom_operations.copy()
+ new_custom_operations = []
for name in operations_to_serialize:
operation = custom_operations[name]
- new_custom_operations = _write_custom_operation(
- custom_operations_buffer, name, operation, custom_operations
+ new_custom_operations.extend(
+ _write_custom_operation(
+ custom_operations_buffer, name, operation, custom_operations
+ )
)
file_obj.write(struct.pack(formats.CUSTOM_CIRCUIT_DEF_HEADER_PACK, len(custom_operations)))
| QPY invalid payload generation via `compose()` or `QuantumCircuit.control()`
### Environment
- **Qiskit Terra version**: 0.23.2
- **Python version**: 3.10
- **Operating system**: Linux
### What is happening?
Running `compose()` with `inplace=True` from an input generated with `QuantumCircuit.control()` leads to a circuit that when qpy serialized is not load-able. This points to an internal state that doesn't match the actual data of the instruction object. I expect the mismatch is caused by the number of arguments or qubits the gate reported in qc which is incorrect
The specific failure in this case is:
```
Traceback (most recent call last):
File "/tmp/test_qpy_roundtrip.py", line 17, in <module>
new_qc = load(fd)[0]
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/interface.py", line 269, in load
loader(
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/binary_io/circuits.py", line 905, in read_circuit
_read_instruction(file_obj, circ, out_registers, custom_operations, version, vectors)
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/binary_io/circuits.py", line 161, in _read_instruction
struct.unpack(
struct.error: unpack requires a buffer of 33 bytes
```
### How can we reproduce the issue?
```python
import io
from qiskit.circuit.random import random_circuit
import numpy as np
from qiskit import QuantumCircuit
from qiskit.qpy import dump, load
qc0 = random_circuit(2, 2, seed=1).decompose(reps=1)
qc1 = random_circuit(2, 2, seed=1).decompose(reps=1)
qc = QuantumCircuit(3)
qc.compose(qc0.control(1), [0,1,2], inplace=True)
qc.compose(qc1.control(1), [0,1,2], inplace=True)
with io.BytesIO() as fd:
dump(qc, fd)
fd.seek(0)
new_qc = load(fd)[0]
assert qc == new_qc
```
### What should happen?
This should not error during the `load()` call
### Any suggestions?
I believe something about the compose call is corrupting the internal state of the circuit which is leading to a QPY payload that has a mismatch between a size
| For anyone experiencing this you can work around this failure by changing:
```python
qc.compose(qc0.control(1), [0,1,2], inplace=True)
qc.compose(qc1.control(1), [0,1,2], inplace=True)
```
to
```python
qc.append(qc0.control(1), [0,1,2])
qc.append(qc1.control(1), [0,1,2])
```
I think something's a bit odd about `QuantumCircuit.control`: it constructs a gate, from the circuit, controls that, and adds the resulting gate to a circuit. That means that when you compose it onto a circuit, it's _still_ a wrapped custom instruction rather than having been properly inlined.
If I change `QuantumCircuit.control` to just return `circuit_to_gate(self).control(...).definition`, the QPY stuff works fine and that code makes more sense. That said, it's not necessarily the correct fix for here (unless there's an actual bug in `QuantumCircuit.control`), because the current form _should_ still be producing a valid circuit that roundtrips through QPY.
Hmm, that seems odd qpy shouldn't care about it being wrapped in a custom instruction. I wonder if it's the same bug as https://github.com/Qiskit/qiskit-terra/issues/8941 where the controlled gates are ending up with the same names and that's causing issues.
I'm suspicious that there's a mistake in the recursive handling, when there's multiple custom instructions that all contain other custom instructions. I'm fairly confident the issue happens during the QPY dump, not the read. I instrumented `write_circuit` with a `print(circuit.name)`, and it shows an asymmetry between how it handles the two gates - it shows that it touches `qc.data[1].operation._definition` and `qc.data[1].operation.base_gate.definition`, but only one of those two things for `qc.data[0]`.
Hi all, thanks for looking into this issue !
The workaround proposed above (with `inplace=False`) will create two empty circuits though right ?
From what I could understand, when `qpy` dumps the base circuit (here: https://github.com/Qiskit/qiskit-terra/blob/16f6adb310719619f5cc07d314a95f12d6ea07c4/qiskit/qpy/binary_io/circuits.py#L649) something might be going wrong with the format. So when reading the `.qpy` file, the `reader` manages to read the first base circuit but fails at the second.
Thanks again !
The key to the workaround I suggested in https://github.com/Qiskit/qiskit-terra/issues/9746#issuecomment-1458534286 is that it's using the `append()` method instead of `compose()` and that is always done in place. So taking the OP code it returns a circuit that looks like:
```
┌───────────────┐┌───────────────┐
q_0: ┤0 ├┤0 ├
│ ││ │
q_1: ┤1 c_circuit-88 ├┤1 c_circuit-91 ├
│ ││ │
q_2: ┤2 ├┤2 ├
└───────────────┘└───────────────┘
```
which decomposed looks like:
```
q_0: ───────■──────────────■───────
┌──────┴──────┐┌──────┴──────┐
q_1: ┤0 ├┤0 ├
│ circuit-88 ││ circuit-91 │
q_2: ┤1 ├┤1 ├
└─────────────┘└─────────────┘
```
and another layer deeper is:
```
q_0: ──■───■──────────■───■─────────■───■──────────■───■──────────■───■───────»
┌─┴─┐ │P(-π/4) │ │ │ │ ┌─┴─┐ │P(-π/4) │ │ »
q_1: ┤ X ├─■──────────■───┼─────────■───┼────────┤ X ├─■──────────■───┼───────»
└─┬─┘ ┌─┴─┐ │P(π/4) ┌─┴─┐ │P(-π/4) └─┬─┘ ┌─┴─┐ │P(π/4) »
q_2: ──■────────────┤ X ├─■───────┤ X ├─■──────────■────────────┤ X ├─■───────»
└───┘ └───┘ └───┘ »
«
«q_0: ──■───■────────
« │ │
«q_1: ──■───┼────────
« ┌─┴─┐ │P(-π/4)
«q_2: ┤ X ├─■────────
« └───┘
```
The workaround uses `QuantumCircuit.append` not `QuantumCircuit.compose` - `append` is in place as well. The difference is that `append` adds things as a single instruction, whereas `compose` "inlines" the given circuit into the existing one. It so happens that `QuantumCircuit.control` does some pretty weird stuff internally, so you probably wouldn't spot the difference in this case, but for most things, it's important for abstract optimisations in the transpiler (overuse of `compose` is akin to prematurely inlining functions in a classical language).
I don't believe there's anything wrong with the QPY format itself, but I am suspicious that the recursive handling of custom gates (of which this is an example) might be skipping one of the necessary circuits when there's several compound custom gates in succession.
Oh I didn't notice the `compose` to `append` switch sorry ! Indeed that's a good fix !
Interestingly, as of 0.25.0, QPY now throws an error during the re-load, which might suggest where the underlying bug was:
```
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-1-2524f28d100e> in <module>
15 dump(qc, fd)
16 fd.seek(0)
---> 17 new_qc = load(fd)[0]
18
~/code/qiskit/terra/qiskit/qpy/interface.py in load(file_obj, metadata_deserializer)
267 for _ in range(data.num_programs):
268 programs.append(
--> 269 loader(
270 file_obj,
271 data.qpy_version,
~/code/qiskit/terra/qiskit/qpy/binary_io/circuits.py in read_circuit(file_obj, version, metadata_deserializer)
1087 custom_operations = _read_custom_operations(file_obj, version, vectors)
1088 for _instruction in range(num_instructions):
-> 1089 _read_instruction(file_obj, circ, out_registers, custom_operations, version, vectors)
1090
1091 # Read calibrations
~/code/qiskit/terra/qiskit/qpy/binary_io/circuits.py in _read_instruction(file_obj, circuit, registers, custom_operations, version, vectors)
176 else:
177 instruction = formats.CIRCUIT_INSTRUCTION_V2._make(
--> 178 struct.unpack(
179 formats.CIRCUIT_INSTRUCTION_V2_PACK,
180 file_obj.read(formats.CIRCUIT_INSTRUCTION_V2_SIZE),
error: unpack requires a buffer of 33 bytes
```
(or alternatively I broke something with #10392)
Oh no wait, sorry, I _totally_ forgot what the top comment says - the error's the exact same. That's what I get for trying to come back to an issue after a couple of months and not taking the time to re-read everything properly. | 2023-07-31T17:26:31Z | [] | [] |
Traceback (most recent call last):
File "/tmp/test_qpy_roundtrip.py", line 17, in <module>
new_qc = load(fd)[0]
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/interface.py", line 269, in load
loader(
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/binary_io/circuits.py", line 905, in read_circuit
_read_instruction(file_obj, circ, out_registers, custom_operations, version, vectors)
File "/tmp/foo/lib/python3.10/site-packages/qiskit/qpy/binary_io/circuits.py", line 161, in _read_instruction
struct.unpack(
struct.error: unpack requires a buffer of 33 bytes
| 919 |
|||
Qiskit/qiskit | Qiskit__qiskit-1118 | 02200d2cdbbc5057062c35f9002463db7795cdf0 | diff --git a/qiskit/backends/aer/statevector_simulator.py b/qiskit/backends/aer/statevector_simulator.py
--- a/qiskit/backends/aer/statevector_simulator.py
+++ b/qiskit/backends/aer/statevector_simulator.py
@@ -56,8 +56,6 @@ def _run_job(self, job_id, qobj):
QobjInstruction(name='snapshot', params=[final_state_key])
)
result = super()._run_job(job_id, qobj)
- # Replace backend name with current backend
- result.backend_name = self.name
# Extract final state snapshot and move to 'statevector' data field
for experiment_result in result.results.values():
snapshots = experiment_result.snapshots
diff --git a/qiskit/backends/aer/statevector_simulator_py.py b/qiskit/backends/aer/statevector_simulator_py.py
--- a/qiskit/backends/aer/statevector_simulator_py.py
+++ b/qiskit/backends/aer/statevector_simulator_py.py
@@ -70,8 +70,6 @@ def _run_job(self, job_id, qobj):
QobjInstruction(name='snapshot', params=[final_state_key])
)
result = super()._run_job(job_id, qobj)
- # Replace backend name with current backend
- result.backend_name = self.name
# Extract final state snapshot and move to 'statevector' data field
for experiment_result in result.results.values():
snapshots = experiment_result.snapshots
| Can not combine the Result object from the same backend (statevector)
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**: the master branch
- **Python version**: 3.6.5
- **Operating system**: macOS 10.13
### What is the current behavior?
raise error
```
Traceback (most recent call last):
File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 125, in __add__
copy_of_self += other
File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 108, in __iadd__
raise QISKitError('Result objects from different backends cannot be combined.')
qiskit._qiskiterror.QISKitError: 'Result objects from different backends cannot be combined.'
```
### Steps to reproduce the problem
Code
```python
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister
import qiskit as qk
import numpy as np
num_qubits = 2
q = QuantumRegister(num_qubits, name='q')
c = ClassicalRegister(num_qubits, name='c')
circuits = QuantumCircuit(q, c)
param_idx = 0
for qubit in range(num_qubits):
circuits.u3(0.0, 0.0, 0.0, q[qubit])
circuits.u1(3.0, q[qubit])
# circuits.measure(q, c)
my_backend = qk.Aer.get_backend('statevector_simulator')
qobj = qk.compile(circuits=circuits, backend=my_backend)
job = my_backend.run(qobj)
result_a = job.result()
qobj = qk.compile(circuits=circuits, backend=my_backend)
job = my_backend.run(qobj)
result_b = job.result()
result = result_a + result_b
```
### What is the expected behavior?
Result objects are combined without error
### Suggested solutions
None
Note: If I change the backend to `qasm_simulator`, there is no error.
| So I dug into this, the underlying issue is that name in most places is not actually a property/string but instead actually a method. So when result is doing the comparison of the backend_name property it's getting a bound method (which doesn't match for different objects) instead of the string it was expecting.
We can fix this for the statevector simulator case by changing the result code to call `result.backend_name()` instead of `result.backend_name`. However there are cases where backend_name is a string and doing this will break those. Ideally I'd like to see everything be a string/property since it's static and calling a function seems unecessary, but it looks like the assumption that it's function is in a bunch of other places throughout the code. I'll unravel the ball of yarn and figure out a way to preserve our interface compatibility while making it behave consistently for all the backends.
so a quick workaround I can check both?
like comparing `result_a.backend_name() == result_b.backend_name() or result_a.backend_name == result_b.backend_name` | 2018-10-17T18:52:16Z | [] | [] |
Traceback (most recent call last):
File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 125, in __add__
copy_of_self += other
File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 108, in __iadd__
raise QISKitError('Result objects from different backends cannot be combined.')
qiskit._qiskiterror.QISKitError: 'Result objects from different backends cannot be combined.'
| 942 |
|||
Qiskit/qiskit | Qiskit__qiskit-1215 | 9d603f11a350ee77e5cd3fa02c8e61f40ab44440 | diff --git a/qiskit/backends/aer/aerjob.py b/qiskit/backends/aer/aerjob.py
--- a/qiskit/backends/aer/aerjob.py
+++ b/qiskit/backends/aer/aerjob.py
@@ -114,8 +114,12 @@ def status(self):
elif self._future.done():
_status = JobStatus.DONE if self._future.exception() is None else JobStatus.ERROR
else:
- raise JobError('Unexpected behavior of {0}'.format(
- self.__class__.__name__))
+ # Note: There is an undocumented Future state: PENDING, that seems to show up when
+ # the job is enqueued, waiting for someone to pick it up. We need to deal with this
+ # state but there's no public API for it, so we are assuming that if the job is not
+ # in any of the previous states, is PENDING, ergo INITIALIZING for us.
+ _status = JobStatus.INITIALIZING
+
return _status
def backend_name(self):
| test_compiler breaks AerJob status check
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**: master
- **Python version**:
- **Operating system**:
### What is the current behavior?
When running the tests, the following does not work when run *after* the `test_compiler` module:
```python
backend = Aer.get_backend('qasm_simulator')
job_sim = execute(qc, backend)
job_sim.status()
```
```
Traceback (most recent call last):
File "/Users/paul/Desktop/Github_repos/qiskit-core/test/python/test_cpp.py", line 34, in test_aer_status
job_sim.status()
File "/Users/paul/Desktop/Github_repos/qiskit-core/qiskit/backends/aer/aerjob.py", line 37, in _wrapper
return func(self, *args, **kwargs)
File "/Users/paul/Desktop/Github_repos/qiskit-core/qiskit/backends/aer/aerjob.py", line 118, in status
self.__class__.__name__))
qiskit.backends.joberror.JobError: 'Unexpected behavior of AerJob'
```
However, if run *before* that module, it works fine. This is true both when running locally and on Travis. This is blocking #975.
### Steps to reproduce the problem
### What is the expected behavior?
### Suggested solutions
| I'm taking over this one | 2018-11-05T10:38:23Z | [] | [] |
Traceback (most recent call last):
File "/Users/paul/Desktop/Github_repos/qiskit-core/test/python/test_cpp.py", line 34, in test_aer_status
job_sim.status()
File "/Users/paul/Desktop/Github_repos/qiskit-core/qiskit/backends/aer/aerjob.py", line 37, in _wrapper
return func(self, *args, **kwargs)
File "/Users/paul/Desktop/Github_repos/qiskit-core/qiskit/backends/aer/aerjob.py", line 118, in status
self.__class__.__name__))
qiskit.backends.joberror.JobError: 'Unexpected behavior of AerJob'
| 958 |
|||
Qiskit/qiskit | Qiskit__qiskit-1284 | 3fb837f1afd6be505c3139d5f226936d7ff9a1fc | diff --git a/examples/python/teleport.py b/examples/python/teleport.py
--- a/examples/python/teleport.py
+++ b/examples/python/teleport.py
@@ -47,6 +47,7 @@
qc.measure(q[1], c1[0])
# Apply a correction
+qc.barrier(q)
qc.z(q[2]).c_if(c0, 1)
qc.x(q[2]).c_if(c1, 1)
qc.measure(q[2], c2[0])
@@ -57,17 +58,32 @@
###############################################################
# First version: not mapped
-qobj = compile(qc, backend=backend, coupling_map=None, shots=1024)
+initial_layout = {("q", 0): ("q", 0), ("q", 1): ("q", 1),
+ ("q", 2): ("q", 2)}
+qobj = compile(qc, backend=backend, coupling_map=None, shots=1024, initial_layout=initial_layout)
job = backend.run(qobj)
+qobj_exp = qobj.experiments[0]
+print(qobj_exp.header.qubit_labels)
+print(qobj_exp.header.compiled_circuit_qasm)
+print(qobj_exp.header.clbit_labels)
+for i in qobj_exp.instructions:
+ print(i)
+
result = job.result()
print(result)
print(result.get_counts(qc))
# Second version: mapped to 2x8 array coupling graph
-qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024)
+qobj = compile(qc, backend=backend, coupling_map=coupling_map, shots=1024,initial_layout=initial_layout)
+qobj_exp = qobj.experiments[0]
+print(qobj_exp.header.qubit_labels)
+qobj_exp.header.compiled_circuit_qasm = ""
+print(qobj_exp.header.compiled_circuit_qasm)
+print(qobj_exp.header.clbit_labels)
+for i in qobj_exp.instructions:
+ print(i)
job = backend.run(qobj)
result = job.result()
-
print(result)
print(result.get_counts(qc))
diff --git a/qiskit/_quantumcircuit.py b/qiskit/_quantumcircuit.py
--- a/qiskit/_quantumcircuit.py
+++ b/qiskit/_quantumcircuit.py
@@ -11,7 +11,10 @@
Quantum circuit object.
"""
import itertools
+import warnings
from collections import OrderedDict
+from copy import deepcopy
+
from qiskit.qasm import _qasm
from qiskit.unrollers import _unroller
@@ -107,9 +110,9 @@ def __init__(self, *regs, name=None):
self.data = []
# This is a map of registers bound to this circuit, by name.
- self.qregs = OrderedDict()
- self.cregs = OrderedDict()
- self.add(*regs)
+ self.qregs = []
+ self.cregs = []
+ self.add_register(*regs)
@classmethod
def _increment_instances(cls):
@@ -138,10 +141,10 @@ def has_register(self, register):
"""
has_reg = False
if (isinstance(register, QuantumRegister) and
- register in self.qregs.values()):
+ register in self.qregs):
has_reg = True
elif (isinstance(register, ClassicalRegister) and
- register in self.cregs.values()):
+ register in self.cregs):
has_reg = True
return has_reg
@@ -160,8 +163,15 @@ def combine(self, rhs):
self._check_compatible_regs(rhs)
# Make new circuit with combined registers
- combined_qregs = {**self.qregs, **rhs.qregs}.values()
- combined_cregs = {**self.cregs, **rhs.cregs}.values()
+ combined_qregs = deepcopy(self.qregs)
+ combined_cregs = deepcopy(self.cregs)
+
+ for element in rhs.qregs:
+ if element not in self.qregs:
+ combined_qregs.append(element)
+ for element in rhs.cregs:
+ if element not in self.cregs:
+ combined_cregs.append(element)
circuit = QuantumCircuit(*combined_qregs, *combined_cregs)
for gate in itertools.chain(self.data, rhs.data):
gate.reapply(circuit)
@@ -182,8 +192,12 @@ def extend(self, rhs):
self._check_compatible_regs(rhs)
# Add new registers
- self.qregs.update(rhs.qregs)
- self.cregs.update(rhs.cregs)
+ for element in rhs.qregs:
+ if element not in self.qregs:
+ self.qregs.append(element)
+ for element in rhs.cregs:
+ if element not in self.cregs:
+ self.cregs.append(element)
# Add new gates
for gate in rhs.data:
@@ -211,19 +225,27 @@ def _attach(self, instruction):
self.data.append(instruction)
return instruction
- def add(self, *regs):
+ def add_register(self, *regs):
"""Add registers."""
for register in regs:
- if register.name in self.qregs or register.name in self.cregs:
+ if register in self.qregs or register in self.cregs:
raise QISKitError("register name \"%s\" already exists"
% register.name)
if isinstance(register, QuantumRegister):
- self.qregs[register.name] = register
+ self.qregs.append(register)
elif isinstance(register, ClassicalRegister):
- self.cregs[register.name] = register
+ self.cregs.append(register)
else:
raise QISKitError("expected a register")
+ def add(self, *regs):
+ """Add registers."""
+
+ warnings.warn('The add() function is deprecated and will be '
+ 'removed in a future release. Instead use '
+ 'QuantumCircuit.add_register().', DeprecationWarning)
+ self.add_register(*regs)
+
def _check_qreg(self, register):
"""Raise exception if r is not in this circuit or not qreg."""
if not isinstance(register, QuantumRegister):
@@ -263,12 +285,14 @@ def _check_dups(self, qubits):
def _check_compatible_regs(self, rhs):
"""Raise exception if the circuits are defined on incompatible registers"""
- lhs_regs = {**self.qregs, **self.cregs}
- rhs_regs = {**rhs.qregs, **rhs.cregs}
- common_registers = lhs_regs.keys() & rhs_regs.keys()
- for name in common_registers:
- if lhs_regs[name] != rhs_regs[name]:
- raise QISKitError("circuits are not compatible")
+
+ list1 = self.qregs + self.cregs
+ list2 = rhs.qregs + rhs.cregs
+ for element1 in list1:
+ for element2 in list2:
+ if element2.name == element1.name:
+ if element1 != element2:
+ raise QISKitError("circuits are not compatible")
def _gate_string(self, name):
"""Return a QASM string for the named gate."""
@@ -292,9 +316,9 @@ def qasm(self):
for gate_name in self.definitions:
if self.definitions[gate_name]["print"]:
string_temp += self._gate_string(gate_name)
- for register in self.qregs.values():
+ for register in self.qregs:
string_temp += register.qasm() + "\n"
- for register in self.cregs.values():
+ for register in self.cregs:
string_temp += register.qasm() + "\n"
for instruction in self.data:
string_temp += instruction.qasm() + "\n"
diff --git a/qiskit/_register.py b/qiskit/_register.py
--- a/qiskit/_register.py
+++ b/qiskit/_register.py
@@ -84,3 +84,24 @@ def __iter__(self):
form `tuple (Register, int)`.
"""
return zip([self]*self.size, range(self.size))
+
+ def __eq__(self, other):
+ """Two Registers are the same if they are of the same type
+ (i.e. quantum/classical), and have the same name and size.
+
+ Args:
+ other (Register): other Register
+
+ Returns:
+ bool: are self and other equal.
+ """
+ res = False
+ if type(self) is type(other) and \
+ self.name == other.name and \
+ self.size == other.size:
+ res = True
+ return res
+
+ def __hash__(self):
+ """Make object hashable, based on the name and size to hash."""
+ return hash(str(type(self)) + self.name + str(self.size))
diff --git a/qiskit/dagcircuit/_dagcircuit.py b/qiskit/dagcircuit/_dagcircuit.py
--- a/qiskit/dagcircuit/_dagcircuit.py
+++ b/qiskit/dagcircuit/_dagcircuit.py
@@ -50,13 +50,13 @@ def __init__(self):
# Map from a wire's name (reg,idx) to a Bool that is True if the
# wire is a classical bit and False if the wire is a qubit.
- self.wire_type = {}
+ self.wire_type = OrderedDict()
# Map from wire names (reg,idx) to input nodes of the graph
- self.input_map = {}
+ self.input_map = OrderedDict()
# Map from wire names (reg,idx) to output nodes of the graph
- self.output_map = {}
+ self.output_map = OrderedDict()
# Running count of the total number of nodes
self.node_counter = 0
@@ -83,7 +83,7 @@ def __init__(self):
self.cregs = OrderedDict()
# Map of user defined gates to ast nodes defining them
- self.gates = {}
+ self.gates = OrderedDict()
# Output precision for printing floats
self.prec = 10
@@ -1356,9 +1356,9 @@ def fromQuantumCircuit(circuit, expand_gates=True):
"""
dagcircuit = DAGCircuit()
dagcircuit.name = circuit.name
- for register in circuit.qregs.values():
+ for register in circuit.qregs:
dagcircuit.add_qreg(register)
- for register in circuit.cregs.values():
+ for register in circuit.cregs:
dagcircuit.add_creg(register)
# Add user gate definitions
for name, data in circuit.definitions.items():
diff --git a/qiskit/extensions/simulator/load.py b/qiskit/extensions/simulator/load.py
--- a/qiskit/extensions/simulator/load.py
+++ b/qiskit/extensions/simulator/load.py
@@ -47,7 +47,7 @@ def load(self, slot):
"""
tuples = []
if isinstance(self, QuantumCircuit):
- for register in self.qregs.values():
+ for register in self.qregs:
tuples.append(register)
if not tuples:
raise ExtensionError("no qubits for load")
diff --git a/qiskit/extensions/simulator/noise.py b/qiskit/extensions/simulator/noise.py
--- a/qiskit/extensions/simulator/noise.py
+++ b/qiskit/extensions/simulator/noise.py
@@ -46,7 +46,7 @@ def noise(self, switch):
"""
tuples = []
if isinstance(self, QuantumCircuit):
- for register in self.qregs.values():
+ for register in self.qregs:
tuples.append(register)
if not tuples:
raise ExtensionError("no qubits for noise")
diff --git a/qiskit/extensions/simulator/save.py b/qiskit/extensions/simulator/save.py
--- a/qiskit/extensions/simulator/save.py
+++ b/qiskit/extensions/simulator/save.py
@@ -47,7 +47,7 @@ def save(self, slot):
"""
tuples = []
if isinstance(self, QuantumCircuit):
- for register in self.qregs.values():
+ for register in self.qregs:
tuples.append(register)
if not tuples:
raise ExtensionError("no qubits for save")
diff --git a/qiskit/extensions/simulator/snapshot.py b/qiskit/extensions/simulator/snapshot.py
--- a/qiskit/extensions/simulator/snapshot.py
+++ b/qiskit/extensions/simulator/snapshot.py
@@ -47,7 +47,7 @@ def snapshot(self, slot):
"""
tuples = []
if isinstance(self, QuantumCircuit):
- for register in self.qregs.values():
+ for register in self.qregs:
tuples.append(register)
if not tuples:
raise ExtensionError("no qubits for snapshot")
diff --git a/qiskit/extensions/standard/barrier.py b/qiskit/extensions/standard/barrier.py
--- a/qiskit/extensions/standard/barrier.py
+++ b/qiskit/extensions/standard/barrier.py
@@ -38,7 +38,7 @@ def barrier(self, *qargs):
qubits = []
if not qargs: # None
- for qreg in self.qregs.values():
+ for qreg in self.qregs:
for j in range(qreg.size):
qubits.append((qreg, j))
diff --git a/qiskit/quantum_info/__init__.py b/qiskit/quantum_info/__init__.py
--- a/qiskit/quantum_info/__init__.py
+++ b/qiskit/quantum_info/__init__.py
@@ -10,3 +10,4 @@
from .operators.pauli import Pauli, pauli_group
from .states._states import basis_state, random_state, projector
from .states._measures import state_fidelity
+from .operators._measures import process_fidelity
diff --git a/qiskit/quantum_info/operators/_measures.py b/qiskit/quantum_info/operators/_measures.py
new file mode 100644
--- /dev/null
+++ b/qiskit/quantum_info/operators/_measures.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2017, IBM.
+#
+# This source code is licensed under the Apache License, Version 2.0 found in
+# the LICENSE.txt file in the root directory of this source tree.
+
+# pylint: disable=invalid-name,anomalous-backslash-in-string
+
+"""
+A collection of useful quantum information functions for operators.
+
+"""
+
+import numpy as np
+
+
+def process_fidelity(channel1, channel2):
+ """Return the process fidelity between two quantum channels.
+
+ Currently the input must be a unitary (until we decide on the channel)
+ For a unitary channels the process fidelity is given by
+ F_p(U, U) = abs(Tr[ U^dagger U ])^2/d^2
+
+ Args:
+ channel1 (array_like): a quantum unitary operator.
+ channel2 (array_like): a quantum unitary operator.
+
+ Returns:
+ array_like: The state fidelity F(state1, state2).
+ """
+ # convert input to numpy arrays
+ s1 = np.array(channel1)
+ s2 = np.array(channel2)
+
+ # fidelity of two unitary vectors
+ overlap = np.trace(np.dot(s1.conj().transpose(), s2))
+ f_p = abs(overlap)**2 / (len(s1)**2)
+ return f_p
diff --git a/qiskit/tools/_compiler.py b/qiskit/tools/_compiler.py
--- a/qiskit/tools/_compiler.py
+++ b/qiskit/tools/_compiler.py
@@ -10,7 +10,6 @@
import uuid
import logging
-
from qiskit import transpiler
from qiskit.transpiler._passmanager import PassManager
from qiskit.qobj import Qobj, QobjConfig, QobjExperiment, QobjItem, QobjHeader
diff --git a/qiskit/unrollers/_circuitbackend.py b/qiskit/unrollers/_circuitbackend.py
--- a/qiskit/unrollers/_circuitbackend.py
+++ b/qiskit/unrollers/_circuitbackend.py
@@ -56,14 +56,14 @@ def new_qreg(self, qreg):
qreg = QuantumRegister object
"""
- self.circuit.add(qreg)
+ self.circuit.add_register(qreg)
def new_creg(self, creg):
"""Create a new classical register.
creg = ClassicalRegister object
"""
- self.circuit.add(creg)
+ self.circuit.add_register(creg)
def define_gate(self, name, gatedata):
"""Define a new quantum gate.
@@ -77,26 +77,35 @@ def define_gate(self, name, gatedata):
def _map_qubit(self, qubit):
"""Map qubit tuple (regname, index) to (QuantumRegister, index)."""
+
qregs = self.circuit.qregs
- if qubit[0] not in qregs:
+ regname = qubit[0]
+ qregs_names = [element.name for element in qregs]
+ if regname not in qregs_names:
raise _backenderror.BackendError(
"qreg %s does not exist" % qubit[0])
- return (qregs[qubit[0]], qubit[1])
+ index = qregs_names.index(regname)
+ return (qregs[index], qubit[1])
def _map_bit(self, bit):
"""Map bit tuple (regname, index) to (ClassicalRegister, index)."""
cregs = self.circuit.cregs
- if bit[0] not in cregs:
+ regname = bit[0]
+ cregs_names = [element.name for element in cregs]
+ if regname not in cregs_names:
raise _backenderror.BackendError(
"creg %s does not exist" % bit[0])
- return (cregs[bit[0]], bit[1])
+ index = cregs_names.index(regname)
+ return (cregs[index], bit[1])
def _map_creg(self, creg):
"""Map creg name to ClassicalRegister."""
cregs = self.circuit.cregs
- if creg not in cregs:
+ cregs_names = [element.name for element in cregs]
+ if creg not in cregs_names:
raise _backenderror.BackendError("creg %s does not exist" % creg)
- return cregs[creg]
+ index = cregs_names.index(creg)
+ return cregs[index]
def u(self, arg, qubit, nested_scope=None):
"""Fundamental single qubit gate.
diff --git a/qiskit/unrollers/_jsonbackend.py b/qiskit/unrollers/_jsonbackend.py
--- a/qiskit/unrollers/_jsonbackend.py
+++ b/qiskit/unrollers/_jsonbackend.py
@@ -34,6 +34,8 @@
]
}
"""
+from collections import OrderedDict
+
from qiskit.unrollers._backenderror import BackendError
from qiskit.unrollers._unrollerbackend import UnrollerBackend
@@ -60,8 +62,8 @@ def __init__(self, basis=None):
self._number_of_cbits = 0
self._qubit_order = []
self._cbit_order = []
- self._qubit_order_internal = {}
- self._cbit_order_internal = {}
+ self._qubit_order_internal = OrderedDict()
+ self._cbit_order_internal = OrderedDict()
self.creg = None
self.cval = None
diff --git a/qiskit/unrollers/_unroller.py b/qiskit/unrollers/_unroller.py
--- a/qiskit/unrollers/_unroller.py
+++ b/qiskit/unrollers/_unroller.py
@@ -8,6 +8,7 @@
"""
OPENQASM interpreter.
"""
+from collections import OrderedDict
from qiskit._quantumregister import QuantumRegister
from qiskit._classicalregister import ClassicalRegister
from ._unrollererror import UnrollerError
@@ -32,11 +33,11 @@ def __init__(self, ast, backend=None, precision=15, filename=None):
# OPENQASM version number
self.version = 0.0
# Dict of qreg names and sizes
- self.qregs = {}
+ self.qregs = OrderedDict()
# Dict of creg names and sizes
- self.cregs = {}
+ self.cregs = OrderedDict()
# Dict of gates names and properties
- self.gates = {}
+ self.gates = OrderedDict()
# List of dictionaries mapping local parameter ids to expression Nodes
self.arg_stack = [{}]
# List of dictionaries mapping local bit ids to global ids (name, idx)
| The `test_online_qasm_simulator_two_registers` is failing in master
### What is the current behavior?
The test is failing in the `master` branch with the following error:
```
======================================================================
FAIL: test_online_qasm_simulator_two_registers (python.ibmq.test_ibmq_qasm_simulator.TestIbmqQasmSimulator)
Test online_qasm_simulator_two_registers.
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/Qiskit/qiskit-terra/test/python/common.py", line 373, in _wrapper
return decorated_func(self, *args, **kwargs)
File "/home/travis/build/Qiskit/qiskit-terra/test/python/ibmq/test_ibmq_qasm_simulator.py", line 109, in test_online_qasm_simulator_two_registers
self.assertEqual(result1, {'00 01': 1024})
AssertionError: {'01 00': 1024} != {'00 01': 1024}
- {'01 00': 1024}
? ---
+ {'00 01': 1024}
? +++
```
### Steps to reproduce the problem
The failure is intermittent. It seems related with running all the test suite. When running isolated, it seems not to be failing.
### What is the expected behavior?
The test should pass regardless of other tests.
### Suggested solutions
It will be temporally marked as an expected failure but we should dig into what happens.
| It finally failed for me locally in the tests. However, I ran the test 1000 times by itself and could not reproduce it | 2018-11-17T17:51:49Z | [] | [] |
Traceback (most recent call last):
File "/home/travis/build/Qiskit/qiskit-terra/test/python/common.py", line 373, in _wrapper
return decorated_func(self, *args, **kwargs)
File "/home/travis/build/Qiskit/qiskit-terra/test/python/ibmq/test_ibmq_qasm_simulator.py", line 109, in test_online_qasm_simulator_two_registers
self.assertEqual(result1, {'00 01': 1024})
AssertionError: {'01 00': 1024} != {'00 01': 1024}
| 971 |
|||
Qiskit/qiskit | Qiskit__qiskit-1295 | 77dc51b93e7312bbff8f5acf7d8242232bd6624f | diff --git a/qiskit/backends/ibmq/credentials/_configrc.py b/qiskit/backends/ibmq/credentials/_configrc.py
--- a/qiskit/backends/ibmq/credentials/_configrc.py
+++ b/qiskit/backends/ibmq/credentials/_configrc.py
@@ -9,6 +9,7 @@
Utilities for reading and writing credentials from and to configuration files.
"""
+import warnings
import os
from ast import literal_eval
from collections import OrderedDict
@@ -116,15 +117,17 @@ def store_credentials(credentials, overwrite=False, filename=None):
location is used (`HOME/.qiskit/qiskitrc`).
Raises:
- QISKitError: If credentials already exists and overwrite=False; or if
- the account_name could not be assigned.
+ QISKitError: if the account_name could not be assigned.
"""
# Read the current providers stored in the configuration file.
filename = filename or DEFAULT_QISKITRC_FILE
stored_credentials = read_credentials_from_qiskitrc(filename)
+ # Check if duplicated credentials are already stored. By convention,
+ # we assume (hub, group, project) is always unique.
if credentials.unique_id() in stored_credentials and not overwrite:
- raise QISKitError('Credentials already present and overwrite=False')
+ warnings.warn('Credentials already present. Set overwrite=True to overwrite.')
+ return
# Append and write the credentials to file.
stored_credentials[credentials.unique_id()] = credentials
diff --git a/qiskit/backends/ibmq/credentials/credentials.py b/qiskit/backends/ibmq/credentials/credentials.py
--- a/qiskit/backends/ibmq/credentials/credentials.py
+++ b/qiskit/backends/ibmq/credentials/credentials.py
@@ -22,7 +22,7 @@ class Credentials(object):
"""IBM Q account credentials.
Note that, by convention, two credentials that have the same hub, group
- and token (regardless of other attributes) are considered equivalent.
+ and project (regardless of other attributes) are considered equivalent.
The `unique_id()` returns the unique identifier.
"""
diff --git a/qiskit/backends/ibmq/ibmqprovider.py b/qiskit/backends/ibmq/ibmqprovider.py
--- a/qiskit/backends/ibmq/ibmqprovider.py
+++ b/qiskit/backends/ibmq/ibmqprovider.py
@@ -116,7 +116,7 @@ def enable_account(self, token, url=QE_URL, **kwargs):
self._append_account(credentials)
- def save_account(self, token, url=QE_URL, **kwargs):
+ def save_account(self, token, url=QE_URL, overwrite=False, **kwargs):
"""Save the account to disk for future use.
Login into Quantum Experience or IBMQ using the provided credentials,
@@ -127,20 +127,13 @@ def save_account(self, token, url=QE_URL, **kwargs):
token (str): Quantum Experience or IBM Q API token.
url (str): URL for Quantum Experience or IBM Q (for IBM Q,
including the hub, group and project in the URL).
+ overwrite (bool): overwrite existing credentials.
**kwargs (dict):
* proxies (dict): Proxy configuration for the API.
* verify (bool): If False, ignores SSL certificates errors
"""
credentials = Credentials(token, url, **kwargs)
-
- # Check if duplicated credentials are already stored. By convention,
- # we assume (hub, group, project) is always unique.
- stored_credentials = read_credentials_from_qiskitrc()
-
- if credentials.unique_id() in stored_credentials.keys():
- warnings.warn('Credentials are already stored.')
- else:
- store_credentials(credentials)
+ store_credentials(credentials, overwrite=overwrite)
def active_accounts(self):
"""List all accounts currently in the session.
| credentials failed for qiskit ver 0.6.1
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**: 0.6.1
- **Python version**: 3.7.0
- **Operating system**:MAC OSX 10.13.6
### What is the current behavior?
After I acquired fresh token from https://quantumexperience.ng.bluemix.net/qx/account/advanced
IBMQ.load_accounts() fails.
### Steps to reproduce the problem
```
from qiskit import IBMQ
myToken='b6abe11442c9a...'
IBMQ.save_account(myToken)
IBMQ.load_accounts()
```
Results with
```
Traceback (most recent call last):
File "/anaconda3/lib/python3.7/site-packages/qiskit/backends/ibmq/ibmqsingleprovider.py", line 71, in _authenticate
credentials.verify)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 478, in __init__
self.req = _Request(token, config=config, verify=verify)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 253, in __init__
ntlm_credentials=self.ntlm_credentials)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 95, in __init__
self.obtain_token(config=self.config)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 159, in obtain_token
raise CredentialsError('error during login: %s' % error_message)
IBMQuantumExperience.IBMQuantumExperience.CredentialsError: error during login: Wrong user or password, check your credentials.
```
### What is the expected behavior?
Would be better if IBMQ.load_accounts() accepted me. All worked well w/ ver 0.5.
### Suggested solutions
| Can you try enable_account or regenerating the token. Your code should work. If you type `IBMQ.stored_accounts()` do you see the account.
@pacomf I can confirm this has happened to me today as well.
I cant reproduce the bug, i regenerate my APIToken and it works fine using qiskit terra... is it still happening? Can you send me more details?
It happened for about 5 hours on the weekend. However, @nonhermitian could run at the same time and then it started working again.
Mmmm, maybe an issue with the API... we will investigate it
I will add that, when it happened to me, I could log into some accounts and not others.
Hi @jaygambetta,
your tip helped. IBMQ.stored_accounts() has returned some old token, not the new one.
Looks like IBMQ.save_account(myToken) is unable to replace token if it exist - I leave it to you to decide if it is a bug or a feature.
My hack around it is to execute first: IBMQ.delete_accounts()
to clear my old token. So this sequence always works:
`
IBMQ.delete_accounts()
myToken='b6abe11442c9a...'
IBMQ.save_account(myToken)
IBMQ.load_accounts()
`
I can move on, closing thus ticket.
Thanks for help
Jan
Let's leave this open and investigate whether there's a bug with `IBMQ.save_account()` re-writing old tokens.
@diego-plan9 can you please have a look?
Yes - thanks @balewski for the information, which is spot on - currently, `IBMQ.save_account()` will just print a warning and do nothing else if old credentials are present:
https://github.com/Qiskit/qiskit-terra/blob/master/qiskit/backends/ibmq/ibmqprovider.py#L140-L143
> Looks like IBMQ.save_account(myToken) is unable to replace token if it exist - I leave it to you to decide if it is a bug or a feature.
Actually ... I can't decide if it is a bug or a feature either! :thinking: In the original draft implementation, the `.save_account()` (`.add_account()` by that time) method was [raising an Exception](https://github.com/Qiskit/qiskit-terra/blob/746245e29c5cadc44dc37851b19a4150b4e86cd8/qiskit/backends/ibmq/ibmqprovider.py#L111) in the case of trying to store a duplicate account. This was later changed to a warning, I'm unsure if by design and as a hard requisite from Jupyter-users needs, or also related to the slight tuning of the method functionality (ie. not authenticating during the call, just storing in disk). So I'm actually de-assigning myself, as probably the rest of the team has a more fresh view of the design decisions related to #1000.
I think we have several options:
* consider that not overwriting and raising a warning is indeed the desired behavior: the main drawback is that the warning might be easy to miss (and was probably the source of confusion in this issue).
* tune the method a bit in order to accept an `overwrite=True` optional parameter or a similar approach: the `credentials` module already has the needed parts in place, the main drawback would be that we touch a bit the public API.
* be a bit more restrictive and promote the warning back to an exception: it might affect users running the method twice and already used to not raising a warning (ie. maybe notebook users).
One way or the other, I think we need to make sure that the flow for updating an existing stored token is a bit smoother than the delete-save workaround proposed by @balewski, as it seems a relatively common use case.
From external user perspective:
It happens rather often that the ibmq_16_melbourne or even sometimes ibmqx4 does not accept the job, throws some 'general error', despite your web-page says both hardwares are operational.
Then, it is natural to (wrongly?) guess perhaps my token is invalid.
Then, I'd ask for a new token and try to use it - hoping it will help.
For such train of though the natural solution is assume 'user knows what he wants'. If user wants to replace the token by calling save_account(), just replace it. You can issue a warning that there was an old token (still valid), but why not just replace token each time user calls IBMQ.save_account(myToken) ?
Would this have any negative effect on your end?
Thanks
Jan
I think save_account should not raise an exception. Overwriting is not bad behavior. Similar to overwriting a key in a dict or something. Should just work.
@ajavadia is there an update.
Hi,
there is some inconsistency between the devices status you show here:
https://quantumexperience.ng.bluemix.net/qx/account/advanced
and actual avaliability.
At this moment, both ibmqx4 and ibmq_16_melbourne are reported to work.
However,. when I try to submit my circuit using Qiskit ver: 0.6.1 I get the error below for either.
Got a 400 code response to https://quantumexperience.ng.bluemix.net/api/Jobs?access_token=VCgYWnMUUBaYeT5gSmGO14cX93Foo4rccsLUVvIjf3bwYEZNjxlDcRmPArS2wZ25: {"error":{"status":400,"message":"Generic error","code":"GENERIC_ERROR"}}
Note, my token is correct, because I can submit the circuit to your simulator
'backend': 'ibmq_qasm_simulator',
'jobId2': '1814808',
'startTime': '2018-11-09 17:53:28'}
Can you have a look ?
Thanks
Jan | 2018-11-19T08:27:15Z | [] | [] |
Traceback (most recent call last):
File "/anaconda3/lib/python3.7/site-packages/qiskit/backends/ibmq/ibmqsingleprovider.py", line 71, in _authenticate
credentials.verify)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 478, in __init__
self.req = _Request(token, config=config, verify=verify)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 253, in __init__
ntlm_credentials=self.ntlm_credentials)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 95, in __init__
self.obtain_token(config=self.config)
File "/anaconda3/lib/python3.7/site-packages/IBMQuantumExperience/IBMQuantumExperience.py", line 159, in obtain_token
raise CredentialsError('error during login: %s' % error_message)
IBMQuantumExperience.IBMQuantumExperience.CredentialsError: error during login: Wrong user or password, check your credentials.
| 975 |
|||
Qiskit/qiskit | Qiskit__qiskit-1436 | 259c10580d22122e739ed466d306dcd5adb2027f | diff --git a/qiskit/qobj/_qobj.py b/qiskit/qobj/_qobj.py
--- a/qiskit/qobj/_qobj.py
+++ b/qiskit/qobj/_qobj.py
@@ -50,8 +50,6 @@ def _expand_item(cls, obj):
return [cls._expand_item(item) for item in obj]
if isinstance(obj, dict):
return {key: cls._expand_item(value) for key, value in obj.items()}
- if isinstance(obj, QobjItem):
- return obj.as_dict()
if isinstance(obj, numpy.integer):
return int(obj)
if isinstance(obj, numpy.float):
@@ -61,9 +59,11 @@ def _expand_item(cls, obj):
if isinstance(obj, sympy.Basic):
return float(obj.evalf())
if isinstance(obj, numpy.ndarray):
- return obj.tolist()
+ return cls._expand_item(obj.tolist())
if isinstance(obj, complex):
return [obj.real, obj.imag]
+ if hasattr(obj, 'as_dict'):
+ return obj.as_dict()
return obj
@classmethod
| crash when set initial_state with complex vector for simulator
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**: the master branch (Dec. 4th)
- **Python version**: 3.7.1
- **Operating system**: macOS 10.13
### What is the current behavior?
Encounter JSON encoding error
### Steps to reproduce the problem
running with the following qasm and setting
```
OPENQASM 2.0;
include "qelib1.inc";
qreg q[1];
u1(3.14159265358979) q[0];
{'shots': 1, 'config': {'initial_state': array([0.93130364-0.02274014j, 0.2641254 +0.2497883j ])}}
```
error message:
```
Traceback (most recent call last):
File "/Users/rchen/Developer/Quantum/temp/aqua/test/test_operator.py", line 136, in test_create_from_matrix
non_matrix_mode = op.eval('paulis', circuit, backend, run_config=run_config)[0]
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/operator.py", line 779, in eval
has_shared_circuits=has_shared_circuits)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 151, in compile_and_run_circuits
return _reuse_shared_circuits(circuits, backend, backend_config, compile_config, run_config, qjob_config)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 110, in _reuse_shared_circuits
show_circuit_summary=show_circuit_summary)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 239, in compile_and_run_circuits
results.append(job.result(**qjob_config))
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/aerjob.py", line 37, in _wrapper
return func(self, *args, **kwargs)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/aerjob.py", line 92, in result
return self._future.result(timeout=timeout)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/statevector_simulator.py", line 71, in _run_job
result = super()._run_job(job_id, qobj)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/qasm_simulator.py", line 97, in _run_job
result = run(qobj_dict, self._configuration.exe)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/qasm_simulator.py", line 195, in run
cin = json.dumps(qobj).encode()
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type complex is not JSON serializable
```
### What is the expected behavior?
Without crash.
### Suggested solutions
Terra should parse the complex vector to [ [real, imag] [real, imag]].
(I tried with above format, it will work)
| @diego-plan9 can you please look into this? passing a `config` kwarg to `execute()` seems to not serialize correctly...
I think I need more info - but if you are modifying a `Qobj` instance directly, the data (in this case, I assume is appending the `config`) should be as close to the specs as possible, which would mean that the proper way for that information to be stored would be indeed as a bare list of pairs.
@chunfuchen , can you provide more information about how you are using the custom configuration?
@diego-plan9
here is the example script
```python
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister
import qiskit as qk
import numpy as np
np.random.seed(0)
def generate_circuits(num_qubits, parameters, depth=10):
q = QuantumRegister(num_qubits, name='q')
c = ClassicalRegister(num_qubits, name='c')
circuit = QuantumCircuit(q, c)
param_idx = 0
for qubit in range(num_qubits):
circuit.u3(parameters[param_idx], 0.0, 0.0, q[qubit])
circuit.u1(parameters[param_idx+1], q[qubit])
param_idx += 2
for block in range(depth):
circuit.barrier(q)
for qubit in range(num_qubits):
circuit.u3(parameters[param_idx], 0.0, 0.0, q[qubit])
circuit.u1(parameters[param_idx+1], q[qubit])
param_idx += 2
# circuit.barrier(q)
# circuit.measure(q, c)
return circuit
num_circuits = 10
num_qubits = 5
depth = 2
#work
config = {'config': {'initial_state': [[0.93130364, -0.02274014], [0.2641254, 0.2497883]]}}
# does not work
# config = {'config': {'initial_state': np.asarray([0.93130364-0.02274014j, 0.2641254 +0.2497883j ])}}
num_parameters = num_qubits * (depth + 1) * 2
circuits = [generate_circuits(num_qubits, np.random.rand(num_parameters), depth) for _ in range(num_circuits)]
my_backend = qk.Aer.get_backend('statevector_simulator')
qobj = qk.compile(circuits=circuits, backend=my_backend, seed=0, config=config)
qjob = my_backend.run(qobj)
result = qjob.result()
print(result.get_statevector(circuits[0]))
```
Thanks @chunfuchen ! I think there are several forces at play here:
* the `config` is indeed appended to the `Qobj` "directly" in `circuits_to_qobj`. If we still consider `Qobj` to be a rather dummy and bare container, it would make sense that it is stored in the right format (ie. complex as tuple) one it reaches that point.
* whether we can limit all the possible inputs for `config` and reach some kind of universal conversion - which I think we realistically can't . Since it is a field that is loosely defined, and per-backend type, we might be able to perform a "preprocessing" of sorts in the backends, where they might know what configurations are valid for their needs.
* backwards-compatibility: I'm not sure what was the expected behaviour pre-0.7! If possible, it would be nice to preserve it.
So I'm kind of rebounding the question and the decision to @ajavadia , and in general, the compilers project - we have several options, from specifying that the `config` should be passed in qobj-like format (the second option mentioned by @chunfuchen ), to only perform the conversion on a subset of configurations that we know are supported, to fully delegating on backends. Any ideas?
I would say that it should be possible to take a state vector returned by `results.get_statevector()` and feed it into the circuit in `initial_state` with no conversion by the user. Since the former is a NumPy array, the latter should accept that as an input.
@diego-plan9 I found a related bug to this in `QobItem`:
The line
```python
if isinstance(obj, numpy.ndarray):
return obj.tolist()
```
should be
```python
if isinstance(obj, numpy.ndarray):
return cls._expand_item(obj.tolist())
```
to recursively parse arse a complex array, otherwise it will only convert the array to a list, but not serialize the inner complex numbers correctly. | 2018-12-05T20:51:20Z | [] | [] |
Traceback (most recent call last):
File "/Users/rchen/Developer/Quantum/temp/aqua/test/test_operator.py", line 136, in test_create_from_matrix
non_matrix_mode = op.eval('paulis', circuit, backend, run_config=run_config)[0]
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/operator.py", line 779, in eval
has_shared_circuits=has_shared_circuits)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 151, in compile_and_run_circuits
return _reuse_shared_circuits(circuits, backend, backend_config, compile_config, run_config, qjob_config)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 110, in _reuse_shared_circuits
show_circuit_summary=show_circuit_summary)
File "/Users/rchen/Developer/Quantum/temp/aqua/qiskit_aqua/utils/run_circuits.py", line 239, in compile_and_run_circuits
results.append(job.result(**qjob_config))
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/aerjob.py", line 37, in _wrapper
return func(self, *args, **kwargs)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/aerjob.py", line 92, in result
return self._future.result(timeout=timeout)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/statevector_simulator.py", line 71, in _run_job
result = super()._run_job(job_id, qobj)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/qasm_simulator.py", line 97, in _run_job
result = run(qobj_dict, self._configuration.exe)
File "/Users/rchen/Developer/Quantum/qiskit-terra-chenrich/qiskit/backends/aer/qasm_simulator.py", line 195, in run
cin = json.dumps(qobj).encode()
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/local/Cellar/python/3.7.1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type complex is not JSON serializable
| 997 |
|||
Qiskit/qiskit | Qiskit__qiskit-1748 | 186f3bf4fdd2bdcd9d18b2e059aea76209ddda0b | diff --git a/qiskit/tools/visualization/_text.py b/qiskit/tools/visualization/_text.py
--- a/qiskit/tools/visualization/_text.py
+++ b/qiskit/tools/visualization/_text.py
@@ -215,11 +215,13 @@ def __init__(self, label, input_length, order):
class BoxOnQuWireBot(MultiBox, BoxOnQuWire):
""" Draws the bottom part of a box that affects more than one quantum wire"""
- def __init__(self, label, input_length):
+ def __init__(self, label, input_length, bot_connect='─'):
super().__init__(label)
self.top_format = "│ %s │"
+ self.top_pad = " "
+ self.bot_connect = bot_connect
- self.mid_content = self.bot_connect = self.top_connect = ""
+ self.mid_content = self.top_connect = ""
if input_length <= 2:
self.top_connect = label
@@ -755,9 +757,9 @@ def build_layers(self):
layer.set_qubit(instruction['qargs'][0],
BoxOnQuWire(TextDrawing.label_for_box(instruction)))
- elif len(instruction['qubits']) >= 2 and not instruction['cargs']:
+ elif len(instruction['qargs']) >= 2 and not instruction['cargs']:
# multiple qubit gate
- layer.set_qu_multibox(instruction['qubits'], TextDrawing.label_for_box(instruction))
+ layer.set_qu_multibox(instruction['qargs'], TextDrawing.label_for_box(instruction))
else:
raise VisualizationError(
@@ -876,7 +878,8 @@ def connect_with(self, wire_char, label=None):
affected_bits[0].connect(wire_char, ['bot'])
for affected_bit in affected_bits[1:-1]:
affected_bit.connect(wire_char, ['bot', 'top'])
- affected_bits[-1].connect(wire_char, ['top'], label)
+ if not isinstance(affected_bits[-1], MultiBox):
+ affected_bits[-1].connect(wire_char, ['top'], label)
if label:
for affected_bit in affected_bits:
| Using the rzz gate yields an error on circuit drawing
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Terra version**:
qiskit version 0.7.0
- **Python version**:
python 3.6.6
- **Operating system**:
Red Hat Entreprise Server 7.4
### Current behavior
At circuit draw I get the following error
```bash
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 83, in __str__
return str(self.draw(output='text'))
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 413, in __str__
return self.single_string()
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 438, in single_string
return "\n".join(self.lines())
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 472, in lines
layers = self.build_layers()
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 758, in build_layers
elif len(instruction['qubits']) >= 2 and not instruction['cargs']:
KeyError: 'qubits'
```
### Steps to reproduce the problem
```python
from qiskit import *
q = QuantumRegister(2)
qc = QuantumCircuit(q);
qc.rzz(0, q[0], q[1])
print(qc)
```
### What is the expected behavior?
It should draw the circuit with no problem, but here it gives a KeyError on "qubits"
### Suggested solutions
Maybe it is expecting "qargs" instead of "qubits"?
| 2019-02-02T02:13:11Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 83, in __str__
return str(self.draw(output='text'))
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 413, in __str__
return self.single_string()
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 438, in single_string
return "\n".join(self.lines())
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 472, in lines
layers = self.build_layers()
File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 758, in build_layers
elif len(instruction['qubits']) >= 2 and not instruction['cargs']:
KeyError: 'qubits'
| 1,037 |
||||
Qiskit/qiskit | Qiskit__qiskit-1765 | ff091d1ceff3454e793d919b0c19e13a601c908f | diff --git a/qiskit/circuit/gate.py b/qiskit/circuit/gate.py
--- a/qiskit/circuit/gate.py
+++ b/qiskit/circuit/gate.py
@@ -21,7 +21,7 @@ def __init__(self, name, params, qargs, circuit=None):
name = instruction name string
params = list of real parameters (will be converted to symbolic)
qargs = list of pairs (QuantumRegister, index)
- circuit = QuantumCircuit containing this gate
+ circuit = QuantumCircuit or CompositeGate containing this gate
"""
self._is_multi_qubit = False
self._qubit_coupling = [qarg[1] for qarg in qargs]
diff --git a/qiskit/extensions/simulator/snapshot.py b/qiskit/extensions/simulator/snapshot.py
--- a/qiskit/extensions/simulator/snapshot.py
+++ b/qiskit/extensions/simulator/snapshot.py
@@ -9,6 +9,7 @@
Simulator command to snapshot internal simulator representation.
"""
from qiskit import QuantumCircuit
+from qiskit.circuit import CompositeGate
from qiskit import QuantumRegister
from qiskit.circuit import Instruction
from qiskit.extensions.exceptions import ExtensionError
@@ -65,5 +66,6 @@ def snapshot(self, label, snap_type='statevector'):
return self._attach(Snapshot(label, snap_type, qubits, self))
-# Add to QuantumCircuit class
+# Add to QuantumCircuit and CompositeGate classes
QuantumCircuit.snapshot = snapshot
+CompositeGate.snapshot = snapshot
diff --git a/qiskit/extensions/standard/barrier.py b/qiskit/extensions/standard/barrier.py
--- a/qiskit/extensions/standard/barrier.py
+++ b/qiskit/extensions/standard/barrier.py
@@ -9,6 +9,7 @@
Barrier instruction.
"""
from qiskit.circuit import QuantumCircuit
+from qiskit.circuit import CompositeGate
from qiskit.circuit import QuantumRegister
from qiskit.circuit import Instruction
@@ -57,3 +58,4 @@ def barrier(self, *qargs):
QuantumCircuit.barrier = barrier
+CompositeGate.barrier = barrier
diff --git a/qiskit/extensions/standard/ccx.py b/qiskit/extensions/standard/ccx.py
--- a/qiskit/extensions/standard/ccx.py
+++ b/qiskit/extensions/standard/ccx.py
@@ -8,6 +8,7 @@
"""
Toffoli gate. Controlled-Controlled-X.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -79,3 +80,4 @@ def ccx(self, ctl1, ctl2, tgt):
QuantumCircuit.ccx = ccx
+CompositeGate.ccx = ccx
diff --git a/qiskit/extensions/standard/ch.py b/qiskit/extensions/standard/ch.py
--- a/qiskit/extensions/standard/ch.py
+++ b/qiskit/extensions/standard/ch.py
@@ -10,6 +10,7 @@
"""
controlled-H gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -84,3 +85,4 @@ def ch(self, ctl, tgt):
QuantumCircuit.ch = ch
+CompositeGate.ch = ch
diff --git a/qiskit/extensions/standard/crz.py b/qiskit/extensions/standard/crz.py
--- a/qiskit/extensions/standard/crz.py
+++ b/qiskit/extensions/standard/crz.py
@@ -8,6 +8,7 @@
"""
controlled-rz gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -65,3 +66,4 @@ def crz(self, theta, ctl, tgt):
QuantumCircuit.crz = crz
+CompositeGate.crz = crz
diff --git a/qiskit/extensions/standard/cswap.py b/qiskit/extensions/standard/cswap.py
--- a/qiskit/extensions/standard/cswap.py
+++ b/qiskit/extensions/standard/cswap.py
@@ -8,6 +8,7 @@
"""
Fredkin gate. Controlled-SWAP.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -64,3 +65,4 @@ def cswap(self, ctl, tgt1, tgt2):
QuantumCircuit.cswap = cswap
+CompositeGate.cswap = cswap
diff --git a/qiskit/extensions/standard/cu1.py b/qiskit/extensions/standard/cu1.py
--- a/qiskit/extensions/standard/cu1.py
+++ b/qiskit/extensions/standard/cu1.py
@@ -8,6 +8,7 @@
"""
controlled-u1 gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -67,3 +68,4 @@ def cu1(self, theta, ctl, tgt):
QuantumCircuit.cu1 = cu1
+CompositeGate.cu1 = cu1
diff --git a/qiskit/extensions/standard/cu3.py b/qiskit/extensions/standard/cu3.py
--- a/qiskit/extensions/standard/cu3.py
+++ b/qiskit/extensions/standard/cu3.py
@@ -8,6 +8,7 @@
"""
controlled-u3 gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -72,3 +73,4 @@ def cu3(self, theta, phi, lam, ctl, tgt):
QuantumCircuit.cu3 = cu3
+CompositeGate.cu3 = cu3
diff --git a/qiskit/extensions/standard/cx.py b/qiskit/extensions/standard/cx.py
--- a/qiskit/extensions/standard/cx.py
+++ b/qiskit/extensions/standard/cx.py
@@ -10,6 +10,7 @@
"""
controlled-NOT gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -58,3 +59,4 @@ def cx(self, ctl, tgt):
QuantumCircuit.cx = cx
+CompositeGate.cx = cx
diff --git a/qiskit/extensions/standard/cxbase.py b/qiskit/extensions/standard/cxbase.py
--- a/qiskit/extensions/standard/cxbase.py
+++ b/qiskit/extensions/standard/cxbase.py
@@ -8,6 +8,7 @@
"""
Fundamental controlled-NOT gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.decorators import _op_expand
@@ -39,3 +40,4 @@ def cx_base(self, ctl, tgt):
QuantumCircuit.cx_base = cx_base
+CompositeGate.cx_base = cx_base
diff --git a/qiskit/extensions/standard/cy.py b/qiskit/extensions/standard/cy.py
--- a/qiskit/extensions/standard/cy.py
+++ b/qiskit/extensions/standard/cy.py
@@ -10,6 +10,7 @@
"""
controlled-Y gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -62,3 +63,4 @@ def cy(self, ctl, tgt):
QuantumCircuit.cy = cy
+CompositeGate.cy = cy
diff --git a/qiskit/extensions/standard/cz.py b/qiskit/extensions/standard/cz.py
--- a/qiskit/extensions/standard/cz.py
+++ b/qiskit/extensions/standard/cz.py
@@ -10,6 +10,7 @@
"""
controlled-Phase gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -61,3 +62,4 @@ def cz(self, ctl, tgt):
QuantumCircuit.cz = cz
+CompositeGate.cz = cz
diff --git a/qiskit/extensions/standard/h.py b/qiskit/extensions/standard/h.py
--- a/qiskit/extensions/standard/h.py
+++ b/qiskit/extensions/standard/h.py
@@ -10,6 +10,7 @@
"""
Hadamard gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -57,3 +58,4 @@ def h(self, q):
QuantumCircuit.h = h
+CompositeGate.h = h
diff --git a/qiskit/extensions/standard/iden.py b/qiskit/extensions/standard/iden.py
--- a/qiskit/extensions/standard/iden.py
+++ b/qiskit/extensions/standard/iden.py
@@ -10,6 +10,7 @@
"""
Identity gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -53,3 +54,4 @@ def iden(self, q):
QuantumCircuit.iden = iden
+CompositeGate.iden = iden
diff --git a/qiskit/extensions/standard/rx.py b/qiskit/extensions/standard/rx.py
--- a/qiskit/extensions/standard/rx.py
+++ b/qiskit/extensions/standard/rx.py
@@ -10,6 +10,7 @@
"""
Rotation around the x-axis.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -62,3 +63,4 @@ def rx(self, theta, q):
QuantumCircuit.rx = rx
+CompositeGate.rx = rx
diff --git a/qiskit/extensions/standard/ry.py b/qiskit/extensions/standard/ry.py
--- a/qiskit/extensions/standard/ry.py
+++ b/qiskit/extensions/standard/ry.py
@@ -10,6 +10,7 @@
"""
Rotation around the y-axis.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -61,3 +62,4 @@ def ry(self, theta, q):
QuantumCircuit.ry = ry
+CompositeGate.ry = ry
diff --git a/qiskit/extensions/standard/rz.py b/qiskit/extensions/standard/rz.py
--- a/qiskit/extensions/standard/rz.py
+++ b/qiskit/extensions/standard/rz.py
@@ -10,6 +10,7 @@
"""
Rotation around the z-axis.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -61,3 +62,4 @@ def rz(self, phi, q):
QuantumCircuit.rz = rz
+CompositeGate.rz = rz
diff --git a/qiskit/extensions/standard/rzz.py b/qiskit/extensions/standard/rzz.py
--- a/qiskit/extensions/standard/rzz.py
+++ b/qiskit/extensions/standard/rzz.py
@@ -8,6 +8,7 @@
"""
two-qubit ZZ-rotation gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -60,5 +61,6 @@ def rzz(self, theta, qubit1, qubit2):
return self._attach(RZZGate(theta, qubit1, qubit2, self))
-# Add to QuantumCircuit class
+# Add to QuantumCircuit and CompositeGate classes
QuantumCircuit.rzz = rzz
+CompositeGate.rzz = rzz
diff --git a/qiskit/extensions/standard/s.py b/qiskit/extensions/standard/s.py
--- a/qiskit/extensions/standard/s.py
+++ b/qiskit/extensions/standard/s.py
@@ -10,6 +10,7 @@
"""
S=diag(1,i) Clifford phase gate or its inverse.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -99,3 +100,5 @@ def sdg(self, q):
QuantumCircuit.s = s
QuantumCircuit.sdg = sdg
+CompositeGate.s = s
+CompositeGate.sdg = sdg
diff --git a/qiskit/extensions/standard/swap.py b/qiskit/extensions/standard/swap.py
--- a/qiskit/extensions/standard/swap.py
+++ b/qiskit/extensions/standard/swap.py
@@ -10,6 +10,7 @@
"""
SWAP gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -60,3 +61,4 @@ def swap(self, qubit1, qubit2):
QuantumCircuit.swap = swap
+CompositeGate.swap = swap
diff --git a/qiskit/extensions/standard/t.py b/qiskit/extensions/standard/t.py
--- a/qiskit/extensions/standard/t.py
+++ b/qiskit/extensions/standard/t.py
@@ -10,6 +10,7 @@
"""
T=sqrt(S) phase gate or its inverse.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -99,3 +100,5 @@ def tdg(self, q):
QuantumCircuit.t = t
QuantumCircuit.tdg = tdg
+CompositeGate.t = t
+CompositeGate.tdg = tdg
diff --git a/qiskit/extensions/standard/u0.py b/qiskit/extensions/standard/u0.py
--- a/qiskit/extensions/standard/u0.py
+++ b/qiskit/extensions/standard/u0.py
@@ -10,6 +10,7 @@
"""
Single qubit gate cycle idle.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -53,3 +54,4 @@ def u0(self, m, q):
QuantumCircuit.u0 = u0
+CompositeGate.u0 = u0
diff --git a/qiskit/extensions/standard/u1.py b/qiskit/extensions/standard/u1.py
--- a/qiskit/extensions/standard/u1.py
+++ b/qiskit/extensions/standard/u1.py
@@ -10,6 +10,7 @@
"""
Diagonal single qubit gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -55,3 +56,4 @@ def u1(self, theta, q):
QuantumCircuit.u1 = u1
+CompositeGate.u1 = u1
diff --git a/qiskit/extensions/standard/u2.py b/qiskit/extensions/standard/u2.py
--- a/qiskit/extensions/standard/u2.py
+++ b/qiskit/extensions/standard/u2.py
@@ -10,6 +10,7 @@
"""
One-pulse single-qubit gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -61,3 +62,4 @@ def u2(self, phi, lam, q):
QuantumCircuit.u2 = u2
+CompositeGate.u2 = u2
diff --git a/qiskit/extensions/standard/u3.py b/qiskit/extensions/standard/u3.py
--- a/qiskit/extensions/standard/u3.py
+++ b/qiskit/extensions/standard/u3.py
@@ -10,6 +10,7 @@
"""
Two-pulse single-qubit gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -62,3 +63,4 @@ def u3(self, theta, phi, lam, q):
QuantumCircuit.u3 = u3
+CompositeGate.u3 = u3
diff --git a/qiskit/extensions/standard/ubase.py b/qiskit/extensions/standard/ubase.py
--- a/qiskit/extensions/standard/ubase.py
+++ b/qiskit/extensions/standard/ubase.py
@@ -10,6 +10,7 @@
"""
Element of SU(2).
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.decorators import _op_expand
@@ -46,3 +47,4 @@ def u_base(self, theta, phi, lam, q):
QuantumCircuit.u_base = u_base
+CompositeGate.u_base = u_base
diff --git a/qiskit/extensions/standard/x.py b/qiskit/extensions/standard/x.py
--- a/qiskit/extensions/standard/x.py
+++ b/qiskit/extensions/standard/x.py
@@ -10,6 +10,7 @@
"""
Pauli X (bit-flip) gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -59,3 +60,4 @@ def x(self, q):
QuantumCircuit.x = x
+CompositeGate.x = x
diff --git a/qiskit/extensions/standard/y.py b/qiskit/extensions/standard/y.py
--- a/qiskit/extensions/standard/y.py
+++ b/qiskit/extensions/standard/y.py
@@ -10,6 +10,7 @@
"""
Pauli Y (bit-phase-flip) gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -54,3 +55,4 @@ def y(self, q):
QuantumCircuit.y = y
+CompositeGate.y = y
diff --git a/qiskit/extensions/standard/z.py b/qiskit/extensions/standard/z.py
--- a/qiskit/extensions/standard/z.py
+++ b/qiskit/extensions/standard/z.py
@@ -10,6 +10,7 @@
"""
Pauli Z (phase-flip) gate.
"""
+from qiskit.circuit import CompositeGate
from qiskit.circuit import Gate
from qiskit.circuit import QuantumCircuit
from qiskit.circuit import QuantumRegister
@@ -54,3 +55,4 @@ def z(self, q):
QuantumCircuit.z = z
+CompositeGate.z = z
| CompositeGate became unusable - what is the replacement?
### Informations
- **Qiskit Terra version**: 0.7
- **Python version**: 3.6.5
- **Operating system**: Linux
### What is the current behavior?
Commit 7485ed924126b0861ef94d35eccef2d3532d70bf removed the `CompositeGate.X = X` from the ` qiskit/extensions/simulator/*.py` files. Because of this, CompositeGate is not usable as before.
[This code](https://gist.github.com/nelimee/79f54a75371d65a0c00d59af1cebf874) fails at execution with the error
```
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "<input>", line 74, in crzz
File "<input>", line 64, in __init__
AttributeError: 'CRZZGate' object has no attribute 'cu1'
```
### Steps to reproduce the problem
Download [the code](https://gist.github.com/nelimee/79f54a75371d65a0c00d59af1cebf874) and execute it with a version of Qiskit that contains the modifications of 7485ed924126b0861ef94d35eccef2d3532d70bf.
### What is the expected behavior?
The CompositeGate should work as before, i.e. be appended to the quantum circuit.
### Suggested solutions
1. Revert the part of 7485ed924126b0861ef94d35eccef2d3532d70bf that removed the lines `CompositeGate.X = X`.
2. **or** Update the main Changelog with this non-documented removal and provide/document an alternative way to create user-defined custom gates.
| thanks for the input we are thinking about how to fix this. @ajavadia and i were discussing today. I think the best idea is to not try and revert but give an update in the change that it is broken but fix with a method that scales better. | 2019-02-05T16:03:38Z | [] | [] |
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "<input>", line 74, in crzz
File "<input>", line 64, in __init__
AttributeError: 'CRZZGate' object has no attribute 'cu1'
| 1,039 |
|||
Qiskit/qiskit | Qiskit__qiskit-1849 | 91149f910e5530dc01dace328cb6cba0bce950cd | diff --git a/qiskit/tools/visualization/_text.py b/qiskit/tools/visualization/_text.py
--- a/qiskit/tools/visualization/_text.py
+++ b/qiskit/tools/visualization/_text.py
@@ -784,8 +784,11 @@ def build_layers(self):
Raises:
VisualizationError: When the drawing is, for some reason, impossible to be drawn.
"""
+ wire_names = self.wire_names(with_initial_value=True)
+ if not wire_names:
+ return []
- layers = [InputWire.fillup_layer(self.wire_names(with_initial_value=True))]
+ layers = [InputWire.fillup_layer(wire_names)]
for instruction_layer in self.instructions:
layer = Layer(self.qregs, self.cregs)
| Text circuit drawer raises ValueError if an empty circuit is given
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.7.3
- **Python version**: 3.6.8
- **Operating system**: macOS HighSierra
### What is the current behavior?
If I try to draw an empty circuit with the text drawer, it raises ValueError.
### Steps to reproduce the problem
```
# sample.py
from qiskit import QuantumCircuit
qc = QuantumCircuit()
print(qc)
```
```
$ python sample.py
Traceback (most recent call last):
File "c.py", line 3, in <module>
print(qc)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 83, in __str__
return str(self.draw(output='text'))
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 413, in __str__
return self.single_string()
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 438, in single_string
return "\n".join(self.lines())
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 472, in lines
layers = self.build_layers()
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 672, in build_layers
layers.append(InputWire.fillup_layer(self.wire_names(with_initial_value=True)))
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 394, in fillup_layer
longest = max([len(name) for name in names])
ValueError: max() arg is an empty sequence
```
### What is the expected behavior?
No ValueError.
### Suggested solutions
Check whether `names` is empty or not.
| 2019-02-22T19:20:00Z | [] | [] |
Traceback (most recent call last):
File "c.py", line 3, in <module>
print(qc)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 83, in __str__
return str(self.draw(output='text'))
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 413, in __str__
return self.single_string()
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 438, in single_string
return "\n".join(self.lines())
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 472, in lines
layers = self.build_layers()
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 672, in build_layers
layers.append(InputWire.fillup_layer(self.wire_names(with_initial_value=True)))
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 394, in fillup_layer
longest = max([len(name) for name in names])
ValueError: max() arg is an empty sequence
| 1,051 |
||||
Qiskit/qiskit | Qiskit__qiskit-1866 | fb44b4ad18969a89a03e78c9ca4944750edbacb4 | diff --git a/qiskit/tools/visualization/_matplotlib.py b/qiskit/tools/visualization/_matplotlib.py
--- a/qiskit/tools/visualization/_matplotlib.py
+++ b/qiskit/tools/visualization/_matplotlib.py
@@ -462,7 +462,8 @@ def _draw_ops(self, verbose=False):
_wide_gate = 'u2 u3 cu2 cu3'.split()
_barriers = {'coord': [], 'group': []}
next_ops = self._ops.copy()
- next_ops.pop(0)
+ if next_ops:
+ next_ops.pop(0)
this_anc = 0
#
@@ -682,8 +683,12 @@ def _draw_ops(self, verbose=False):
#
# adjust window size and draw horizontal lines
#
- max_anc = max([q_anchors[ii].get_index() for ii in self._qreg_dict])
- n_fold = (max_anc - 1) // self._style.fold
+ anchors = [q_anchors[ii].get_index() for ii in self._qreg_dict]
+ if anchors:
+ max_anc = max(anchors)
+ else:
+ max_anc = 0
+ n_fold = max(0, max_anc - 1) // self._style.fold
# window size
if max_anc > self._style.fold > 0:
self._cond['xmax'] = self._style.fold + 1
| Matplotlib circuit drawer raises IndexError if there is no gate in QuantumCircuit
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.7.3
- **Python version**: 3.6.8
- **Operating system**: macOS HighSierra
### What is the current behavior?
If I try to draw a quantum circuit without any gate, the mpl drawer raises IndexError. The text drawer does not have this issue.
### Steps to reproduce the problem
```
# sample.py
from qiskit import QuantumCircuit, QuantumRegister
qr = QuantumRegister(1)
qc = QuantumCircuit(qr)
print(qc)
qc.draw(filename='output.pdf', output='mpl')
```
```
$ python sample.py
q0_0: |0>
Traceback (most recent call last):
File "c.py", line 5, in <module>
qc.draw(filename='output.pdf', output='mpl')
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 353, in draw
reverse_bits=reverse_bits)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_circuit_visualization.py", line 237, in circuit_drawer
reverse_bits=reverse_bits)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_circuit_visualization.py", line 577, in _matplotlib_circuit_drawer
return qcd.draw(filename)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_matplotlib.py", line 343, in draw
self._draw_ops(verbose)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_matplotlib.py", line 466, in _draw_ops
next_ops.pop(0)
IndexError: pop from empty list
```
### What is the expected behavior?
No IndexError.
### Suggested solutions
Check whether `next_ops` is empty or not.
| @nkanazawa1989 Can you check it? | 2019-02-25T21:01:55Z | [] | [] |
Traceback (most recent call last):
File "c.py", line 5, in <module>
qc.draw(filename='output.pdf', output='mpl')
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 353, in draw
reverse_bits=reverse_bits)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_circuit_visualization.py", line 237, in circuit_drawer
reverse_bits=reverse_bits)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_circuit_visualization.py", line 577, in _matplotlib_circuit_drawer
return qcd.draw(filename)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_matplotlib.py", line 343, in draw
self._draw_ops(verbose)
File "/Users/ima/envs/vqe2/lib/python3.6/site-packages/qiskit/tools/visualization/_matplotlib.py", line 466, in _draw_ops
next_ops.pop(0)
IndexError: pop from empty list
| 1,055 |
|||
Qiskit/qiskit | Qiskit__qiskit-1944 | b56cdf32e67438879faddf91d56eae04724e928b | diff --git a/qiskit/dagcircuit/_dagcircuit.py b/qiskit/dagcircuit/_dagcircuit.py
--- a/qiskit/dagcircuit/_dagcircuit.py
+++ b/qiskit/dagcircuit/_dagcircuit.py
@@ -1336,7 +1336,7 @@ def multigraph_layers(self):
next_layer = []
def collect_runs(self, namelist):
- """Return a set of runs of "op" nodes with the given names.
+ """Return a set of non-conditional runs of "op" nodes with the given names.
For example, "... h q[0]; cx q[0],q[1]; cx q[0],q[1]; h q[1]; .."
would produce the tuple of cx nodes as an element of the set returned
@@ -1357,7 +1357,7 @@ def collect_runs(self, namelist):
for node in tops_node:
nd = self.multi_graph.node[node]
if nd["type"] == "op" and nd["name"] in namelist \
- and not nodes_seen[node]:
+ and nd["condition"] is None and not nodes_seen[node]:
group = [node]
nodes_seen[node] = True
s = list(self.multi_graph.successors(node))
| Mapper error when I try to execute QuantumCircuit as an object of a user defined class
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit version**: 0.7.0
- **Python version**: 3.6.7
- **Operating system**: Ubuntu 18.04
### What is the current behavior?
/home/varun/.local/lib/python3.6/site-packages/marshmallow/schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict
ChangedInMarshmallow3Warning
Please provide the cooeficeints for the intital state in the format a+bj
Coefficient of state zero1+0j
Coefficient of state one0+0j
Provide the square of normalisation denominator1
[1.+0.j 0.+0.j]
The best backend is ibmq_16_melbourne
Traceback (most recent call last):
File "class_counts.py", line 183, in <module>
main()
File "class_counts.py", line 163, in main
sim = obj.simulate(p,b)
File "class_counts.py", line 111, in simulate
dec_state = self.ibmq(b)
File "class_counts.py", line 97, in ibmq
job_exp = execute(self.qc, backend=backend, shots=shots, max_credits=max_credits)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/compiler.py", line 108, in execute
skip_transpiler, seed_mapper, pass_manager, memory)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/compiler.py", line 61, in compile
seed_mapper, pass_manager)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 67, in transpile
'pass_manager': pass_manager})
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/parallel.py", line 93, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 109, in _transpilation
pass_manager=pass_manager)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 199, in transpile_dag
dag = Optimize1qGates().run(dag)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/passes/optimize_1q_gates.py", line 53, in run
raise MapperError("internal error")
qiskit.mapper._mappererror.MapperError: 'internal error'
### Steps to reproduce the problem
The QuantumRegister, ClassicalRegister and QuantumCircuit are intialised when a object is created for the user-defined class I have made. The code ran without errors on the local machine and on the hpc. But when I tried to run it on the ibm Qx_16 it showed this error.
### What is the expected behavior?
I expect to get values of get_counts() for 20 different values for a given parameter. For each value a new object is created and the entire circuit is simulated.
### Suggested solutions
The problem could be because I create a new object every iteration of the loop. I made sure to delete the object after each iteration though,
| Hi @isolatedinformation could you provide some more information about the circuit you were trying to execute when this error occurred?
My circuit consisits of 9 qubits and 9 cbits to record thier outcomes. Basically, I was trying to simulate a noisy channel and perform error correction to retireve the initial message. The noisiness of the channel was tuned by a parameter \gamma. For diiferent values of \gamma, I wanted the statistics of the measuremnt outcome and plotted them. For instance, this attached image was the statistics I got for 20 different values of gamma when run on the HPC.
I created a class to make sure the entire circuit was re initilased for every iteration of \gamma since each iteration is independent of the other. The objects of the class helped achieve this objective.
If it's still not clear, I can email my code to you.
I think this is the same issue as #1871 | 2019-03-10T09:20:58Z | [] | [] |
Traceback (most recent call last):
File "class_counts.py", line 183, in <module>
main()
File "class_counts.py", line 163, in main
sim = obj.simulate(p,b)
File "class_counts.py", line 111, in simulate
dec_state = self.ibmq(b)
File "class_counts.py", line 97, in ibmq
job_exp = execute(self.qc, backend=backend, shots=shots, max_credits=max_credits)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/compiler.py", line 108, in execute
skip_transpiler, seed_mapper, pass_manager, memory)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/compiler.py", line 61, in compile
seed_mapper, pass_manager)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 67, in transpile
'pass_manager': pass_manager})
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/tools/parallel.py", line 93, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 109, in _transpilation
pass_manager=pass_manager)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/_transpiler.py", line 199, in transpile_dag
dag = Optimize1qGates().run(dag)
File "/home/varun/.local/lib/python3.6/site-packages/qiskit/transpiler/passes/optimize_1q_gates.py", line 53, in run
raise MapperError("internal error")
qiskit.mapper._mappererror.MapperError: 'internal error'
| 1,064 |
|||
Qiskit/qiskit | Qiskit__qiskit-1959 | d25e58dde25bd6783815828ce72b35935b764eb3 | diff --git a/qiskit/qobj/models.py b/qiskit/qobj/models.py
--- a/qiskit/qobj/models.py
+++ b/qiskit/qobj/models.py
@@ -10,7 +10,8 @@
from marshmallow.validate import Length, Range, Regexp
from qiskit.validation.base import BaseModel, BaseSchema, bind_schema
-from qiskit.validation.fields import Integer, List, Nested, Raw, String
+from qiskit.validation.fields import (Integer, List, Nested, String,
+ InstructionParameter)
class QobjConditionalSchema(BaseSchema):
@@ -31,7 +32,7 @@ class QobjInstructionSchema(BaseSchema):
# Optional properties.
qubits = List(Integer(validate=Range(min=0)),
validate=Length(min=1))
- params = List(Raw())
+ params = List(InstructionParameter())
memory = List(Integer(validate=Range(min=0)),
validate=Length(min=1))
conditional = Nested(QobjConditionalSchema)
diff --git a/qiskit/validation/fields/__init__.py b/qiskit/validation/fields/__init__.py
--- a/qiskit/validation/fields/__init__.py
+++ b/qiskit/validation/fields/__init__.py
@@ -12,8 +12,8 @@
1. Distinguish a new type, like the ``Complex`` number in this module.
2. Use a new Marshmallow field not used in ``qiskit`` yet.
-Marshamallow fields does not allow model validation so you need to create a new
-field, make it subclass of the Marshamallow field *and* ``ModelTypeValidator``,
+Marshmallow fields does not allow model validation so you need to create a new
+field, make it subclass of the Marshmallow field *and* ``ModelTypeValidator``,
and redefine ``valid_types`` to be the list of valid types. Usually, **the
same types this field deserializes to**. For instance::
@@ -24,45 +24,16 @@ class Boolean(marshmallow.fields.Boolean, ModelTypeValidator):
See ``ModelTypeValidator`` for more subclassing options.
"""
+
from datetime import date, datetime
from marshmallow import fields as _fields
-from marshmallow.utils import is_collection
from qiskit.validation import ModelTypeValidator
from qiskit.validation.fields.polymorphic import ByAttribute, ByType, TryFrom
from qiskit.validation.fields.containers import Nested, List
-
-class Complex(ModelTypeValidator):
- """Field for complex numbers.
-
- Field for parsing complex numbers:
- * deserializes to Python's `complex`.
- * serializes to a tuple of 2 decimals `(real, imaginary)`
- """
-
- valid_types = (complex, )
-
- default_error_messages = {
- 'invalid': '{input} cannot be parsed as a complex number.',
- 'format': '"{input}" cannot be formatted as complex number.',
- }
-
- def _serialize(self, value, attr, obj):
- try:
- return [value.real, value.imag]
- except AttributeError:
- self.fail('format', input=value)
-
- def _deserialize(self, value, attr, data):
- if not is_collection(value) or len(value) != 2:
- self.fail('invalid', input=value)
-
- try:
- return complex(*value)
- except (ValueError, TypeError):
- self.fail('invalid', input=value)
+from .custom import Complex, InstructionParameter
class String(_fields.String, ModelTypeValidator):
diff --git a/qiskit/validation/fields/custom.py b/qiskit/validation/fields/custom.py
new file mode 100644
--- /dev/null
+++ b/qiskit/validation/fields/custom.py
@@ -0,0 +1,90 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2019, IBM.
+#
+# This source code is licensed under the Apache License, Version 2.0 found in
+# the LICENSE.txt file in the root directory of this source tree.
+
+"""Fields custom to Terra to be used with Qiskit validated classes."""
+
+import numpy
+import sympy
+
+from marshmallow.utils import is_collection
+
+from qiskit.validation import ModelTypeValidator
+
+
+class Complex(ModelTypeValidator):
+ """Field for complex numbers.
+
+ Field for parsing complex numbers:
+ * deserializes to Python's `complex`.
+ * serializes to a tuple of 2 decimals `(real, imaginary)`
+ """
+
+ valid_types = (complex, )
+
+ default_error_messages = {
+ 'invalid': '{input} cannot be parsed as a complex number.',
+ 'format': '"{input}" cannot be formatted as complex number.',
+ }
+
+ def _serialize(self, value, attr, obj):
+ try:
+ return [value.real, value.imag]
+ except AttributeError:
+ self.fail('format', input=value)
+
+ def _deserialize(self, value, attr, data):
+ if not is_collection(value) or len(value) != 2:
+ self.fail('invalid', input=value)
+
+ try:
+ return complex(*value)
+ except (ValueError, TypeError):
+ self.fail('invalid', input=value)
+
+
+class InstructionParameter(ModelTypeValidator):
+ """Field for objects used in instruction parameters.
+
+ This field provides support for parsing objects of types that uses by
+ qobj.experiments.instructions.parameters:
+ * basic Python types: complex, int, float, str
+ * ``numpy``: integer, float
+ * ``sympy``: Symbol, Basic
+
+ Note that by using this field, serialization-deserialization round-tripping
+ becomes not possible, as certain types serialize to the same Python basic
+ type (for example, numpy.float and regular float). If possible, it is
+ recommended that more specific and defined fields are used instead.
+ """
+ valid_types = (complex, int, float, str,
+ numpy.integer, numpy.float, sympy.Basic, sympy.Symbol)
+
+ def _serialize(self, value, attr, obj):
+ # pylint: disable=too-many-return-statements
+ if isinstance(value, (float, int, str)):
+ return value
+ if isinstance(value, complex):
+ return [value.real, value.imag]
+ if isinstance(value, numpy.integer):
+ return int(value)
+ if isinstance(value, numpy.float):
+ return float(value)
+ if isinstance(value, sympy.Symbol):
+ return str(value)
+ if isinstance(value, sympy.Basic):
+ if value.is_imaginary:
+ return [float(sympy.re(value)), float(sympy.im(value))]
+ else:
+ return float(value.evalf())
+
+ return self.fail('invalid', input=value)
+
+ def _deserialize(self, value, attr, data):
+ if is_collection(value) and len(value) != 2:
+ return complex(*value)
+
+ return value
| ghz example is failing in qobj
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**:
- **Python version**:
- **Operating system**:
### What is the current behavior?
### Steps to reproduce the problem
### What is the expected behavior?
```
>>> python examples/python/ghz.py
```
```
Traceback (most recent call last):
File "examples/python/ghz.py", line 59, in <module>
result = job.result()
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 186, in result
job_response = self._wait_for_result(timeout=timeout, wait=wait)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 190, in _wait_for_result
self._wait_for_submission(timeout)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 391, in _wait_for_submission
raise self._future_captured_exception
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 330, in _submit_callback
submit_info = self._api.run_job(self._qobj_payload, backend_name=backend_name)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api/ibmqconnector.py", line 144, in run_job
job = self.req.post(url, data=json.dumps(data))
File "/anaconda3/lib/python3.6/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/anaconda3/lib/python3.6/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/anaconda3/lib/python3.6/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/anaconda3/lib/python3.6/json/encoder.py", line 180, in default
o.__class__.__name__)
TypeError: Object of type 'Zero' is not JSON serializable
```
Not sure what is causing this serialization error. @diego-plan9 can you take a look?
| It seems quite similar to the errors in Aer that caused two tests to be skipped during the Qobj PR (as `IBMQProvider` contains also code very similar to Aer's to deal with noise model, etc) - I'll check with @chriseclectic as they can probably be handled at the same time.
a few other examples also fail. it seems the failure happens when trying to run on devices, not Aer.
Seems we are being too lax when handling `qobj.experiment.instructions.parameters`, and is rippling up when using types that cannot be serialized by default - will issue a PR shortly! | 2019-03-12T11:05:25Z | [] | [] |
Traceback (most recent call last):
File "examples/python/ghz.py", line 59, in <module>
result = job.result()
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 186, in result
job_response = self._wait_for_result(timeout=timeout, wait=wait)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 190, in _wait_for_result
self._wait_for_submission(timeout)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 391, in _wait_for_submission
raise self._future_captured_exception
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqjob.py", line 330, in _submit_callback
submit_info = self._api.run_job(self._qobj_payload, backend_name=backend_name)
File "/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api/ibmqconnector.py", line 144, in run_job
job = self.req.post(url, data=json.dumps(data))
File "/anaconda3/lib/python3.6/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/anaconda3/lib/python3.6/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/anaconda3/lib/python3.6/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/anaconda3/lib/python3.6/json/encoder.py", line 180, in default
o.__class__.__name__)
TypeError: Object of type 'Zero' is not JSON serializable
| 1,066 |
|||
Qiskit/qiskit | Qiskit__qiskit-2149 | edc96e5f0581ab6aee40013e7a8e3c6c50feda8e | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -676,6 +676,14 @@ def assign_variables(self, value_dict):
new_circuit.variable_table[variable] = value_dict
return new_circuit
+ @property
+ def unassigned_variables(self):
+ """Returns a set containing any variables which have not yet been assigned."""
+ return {variable
+ for variable, parameterized_instructions in self.variable_table.items()
+ if any(instruction.params[parameter_index].free_symbols
+ for instruction, parameter_index in parameterized_instructions)}
+
def _circuit_from_qasm(qasm):
# pylint: disable=cyclic-import
diff --git a/qiskit/transpiler/preset_passmanagers/default.py b/qiskit/transpiler/preset_passmanagers/default.py
--- a/qiskit/transpiler/preset_passmanagers/default.py
+++ b/qiskit/transpiler/preset_passmanagers/default.py
@@ -25,7 +25,8 @@
from ..passes.mapping.extend_layout import ExtendLayout
-def default_pass_manager(basis_gates, coupling_map, initial_layout, seed_mapper):
+def default_pass_manager(basis_gates, coupling_map, initial_layout,
+ skip_numeric_passes, seed_mapper):
"""
The default pass manager that maps to the coupling map.
@@ -33,6 +34,7 @@ def default_pass_manager(basis_gates, coupling_map, initial_layout, seed_mapper)
basis_gates (list[str]): list of basis gate names supported by the
target. Default: ['u1','u2','u3','cx','id']
initial_layout (Layout or None): If None, trivial layout will be chosen.
+ skip_numeric_passes (bool): If true, skip passes which require fixed parameter values
coupling_map (CouplingMap): coupling map (perhaps custom) to target
in mapping.
seed_mapper (int or None): random seed for the swap_mapper.
@@ -72,8 +74,14 @@ def default_pass_manager(basis_gates, coupling_map, initial_layout, seed_mapper)
pass_manager.append(Unroller(['u1', 'u2', 'u3', 'id', 'cx']))
# Simplify single qubit gates and CXs
- pass_manager.append([Optimize1qGates(), CXCancellation(), Depth(), FixedPoint('depth')],
+ if not skip_numeric_passes:
+ simplification_passes = [Optimize1qGates(), CXCancellation()]
+ else:
+ simplification_passes = [CXCancellation()]
+
+ pass_manager.append(simplification_passes + [Depth(), FixedPoint('depth')],
do_while=lambda property_set: not property_set['depth_fixed_point'])
+
return pass_manager
diff --git a/qiskit/transpiler/transpiler.py b/qiskit/transpiler/transpiler.py
--- a/qiskit/transpiler/transpiler.py
+++ b/qiskit/transpiler/transpiler.py
@@ -101,12 +101,15 @@ def _transpilation(circuit, basis_gates=None, coupling_map=None,
if pass_manager and not pass_manager.working_list:
return circuit
+ is_parametric_circuit = bool(circuit.unassigned_variables)
+
dag = circuit_to_dag(circuit)
del circuit
final_dag = transpile_dag(dag, basis_gates=basis_gates,
coupling_map=coupling_map,
initial_layout=initial_layout,
+ skip_numeric_passes=is_parametric_circuit,
seed_mapper=seed_mapper,
pass_manager=pass_manager)
@@ -117,7 +120,8 @@ def _transpilation(circuit, basis_gates=None, coupling_map=None,
# pylint: disable=redefined-builtin
def transpile_dag(dag, basis_gates=None, coupling_map=None,
- initial_layout=None, seed_mapper=None, pass_manager=None):
+ initial_layout=None, skip_numeric_passes=None,
+ seed_mapper=None, pass_manager=None):
"""Transform a dag circuit into another dag circuit (transpile), through
consecutive passes on the dag.
@@ -135,6 +139,7 @@ def transpile_dag(dag, basis_gates=None, coupling_map=None,
eg. [[0, 2], [1, 2], [1, 3], [3, 4]}
initial_layout (Layout or None): A layout object
+ skip_numeric_passes (bool): If true, skip passes which require fixed parameter values
seed_mapper (int): random seed_mapper for the swap mapper
pass_manager (PassManager): pass manager instance for the transpilation process
If None, a default set of passes are run.
@@ -164,6 +169,7 @@ def transpile_dag(dag, basis_gates=None, coupling_map=None,
pass_manager = default_pass_manager(basis_gates,
CouplingMap(coupling_map),
initial_layout,
+ skip_numeric_passes,
seed_mapper=seed_mapper)
else:
pass_manager = default_pass_manager_simulator(basis_gates)
| Transpiling parameterized circuits for device backends raises
```
>>> qobj = qk.compile(qc, backend=FakeTokyo())
/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/compiler.py:50: DeprecationWarning: qiskit.compile() is deprecated and will be removed in Qiskit Terra 0.9. Please use qiskit.transpile() to transform circuits and qiskit.assemble_circuits() to produce qobj.
DeprecationWarning)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/compiler.py", line 67, in compile
initial_layout, seed_mapper, pass_manager)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 85, in transpile
'pass_manager': pass_manager})
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 93, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 143, in _transpilation
pass_manager=pass_manager)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 234, in transpile_dag
dag = pass_manager.run_passes(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 129, in run_passes
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 169, in _do_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 62, in run
left_parameters = tuple([float(x) for x in left_parameters])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 62, in <listcomp>
left_parameters = tuple([float(x) for x in left_parameters])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/lib/python3.5/site-packages/sympy/core/expr.py", line 256, in __float__
raise TypeError("can't convert expression to float")
TypeError: can't convert expression to float
```
| I'm not sure this is going to be fixable, we switched optimize 1q gates in #1738 to resolve things numerically which resulted in a very significant speed up. If we're passing in sympy expressions for parameters which are not able to represented as a float I don't see how we can run optimize 1q on them short of adding back sympy to the pass (which I don't want to do) so that we can call simplify() in the hopes that the terms in the expressions for gates cancel without needing the undefined parameter in the expression to do the evaluation. (I'm not sure if it could provide any meaningful optimization if the parameters aren't defined though)
We need to make the transpiler be smart here and use a PassManager that does not invoke `Optimize1qGates` (or any other pass that involves numerical optimizations based on gate parameters -- another example is the `ConsolidateBlocks` pass in #2134) | 2019-04-17T22:14:56Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/compiler.py", line 67, in compile
initial_layout, seed_mapper, pass_manager)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 85, in transpile
'pass_manager': pass_manager})
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 93, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 143, in _transpilation
pass_manager=pass_manager)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpiler.py", line 234, in transpile_dag
dag = pass_manager.run_passes(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 129, in run_passes
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 169, in _do_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 62, in run
left_parameters = tuple([float(x) for x in left_parameters])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 62, in <listcomp>
left_parameters = tuple([float(x) for x in left_parameters])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/lib/python3.5/site-packages/sympy/core/expr.py", line 256, in __float__
raise TypeError("can't convert expression to float")
TypeError: can't convert expression to float
| 1,113 |
|||
Qiskit/qiskit | Qiskit__qiskit-2169 | da338d8ff9ca7e67ec675aac9414b9976341c580 | diff --git a/qiskit/visualization/matplotlib.py b/qiskit/visualization/matplotlib.py
--- a/qiskit/visualization/matplotlib.py
+++ b/qiskit/visualization/matplotlib.py
@@ -787,9 +787,15 @@ def _draw_ops(self, verbose=False):
def param_parse(v, pimode=False):
for i, e in enumerate(v):
if pimode:
- v[i] = MatplotlibDrawer.format_pi(e)
+ try:
+ v[i] = MatplotlibDrawer.format_pi(e)
+ except TypeError:
+ v[i] = str(e)
else:
- v[i] = MatplotlibDrawer.format_numeric(e)
+ try:
+ v[i] = MatplotlibDrawer.format_numeric(e)
+ except TypeError:
+ v[i] = str(e)
if v[i].startswith('-'):
v[i] = '$-$' + v[i][1:]
param = ', '.join(v)
| Visualization support for parameterized circuits
Following #2103, gate params can be sympy expressions
```
>>> theta = sympy.Symbol('theta')
>>> qc = qk.QuantumCircuit(cr, qr)
>>> qc.rx(theta, qr[0])
<qiskit.extensions.standard.rx.RXGate object at 0x116d5c1d0>
>>> qc.measure(qr[0], cr[0])
<qiskit.circuit.measure.Measure object at 0x11ed71c50>
>>> print(qc.draw())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 437, in __str__
return self.single_string()
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 462, in single_string
return "\n".join(self.lines())
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 497, in lines
layers = self.build_layers()
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 820, in build_layers
self._instruction_to_gate(instruction, layer)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 780, in _instruction_to_gate
BoxOnQuWire(TextDrawing.label_for_box(instruction)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 618, in label_for_box
params = TextDrawing.params_for_label(instruction)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 610, in params_for_label
return ['%.5g' % i for i in instruction.op.params
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 611, in <listcomp>
if not isinstance(i, (numpy.ndarray, sympy.Matrix))]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/lib/python3.5/site-packages/sympy/core/expr.py", line 256, in __float__
raise TypeError("can't convert expression to float")
TypeError: can't convert expression to float
```
| Is this on all backends or just text?
Ok, I just tested this locally, mpl also fails for the same reason casting trying to cast the sympy expression to a float which it can't do because there is no value. But latex actually works:
without any modifications. I think this is a first where latex is the one without a bug! :)
I'm self-assigning the text part.
Well done latex drawer! :)
Text drawer fix in #2168. When merged, this issue should be renamed to be mpl specific. | 2019-04-22T16:42:48Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 437, in __str__
return self.single_string()
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 462, in single_string
return "\n".join(self.lines())
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 497, in lines
layers = self.build_layers()
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 820, in build_layers
self._instruction_to_gate(instruction, layer)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 780, in _instruction_to_gate
BoxOnQuWire(TextDrawing.label_for_box(instruction)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 618, in label_for_box
params = TextDrawing.params_for_label(instruction)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 610, in params_for_label
return ['%.5g' % i for i in instruction.op.params
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/visualization/text.py", line 611, in <listcomp>
if not isinstance(i, (numpy.ndarray, sympy.Matrix))]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/lib/python3.5/site-packages/sympy/core/expr.py", line 256, in __float__
raise TypeError("can't convert expression to float")
TypeError: can't convert expression to float
| 1,118 |
|||
Qiskit/qiskit | Qiskit__qiskit-2350 | 09ed6a15b068259d5e36d55aa0973af5b8099287 | diff --git a/qiskit/converters/qobj_to_circuits.py b/qiskit/converters/qobj_to_circuits.py
--- a/qiskit/converters/qobj_to_circuits.py
+++ b/qiskit/converters/qobj_to_circuits.py
@@ -30,7 +30,7 @@ def qobj_to_circuits(qobj):
"""
warnings.warn('qiskit.converters.qobj_to_circuit() is deprecated and will '
'be removed in Qiskit Terra 0.9. Please use '
- 'qiskit.compiler.disassemble_circuits() to convert a qobj '
+ 'qiskit.assembler.disassemble() to convert a qobj '
'to list of circuits.', DeprecationWarning)
variables = disassemble(qobj)
| disassemble_circuits() suggested in qobj_to_circuits.py DeprecationWarning doesn't exist
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.8.0
- **Python version**: 3.7.2
- **Operating system**: macOS
`qobj_to_circuits` gives the following `DeprecationWarning`:
```python
.../qiskit/converters/qobj_to_circuits.py:34: DeprecationWarning: qiskit.converters.qobj_to_circuit() is deprecated and will be removed in Qiskit Terra 0.9. Please use qiskit.compiler.disassemble_circuits() to convert a qobj to list of circuits.
```
but `qiskit.compiler.disassemble_circuits()` doesn't exist.
### What is the current behavior?
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'disassemble_circuits' from 'qiskit.compiler' (/Users/matteo/Work/projects/ibmq/env/lib/python3.7/site-packages/qiskit/compiler/__init__.py)
```
### Steps to reproduce the problem
1. Installed qiskit in a new python virtualenv with `pip install qiskit`
2. `from qiskit.compiler import disassemble_circuits`
```
>>> qiskit.__qiskit_version__
{'qiskit': '0.10.0', 'qiskit-terra': '0.8.0', 'qiskit-ignis': '0.1.1', 'qiskit-aer': '0.2.0', 'qiskit-ibmq-provider': '0.2.1', 'qiskit-aqua': '0.5.0'}
```
### What is the expected behavior?
If a function is deprecated, and the warning suggests to use a new function, this function should exist in the current release.
### Suggested solutions
Implement the function or change the deprecation warning.
| Sorry there seems to be a mistake in the deprecation message. For now please use
```from qiskit.assembler import disassemble```
@mtreinish I think `disassemble` should be added under the `qiskit.compile` namespace.
yeah this is a bug we did not update the disassemble warning when we changed API. This was probably my fault.
It was when I originally added the function (and deprecation message) in #2137 but it looks like that was changed in #2244 right before the release without updating the deprecation message.
so yeah my fault :-( | 2019-05-08T14:09:44Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'disassemble_circuits' from 'qiskit.compiler' (/Users/matteo/Work/projects/ibmq/env/lib/python3.7/site-packages/qiskit/compiler/__init__.py)
| 1,148 |
|||
Qiskit/qiskit | Qiskit__qiskit-2573 | 3aa97b11f3104113d0ae4e754da8f7e75d07a917 | diff --git a/qiskit/dagcircuit/dagcircuit.py b/qiskit/dagcircuit/dagcircuit.py
--- a/qiskit/dagcircuit/dagcircuit.py
+++ b/qiskit/dagcircuit/dagcircuit.py
@@ -1142,7 +1142,8 @@ def collect_runs(self, namelist):
s = list(self._multi_graph.successors(node))
while len(s) == 1 and \
s[0].type == "op" and \
- s[0].name in namelist:
+ s[0].name in namelist and \
+ s[0].condition is None:
group.append(s[0])
nodes_seen[s[0]] = True
s = list(self._multi_graph.successors(s[0]))
| internal error from optimize_1q_gates from conditional cy gate
```
>>> qc = qk.QuantumCircuit(2,2)
>>> qc.cy(0,1).c_if(qc.cregs[0], 0)
<qiskit.circuit.instructionset.InstructionSet object at 0x12c540160>
>>> qk.transpile(qc, backend=FakeTenerife())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 147, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_configs)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 100, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 168, in _transpile_circuit
return transpile_circuit(circuit, transpile_config)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpile_circuit.py", line 62, in transpile_circuit
return pass_manager.run(circuit)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 147, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 180, in _do_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 53, in run
raise TranspilerError("internal error")
qiskit.transpiler.exceptions.TranspilerError: 'internal error'
```
| It looks like this is potentially caused by an issue in `dagcircuit.collect_runs()` It's supposed to return a list of non-conditional runs of op nodes with the given names. But in the cy().c_if() example above a conditional is being returned which is triggering the if for a condition here: https://github.com/Qiskit/qiskit-terra/blob/master/qiskit/transpiler/passes/optimize_1q_gates.py#L50 | 2019-06-04T18:14:51Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 147, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_configs)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 100, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 168, in _transpile_circuit
return transpile_circuit(circuit, transpile_config)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpile_circuit.py", line 62, in transpile_circuit
return pass_manager.run(circuit)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 147, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 180, in _do_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/optimize_1q_gates.py", line 53, in run
raise TranspilerError("internal error")
qiskit.transpiler.exceptions.TranspilerError: 'internal error'
| 1,194 |
|||
Qiskit/qiskit | Qiskit__qiskit-2661 | 2151cb92497836577d9610d28d78dade1b566f24 | diff --git a/qiskit/version.py b/qiskit/version.py
--- a/qiskit/version.py
+++ b/qiskit/version.py
@@ -108,7 +108,10 @@ def _get_qiskit_versions():
pass
cmd = [sys.executable, '-m', 'pip', 'freeze']
- reqs = _minimal_ext_cmd(cmd)
+ try:
+ reqs = _minimal_ext_cmd(cmd)
+ except Exception:
+ return out_dict
reqs_dict = {}
for req in reqs.split():
req_parts = req.decode().split('==')
| Initializing __qiskit_version__ raises OSError
### Information
- **Qiskit Terra version**: 0.9.0, master branch
- **Python version**: 3.6.6
- **Operating system**: Debian GNU/Linux 9 (stretch)
### What is the current behavior?
Since Terra 0.9.0 the way to find out the used Qiskit-packages has changed. In version.py, the method `_get_qiskit_versions()` is called, which calls `_minimal_ext_cmd` to get the pip freeze output to parse for the used qiskit package versions. The call to this function raises an OSError because subprocess.Popen has returncode 1.
Traceback (most recent call last):
File "./docs/example_qiskit_entangle.py", line 20, in <module>
from qiskit.validation.base import Obj
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/__init__.py", line 47, in <module>
from qiskit.providers.basicaer import BasicAer
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/providers/__init__.py", line 19, in <module>
from .basebackend import BaseBackend
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/providers/basebackend.py", line 23, in <module>
from qiskit.version import VERSION as __version__
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 117, in <module>
__qiskit_version__ = _get_qiskit_versions()
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 86, in _get_qiskit_versions
reqs = _minimal_ext_cmd(cmd)
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 41, in _minimal_ext_cmd
raise OSError
### Steps to reproduce the problem
This happens on our Debian Jenkins environment, where by default the environment variable HOME is set to root directory. Further the Jenkins user is not running under root. We suspect that the pip freeze command uses the HOME directory somehow and fails because it has no rights on the root directory.
What we did in our investigation to make it work on our environment, we set 'HOME' to a writeable directory for the Jenkins user and had to add environment variable 'HOME' to the env parameter of subprocess.Popen (in function _minimal_ext_cmd),
| I'm not sure what's going on with `pip --freeze` here. I tried `HOME=/ pip --freeze` from within my terra venv (on mac) and it seemed to work okay.
It's a bug that we `raise OSError` here. We should include at least stderr and the return code.
I agree this is a bug, it's my mistake I actually realized this yesterday after #2652 merged that we don't have any error handling. Nothing should raise an exception from the version module because it gets executed at import time, we should catch it and make it non-fatal. Having the pip versions in qiskit_version is not critical so if pip fails for whatever reason we should just ignore it and move on. I'll push a patch up to fix this shortly. | 2019-06-20T14:53:18Z | [] | [] |
Traceback (most recent call last):
File "./docs/example_qiskit_entangle.py", line 20, in <module>
from qiskit.validation.base import Obj
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/__init__.py", line 47, in <module>
from qiskit.providers.basicaer import BasicAer
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/providers/__init__.py", line 19, in <module>
from .basebackend import BaseBackend
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/providers/basebackend.py", line 23, in <module>
from qiskit.version import VERSION as __version__
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 117, in <module>
__qiskit_version__ = _get_qiskit_versions()
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 86, in _get_qiskit_versions
reqs = _minimal_ext_cmd(cmd)
File "/var/jenkins_home/workspace/SDK_dev/venv/lib/python3.6/site-packages/qiskit/version.py", line 41, in _minimal_ext_cmd
raise OSError
### Steps to reproduce the problem
This happens on our Debian Jenkins environment, where by default the environment variable HOME is set to root directory. Further the Jenkins user is not running under root. We suspect that the pip freeze command uses the HOME directory somehow and fails because it has no rights on the root directory.
| 1,206 |
|||
Qiskit/qiskit | Qiskit__qiskit-2783 | d9f36863258dd94d2d84c87f2e8518980a4a9df5 | diff --git a/qiskit/dagcircuit/dagcircuit.py b/qiskit/dagcircuit/dagcircuit.py
--- a/qiskit/dagcircuit/dagcircuit.py
+++ b/qiskit/dagcircuit/dagcircuit.py
@@ -256,7 +256,7 @@ def apply_operation_back(self, op, qargs=None, cargs=None, condition=None):
cargs = cargs or []
all_cbits = self._bits_in_condition(condition)
- all_cbits.extend(cargs)
+ all_cbits = set(all_cbits).union(cargs)
self._check_condition(op.name, condition)
self._check_bits(qargs, self.output_map)
@@ -799,6 +799,20 @@ def substitute_node_with_dag(self, node, input_dag, wires=None):
pred_map, succ_map = self._make_pred_succ_maps(node)
full_pred_map, full_succ_map = self._full_pred_succ_maps(pred_map, succ_map,
input_dag, wire_map)
+
+ if condition_bit_list:
+ # If we are replacing a conditional node, map input dag through
+ # wire_map to verify that it will not modify any of the conditioning
+ # bits.
+ condition_bits = set(condition_bit_list)
+
+ for op_node in input_dag.op_nodes():
+ mapped_cargs = {wire_map[carg] for carg in op_node.cargs}
+
+ if condition_bits & mapped_cargs:
+ raise DAGCircuitError('Mapped DAG would alter clbits '
+ 'on which it would be conditioned.')
+
# Now that we know the connections, delete node
self._multi_graph.remove_node(node)
diff --git a/qiskit/extensions/simulator/snapshot.py b/qiskit/extensions/simulator/snapshot.py
--- a/qiskit/extensions/simulator/snapshot.py
+++ b/qiskit/extensions/simulator/snapshot.py
@@ -20,7 +20,7 @@
from qiskit import QuantumCircuit
from qiskit.circuit.quantumregister import QuantumRegister
from qiskit.circuit import Instruction
-from qiskit.extensions.exceptions import ExtensionError
+from qiskit.extensions.exceptions import QiskitError, ExtensionError
class Snapshot(Instruction):
@@ -89,6 +89,9 @@ def label(self, name):
else:
raise TypeError('label expects a string')
+ def c_if(self, classical, val):
+ raise QiskitError('Snapshots are simulator directives and cannot be conditional.')
+
def snapshot(self,
label,
diff --git a/qiskit/extensions/standard/barrier.py b/qiskit/extensions/standard/barrier.py
--- a/qiskit/extensions/standard/barrier.py
+++ b/qiskit/extensions/standard/barrier.py
@@ -18,6 +18,7 @@
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.quantumregister import QuantumRegister
from qiskit.circuit import Instruction
+from qiskit.exceptions import QiskitError
class Barrier(Instruction):
@@ -34,6 +35,9 @@ def inverse(self):
def broadcast_arguments(self, qargs, cargs):
yield [qarg for sublist in qargs for qarg in sublist], []
+ def c_if(self, classical, val):
+ raise QiskitError('Barriers are compiler directives and cannot be conditional.')
+
def barrier(self, *qargs):
"""Apply barrier to circuit.
| Measures conditioned on register containing the target bit generate an invalid DAG
When attempting to condition a `measure` on the register containing the target bit:
```
>>> qc = qk.QuantumCircuit(1,1)
>>> qc.measure(0,0).c_if(qc.cregs[0],0)
>>> qc.depth()
1
>>> qk.converters.circuit_to_dag(qc).depth()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 574, in depth
raise DAGCircuitError("not a DAG")
qiskit.dagcircuit.exceptions.DAGCircuitError: 'not a DAG'
```
The drawer also has difficulty with them:
```
>>> qc = qk.QuantumCircuit(1,1)
>>> qc.measure(0,0).c_if(qc.cregs[0],0)
>>> print(qc)
q_0: |0>
c_0: 0
>>> qc = qk.QuantumCircuit(2,1)
>>> qc.h([0,1])
>>> qc.measure(0,0)
>>> qc.measure(1,0).c_if(qc.cregs[0], 0)
>>> print(qc)
┌───┐┌─┐
q_0: |0>┤ H ├┤M├
├───┤└╥┘
q_1: |0>┤ H ├─╫─
└───┘ ║
c_0: 0 ══════╩═
```
Conditioning a `measure` on a separate register seems to work okay
```
>>> qr = qk.QuantumRegister(2)
>>> cr1 = qk.ClassicalRegister(1)
>>> cr2 = qk.ClassicalRegister(1)
>>> qc = qk.QuantumCircuit(qr, cr1, cr2)
>>> qc.h(qr)
>>> qc.measure(qr[0], cr1[0])
>>> qc.measure(qr[1], cr2[0]).c_if(cr1, 0)
>>> qc.depth()
3
>>> qk.converters.circuit_to_dag(qc).depth()
3
>>> print(qc)
┌───┐┌─┐
q3_0: |0>┤ H ├┤M├───────
├───┤└╥┘ ┌─┐
q3_1: |0>┤ H ├─╫───┤M├──
└───┘ ║ ┌─┴┴┴─┐
c2_0: 0 ══════╩═╡ = 0 ╞
└──║──┘
c3_0: 0 ═══════════╩═══
```
| TIL that conditional measurements are a thing.
This is actually probably not a bug, save for in the random testing. There is no causality defined in this situation, so you get the cyclic graph.
I don't see the lack of causality here. `qc.measure(1,0).c_if(qc.cregs[0], 0)` to me decomposes as "Check the value of creg0; if 0, trigger a measure of qubit 1 into clbit 0" which should be well defined. The presence or absence of the measure is dependent on the value of `clbit 0` is at the start of the operation, not what it will be at the end.
That said, I don't know of an algorithm or use case that requires a conditional measure, I wouldn't be opposed to not supporting them (this is the first time I came across them as well) but then we should raise early when building them, rather than throwing `not a DAG` down the road. We also allow conditional barriers, by the way, which seems more obviously a bug.
Actually I agree with you. I was wrong, there is an order. | 2019-07-12T20:40:07Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 574, in depth
raise DAGCircuitError("not a DAG")
qiskit.dagcircuit.exceptions.DAGCircuitError: 'not a DAG'
| 1,229 |
|||
Qiskit/qiskit | Qiskit__qiskit-2931 | 5303420daa8be87865593b1b5c8a943ae910b82d | diff --git a/qiskit/transpiler/passes/consolidate_blocks.py b/qiskit/transpiler/passes/consolidate_blocks.py
--- a/qiskit/transpiler/passes/consolidate_blocks.py
+++ b/qiskit/transpiler/passes/consolidate_blocks.py
@@ -19,7 +19,7 @@
The blocks are collected by a previous pass, such as Collect2qBlocks.
"""
-from qiskit.circuit import QuantumRegister, QuantumCircuit, Qubit
+from qiskit.circuit import QuantumRegister, QuantumCircuit
from qiskit.dagcircuit import DAGCircuit
from qiskit.quantum_info.operators import Operator
from qiskit.quantum_info.synthesis import TwoQubitBasisDecomposer
@@ -57,12 +57,7 @@ def run(self, dag):
new_dag.add_creg(creg)
# compute ordered indices for the global circuit wires
- global_index_map = {}
- for wire in dag.wires:
- if not isinstance(wire, Qubit):
- continue
- global_qregs = list(dag.qregs.values())
- global_index_map[wire] = global_qregs.index(wire.register) + wire.index
+ global_index_map = {wire: idx for idx, wire in enumerate(dag.qubits())}
blocks = self.property_set['block_list']
# just to make checking if a node is in any block easier
| ConsolidateBlocks raises for CX between two registers
From https://travis-ci.com/Qiskit/qiskit-terra/jobs/216588160#L6863:
```
>>> qr1 = qk.QuantumRegister(1)
>>> qr2 = qk.QuantumRegister(2)
>>> qc = qk.QuantumCircuit(qr2, qr1, cr)
>>> qc.cx(qr1[0], qr2[1])
>>> qc.measure(qr1[0], cr[0])
>>> qk.transpile(qc, optimization_level=3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 187, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_configs)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 100, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 208, in _transpile_circuit
return transpile_circuit(circuit, transpile_config)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpile_circuit.py", line 65, in transpile_circuit
return pass_manager.run(circuit)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 171, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 202, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 215, in _run_this_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/consolidate_blocks.py", line 93, in run
subcirc.append(nd.op, [q[block_index_map[i]] for i in nd.qargs])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 359, in append
instructions.add(self._append(instruction, qarg, carg), qarg, carg)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 382, in _append
self._check_dups(qargs)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 438, in _check_dups
raise QiskitError("duplicate qubit arguments")
qiskit.exceptions.QiskitError: 'duplicate qubit arguments'
```
| .. and this one if you don't mind @maddy-tod :) | 2019-08-07T11:33:54Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 187, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_configs)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 100, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 208, in _transpile_circuit
return transpile_circuit(circuit, transpile_config)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/transpile_circuit.py", line 65, in transpile_circuit
return pass_manager.run(circuit)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 171, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 202, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 215, in _run_this_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/consolidate_blocks.py", line 93, in run
subcirc.append(nd.op, [q[block_index_map[i]] for i in nd.qargs])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 359, in append
instructions.add(self._append(instruction, qarg, carg), qarg, carg)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 382, in _append
self._check_dups(qargs)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 438, in _check_dups
raise QiskitError("duplicate qubit arguments")
qiskit.exceptions.QiskitError: 'duplicate qubit arguments'
| 1,259 |
|||
Qiskit/qiskit | Qiskit__qiskit-2947 | 85ec21f6db77a316c661f47a8906e7cadf1b09f9 | diff --git a/qiskit/circuit/parameter.py b/qiskit/circuit/parameter.py
--- a/qiskit/circuit/parameter.py
+++ b/qiskit/circuit/parameter.py
@@ -15,6 +15,8 @@
Parameter Class for variable parameters.
"""
+from uuid import uuid4
+
import sympy
from .parameterexpression import ParameterExpression
@@ -22,6 +24,27 @@
class Parameter(ParameterExpression):
"""Parameter Class for variable parameters"""
+
+ def __new__(cls, _, uuid=None):
+ # Parameter relies on self._uuid being set prior to other attributes
+ # (e.g. symbol_map) which may depend on self._uuid for Parameter's hash
+ # or __eq__ functions.
+
+ obj = object.__new__(cls)
+
+ if uuid is None:
+ obj._uuid = uuid4()
+ else:
+ obj._uuid = uuid
+
+ return obj
+
+ def __getnewargs__(self):
+ # Unpickling won't in general call __init__ but will always call
+ # __new__. Specify arguments to be passed to __new__ when unpickling.
+
+ return (self.name, self._uuid)
+
def __init__(self, name):
self._name = name
@@ -48,3 +71,9 @@ def __deepcopy__(self, memo=None):
def __repr__(self):
return '{}({})'.format(self.__class__.__name__, self.name)
+
+ def __eq__(self, other):
+ return isinstance(other, Parameter) and self._uuid == other._uuid
+
+ def __hash__(self):
+ return hash(self._uuid)
| assemble.py _expand_parameters(circuits, run_config) apparently broken
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master
- **Python version**: 3.6.8
- **Operating system**: Linux
### What is the current behavior?
`Parameter` binding does not succeed as reported by user `@Adrian Auer` in Qiskit Slack.
### Steps to reproduce the problem
```
from qiskit import Aer, QuantumCircuit, QuantumRegister, execute
from qiskit.circuit import Parameter
# create m = 2 circuits
qr = QuantumRegister(1)
quantum_circuit_1 = QuantumCircuit(qr)
quantum_circuit_2 = QuantumCircuit(qr)
theta = Parameter('theta')
# add parametrized gates
quantum_circuit_1.u3(theta, 0, 0, qr[0])
quantum_circuit_2.u3(theta, 3.14, 0, qr[0])
circuits = [quantum_circuit_1, quantum_circuit_2]
# inspect parameters property
for circuit in circuits:
print(circuit.parameters)
# bind parameter to n = 1 values
job = execute(circuits,
Aer.get_backend('qasm_simulator'),
shots=512,
parameter_binds=[{theta: 1}])
```
Result is error:
```
Traceback (most recent call last):
File "adrian_auer_example.py", line 25, in <module>
parameter_binds=[{theta: 1}])
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/execute.py", line 218, in execute
run_config=run_config
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/compiler/assemble.py", line 149, in assemble
run_config=run_config)
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/compiler/assemble.py", line 298, in _expand_parameters
'Circuit parameters: {}').format(all_bind_parameters, all_circuit_parameters))
qiskit.exceptions.QiskitError: 'Mismatch between run_config.parameter_binds and all circuit parameters. Parameter binds: [dict_keys([Parameter(theta)])] Circuit parameters: [{Parameter(theta)}, {Parameter(theta)}]'
```
### What is the expected behavior?
Parameter would bind and circuits would execute.
### Suggested solutions
In `qiskit/compiler/assembly.py:_expand_parameters` lines 293-294 both of the following tests are failing:
```
or any(unique_parameters != bind_params for bind_params in all_bind_parameters) \
or any(unique_parameters != parameters for parameters in all_circuit_parameters):
```
It appears to be because `unique_parameters` is a `list` of `Parameter` each of which is being compared to the elements of a list of dictionaries.
The comparison should be re-examined so that types match up.
| This bug is similar to #2429 . Right now, `Parameter`s depend on python identity for equality, but when they are serialized and sent to another process (here by `parallel_map` inside `transpile` inside `execute`), and return, they are instantiated with a new identity and so no longer treated as equal.
The comparison in lines 293-294 of `assembly.py` attempts to check that there is only one set of parameters (`unique_parameters`), which is fully used by every circuit and fully bound by every set of bindings.
Just prior to the comparison, `unique_parameters` should hold the single instance of `theta` used to build the circuits, e.g. `{Parameter(theta)}`, but:
```
(Pdb) p unique_parameters
{Parameter(theta), Parameter(theta), Parameter(theta)}
(Pdb) p [id(p) for p in unique_parameters]
[5126415024, 5126414408, 5126158936]
```
As a possible workaround, each circuit could be transpiled one-by-one, and then assembled as a group and executed. Alternately, `phi` could be made a parameter to the `u3` gate, and then bound along with `theta`. | 2019-08-08T20:19:27Z | [] | [] |
Traceback (most recent call last):
File "adrian_auer_example.py", line 25, in <module>
parameter_binds=[{theta: 1}])
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/execute.py", line 218, in execute
run_config=run_config
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/compiler/assemble.py", line 149, in assemble
run_config=run_config)
File "/home/jax/work/QISKit/DEV/qiskit-terra/qiskit/compiler/assemble.py", line 298, in _expand_parameters
'Circuit parameters: {}').format(all_bind_parameters, all_circuit_parameters))
qiskit.exceptions.QiskitError: 'Mismatch between run_config.parameter_binds and all circuit parameters. Parameter binds: [dict_keys([Parameter(theta)])] Circuit parameters: [{Parameter(theta)}, {Parameter(theta)}]'
| 1,263 |
|||
Qiskit/qiskit | Qiskit__qiskit-3051 | b0a4d01143133438bd2d123f23b5ac48289ebedf | diff --git a/qiskit/visualization/bloch.py b/qiskit/visualization/bloch.py
--- a/qiskit/visualization/bloch.py
+++ b/qiskit/visualization/bloch.py
@@ -53,6 +53,7 @@
import os
import numpy as np
+from matplotlib import get_backend
import matplotlib.pyplot as plt # pylint: disable=import-error
from matplotlib.patches import FancyArrowPatch # pylint: disable=import-error
from mpl_toolkits.mplot3d import (Axes3D, proj3d) # pylint: disable=import-error
@@ -626,7 +627,9 @@ def save(self, name=None, output='png', dirc=None):
self.fig.savefig(name)
self.savenum += 1
if self.fig:
- plt.close(self.fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(self.fig)
def _hide_tick_lines_and_labels(axis):
diff --git a/qiskit/visualization/counts_visualization.py b/qiskit/visualization/counts_visualization.py
--- a/qiskit/visualization/counts_visualization.py
+++ b/qiskit/visualization/counts_visualization.py
@@ -25,6 +25,7 @@
from .exceptions import VisualizationError
if HAS_MATPLOTLIB:
+ from matplotlib import get_backend
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
@@ -187,5 +188,7 @@ def plot_histogram(data, figsize=(7, 5), color=None, number_to_keep=None,
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.0), ncol=1,
borderaxespad=0, frameon=True, fontsize=12)
if fig:
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
diff --git a/qiskit/visualization/gate_map.py b/qiskit/visualization/gate_map.py
--- a/qiskit/visualization/gate_map.py
+++ b/qiskit/visualization/gate_map.py
@@ -23,6 +23,7 @@
if HAS_MATPLOTLIB:
import matplotlib
+ from matplotlib import get_backend
import matplotlib.pyplot as plt # pylint: disable=import-error
import matplotlib.patches as mpatches
import matplotlib.cm as cm
@@ -232,7 +233,9 @@ def plot_gate_map(backend, figsize=None,
ax.set_xlim([-1, x_max+1])
ax.set_ylim([-(y_max+1), 1])
if not input_axes:
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
return None
@@ -448,6 +451,7 @@ def plot_error_map(backend, figsize=(12, 9), show_title=True):
if show_title:
fig.suptitle('{name} Error Map'.format(name=backend.name()),
fontsize=24, y=0.9)
-
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
diff --git a/qiskit/visualization/matplotlib.py b/qiskit/visualization/matplotlib.py
--- a/qiskit/visualization/matplotlib.py
+++ b/qiskit/visualization/matplotlib.py
@@ -26,6 +26,7 @@
import numpy as np
try:
+ from matplotlib import get_backend
from matplotlib import patches
from matplotlib import pyplot as plt
HAS_MATPLOTLIB = True
@@ -484,7 +485,9 @@ def draw(self, filename=None, verbose=False):
if filename:
self.figure.savefig(filename, dpi=self._style.dpi,
bbox_inches='tight')
- plt.close(self.figure)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(self.figure)
return self.figure
def _draw_regs(self):
diff --git a/qiskit/visualization/pulse_visualization.py b/qiskit/visualization/pulse_visualization.py
--- a/qiskit/visualization/pulse_visualization.py
+++ b/qiskit/visualization/pulse_visualization.py
@@ -22,6 +22,9 @@
from qiskit.visualization.exceptions import VisualizationError
from qiskit.visualization.pulse import matplotlib as _matplotlib
+if _matplotlib.HAS_MATPLOTLIB:
+ from matplotlib import get_backend
+
def pulse_drawer(data, dt=1, style=None, filename=None,
interp_method=None, scaling=None, channels_to_plot=None,
@@ -50,7 +53,10 @@ def pulse_drawer(data, dt=1, style=None, filename=None,
matplotlib.figure: A matplotlib figure object for the pulse envelope
Raises:
VisualizationError: when invalid data is given or lack of information
+ ImportError: when matplotlib is not installed
"""
+ if not _matplotlib.HAS_MATPLOTLIB:
+ raise ImportError('Must have Matplotlib installed.')
if isinstance(data, SamplePulse):
drawer = _matplotlib.SamplePulseDrawer(style=style)
image = drawer.draw(data, dt=dt, interp_method=interp_method, scaling=scaling)
@@ -66,7 +72,9 @@ def pulse_drawer(data, dt=1, style=None, filename=None,
if filename:
image.savefig(filename, dpi=drawer.style.dpi, bbox_inches='tight')
- _matplotlib.plt.close(image)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ _matplotlib.plt.close(image)
if image and interactive:
image.show()
return image
diff --git a/qiskit/visualization/state_visualization.py b/qiskit/visualization/state_visualization.py
--- a/qiskit/visualization/state_visualization.py
+++ b/qiskit/visualization/state_visualization.py
@@ -26,6 +26,7 @@
from .matplotlib import HAS_MATPLOTLIB
if HAS_MATPLOTLIB:
+ from matplotlib import get_backend
from matplotlib.ticker import MaxNLocator
from matplotlib import pyplot as plt
from matplotlib.patches import FancyArrowPatch
@@ -127,7 +128,9 @@ def plot_state_hinton(rho, title='', figsize=None):
if title:
fig.suptitle(title, fontsize=16)
plt.tight_layout()
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
@@ -158,7 +161,9 @@ def plot_bloch_vector(bloch, title="", ax=None, figsize=None):
if ax is None:
fig = B.fig
fig.set_size_inches(figsize[0], figsize[1])
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
return None
@@ -198,7 +203,9 @@ def plot_bloch_multivector(rho, title='', figsize=None):
plot_bloch_vector(bloch_state, "qubit " + str(i), ax=ax,
figsize=figsize)
fig.suptitle(title, fontsize=16)
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
@@ -347,7 +354,9 @@ def plot_state_city(rho, title="", figsize=None, color=None,
tick.label.set_fontsize(14)
plt.suptitle(title, fontsize=16)
plt.tight_layout()
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
@@ -396,7 +405,9 @@ def plot_state_paulivec(rho, title="", figsize=None, color=None):
for tick in ax.xaxis.get_major_ticks()+ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
ax.set_title(title, fontsize=16)
- plt.close(fig)
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
@@ -626,8 +637,9 @@ def plot_state_qsphere(rho, figsize=None):
verticalalignment='center', fontsize=14)
fig.tight_layout()
- plt.close(fig)
-
+ if get_backend() in ['module://ipykernel.pylab.backend_inline',
+ 'nbAgg']:
+ plt.close(fig)
return fig
| circuit.draw() interactive failed when used in python Shell
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.8.2
- **Python version**: 3.7.3
- **Operating system**: Windows 10
### What is the current behavior?
Having created an simple circuit. Trying to draw it with circuit.draw(output = 'mpl', interactive = True). The command produces some error as follows:
>>> circuit.draw(output = 'mpl', interactive = True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\pc\.conda\envs\qcircuit\lib\site-packages\qiskit\circuit\quantumcircuit.py", line 487, in draw
justify=justify)
File "C:\Users\pc\.conda\envs\qcircuit\lib\site-packages\qiskit\visualization\circuit_visualization.py", line 218, in circuit_drawer
image.show()
File "C:\Users\pc\AppData\Roaming\Python\Python37\site-packages\matplotlib\figure.py", line 450, in show
manager.show()
File "C:\Users\pc\AppData\Roaming\Python\Python37\site-packages\matplotlib\backends\_backend_tk.py", line 546, in show
self.canvas._tkcanvas.bind("<Destroy>", destroy)
File "C:\Users\pc\.conda\envs\qcircuit\lib\tkinter\__init__.py", line 1251, in bind
return self._bind(('bind', self._w), sequence, func, add)
File "C:\Users\pc\.conda\envs\qcircuit\lib\tkinter\__init__.py", line 1206, in _bind
self.tk.call(what + (sequence, cmd))
_tkinter.TclError: can't invoke "bind" command: application has been destroyed
### Steps to reproduce the problem
Open anaconda prompt. Activate the environment created specifically for qiskit. Open python shell and type the following commands:
>>> import qiskit
>>> from qiskit import QuantumCircuit
>>> circuit = QuantumCircuit(2,2)
>>> circuit.h(0)
>>> circuit.draw(output = 'mpl', interactive = True)
The error will show up.
### What is the expected behavior?
I expect some interactive panel popping up like when I use plt.show() to show some matplotlib figures.
If this is not the correct way to get a mpl form of a circuit figure showing please tell me which reference should I be looking into.
| Hi @skxsky I agree this is unexpected behaviour. This happens because we close the figure before returning it, to prevent it from rendering twice in Jupyter notebooks. If you run this code from a Jupyter notebook it should work, or you can save the image using
`circuit.draw(output = 'mpl', filename="my_circuit.png")`. I will try to have a look at getting the behaviour more inline with what is expected.
@maddy-tod I see. So rendering an interactive image in python shell is just not a feature. Apart from simply printing the circuit, are there any other ways to show a clearer image in python shell? If I were to use scripts then saving the image would work fine for me.
@skxsky no, printing the circuit or saving the image are the only options at the moment. I will try to get the interactive element fixed ASAP!
@maddy-tod Thanks, that solves my puzzles. | 2019-08-28T12:02:28Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\pc\.conda\envs\qcircuit\lib\site-packages\qiskit\circuit\quantumcircuit.py", line 487, in draw
justify=justify)
File "C:\Users\pc\.conda\envs\qcircuit\lib\site-packages\qiskit\visualization\circuit_visualization.py", line 218, in circuit_drawer
image.show()
File "C:\Users\pc\AppData\Roaming\Python\Python37\site-packages\matplotlib\figure.py", line 450, in show
manager.show()
File "C:\Users\pc\AppData\Roaming\Python\Python37\site-packages\matplotlib\backends\_backend_tk.py", line 546, in show
self.canvas._tkcanvas.bind("<Destroy>", destroy)
File "C:\Users\pc\.conda\envs\qcircuit\lib\tkinter\__init__.py", line 1251, in bind
return self._bind(('bind', self._w), sequence, func, add)
File "C:\Users\pc\.conda\envs\qcircuit\lib\tkinter\__init__.py", line 1206, in _bind
self.tk.call(what + (sequence, cmd))
_tkinter.TclError: can't invoke "bind" command: application has been destroyed
| 1,278 |
|||
Qiskit/qiskit | Qiskit__qiskit-3079 | 54ea1b9ad78a2ccf3595284410c4da72cb941ef0 | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -355,24 +355,8 @@ def append(self, instruction, qargs=None, cargs=None):
expanded_cargs = [self.cbit_argument_conversion(carg) for carg in cargs or []]
instructions = InstructionSet()
-
- # When broadcasting was handled by decorators (prior to #2282), append
- # received multiple distinct instruction instances, one for each expanded
- # arg. With broadcasting as part of QuantumCircuit.append, the
- # instruction instance is constructed before append is called. However,
- # (at least) ParameterTable expects instruction instances to be unique
- # within a circuit, so make instruction deepcopies for expanded_args[1:].
-
- first_instruction = True
for (qarg, carg) in instruction.broadcast_arguments(expanded_qargs, expanded_cargs):
- if first_instruction:
- instructions.add(
- self._append(instruction, qarg, carg), qarg, carg)
- first_instruction = False
- else:
- instructions.add(
- self._append(deepcopy(instruction), qarg, carg), qarg, carg)
-
+ instructions.add(self._append(instruction, qarg, carg), qarg, carg)
return instructions
def _append(self, instruction, qargs, cargs):
@@ -410,7 +394,9 @@ def _append(self, instruction, qargs, cargs):
for parameter in param.parameters:
if parameter in current_parameters:
- self._parameter_table[parameter].append((instruction, param_index))
+ if not self._check_dup_param_spec(self._parameter_table[parameter],
+ instruction, param_index):
+ self._parameter_table[parameter].append((instruction, param_index))
else:
if parameter.name in {p.name for p in current_parameters}:
raise QiskitError(
@@ -419,6 +405,12 @@ def _append(self, instruction, qargs, cargs):
return instruction
+ def _check_dup_param_spec(self, parameter_spec_list, instruction, param_index):
+ for spec in parameter_spec_list:
+ if spec[0] is instruction and spec[1] == param_index:
+ return True
+ return False
+
def add_register(self, *regs):
"""Add registers."""
if not regs:
| ParameterTable expects Instructions to be used only once within a circuit
See the bug reported in #3008 . The implementation of `ParameterTable` and the associated binding machinery operate under the assumption that a given `Instruction` instance will appear in only one gate in a circuit, but this isn't guaranteed.
e.g. a user could write:
```
>>> import qiskit as qk
>>> p = qk.circuit.Parameter('p')
>>> qc = qk.QuantumCircuit(2)
>>> rz = qk.extensions.standard.RZGate(p)
>>> qc.append(rz, [0], [])
>>> qc.append(rz, [1], [])
>>> print(qc)
┌───────┐
q_0: |0>┤ Rz(p) ├
├───────┤
q_1: |0>┤ Rz(p) ├
└───────┘
>>> qc.bind_parameters({p: 3})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 875, in bind_parameters
new_circuit._bind_parameter(parameter, value)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 897, in _bind_parameter
instr.params[param_index] = instr.params[param_index].bind({parameter: value})
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/parameterexpression.py", line 68, in bind
self._raise_if_passed_unknown_parameters(parameter_values.keys())
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/parameterexpression.py", line 136, in _raise_if_passed_unknown_parameters
[str(p) for p in unknown_parameters]))
qiskit.exceptions.QiskitError: "Cannot bind Parameters (['p']) not present in expression."
```
Users of `Instruction.repeat` would see the same problem.
#3013 worked around this by forcing deepcopies of instructions when broadcasting, but this doesn't resolve the general problem.
| 2019-09-06T21:32:06Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 875, in bind_parameters
new_circuit._bind_parameter(parameter, value)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 897, in _bind_parameter
instr.params[param_index] = instr.params[param_index].bind({parameter: value})
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/parameterexpression.py", line 68, in bind
self._raise_if_passed_unknown_parameters(parameter_values.keys())
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/parameterexpression.py", line 136, in _raise_if_passed_unknown_parameters
[str(p) for p in unknown_parameters]))
qiskit.exceptions.QiskitError: "Cannot bind Parameters (['p']) not present in expression."
| 1,283 |
||||
Qiskit/qiskit | Qiskit__qiskit-3675 | 21a3424368afc75afe3f695f72654320cbb16795 | diff --git a/qiskit/converters/ast_to_dag.py b/qiskit/converters/ast_to_dag.py
--- a/qiskit/converters/ast_to_dag.py
+++ b/qiskit/converters/ast_to_dag.py
@@ -48,8 +48,11 @@
from qiskit.extensions.standard.rz import RZGate
from qiskit.extensions.standard.cu1 import Cu1Gate
from qiskit.extensions.standard.ch import CHGate
+from qiskit.extensions.standard.crx import CrxGate
+from qiskit.extensions.standard.cry import CryGate
from qiskit.extensions.standard.crz import CrzGate
from qiskit.extensions.standard.cu3 import Cu3Gate
+from qiskit.extensions.standard.rxx import RXXGate
from qiskit.extensions.standard.rzz import RZZGate
@@ -106,6 +109,7 @@ class AstInterpreter:
"sdg": SdgGate,
"swap": SwapGate,
"rx": RXGate,
+ "rxx": RXXGate,
"ry": RYGate,
"rz": RZGate,
"rzz": RZZGate,
@@ -115,6 +119,8 @@ class AstInterpreter:
"cy": CyGate,
"cz": CzGate,
"ch": CHGate,
+ "crx": CrxGate,
+ "cry": CryGate,
"crz": CrzGate,
"cu1": Cu1Gate,
"cu3": Cu3Gate,
| Reading QASM strings with Ion-Trap Gates broken
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.11.0
- **Python version**: 3.7.5
- **Operating system**: Windows
### What is the current behavior?
Cannot construct a quantum Circuit object for non-superconducting gates, even though ion trap gates (RXX, MS, etc) are somewhat supported.
### Steps to reproduce the problem
```python
>>> from qiskit import QuantumCircuit
>>> qc = QuantumCircuit(3)
>>> qc.rxx(3.14, 0, 2)
<qiskit.circuit.instructionset.InstructionSet object at 0x0000018552A839C8>
>>> qc.from_qasm_str(qc.qasm())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "[...]\site-packages\qiskit\circuit\quantumcircuit.py", line 1174, in from_qasm_str
return _circuit_from_qasm(qasm)
File "[...]\site-packages\qiskit\circuit\quantumcircuit.py", line 1241, in _circuit_from_qasm
ast = qasm.parse()
File "[...]\site-packages\qiskit\qasm\qasm.py", line 69, in parse
return qasm_p.parse(self._data)
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 1089, in parse
self.parser.parse(data, lexer=self.lexer, debug=self.parse_deb)
File "[...]\site-packages\ply\yacc.py", line 333, in parse
return self.parseopt_notrack(input, lexer, debug, tracking, tokenfunc)
File "[...]\site-packages\ply\yacc.py", line 1120, in parseopt_notrack
p.callable(pslice)
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 660, in p_unitary_op_4
self.verify_as_gate(program[1], program[5], arglist=program[3])
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 133, in verify_as_gate
+ "', line", str(obj.line), 'file', obj.file)
qiskit.qasm.exceptions.QasmError: "Cannot find gate definition for 'rxx', line 4 file "
>>> QuantumCircuit.from_qasm_str(qc.qasm())
[SAME ERROR MESSAGE]
```
### What is the expected behavior?
Qiskit should be able to generate a ``QuantumCircuit`` object from a QASM string which includes extension gates.
### Suggested solutions
It appears that the QASM parser needs to be aware of the non-IBM/superconducting gates. Confirm? So a new "iontrap.inc" file (or similar) needs to be created and placed in a path that the QASM parser can recognize? Is there any documentation on writing a new include file?
| Can I have a go at it?
I am new to this, so please correct me if I am wrong at any point. I believe we would need to add the rxx gate (and any other gates defined in qiskit/extensions for full support) in qelib1.inc | 2020-01-03T14:19:05Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "[...]\site-packages\qiskit\circuit\quantumcircuit.py", line 1174, in from_qasm_str
return _circuit_from_qasm(qasm)
File "[...]\site-packages\qiskit\circuit\quantumcircuit.py", line 1241, in _circuit_from_qasm
ast = qasm.parse()
File "[...]\site-packages\qiskit\qasm\qasm.py", line 69, in parse
return qasm_p.parse(self._data)
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 1089, in parse
self.parser.parse(data, lexer=self.lexer, debug=self.parse_deb)
File "[...]\site-packages\ply\yacc.py", line 333, in parse
return self.parseopt_notrack(input, lexer, debug, tracking, tokenfunc)
File "[...]\site-packages\ply\yacc.py", line 1120, in parseopt_notrack
p.callable(pslice)
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 660, in p_unitary_op_4
self.verify_as_gate(program[1], program[5], arglist=program[3])
File "[...]\site-packages\qiskit\qasm\qasmparser.py", line 133, in verify_as_gate
+ "', line", str(obj.line), 'file', obj.file)
qiskit.qasm.exceptions.QasmError: "Cannot find gate definition for 'rxx', line 4 file "
| 1,368 |
|||
Qiskit/qiskit | Qiskit__qiskit-3869 | c59783a5739dd7f2d25ead7549bb95c642d69e9a | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -124,6 +124,13 @@ class QuantumCircuit:
extension_lib = "include \"qelib1.inc\";"
def __init__(self, *regs, name=None):
+ if any([not isinstance(reg, (QuantumRegister, ClassicalRegister)) for reg in regs]):
+ try:
+ regs = tuple(int(reg) for reg in regs)
+ except Exception:
+ raise CircuitError("Circuit args must be Registers or be castable to an int" +
+ "(%s '%s' was provided)"
+ % ([type(reg).__name__ for reg in regs], regs))
if name is None:
name = self.cls_prefix() + str(self.cls_instances())
if sys.platform != "win32" and not is_main_process():
| QuantumCircuit constructor fails if n_qubits is np.int64
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues to confirm this idea does not exist. -->
### What is the expected enhancement?
```
Python 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> import qiskit as qk
>>> n = np.int64(12)
>>> qr = qk.QuantumRegister(n)
>>> qc = qk.QuantumCircuit(n)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 157, in __init__
self.add_register(*regs)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 541, in add_register
if register.name in [reg.name for reg in self.qregs + self.cregs]:
AttributeError: 'numpy.int64' object has no attribute 'name'
```
Similar to as was added for `Register`s in #2288, the `QuantumCircuit` constructor should test if a provided argument can be cast to an int before it raises an error.
For reference, here is where `Register` does the check:
https://github.com/Qiskit/qiskit-terra/blob/2ee7a3a/qiskit/circuit/register.py#L39
and where `QuantumCircuit` makes new `Registers` from the provided `int`:
https://github.com/Qiskit/qiskit-terra/blob/703c9a3/qiskit/circuit/quantumcircuit.py#L522
| @1ucian0 have you made progress on this? I'd like to give it a go if not (I'm new to qiskit and open source and this looks like a nice first issue to try out!)
Sure! Go ahead!
Yay great I'll get cracking! Is there a corresponding test file that needs updating as well?
Yes, tests should be added (maybe in the files we already have?). You can use the code from OP for that. | 2020-02-20T23:12:04Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 157, in __init__
self.add_register(*regs)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 541, in add_register
if register.name in [reg.name for reg in self.qregs + self.cregs]:
AttributeError: 'numpy.int64' object has no attribute 'name'
| 1,398 |
|||
Qiskit/qiskit | Qiskit__qiskit-4366 | 77b38a925a48f6976b17611a8b7ca3c77b4c827c | diff --git a/qiskit/circuit/library/standard_gates/__init__.py b/qiskit/circuit/library/standard_gates/__init__.py
--- a/qiskit/circuit/library/standard_gates/__init__.py
+++ b/qiskit/circuit/library/standard_gates/__init__.py
@@ -84,5 +84,17 @@
from .y import YGate, CYGate
from .z import ZGate, CZGate
-from .boolean_logical_gates import logical_and, logical_or
from .multi_control_rotation_gates import mcrx, mcry, mcrz
+
+# deprecated gates
+from .boolean_logical_gates import logical_and, logical_or
+from .u1 import Cu1Gate
+from .u3 import Cu3Gate
+from .x import CnotGate, ToffoliGate
+from .swap import FredkinGate
+from .i import IdGate
+from .rx import CrxGate
+from .ry import CryGate
+from .rz import CrzGate
+from .y import CyGate
+from .z import CzGate
| Old classes are not accessible via qiskit.extensions
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.14.0
- **Python version**: All
- **Operating system**: Any
### What is the current behavior?
Running
```
from qiskit.extensions import Cu3Gate
```
Raises an `ImportError`
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'Cu3Gate' from 'qiskit.extensions' (/home/mtreinish/git/qiskit/qiskit/.tox/lint/lib/python3.8/site-packages/qiskit/extensions/__init__.py)
```
as do other classes which were deprecated as part of the 0.13.0 release cleanup. It worked fine on qiskit-terra 0.13.0 (also without a deprecation warning, which I thought it would raise). This is a big breakage and needs to be fixed in a quick 0.14.1 release.
### Steps to reproduce the problem
```
from qiskit.extensions import Cu3Gate
```
### What is the expected behavior?
This works, this is likely fallout from rushing through #4035
### Suggested solutions
Fix this
| 2020-04-30T21:54:14Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'Cu3Gate' from 'qiskit.extensions' (/home/mtreinish/git/qiskit/qiskit/.tox/lint/lib/python3.8/site-packages/qiskit/extensions/__init__.py)
| 1,465 |
||||
Qiskit/qiskit | Qiskit__qiskit-447 | 06482631beceba0a3571e925dcdb1a23b97ecdbf | diff --git a/qiskit/mapper/_compiling.py b/qiskit/mapper/_compiling.py
--- a/qiskit/mapper/_compiling.py
+++ b/qiskit/mapper/_compiling.py
@@ -22,7 +22,7 @@
import math
import numpy as np
-from scipy.linalg import expm
+import scipy.linalg as la
from ._mappererror import MapperError
@@ -40,7 +40,7 @@ def euler_angles_1q(unitary_matrix):
small = 1e-10
if unitary_matrix.shape != (2, 2):
raise MapperError("compiling.euler_angles_1q expected 2x2 matrix")
- phase = np.linalg.det(unitary_matrix)**(-1.0/2.0)
+ phase = la.det(unitary_matrix)**(-1.0/2.0)
U = phase * unitary_matrix # U in SU(2)
# OpenQASM SU(2) parameterization:
# U[0, 0] = exp(-i(phi+lambda)/2) * cos(theta/2)
@@ -78,7 +78,7 @@ def euler_angles_1q(unitary_matrix):
Rzlambda = np.array([[np.exp(-1j*lamb/2.0), 0],
[0, np.exp(1j*lamb/2.0)]], dtype=complex)
V = np.dot(Rzphi, np.dot(Rytheta, Rzlambda))
- if np.linalg.norm(V - U) > small:
+ if la.norm(V - U) > small:
raise MapperError("compiling.euler_angles_1q incorrect result")
return theta, phi, lamb, "U(%.15f,%.15f,%.15f)" % (theta, phi, lamb)
@@ -159,14 +159,14 @@ def two_qubit_kak(unitary_matrix):
"""
if unitary_matrix.shape != (4, 4):
raise MapperError("compiling.two_qubit_kak expected 4x4 matrix")
- phase = np.linalg.det(unitary_matrix)**(-1.0/4.0)
+ phase = la.det(unitary_matrix)**(-1.0/4.0)
# Make it in SU(4), correct phase at the end
U = phase * unitary_matrix
# B changes to the Bell basis
- B = (1.0/math.sqrt(2)) * np.array([[1, 1j, 0, 0],
- [0, 0, 1j, 1],
- [0, 0, 1j, -1],
- [1, -1j, 0, 0]], dtype=complex)
+ B = (1.0/np.sqrt(2)) * np.array([[1, 1j, 0, 0],
+ [0, 0, 1j, 1],
+ [0, 0, 1j, -1],
+ [1, -1j, 0, 0]], dtype=complex)
# U' = Bdag . U . B
Uprime = np.dot(np.transpose(B.conjugate()), np.dot(U, B))
# M^2 = trans(U') . U'
@@ -174,9 +174,9 @@ def two_qubit_kak(unitary_matrix):
# Diagonalize M2
# Must use diagonalization routine which finds a real orthogonal matrix P
# when M2 is real.
- D, P = np.linalg.eig(M2)
+ D, P = la.eig(M2)
# If det(P) == -1, apply a swap to make P in SO(4)
- if abs(np.linalg.det(P)+1) < 1e-5:
+ if abs(la.det(P)+1) < 1e-5:
swap = np.array([[1, 0, 0, 0],
[0, 0, 1, 0],
[0, 1, 0, 0],
@@ -185,15 +185,15 @@ def two_qubit_kak(unitary_matrix):
D = np.diag(np.dot(swap, np.dot(np.diag(D), swap)))
Q = np.diag(np.sqrt(D)) # array from elementwise sqrt
# Want to take square root so that Q has determinant 1
- if abs(np.linalg.det(Q)+1) < 1e-5:
+ if abs(la.det(Q)+1) < 1e-5:
Q[0, 0] = -Q[0, 0]
- Kprime = np.dot(Uprime, np.dot(P, np.dot(np.linalg.inv(Q),
+ Kprime = np.dot(Uprime, np.dot(P, np.dot(la.inv(Q),
np.transpose(P))))
K1 = np.dot(B, np.dot(Kprime, np.dot(P, np.transpose(B.conjugate()))))
A = np.dot(B, np.dot(Q, np.transpose(B.conjugate())))
K2 = np.dot(B, np.dot(np.transpose(P), np.transpose(B.conjugate())))
KAK = np.dot(K1, np.dot(A, K2))
- if np.linalg.norm(KAK - U, 2) > 1e-6:
+ if la.norm(KAK - U, 2) > 1e-6:
raise MapperError("compiling.two_qubit_kak: " +
"unknown error in KAK decomposition")
# Compute parameters alpha, beta, gamma so that
@@ -210,9 +210,9 @@ def two_qubit_kak(unitary_matrix):
# K1 = kron(U1, U2) and K2 = kron(V1, V2)
# Find the matrices U1, U2, V1, V2
L = K1[0:2, 0:2]
- if np.linalg.norm(L) < 1e-9:
+ if la.norm(L) < 1e-9:
L = K1[0:2, 2:4]
- if np.linalg.norm(L) < 1e-9:
+ if la.norm(L) < 1e-9:
L = K1[2:4, 2:4]
Q = np.dot(L, np.transpose(L.conjugate()))
U2 = L / np.sqrt(Q[0, 0])
@@ -223,9 +223,9 @@ def two_qubit_kak(unitary_matrix):
U1[1, 0] = R[2, 0]
U1[1, 1] = R[2, 2]
L = K2[0:2, 0:2]
- if np.linalg.norm(L) < 1e-9:
+ if la.norm(L) < 1e-9:
L = K2[0:2, 2:4]
- if np.linalg.norm(L) < 1e-9:
+ if la.norm(L) < 1e-9:
L = K2[2:4, 2:4]
Q = np.dot(L, np.transpose(L.conjugate()))
V2 = L / np.sqrt(Q[0, 0])
@@ -235,12 +235,12 @@ def two_qubit_kak(unitary_matrix):
V1[0, 1] = R[0, 2]
V1[1, 0] = R[2, 0]
V1[1, 1] = R[2, 2]
- if np.linalg.norm(np.kron(U1, U2) - K1) > 1e-4 or \
- np.linalg.norm(np.kron(V1, V2) - K2) > 1e-4:
+ if la.norm(np.kron(U1, U2) - K1) > 1e-4 or \
+ la.norm(np.kron(V1, V2) - K2) > 1e-4:
raise MapperError("compiling.two_qubit_kak: " +
"error in SU(2) x SU(2) part")
- test = expm(1j*(alpha * xx + beta * yy + gamma * zz))
- if np.linalg.norm(A - test) > 1e-4:
+ test = la.expm(1j*(alpha * xx + beta * yy + gamma * zz))
+ if la.norm(A - test) > 1e-4:
raise MapperError("compiling.two_qubit_kak: " +
"error in A part")
# Circuit that implements K1 * A * K2 (up to phase), using
@@ -286,7 +286,7 @@ def two_qubit_kak(unitary_matrix):
V = np.dot(g6, V)
V = np.dot(g7, V)
- if np.linalg.norm(V - U*phase.conjugate()) > 1e-6:
+ if la.norm(V - U*phase.conjugate()) > 1e-6:
raise MapperError("compiling.two_qubit_kak: " +
"sequence incorrect, unknown error")
@@ -387,11 +387,11 @@ def two_qubit_kak(unitary_matrix):
V = np.dot(np.kron(np.identity(2),
rz_array(gate["params"][1])), V)
# Put V in SU(4) and test up to global phase
- V = np.linalg.det(V)**(-1.0/4.0) * V
- if np.linalg.norm(V - U) > 1e-6 and \
- np.linalg.norm(1j*V - U) > 1e-6 and \
- np.linalg.norm(-1*V - U) > 1e-6 and \
- np.linalg.norm(-1j*V - U) > 1e-6:
+ V = la.det(V)**(-1.0/4.0) * V
+ if la.norm(V - U) > 1e-6 and \
+ la.norm(1j*V - U) > 1e-6 and \
+ la.norm(-1*V - U) > 1e-6 and \
+ la.norm(-1j*V - U) > 1e-6:
raise MapperError("compiling.two_qubit_kak: " +
"sequence incorrect, unknown error")
| two-qubit-kak error when computing phase
<!--- Provide a general summary of the issue in the Title above -->
When I run the following program, I encountered an error:
```
#setup
from qiskit import QuantumProgram
import Qconfig
qp = QuantumProgram()
qp.set_api(Qconfig.APItoken, Qconfig.config['url'])
from qiskit.mapper import two_qubit_kak
import numpy as np
perm = np.array([[0.,0.,0.,1.], [1.,0.,0.,0.], [0.,1.,0.,0.], [0.,0.,1.,0.] ])
permCircuit = two_qubit_kak(perm)
print(perm)
print(permCircuit)
```
## Expected Behavior
<!--- If you're describing a bug, tell us what should happen -->
<!--- If you're suggesting a change/improvement, tell us how it should work -->
It should produce a circuit for the unitary matrix below:
```
[
[0,0,0,1],
[1,0,0,0],
[0,1,0,0],
[0,0,1,0]
]
```
## Current Behavior
<!--- If describing a bug, tell us what happens instead of the expected behavior -->
<!--- If suggesting a change/improvement, explain the difference from current behavior -->
It gives an error message:
```
/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/qiskit/mapper/_compiling.py:162: RuntimeWarning: invalid value encountered in double_scalars
phase = np.linalg.det(unitary_matrix)**(-1.0/4.0)
Traceback (most recent call last):
File "test_u.py", line 15, in <module>
permCircuit = two_qubit_kak(perm)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/qiskit/mapper/_compiling.py", line 177, in two_qubit_kak
D, P = np.linalg.eig(M2)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 1143, in eig
_assertFinite(a)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 216, in _assertFinite
raise LinAlgError("Array must not contain infs or NaNs")
numpy.linalg.linalg.LinAlgError: Array must not contain infs or NaNs
```
## Possible Solution
<!--- Not obligatory, but suggest a fix/reason for the bug, -->
<!--- or ideas how to implement the addition or change -->
## Steps to Reproduce (for bugs)
<!--- Provide a link to a live example, or an unambiguous set of steps to -->
<!--- reproduce this bug. Include code to reproduce, if relevant -->
1.
2.
3.
4.
## Context
<!--- How has this issue affected you? What are you trying to accomplish? -->
<!--- Providing context helps us come up with a solution that is most useful in the real world -->
## Your Environment
<!--- Include as many relevant details about the environment you experienced the bug in -->
* Version used:
* Environment name and version (e.g. Python 3.6.1):
* Operating System and version:
| Hi @rraymondhp the problem is that eig does not behave as needed for all inputs. Here is one way to fix the problem. If M2 is close to real, round it to a real type before calling eig. If M2 is symmetric, use eigh. If M2 is already diagonal, substitute D and P and skip the call to eig.
Actually it is a problem that occurs here:
https://github.com/QISKit/qiskit-sdk-py/blob/06482631beceba0a3571e925dcdb1a23b97ecdbf/qiskit/mapper/_compiling.py#L162
The NumPy `det` function is being called, and returns a `np.float64` type. The problem occurs when doing `np.float64(-1)**(-1.0/4.0)`, that leads to a `nan` being returned.
In contrast, using the SciPy routine `scipy.linalg.det` returns a generic `float` type and the same computation succeeds. This is because the NumPy data types do not support negative fractional powers for negative numbers unless the data type is `complex`. The solution is to either use the SciPy routine, or cast the return value from `np.linalg.det` into `complex`.
I can submit a Pull. | 2018-05-04T13:41:35Z | [] | [] |
Traceback (most recent call last):
File "test_u.py", line 15, in <module>
permCircuit = two_qubit_kak(perm)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/qiskit/mapper/_compiling.py", line 177, in two_qubit_kak
D, P = np.linalg.eig(M2)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 1143, in eig
_assertFinite(a)
File "/Users/rraymondhp/miniconda3/envs/QISKitenv/lib/python3.6/site-packages/numpy/linalg/linalg.py", line 216, in _assertFinite
raise LinAlgError("Array must not contain infs or NaNs")
numpy.linalg.linalg.LinAlgError: Array must not contain infs or NaNs
| 1,481 |
|||
Qiskit/qiskit | Qiskit__qiskit-4584 | 93a51c815ffa1f9ee3e894ec4e576f5c75128d74 | diff --git a/qiskit/circuit/add_control.py b/qiskit/circuit/add_control.py
--- a/qiskit/circuit/add_control.py
+++ b/qiskit/circuit/add_control.py
@@ -102,12 +102,6 @@ def control(operation: Union[Gate, ControlledGate],
# pylint: disable=unused-import
import qiskit.circuit.library.standard_gates.multi_control_rotation_gates
- # check args
- if num_ctrl_qubits == 0:
- return operation
- elif num_ctrl_qubits < 0:
- raise CircuitError('number of control qubits must be positive integer')
-
q_control = QuantumRegister(num_ctrl_qubits, name='control')
q_target = QuantumRegister(operation.num_qubits, name='target')
q_ancillae = None # TODO: add
diff --git a/qiskit/circuit/controlledgate.py b/qiskit/circuit/controlledgate.py
--- a/qiskit/circuit/controlledgate.py
+++ b/qiskit/circuit/controlledgate.py
@@ -85,10 +85,8 @@ def __init__(self, name: str, num_qubits: int, params: List,
qc2.draw()
"""
super().__init__(name, num_qubits, params, label=label)
- if num_ctrl_qubits < num_qubits:
- self.num_ctrl_qubits = num_ctrl_qubits
- else:
- raise CircuitError('number of control qubits must be less than the number of qubits')
+ self._num_ctrl_qubits = 1
+ self.num_ctrl_qubits = num_ctrl_qubits
self.base_gate = None
if definition:
self.definition = definition
@@ -132,6 +130,31 @@ def definition(self, excited_def: List):
"""Set controlled gate definition with closed controls."""
super(Gate, self.__class__).definition.fset(self, excited_def)
+ @property
+ def num_ctrl_qubits(self):
+ """Get number of control qubits.
+
+ Returns:
+ int: The number of control qubits for the gate.
+ """
+ return self._num_ctrl_qubits
+
+ @num_ctrl_qubits.setter
+ def num_ctrl_qubits(self, num_ctrl_qubits):
+ """Set the number of control qubits.
+
+ Args:
+ num_ctrl_qubits (int): The number of control qubits in [1, num_qubits-1].
+
+ Raises:
+ CircuitError: num_ctrl_qubits is not an integer in [1, num_qubits - 1].
+ """
+ if (num_ctrl_qubits == int(num_ctrl_qubits) and
+ 1 <= num_ctrl_qubits < self.num_qubits):
+ self._num_ctrl_qubits = num_ctrl_qubits
+ else:
+ raise CircuitError('The number of control qubits must be in [1, num_qubits-1]')
+
@property
def ctrl_state(self) -> int:
"""Return the control state of the gate as a decimal integer."""
diff --git a/qiskit/circuit/library/standard_gates/x.py b/qiskit/circuit/library/standard_gates/x.py
--- a/qiskit/circuit/library/standard_gates/x.py
+++ b/qiskit/circuit/library/standard_gates/x.py
@@ -739,7 +739,7 @@ class MCXGate(ControlledGate):
def __new__(cls, num_ctrl_qubits=None, label=None, ctrl_state=None):
"""Create a new MCX instance.
- Depending on the number of controls, this creates an explicit X, CX, CCX, C3X or C4X
+ Depending on the number of controls, this creates an explicit CX, CCX, C3X or C4X
instance or a generic MCX gate.
"""
# these gates will always be implemented for all modes of the MCX if the number of control
@@ -748,8 +748,6 @@ def __new__(cls, num_ctrl_qubits=None, label=None, ctrl_state=None):
1: CXGate,
2: CCXGate
}
- if num_ctrl_qubits == 0:
- return XGate(label=label)
if num_ctrl_qubits in explicit.keys():
gate_class = explicit[num_ctrl_qubits]
gate = gate_class.__new__(gate_class, label=label, ctrl_state=ctrl_state)
| When `num_ctrl_qubits=0`, creating controlled gates will produce an `AttributeError`
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.14.1
- **Python version**: 3.8
- **Operating system**: both Windows and Linux
### What is the current behavior?
When `num_ctrl_qubits=0`, creating controlled gates will produce an `AttributeError`.
### Steps to reproduce the problem
```
>>> from qiskit.circuit.library import ZGate
>>> ZGate().control(0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/library/standard_gates/z.py", line 100, in control
return super().control(num_ctrl_qubits=num_ctrl_qubits, label=label, ctrl_state=ctrl_state)
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/gate.py", line 132, in control
return add_control(self, num_ctrl_qubits, label, ctrl_state)
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/add_control.py", line 70, in add_control
cgate.base_gate.label = operation.label
AttributeError: 'ZGate' object has no attribute 'base_gate'
```
### What is the expected behavior?
Create a gate without control bits successfully.
### Suggested solutions
This bug happens because the `control` function in`qiskit/circuit/add_control.py` [directly returns `operation` (the first argument) when `num_ctrl_qubits=0`](https://github.com/Qiskit/qiskit-terra/blob/4f804108bba528aa95e46838235754778e0cb68c/qiskit/circuit/add_control.py#L106), but the `add_control` function [expects that the return value from the `control` function has the `base_gate` attribute](https://github.com/Qiskit/qiskit-terra/blob/4f804108bba528aa95e46838235754778e0cb68c/qiskit/circuit/add_control.py#L70).
Either the `control` function should always return a `Gate` object with the `base_gate` attribute, or the `add_control` function should not assume that the `base_gate` attribute always exists.
| Another option would be to raise an exception if num_ctrl_qubits=0. Then whenever `control` is called a `ControlledGate` is always returned. Letting num_ctrl_qubits=0 doesn't seem necessary to support since one could just use the original gate.
Indeed a `ControlledGate` with 0 control bits does not seem to be necessary to be supported. However having such support would be a little bit more user-friendly. I discovered this bug when I try to implement "multiply amplitude by -1 when all `n` qubits are `|1>`" for arbitrary `n` with the following code:
```python
circuit.append(ZGate().control(n - 1), range(n))
```
The code fails when `n=1`, so I have to deal with this case separately. | 2020-06-16T09:57:15Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/library/standard_gates/z.py", line 100, in control
return super().control(num_ctrl_qubits=num_ctrl_qubits, label=label, ctrl_state=ctrl_state)
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/gate.py", line 132, in control
return add_control(self, num_ctrl_qubits, label, ctrl_state)
File "/usr/local/lib/python3.8/dist-packages/qiskit/circuit/add_control.py", line 70, in add_control
cgate.base_gate.label = operation.label
AttributeError: 'ZGate' object has no attribute 'base_gate'
| 1,505 |
|||
Qiskit/qiskit | Qiskit__qiskit-4596 | 9a5d8577c10c58e28cd9d139c6a0aa0faf8bd868 | diff --git a/qiskit/transpiler/passes/basis/unroll_3q_or_more.py b/qiskit/transpiler/passes/basis/unroll_3q_or_more.py
--- a/qiskit/transpiler/passes/basis/unroll_3q_or_more.py
+++ b/qiskit/transpiler/passes/basis/unroll_3q_or_more.py
@@ -36,6 +36,9 @@ def run(self, dag):
# TODO: allow choosing other possible decompositions
rule = node.op.definition
if not rule:
+ if rule == []: # empty node
+ dag.remove_op_node(node)
+ continue
raise QiskitError("Cannot unroll all 3q or more gates. "
"No rule to expand instruction %s." %
node.op.name)
| Cannot unroll identity matrix of more than 2 qubits when coupling_map is set
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.14.1
- **Python version**: 3.8
- **Operating system**: both Windows and Linux
### What is the current behavior?
The `transpile` function fails to unroll an `UnitaryGate` containing an identity matrix of more than 2 qubits when the `backend` argument is set to be a remote quantum computer or the `coupling_map` argument is set.
### Steps to reproduce the problem
```
>>> import numpy as np
>>> from qiskit import IBMQ, QuantumCircuit, transpile
>>> from qiskit.extensions import UnitaryGate
>>> provider = IBMQ.load_account()
>>> backend = provider.get_backend('ibmq_london') # arbitrary backend with at least 3 qubits
>>> circuit = QuantumCircuit(3)
>>> gate = UnitaryGate(np.eye(2 ** 3))
>>> circuit.append(gate, range(3))
<qiskit.circuit.instructionset.InstructionSet object at 0x7ff8b93a60d0>
>>> transpile(circuit, backend=backend)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 210, in transpile circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/usr/local/lib/python3.8/dist-packages/qiskit/tools/parallel.py", line 105, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 306, in _transpile_circuit
return pass_manager.run(circuit, callback=transpile_config['callback'],
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 214, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 277, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 115, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 145, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 157, in _run_this_pass
new_dag = pass_.run(dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 39, in run
raise QiskitError("Cannot unroll all 3q or more gates. "
qiskit.exceptions.QiskitError: 'Cannot unroll all 3q or more gates. No rule to expand instruction circuit9_dg.'
```
Notes:
- This bug only happens when the `backend` argument is set to be a remote quantum computer or the `coupling_map` argument is set to be a coupling map of a remote quantum computer. Calling `transpile(circuit, basis_gates=['u1', 'u2', 'u3', 'cx', 'id'])` works fine.
- This bug only happens when the `UnitaryGate` contains an identity matrix of more than 2 qubits.
### What is the expected behavior?
Successfully transpile the circuit.
| 2020-06-19T19:50:53Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 210, in transpile circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/usr/local/lib/python3.8/dist-packages/qiskit/tools/parallel.py", line 105, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 306, in _transpile_circuit
return pass_manager.run(circuit, callback=transpile_config['callback'],
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 214, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 277, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 115, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 145, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 157, in _run_this_pass
new_dag = pass_.run(dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 39, in run
raise QiskitError("Cannot unroll all 3q or more gates. "
qiskit.exceptions.QiskitError: 'Cannot unroll all 3q or more gates. No rule to expand instruction circuit9_dg.'
| 1,508 |
||||
Qiskit/qiskit | Qiskit__qiskit-4597 | 81603cc65fc558c2f6b2535d29bd42d62bcc62ea | diff --git a/qiskit/visualization/latex.py b/qiskit/visualization/latex.py
--- a/qiskit/visualization/latex.py
+++ b/qiskit/visualization/latex.py
@@ -292,7 +292,7 @@ def _get_image_depth(self):
columns = 2
# add extra column if needed
- if self.cregbundle and self.ops[0][0].name == "measure":
+ if self.cregbundle and (self.ops[0][0].name == "measure" or self.ops[0][0].condition):
columns += 1
# all gates take up 1 column except from those with labels (ie cu1)
@@ -387,7 +387,7 @@ def _build_latex_array(self, aliases=None):
column = 1
# Leave a column to display number of classical registers if needed
- if self.cregbundle and self.ops[0][0].name == "measure":
+ if self.cregbundle and (self.ops[0][0].name == "measure" or self.ops[0][0].condition):
column += 1
for layer in self.ops:
num_cols_used = 1
@@ -423,8 +423,9 @@ def _build_latex_array(self, aliases=None):
temp.sort(key=int)
bottom = temp[len(pos_array) - 1]
gap = pos_cond - bottom
- for i in range(self.cregs[if_reg]):
- if if_value[i] == '1':
+ creg_rng = 1 if self.cregbundle else self.cregs[if_reg]
+ for i in range(creg_rng):
+ if (if_value[i] == '1' or (self.cregbundle and int(if_value) > 0)):
self._latex[pos_cond + i][column] = \
"\\control \\cw \\cwx[-" + str(gap) + "]"
gap = 1
@@ -551,8 +552,9 @@ def _build_latex_array(self, aliases=None):
self._latex[pos_1][column] = ("\\gate{%s}" % nm)
gap = pos_2 - pos_1
- for i in range(self.cregs[if_reg]):
- if if_value[i] == '1':
+ creg_rng = 1 if self.cregbundle else self.cregs[if_reg]
+ for i in range(creg_rng):
+ if (if_value[i] == '1' or (self.cregbundle and int(if_value) > 0)):
self._latex[pos_2 + i][column] = \
"\\control \\cw \\cwx[-" + str(gap) + "]"
gap = 1
@@ -623,8 +625,9 @@ def _build_latex_array(self, aliases=None):
bottom = temp[1]
gap = pos_3 - bottom
- for i in range(self.cregs[if_reg]):
- if if_value[i] == '1':
+ creg_rng = 1 if self.cregbundle else self.cregs[if_reg]
+ for i in range(creg_rng):
+ if (if_value[i] == '1' or (self.cregbundle and int(if_value) > 0)):
self._latex[pos_3 + i][column] = \
"\\control \\cw \\cwx[-" + str(gap) + "]"
gap = 1
@@ -831,8 +834,9 @@ def _build_latex_array(self, aliases=None):
bottom = temp[2]
gap = pos_4 - bottom
- for i in range(self.cregs[if_reg]):
- if if_value[i] == '1':
+ creg_rng = 1 if self.cregbundle else self.cregs[if_reg]
+ for i in range(creg_rng):
+ if (if_value[i] == '1' or (self.cregbundle and int(if_value) > 0)):
self._latex[pos_4 + i][column] = \
"\\control \\cw \\cwx[-" + str(gap) + "]"
gap = 1
| Latex drawer fails with conditional and cregbundle=True
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: Current master
- **Python version**: 3.8
- **Operating system**: Ubuntu 18.04
### What is the current behavior?
The latex drawer fails on an index out of range when there is a conditional on the last creg and cregbundle=True. This was discovered in test_teleport in test_visualization.py when circuit_drawer sets the default cregbundle to True.
### Steps to reproduce the problem
The following code works with cregbundle=False,
```
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister
qr = QuantumRegister(3, 'q')
cr = ClassicalRegister(3, 'c')
qc = QuantumCircuit(qr, cr)
qc.x(qr[2]).c_if(cr, 2)
c = qc.draw(output='latex_source', cregbundle=True)
```
and fails with this if cregbundle is True.
```
Traceback (most recent call last):
File "test_latex_creg.py", line 6, in <module>
c = qc.draw(output='latex_source', cregbundle=True)
File "/home/ed/qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 994, in draw
return circuit_drawer(self, scale=scale,
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/circuit_visualization.py", line 306, in circuit_drawer
return _generate_latex_source(circuit,
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/circuit_visualization.py", line 605, in _generate_latex_source
latex = qcimg.latex()
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/latex.py", line 149, in latex
self._build_latex_array(aliases)
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/latex.py", line 561, in _build_latex_array
self._latex[pos_2 + i][column] = \
IndexError: list index out of range
```
### What is the expected behavior?
### Suggested solutions
| 2020-06-22T06:33:09Z | [] | [] |
Traceback (most recent call last):
File "test_latex_creg.py", line 6, in <module>
c = qc.draw(output='latex_source', cregbundle=True)
File "/home/ed/qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 994, in draw
return circuit_drawer(self, scale=scale,
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/circuit_visualization.py", line 306, in circuit_drawer
return _generate_latex_source(circuit,
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/circuit_visualization.py", line 605, in _generate_latex_source
latex = qcimg.latex()
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/latex.py", line 149, in latex
self._build_latex_array(aliases)
File "/home/ed/qiskit/qiskit-terra/qiskit/visualization/latex.py", line 561, in _build_latex_array
self._latex[pos_2 + i][column] = \
IndexError: list index out of range
| 1,509 |
||||
Qiskit/qiskit | Qiskit__qiskit-4747 | f10e312f09342d152865b26c122eb3ee24c89b2a | diff --git a/qiskit/dagcircuit/dagcircuit.py b/qiskit/dagcircuit/dagcircuit.py
--- a/qiskit/dagcircuit/dagcircuit.py
+++ b/qiskit/dagcircuit/dagcircuit.py
@@ -938,10 +938,16 @@ def node_eq(node_self, node_other):
return rx.is_isomorphic_node_match(self._multi_graph, other._multi_graph, node_eq)
- def topological_nodes(self):
+ def topological_nodes(self, key=None):
"""
Yield nodes in topological order.
+ Args:
+ key (Callable): A callable which will take a DAGNode object and
+ return a string sort key. If not specified the
+ :attr:`~qiskit.dagcircuit.DAGNode.sort_key` attribute will be
+ used as the sort key for each node.
+
Returns:
generator(DAGOpNode, DAGInNode, or DAGOutNode): node in topological order
"""
@@ -949,16 +955,27 @@ def topological_nodes(self):
def _key(x):
return x.sort_key
- return iter(rx.lexicographical_topological_sort(self._multi_graph, key=_key))
+ if key is None:
+ key = _key
+
+ return iter(rx.lexicographical_topological_sort(self._multi_graph, key=key))
- def topological_op_nodes(self):
+ def topological_op_nodes(self, key=None):
"""
Yield op nodes in topological order.
+ Allowed to pass in specific key to break ties in top order
+
+ Args:
+ key (Callable): A callable which will take a DAGNode object and
+ return a string sort key. If not specified the
+ :attr:`~qiskit.dagcircuit.DAGNode.sort_key` attribute will be
+ used as the sort key for each node.
+
Returns:
generator(DAGOpNode): op node in topological order
"""
- return (nd for nd in self.topological_nodes() if isinstance(nd, DAGOpNode))
+ return (nd for nd in self.topological_nodes(key) if isinstance(nd, DAGOpNode))
def substitute_node_with_dag(self, node, input_dag, wires=None):
"""Replace one node with dag.
diff --git a/qiskit/transpiler/passes/__init__.py b/qiskit/transpiler/passes/__init__.py
--- a/qiskit/transpiler/passes/__init__.py
+++ b/qiskit/transpiler/passes/__init__.py
@@ -174,6 +174,7 @@
from .optimization import Optimize1qGates
from .optimization import Optimize1qGatesDecomposition
from .optimization import Collect2qBlocks
+from .optimization import CollectMultiQBlocks
from .optimization import ConsolidateBlocks
from .optimization import CommutationAnalysis
from .optimization import CommutativeCancellation
diff --git a/qiskit/transpiler/passes/optimization/__init__.py b/qiskit/transpiler/passes/optimization/__init__.py
--- a/qiskit/transpiler/passes/optimization/__init__.py
+++ b/qiskit/transpiler/passes/optimization/__init__.py
@@ -15,6 +15,7 @@
from .optimize_1q_gates import Optimize1qGates
from .optimize_1q_decomposition import Optimize1qGatesDecomposition
from .collect_2q_blocks import Collect2qBlocks
+from .collect_multiqubit_blocks import CollectMultiQBlocks
from .consolidate_blocks import ConsolidateBlocks
from .commutation_analysis import CommutationAnalysis
from .commutative_cancellation import CommutativeCancellation
diff --git a/qiskit/transpiler/passes/optimization/collect_multiqubit_blocks.py b/qiskit/transpiler/passes/optimization/collect_multiqubit_blocks.py
new file mode 100644
--- /dev/null
+++ b/qiskit/transpiler/passes/optimization/collect_multiqubit_blocks.py
@@ -0,0 +1,226 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2017, 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Collect sequences of uninterrupted gates acting on a number of qubits."""
+
+from qiskit.transpiler.basepasses import AnalysisPass
+from qiskit.circuit import Gate
+from qiskit.dagcircuit import DAGOpNode, DAGInNode
+
+
+class CollectMultiQBlocks(AnalysisPass):
+ """Collect sequences of uninterrupted gates acting on groups of qubits.
+ max_block_size specifies the maximum number of qubits that can be acted upon
+ by any single group of gates
+
+ Traverse the DAG and find blocks of gates that act consecutively on
+ groups of qubits. Write the blocks to propert_set as a list of blocks
+ of the form:
+ [[g0, g1, g2], [g4, g5]]
+ Blocks are reported in a valid topological order. Further, the gates
+ within each block are also reported in topological order
+ Some gates may not be present in any block (e.g. if the number
+ of operands is greater than max_block_size)
+
+ A Disjont Set Union data structure (DSU) is used to maintain blocks as
+ gates are processed. This data structure points each qubit to a set at all
+ times and the sets correspond to current blocks. These change over time
+ and the data structure allows these changes to be done quickly.
+ """
+
+ def __init__(self, max_block_size=2):
+ super().__init__()
+ self.parent = {} # parent array for the union
+
+ # the dicts belowed are keyed by a qubit signifying the root of a
+ # set in the DSU data structure
+ self.bit_groups = {} # current groups of bits stored at top of trees
+ self.gate_groups = {} # current gate lists for the groups
+
+ self.max_block_size = max_block_size # maximum block size
+
+ def find_set(self, index):
+ """DSU function for finding root of set of items
+ If my parent is myself, I am the root. Otherwise we recursively
+ find the root for my parent. After that, we assign my parent to be
+ my root, saving recursion in the future.
+ """
+
+ if index not in self.parent:
+ self.parent[index] = index
+ self.bit_groups[index] = [index]
+ self.gate_groups[index] = []
+ if self.parent[index] == index:
+ return index
+ self.parent[index] = self.find_set(self.parent[index])
+ return self.parent[index]
+
+ def union_set(self, set1, set2):
+ """DSU function for unioning two sets together
+ Find the roots of each set. Then assign one to have the other
+ as its parent, thus liking the sets.
+ Merges smaller set into larger set in order to have better runtime
+ """
+
+ set1 = self.find_set(set1)
+ set2 = self.find_set(set2)
+ if set1 == set2:
+ return
+ if len(self.gate_groups[set1]) < len(self.gate_groups[set2]):
+ set1, set2 = set2, set1
+ self.parent[set2] = set1
+ self.gate_groups[set1].extend(self.gate_groups[set2])
+ self.bit_groups[set1].extend(self.bit_groups[set2])
+ self.gate_groups[set2].clear()
+ self.bit_groups[set2].clear()
+
+ def run(self, dag):
+ """Run the CollectMultiQBlocks pass on `dag`.
+
+ The blocks contain "op" nodes in topological sort order
+ such that all gates in a block act on the same set of
+ qubits and are adjacent in the circuit.
+
+ The blocks are built by examining predecessors and successors of
+ "cx" gates in the circuit. u1, u2, u3, cx, id gates will be included.
+
+ After the execution, ``property_set['block_list']`` is set to
+ a list of tuples of ``DAGNode`` objects
+ """
+
+ self.parent = {} # reset all variables on run
+ self.bit_groups = {}
+ self.gate_groups = {}
+
+ block_list = []
+
+ def collect_key(x):
+ """special key function for topological ordering.
+ Heuristic for this is to push all gates involving measurement
+ or barriers, etc. as far back as possible (because they force
+ blocks to end). After that, we process gates in order of lowest
+ number of qubits acted on to largest number of qubits acted on
+ because these have less chance of increasing the size of blocks
+ The key also processes all the non operation notes first so that
+ input nodes do not mess with the top sort of op nodes
+ """
+ if isinstance(x, DAGInNode):
+ return "a"
+ if not isinstance(x, DAGOpNode):
+ return "d"
+ if isinstance(x.op, Gate):
+ if x.op.is_parameterized() or x.op.condition is not None:
+ return "c"
+ return "b" + chr(ord("a") + len(x.qargs))
+ return "d"
+
+ op_nodes = dag.topological_op_nodes(key=collect_key)
+ qubit_indices = {bit: index for index, bit in enumerate(dag.qubits)}
+
+ for nd in op_nodes:
+ can_process = True
+ makes_too_big = False
+
+ # check if the node is a gate and if it is parameterized
+ if (
+ nd.op.condition is not None
+ or nd.op.is_parameterized()
+ or not isinstance(nd.op, Gate)
+ ):
+ can_process = False
+
+ cur_qubits = {qubit_indices[bit] for bit in nd.qargs}
+
+ if can_process:
+ # if the gate is valid, check if grouping up the bits
+ # in the gate would fit within our desired max size
+ c_tops = set()
+ for bit in cur_qubits:
+ c_tops.add(self.find_set(bit))
+ tot_size = 0
+ for group in c_tops:
+ tot_size += len(self.bit_groups[group])
+ if tot_size > self.max_block_size:
+ makes_too_big = True
+
+ if not can_process:
+ # resolve the case where we cannot process this node
+ for bit in cur_qubits:
+ # create a gate out of me
+ bit = self.find_set(bit)
+ if len(self.gate_groups[bit]) == 0:
+ continue
+ block_list.append(self.gate_groups[bit][:])
+ cur_set = set(self.bit_groups[bit])
+ for v in cur_set:
+ # reset this bit
+ self.parent[v] = v
+ self.bit_groups[v] = [v]
+ self.gate_groups[v] = []
+
+ if makes_too_big:
+ # adding in all of the new qubits would make the group too big
+ # we must block off sub portions of the groups until the new
+ # group would no longer be too big
+ savings = {}
+ tot_size = 0
+ for bit in cur_qubits:
+ top = self.find_set(bit)
+ if top in savings.keys():
+ savings[top] = savings[top] - 1
+ else:
+ savings[top] = len(self.bit_groups[top]) - 1
+ tot_size += len(self.bit_groups[top])
+ slist = []
+ for item, value in savings.items():
+ slist.append((value, item))
+ slist.sort(reverse=True)
+ savings_need = tot_size - self.max_block_size
+ for item in slist:
+ # remove groups until the size created would be acceptable
+ # start with blocking out the group that would decrease
+ # the new size the most. This heuristic for which blocks we
+ # create does not necessarily give the optimal blocking. Other
+ # heuristics may be worth considering
+ if savings_need > 0:
+ savings_need = savings_need - item[0]
+ if len(self.gate_groups[item[1]]) >= 1:
+ block_list.append(self.gate_groups[item[1]][:])
+ cur_set = set(self.bit_groups[item[1]])
+ for v in cur_set:
+ self.parent[v] = v
+ self.bit_groups[v] = [v]
+ self.gate_groups[v] = []
+
+ if can_process:
+ # if the operation is a gate, either skip it if it is too large
+ # or group up all of the qubits involved in the gate
+ if len(cur_qubits) > self.max_block_size:
+ # gates acting on more qubits than max_block_size cannot
+ # be a part of any block and thus we skip them here.
+ # we have already finalized the blocks involving the gate's
+ # qubits in the above makes_too_big block
+ continue # unable to be part of a group
+ prev = -1
+ for bit in cur_qubits:
+ if prev != -1:
+ self.union_set(prev, bit)
+ prev = bit
+ self.gate_groups[self.find_set(prev)].append(nd)
+ # need to turn all groups that still exist into their own blocks
+ for index in self.parent:
+ if self.parent[index] == index and len(self.gate_groups[index]) != 0:
+ block_list.append(self.gate_groups[index][:])
+
+ self.property_set["block_list"] = block_list
+
+ return dag
| Cannot unroll identity matrix of more than 2 qubits when coupling_map is set
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.14.1
- **Python version**: 3.8
- **Operating system**: both Windows and Linux
### What is the current behavior?
The `transpile` function fails to unroll an `UnitaryGate` containing an identity matrix of more than 2 qubits when the `backend` argument is set to be a remote quantum computer or the `coupling_map` argument is set.
### Steps to reproduce the problem
```
>>> import numpy as np
>>> from qiskit import IBMQ, QuantumCircuit, transpile
>>> from qiskit.extensions import UnitaryGate
>>> provider = IBMQ.load_account()
>>> backend = provider.get_backend('ibmq_london') # arbitrary backend with at least 3 qubits
>>> circuit = QuantumCircuit(3)
>>> gate = UnitaryGate(np.eye(2 ** 3))
>>> circuit.append(gate, range(3))
<qiskit.circuit.instructionset.InstructionSet object at 0x7ff8b93a60d0>
>>> transpile(circuit, backend=backend)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 210, in transpile circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/usr/local/lib/python3.8/dist-packages/qiskit/tools/parallel.py", line 105, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 306, in _transpile_circuit
return pass_manager.run(circuit, callback=transpile_config['callback'],
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 214, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 277, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 115, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 145, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 157, in _run_this_pass
new_dag = pass_.run(dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 39, in run
raise QiskitError("Cannot unroll all 3q or more gates. "
qiskit.exceptions.QiskitError: 'Cannot unroll all 3q or more gates. No rule to expand instruction circuit9_dg.'
```
Notes:
- This bug only happens when the `backend` argument is set to be a remote quantum computer or the `coupling_map` argument is set to be a coupling map of a remote quantum computer. Calling `transpile(circuit, basis_gates=['u1', 'u2', 'u3', 'cx', 'id'])` works fine.
- This bug only happens when the `UnitaryGate` contains an identity matrix of more than 2 qubits.
### What is the expected behavior?
Successfully transpile the circuit.
| 2020-07-17T17:23:30Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 210, in transpile circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/usr/local/lib/python3.8/dist-packages/qiskit/tools/parallel.py", line 105, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/usr/local/lib/python3.8/dist-packages/qiskit/compiler/transpile.py", line 306, in _transpile_circuit
return pass_manager.run(circuit, callback=transpile_config['callback'],
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 214, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passmanager.py", line 277, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 115, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 145, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/runningpassmanager.py", line 157, in _run_this_pass
new_dag = pass_.run(dag)
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 54, in run
decomposition = self.run(decomposition) # recursively unroll
File "/usr/local/lib/python3.8/dist-packages/qiskit/transpiler/passes/basis/unroll_3q_or_more.py", line 39, in run
raise QiskitError("Cannot unroll all 3q or more gates. "
qiskit.exceptions.QiskitError: 'Cannot unroll all 3q or more gates. No rule to expand instruction circuit9_dg.'
| 1,531 |
||||
Qiskit/qiskit | Qiskit__qiskit-4840 | 1468ffb55a70c949147bac3cac052bd483b801bd | diff --git a/qiskit/transpiler/passes/optimization/consolidate_blocks.py b/qiskit/transpiler/passes/optimization/consolidate_blocks.py
--- a/qiskit/transpiler/passes/optimization/consolidate_blocks.py
+++ b/qiskit/transpiler/passes/optimization/consolidate_blocks.py
@@ -60,7 +60,10 @@ def __init__(self,
self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
elif basis_gates is not None:
kak_basis_gate = unitary_synthesis._choose_kak_gate(basis_gates)
- self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
+ if kak_basis_gate is not None:
+ self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
+ else:
+ self.decomposer = None
else:
self.decomposer = TwoQubitBasisDecomposer(CXGate())
| Transpiling 1q circuit at optimization level 3 breaks in 1q basis
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 5bdd12db84b2ef13292d26cd6750fa8fabd61bb3
- **Python version**: 3.7.5
- **Operating system**: MacOs Catalina
### What is the current behavior?
The following code breaks if `basis_gates=['u3']` and `optimization_level=3`.
It works for `basis_gates=['u3','cx']` or if `optimization_level<=2`.
```python
qc = QuantumCircuit(1)
qc.x(0)
transpile(qc, basis_gates=['u3'], optimization_level=3)
```
Edit: changed to a simpler example.
### Traceback
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py", line 214, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py", line 108, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py", line 304, in _transpile_circuit
pass_manager = level_3_pass_manager(pass_manager_config)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/preset_passmanagers/level3.py", line 176, in level_3_pass_manager
ConsolidateBlocks(basis_gates=basis_gates),
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/basepasses.py", line 31, in __call__
pass_instance = type.__call__(cls, *args, **kwargs)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/passes/optimization/consolidate_blocks.py", line 63, in __init__
self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/quantum_info/synthesis/two_qubit_decompose.py", line 293, in __init__
basis = self.basis = TwoQubitWeylDecomposition(Operator(gate).data)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/quantum_info/operators/operator.py", line 103, in __init__
raise QiskitError("Invalid input data format for Operator")
qiskit.exceptions.QiskitError: 'Invalid input data format for Operator'
```
### What is the expected behavior?
Transpile with only a single qubit gate, if possible (or throw a meaningful error).
| i got expected behaviour , try updating or moving to another low version of terra or other
On stable it still works, right, but we should fix it on the master version (ideally before the release). Which versions did you try for transpiling? I'm running on 5bdd12db84b2ef13292d26cd6750fa8fabd61bb3 (version of July 30th).
| 2020-07-31T20:17:47Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py", line 214, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/tools/parallel.py", line 108, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/compiler/transpile.py", line 304, in _transpile_circuit
pass_manager = level_3_pass_manager(pass_manager_config)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/preset_passmanagers/level3.py", line 176, in level_3_pass_manager
ConsolidateBlocks(basis_gates=basis_gates),
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/basepasses.py", line 31, in __call__
pass_instance = type.__call__(cls, *args, **kwargs)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/transpiler/passes/optimization/consolidate_blocks.py", line 63, in __init__
self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/quantum_info/synthesis/two_qubit_decompose.py", line 293, in __init__
basis = self.basis = TwoQubitWeylDecomposition(Operator(gate).data)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/quantum_info/operators/operator.py", line 103, in __init__
raise QiskitError("Invalid input data format for Operator")
qiskit.exceptions.QiskitError: 'Invalid input data format for Operator'
| 1,547 |
|||
Qiskit/qiskit | Qiskit__qiskit-4887 | 5edca05b5188373726f3cc667e1f05bb067048a3 | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -25,6 +25,7 @@
from qiskit.util import is_main_process
from qiskit.circuit.instruction import Instruction
from qiskit.circuit.gate import Gate
+from qiskit.circuit.parameter import Parameter
from qiskit.qasm.qasm import Qasm
from qiskit.circuit.exceptions import CircuitError
from .parameterexpression import ParameterExpression
@@ -823,6 +824,12 @@ def append(self, instruction, qargs=None, cargs=None):
if not isinstance(instruction, Instruction) and hasattr(instruction, "to_instruction"):
instruction = instruction.to_instruction()
+ # Make copy of parameterized gate instances
+ if hasattr(instruction, 'params'):
+ is_parameter = any([isinstance(param, Parameter) for param in instruction.params])
+ if is_parameter:
+ instruction = copy.deepcopy(instruction)
+
expanded_qargs = [self.qbit_argument_conversion(qarg) for qarg in qargs or []]
expanded_cargs = [self.cbit_argument_conversion(carg) for carg in cargs or []]
| Re-using parameterized gate instances breaks upon in-place parameter assigning
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: e03cb1b7fd
- **Python version**: 3.7.7
- **Operating system**: macOS catalina
### What is the current behavior?
When placing the same parameterized gate into multiple circuits and binding the parameter value in place, the parameter in the gate instance is updated but the circuits containing this gate still have the old parameter in their parameter table (at least I think that's what's happening). This leads to issues as:
```python
>>> from qiskit.circuit.library import RXGate
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> a, b = Parameter('a'), Parameter('b')
>>> rx = RXGate(a)
>>> qc0, qc1 = QuantumCircuit(1), QuantumCircuit(1)
>>> qc0.append(rx, [0])
>>> qc1.append(rx, [0])
>>> qc0.assign_parameters({a: b}, inplace=True)
>>> qc1.to_gate()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 836, in to_gate
return circuit_to_gate(self, parameter_map, label=label)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/converters/circuit_to_gate.py", line 88, in circuit_to_gate
target = circuit.assign_parameters(parameter_dict, inplace=False)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1669, in assign_parameters
bound_circuit._substitute_parameter(parameter, value)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1735, in _substitute_parameter
new_param = instr.params[param_index].subs({old_parameter: new_parameter_expr})
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/parameter.py", line 55, in subs
return parameter_map[self]
KeyError: Parameter(b)
```
I don't think this is very pressing but definitely should be fixed in the future.
### Suggested solutions
* Copy the gate parameters upon appending, or
* Make the circuit not explicitly store the parameters since they are already in the gate
These options have different behaviour, in the second one can change the parameter of the gates "from the outside" by modifying the gate instance. Probably the safer behaviour is therefore the first option.
| 2020-08-07T01:40:10Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 836, in to_gate
return circuit_to_gate(self, parameter_map, label=label)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/converters/circuit_to_gate.py", line 88, in circuit_to_gate
target = circuit.assign_parameters(parameter_dict, inplace=False)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1669, in assign_parameters
bound_circuit._substitute_parameter(parameter, value)
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1735, in _substitute_parameter
new_param = instr.params[param_index].subs({old_parameter: new_parameter_expr})
File "/Users/jul/Work/Qiskit/qiskit-terra/qiskit/circuit/parameter.py", line 55, in subs
return parameter_map[self]
KeyError: Parameter(b)
| 1,557 |
||||
Qiskit/qiskit | Qiskit__qiskit-4955 | 6220b8cddebd1cd24c2b1eef1cdf258979649550 | diff --git a/qiskit/extensions/unitary.py b/qiskit/extensions/unitary.py
--- a/qiskit/extensions/unitary.py
+++ b/qiskit/extensions/unitary.py
@@ -19,7 +19,7 @@
from qiskit.circuit import Gate, ControlledGate
from qiskit.circuit import QuantumCircuit
-from qiskit.circuit import QuantumRegister
+from qiskit.circuit import QuantumRegister, Qubit
from qiskit.circuit.exceptions import CircuitError
from qiskit.circuit._utils import _compute_control_matrix
from qiskit.circuit.library.standard_gates import U3Gate
@@ -213,9 +213,14 @@ def validate_parameter(self, parameter):
def unitary(self, obj, qubits, label=None):
"""Apply unitary gate to q."""
+ gate = UnitaryGate(obj, label=label)
if isinstance(qubits, QuantumRegister):
qubits = qubits[:]
- return self.append(UnitaryGate(obj, label=label), qubits, [])
+ # for single qubit unitary gate, allow an 'int' or a 'list of ints' as qubits.
+ if gate.num_qubits == 1:
+ if isinstance(qubits, (int, Qubit)) or len(qubits) > 1:
+ qubits = [qubits]
+ return self.append(gate, qubits, [])
QuantumCircuit.unitary = unitary
| QuantumCircuit.unitary doesn't accept single integer qargs
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master @ 251930a
- **Python version**: 3.5
- **Operating system**: osx
### What is the current behavior?
```
>>> import qiskit as qk
>>> qc = qk.QuantumCircuit(1)
>>> qc.x(0)
>>> qc.barrier(0)
>>> qc.unitary([[0,1], [1,0]], 0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/extensions/unitary.py", line 211, in unitary
return self.append(UnitaryGate(obj, label=label), qubits, [])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 796, in append
for (qarg, carg) in instruction.broadcast_arguments(expanded_qargs, expanded_cargs):
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/gate.py", line 212, in broadcast_arguments
'The amount of qubit/clbit arguments does not match the gate expectation.')
qiskit.circuit.exceptions.CircuitError: 'The amount of qubit/clbit arguments does not match the gate expectation.'
>>> qc.unitary([[0,1], [1,0]], [0]) # This works
```
### Steps to reproduce the problem
### What is the expected behavior?
### Suggested solutions
| 2020-08-20T10:37:25Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/extensions/unitary.py", line 211, in unitary
return self.append(UnitaryGate(obj, label=label), qubits, [])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 796, in append
for (qarg, carg) in instruction.broadcast_arguments(expanded_qargs, expanded_cargs):
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/circuit/gate.py", line 212, in broadcast_arguments
'The amount of qubit/clbit arguments does not match the gate expectation.')
qiskit.circuit.exceptions.CircuitError: 'The amount of qubit/clbit arguments does not match the gate expectation.'
| 1,569 |
||||
Qiskit/qiskit | Qiskit__qiskit-5020 | 9165f396a124fdc43e5288a7c9f3c2f70018ad66 | diff --git a/qiskit/circuit/instruction.py b/qiskit/circuit/instruction.py
--- a/qiskit/circuit/instruction.py
+++ b/qiskit/circuit/instruction.py
@@ -267,7 +267,20 @@ def inverse(self):
"""
if self.definition is None:
raise CircuitError("inverse() not implemented for %s." % self.name)
- inverse_gate = self.copy(name=self.name + '_dg')
+
+ from qiskit.circuit import QuantumCircuit, Gate # pylint: disable=cyclic-import
+ if self.num_clbits:
+ inverse_gate = Instruction(name=self.name + '_dg',
+ num_qubits=self.num_qubits,
+ num_clbits=self.num_clbits,
+ params=self.params.copy())
+
+ else:
+ inverse_gate = Gate(name=self.name + '_dg',
+ num_qubits=self.num_qubits,
+ params=self.params.copy())
+
+ inverse_gate.definition = QuantumCircuit(*self.definition.qregs, *self.definition.cregs)
inverse_gate.definition._data = [(inst.inverse(), qargs, cargs)
for inst, qargs, cargs in reversed(self._definition)]
diff --git a/qiskit/circuit/library/standard_gates/sx.py b/qiskit/circuit/library/standard_gates/sx.py
--- a/qiskit/circuit/library/standard_gates/sx.py
+++ b/qiskit/circuit/library/standard_gates/sx.py
@@ -76,6 +76,10 @@ def _define(self):
qc.data = rules
self.definition = qc
+ def inverse(self):
+ """Return inverse SX gate (i.e. SXdg)."""
+ return SXdgGate()
+
def control(self, num_ctrl_qubits=1, label=None, ctrl_state=None):
"""Return a (multi-)controlled-SX gate.
@@ -150,6 +154,10 @@ def _define(self):
qc.data = rules
self.definition = qc
+ def inverse(self):
+ """Return inverse SXdg gate (i.e. SX)."""
+ return SXGate()
+
def to_matrix(self):
"""Return a numpy.array for the SXdg gate."""
return numpy.array([[1 - 1j, 1 + 1j],
diff --git a/qiskit/circuit/library/standard_gates/x.py b/qiskit/circuit/library/standard_gates/x.py
--- a/qiskit/circuit/library/standard_gates/x.py
+++ b/qiskit/circuit/library/standard_gates/x.py
@@ -515,7 +515,7 @@ def control(self, num_ctrl_qubits=1, label=None, ctrl_state=None):
def inverse(self):
"""Invert this gate. The C3X is its own inverse."""
- return C3XGate(angle=self._angle, ctrl_state=self.ctrl_state)
+ return C3XGate(angle=-self._angle, ctrl_state=self.ctrl_state)
# This matrix is only correct if the angle is pi/4
# def to_matrix(self):
@@ -747,6 +747,10 @@ def __init__(self, num_ctrl_qubits, label=None, ctrl_state=None, _name='mcx'):
num_ctrl_qubits=num_ctrl_qubits, label=label,
ctrl_state=ctrl_state, base_gate=XGate())
+ def inverse(self):
+ """Invert this gate. The MCX is its own inverse."""
+ return MCXGate(num_ctrl_qubits=self.num_ctrl_qubits, ctrl_state=self.ctrl_state)
+
@staticmethod
def get_num_ancilla_qubits(num_ctrl_qubits, mode='noancilla'):
"""Get the number of required ancilla qubits without instantiating the class.
@@ -806,6 +810,10 @@ class MCXGrayCode(MCXGate):
def __init__(self, num_ctrl_qubits, label=None, ctrl_state=None):
super().__init__(num_ctrl_qubits, label=label, ctrl_state=ctrl_state, _name='mcx_gray')
+ def inverse(self):
+ """Invert this gate. The MCX is its own inverse."""
+ return MCXGrayCode(num_ctrl_qubits=self.num_ctrl_qubits, ctrl_state=self.ctrl_state)
+
def _define(self):
"""Define the MCX gate using the Gray code."""
# pylint: disable=cyclic-import
@@ -835,6 +843,10 @@ def get_num_ancilla_qubits(num_ctrl_qubits, mode='recursion'):
"""Get the number of required ancilla qubits."""
return MCXGate.get_num_ancilla_qubits(num_ctrl_qubits, mode)
+ def inverse(self):
+ """Invert this gate. The MCX is its own inverse."""
+ return MCXRecursive(num_ctrl_qubits=self.num_ctrl_qubits, ctrl_state=self.ctrl_state)
+
def _define(self):
"""Define the MCX gate using recursion."""
# pylint: disable=cyclic-import
@@ -891,6 +903,12 @@ def __init__(self, num_ctrl_qubits, dirty_ancillas=False, label=None, ctrl_state
super().__init__(num_ctrl_qubits, label=label, ctrl_state=ctrl_state, _name='mcx_vchain')
self._dirty_ancillas = dirty_ancillas
+ def inverse(self):
+ """Invert this gate. The MCX is its own inverse."""
+ return MCXVChain(num_ctrl_qubits=self.num_ctrl_qubits,
+ dirty_ancillas=self._dirty_ancillas,
+ ctrl_state=self.ctrl_state)
+
@staticmethod
def get_num_ancilla_qubits(num_ctrl_qubits, mode='v-chain'):
"""Get the number of required ancilla qubits."""
| Inverse of MCX gates generated without qubits for ancillae
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master @ 7d79ab0
- **Python version**: 3.5
- **Operating system**: osx
### What is the current behavior?
Inverting an mcx gate (with ancilla) falls back to `ControlledGate.inverse` which builds the definition of the inverse gate without ancilla. This leads to an error when transpiling because the inverse gate uses fewer qubits than the original.
### Steps to reproduce the problem
```
>>> import qiskit as qk
>>> qc = qk.QuantumCircuit(5)
>>> qc.mcx([0,1,2],4,[3], mode='v-chain')
>>> qk.transpile(qc, basis_gates=['u3', 'cx']).count_ops()
OrderedDict([('u3', 16), ('cx', 12)])
>>> qk.transpile(qc.inverse(), basis_gates=['u3', 'cx']).count_ops()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 214, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 106, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 315, in _transpile_circuit
output_name=transpile_config['output_name'])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 212, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 275, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 143, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 155, in _run_this_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/basis/unroll_custom_definitions.py", line 88, in run
dag.substitute_node_with_dag(node, unrolled_dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 842, in substitute_node_with_dag
self._check_wires_list(wires, node)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 716, in _check_wires_list
% (wire_tot, len(wires)))
qiskit.dagcircuit.exceptions.DAGCircuitError: 'expected 5 wires, got 4'
```
```
>>> print(MCXVChain(num_ctrl_qubits=3).definition)
┌───────┐ ┌───────┐
q_0: ┤0 ├─────┤0 ├
│ │ │ │
q_1: ┤1 ├─────┤1 ├
│ │ │ │
q_2: ┤ RCCX ├──■──┤ RCCX ├
│ │┌─┴─┐│ │
q_3: ┤ ├┤ X ├┤ ├
│ │└─┬─┘│ │
q_4: ┤2 ├──■──┤2 ├
└───────┘ └───────┘
>>> print(MCXVChain(num_ctrl_qubits=3).inverse().definition)
q_0: ──■──
│
q_1: ──■──
│
q_2: ──■──
┌─┴─┐
q_3: ┤ X ├
└───┘
```
### Suggested solutions
Adding something to `class MCXGate` like
```
def inverse(self):
return self.copy()
```
| 2020-09-02T21:06:10Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 214, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/tools/parallel.py", line 106, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/compiler/transpile.py", line 315, in _transpile_circuit
output_name=transpile_config['output_name'])
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 212, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passmanager.py", line 275, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 143, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/runningpassmanager.py", line 155, in _run_this_pass
new_dag = pass_.run(dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/transpiler/passes/basis/unroll_custom_definitions.py", line 88, in run
dag.substitute_node_with_dag(node, unrolled_dag)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 842, in substitute_node_with_dag
self._check_wires_list(wires, node)
File "/Users/kevin.krsulichibm.com/q/qiskit-terra/qiskit/dagcircuit/dagcircuit.py", line 716, in _check_wires_list
% (wire_tot, len(wires)))
qiskit.dagcircuit.exceptions.DAGCircuitError: 'expected 5 wires, got 4'
| 1,578 |
||||
Qiskit/qiskit | Qiskit__qiskit-5051 | 2a2b504f97e555571ec952d057aedcb8c344b1bb | diff --git a/qiskit/execute.py b/qiskit/execute.py
--- a/qiskit/execute.py
+++ b/qiskit/execute.py
@@ -243,8 +243,7 @@ def execute(experiments, backend,
coupling_map=coupling_map,
seed_transpiler=seed_transpiler,
backend_properties=backend_properties,
- initial_layout=initial_layout,
- backend=backend)
+ initial_layout=initial_layout)
experiments = pass_manager.run(experiments)
else:
# transpiling the circuits using given transpile options
| Cannot use `execute` with a `PassManager`
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.15.1
- **Python version**: 3.8.5
- **Operating system**: Ubuntu 18.04
### What is the current behavior?
Providing any `PassManager` to the `execute` method will throw an exception as it fails the `_check_conflicting_arguments` call - this method fails if a backend (a _required_ argument) is provided.
### Steps to reproduce the problem
```
from qiskit.transpiler import PassManager
from qiskit import QuantumCircuit, execute
from qiskit.providers.aer import Aer
qc = QuantumCircuit(2)
qc.h(0)
qc.measure_all()
b = Aer.get_backend('qasm_simulator')
job = execute(qc, b, pass_manager = PassManager())
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/execute.py", line 243, in execute
_check_conflicting_argument(optimization_level=optimization_level,
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/execute.py", line 302, in _check_conflicting_argument
raise QiskitError("The parameters pass_manager conflicts with the following "
qiskit.exceptions.QiskitError: 'The parameters pass_manager conflicts with the following parameter(s): backend.'
```
### What is the expected behavior?
This should not throw an exception since there are no conflicts here.
### Suggested solutions
Remove `backend` from the list of arguments checked for conflicts. Or, alternatively, deprecate the use of the `pass_manager` argument (it is currently unusable anyway) to encourage users to use `PassManager.run()` and `assemble` instead.
| Can I help with this?
I think it is, indeed, a bug. I will remove `backend` as conflicting.
> it is currently unusable anyway.
I'm curious. Why? | 2020-09-10T01:12:26Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/execute.py", line 243, in execute
_check_conflicting_argument(optimization_level=optimization_level,
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/execute.py", line 302, in _check_conflicting_argument
raise QiskitError("The parameters pass_manager conflicts with the following "
qiskit.exceptions.QiskitError: 'The parameters pass_manager conflicts with the following parameter(s): backend.'
| 1,585 |
|||
Qiskit/qiskit | Qiskit__qiskit-5060 | 1c89cc59c8ec45311748e3f4c37e843068155d51 | diff --git a/qiskit/circuit/library/template_circuits/__init__.py b/qiskit/circuit/library/template_circuits/__init__.py
new file mode 100644
--- /dev/null
+++ b/qiskit/circuit/library/template_circuits/__init__.py
@@ -0,0 +1,11 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2017, 2020.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
| Aqua build fails: `No module named 'qiskit.circuit.library.template_circuits'`
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: Latest sources after https://github.com/Qiskit/qiskit-terra/commit/1c89cc59c8ec45311748e3f4c37e843068155d51
- **Python version**: any
- **Operating system**: any
### What is the current behavior?
1. `from qiskit.circuit.library import NLocal` fails.
2. The aqua build fails:
```
Failed to import test module: test.optimization
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/unittest/loader.py", line 470, in _find_test_path
package = self._get_module_from_name(name)
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/unittest/loader.py", line 377, in _get_module_from_name
__import__(name)
File "/home/runner/work/qiskit-aqua/qiskit-aqua/test/__init__.py", line 15, in <module>
from .base_test_case import QiskitBaseTestCase
File "/home/runner/work/qiskit-aqua/qiskit-aqua/test/base_test_case.py", line 23, in <module>
from qiskit.aqua import set_logging_level, QiskitLogDomains
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/__init__.py", line 41, in <module>
import qiskit.extensions
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/extensions/__init__.py", line 47, in <module>
from qiskit.circuit.library.standard_gates import *
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/circuit/library/__init__.py", line 245, in <module>
from .template_circuits import *
ModuleNotFoundError: No module named 'qiskit.circuit.library.template_circuits'
```
### Steps to reproduce the problem
Logs from the aqua build: https://github.com/Qiskit/qiskit-aqua/runs/1100653018
### What is the expected behavior?
No errors.
### Suggested solutions
There's missing `qiskit/circuit/library/template_circuits/__init__.py`
| 2020-09-11T14:15:39Z | [] | [] |
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/unittest/loader.py", line 470, in _find_test_path
package = self._get_module_from_name(name)
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/unittest/loader.py", line 377, in _get_module_from_name
__import__(name)
File "/home/runner/work/qiskit-aqua/qiskit-aqua/test/__init__.py", line 15, in <module>
from .base_test_case import QiskitBaseTestCase
File "/home/runner/work/qiskit-aqua/qiskit-aqua/test/base_test_case.py", line 23, in <module>
from qiskit.aqua import set_logging_level, QiskitLogDomains
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/__init__.py", line 41, in <module>
import qiskit.extensions
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/extensions/__init__.py", line 47, in <module>
from qiskit.circuit.library.standard_gates import *
File "/opt/hostedtoolcache/Python/3.8.5/x64/lib/python3.8/site-packages/qiskit/circuit/library/__init__.py", line 245, in <module>
from .template_circuits import *
ModuleNotFoundError: No module named 'qiskit.circuit.library.template_circuits'
| 1,587 |
||||
Qiskit/qiskit | Qiskit__qiskit-5286 | 70f476bba8041b5e2ca4df923f3db829f60876e3 | diff --git a/qiskit/quantum_info/states/statevector.py b/qiskit/quantum_info/states/statevector.py
--- a/qiskit/quantum_info/states/statevector.py
+++ b/qiskit/quantum_info/states/statevector.py
@@ -677,6 +677,7 @@ def _evolve_instruction(statevec, obj, qargs=None):
obj.name, type(obj.definition)))
if obj.definition.global_phase:
statevec._data *= np.exp(1j * float(obj.definition.global_phase))
+ qubits = {qubit: i for i, qubit in enumerate(obj.definition.qubits)}
for instr, qregs, cregs in obj.definition:
if cregs:
raise QiskitError(
@@ -684,8 +685,8 @@ def _evolve_instruction(statevec, obj, qargs=None):
instr.name))
# Get the integer position of the flat register
if qargs is None:
- new_qargs = [tup.index for tup in qregs]
+ new_qargs = [qubits[tup] for tup in qregs]
else:
- new_qargs = [qargs[tup.index] for tup in qregs]
+ new_qargs = [qargs[qubits[tup]] for tup in qregs]
Statevector._evolve_instruction(statevec, instr, qargs=new_qargs)
return statevec
| Statevector.from_instruction fails for custom controlled gates
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: latest
- **Python version**:
- **Operating system**:
### What is the current behavior?
```python
import numpy as np
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
## Create 7mod15 gate
N = 15
m = int(np.ceil(np.log2(N)))
U_qc = QuantumCircuit(m)
U_qc.x(range(m))
U_qc.swap(1, 2)
U_qc.swap(2, 3)
U_qc.swap(0, 3)
U = U_qc.to_gate()
U.name ='{}Mod{}'.format(7, N)
U_cntrl = U.control()
qc = QuantumCircuit(5)
qc.append(U_cntrl, range(5))
Statevector.from_instruction(qc)
```
gives
Traceback (most recent call last):
File "<ipython-input-127-b464b6ab1295>", line 1, in <module>
Statevector.from_instruction(qc)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 529, in from_instruction
return Statevector._evolve_instruction(vec, instruction)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 690, in _evolve_instruction
Statevector._evolve_instruction(statevec, instr, qargs=new_qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 690, in _evolve_instruction
Statevector._evolve_instruction(statevec, instr, qargs=new_qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 661, in _evolve_instruction
return Statevector._evolve_operator(statevec, Operator(mat), qargs=qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 642, in _evolve_operator
np.reshape(statevec.data, pre_tensor_shape), axes), contract_shape)
File "<__array_function__ internals>", line 6, in transpose
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 653, in transpose
return _wrapfunc(a, 'transpose', axes)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 58, in _wrapfunc
return bound(*args, **kwds)
ValueError: axes don't match array
### Steps to reproduce the problem
### What is the expected behavior?
### Suggested solutions
| 2020-10-23T21:36:20Z | [] | [] |
Traceback (most recent call last):
File "<ipython-input-127-b464b6ab1295>", line 1, in <module>
Statevector.from_instruction(qc)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 529, in from_instruction
return Statevector._evolve_instruction(vec, instruction)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 690, in _evolve_instruction
Statevector._evolve_instruction(statevec, instr, qargs=new_qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 690, in _evolve_instruction
Statevector._evolve_instruction(statevec, instr, qargs=new_qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 661, in _evolve_instruction
return Statevector._evolve_operator(statevec, Operator(mat), qargs=qargs)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/quantum_info/states/statevector.py", line 642, in _evolve_operator
np.reshape(statevec.data, pre_tensor_shape), axes), contract_shape)
File "<__array_function__ internals>", line 6, in transpose
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 653, in transpose
return _wrapfunc(a, 'transpose', axes)
File "/opt/miniconda3/envs/qiskit/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 58, in _wrapfunc
return bound(*args, **kwds)
ValueError: axes don't match array
| 1,614 |
||||
Qiskit/qiskit | Qiskit__qiskit-5554 | 21e2898c711790a6200dc90ee0952c90471dd711 | diff --git a/qiskit/quantum_info/synthesis/one_qubit_decompose.py b/qiskit/quantum_info/synthesis/one_qubit_decompose.py
--- a/qiskit/quantum_info/synthesis/one_qubit_decompose.py
+++ b/qiskit/quantum_info/synthesis/one_qubit_decompose.py
@@ -19,8 +19,8 @@
import scipy.linalg as la
from qiskit.circuit.quantumcircuit import QuantumCircuit
-from qiskit.circuit.library.standard_gates import (PhaseGate, U3Gate,
- U1Gate, RXGate, RYGate,
+from qiskit.circuit.library.standard_gates import (UGate, PhaseGate, U3Gate,
+ U2Gate, U1Gate, RXGate, RYGate,
RZGate, RGate, SXGate)
from qiskit.exceptions import QiskitError
from qiskit.quantum_info.operators.predicates import is_unitary_matrix
@@ -29,6 +29,7 @@
ONE_QUBIT_EULER_BASIS_GATES = {
'U3': ['u3'],
+ 'U321': ['u3', 'u2', 'u1'],
'U': ['u'],
'PSX': ['p', 'sx'],
'U1X': ['u1', 'rx'],
@@ -69,6 +70,9 @@ class OneQubitEulerDecomposer:
* - 'U3'
- :math:`Z(\phi) Y(\theta) Z(\lambda)`
- :math:`e^{i\gamma} U_3(\theta,\phi,\lambda)`
+ * - 'U321'
+ - :math:`Z(\phi) Y(\theta) Z(\lambda)`
+ - :math:`e^{i\gamma} U_3(\theta,\phi,\lambda)`
* - 'U'
- :math:`Z(\phi) Y(\theta) Z(\lambda)`
- :math:`e^{i\gamma} U_3(\theta,\phi,\lambda)`
@@ -93,7 +97,7 @@ class OneQubitEulerDecomposer:
def __init__(self, basis='U3'):
"""Initialize decomposer
- Supported bases are: 'U', 'PSX', 'ZSX', 'U3', 'U1X', 'RR', 'ZYZ', 'ZXZ', 'XYX'.
+ Supported bases are: 'U', 'PSX', 'ZSX', 'U321', 'U3', 'U1X', 'RR', 'ZYZ', 'ZXZ', 'XYX'.
Args:
basis (str): the decomposition basis [Default: 'U3']
@@ -155,6 +159,7 @@ def basis(self):
def basis(self, basis):
"""Set the decomposition basis."""
basis_methods = {
+ 'U321': (self._params_u3, self._circuit_u321),
'U3': (self._params_u3, self._circuit_u3),
'U': (self._params_u3, self._circuit_u),
'PSX': (self._params_u1x, self._circuit_psx),
@@ -280,13 +285,12 @@ def _circuit_zxz(theta,
phi,
lam,
phase,
- simplify=False,
+ simplify=True,
atol=DEFAULT_ATOL):
+ circuit = QuantumCircuit(1, global_phase=phase)
if simplify and np.isclose(theta, 0.0, atol=atol):
- circuit = QuantumCircuit(1, global_phase=phase)
circuit.append(RZGate(phi + lam), [0])
return circuit
- circuit = QuantumCircuit(1, global_phase=phase)
if not simplify or not np.isclose(lam, 0.0, atol=atol):
circuit.append(RZGate(lam), [0])
if not simplify or not np.isclose(theta, 0.0, atol=atol):
@@ -326,6 +330,24 @@ def _circuit_u3(theta,
circuit.append(U3Gate(theta, phi, lam), [0])
return circuit
+ @staticmethod
+ def _circuit_u321(theta,
+ phi,
+ lam,
+ phase,
+ simplify=True,
+ atol=DEFAULT_ATOL):
+ rtol = 1e-9 # default is 1e-5, too far from atol=1e-12
+ circuit = QuantumCircuit(1, global_phase=phase)
+ if simplify and (np.isclose(theta, 0.0, atol=atol, rtol=rtol)):
+ if not np.isclose(phi+lam, [0.0, 2*np.pi], atol=atol, rtol=rtol).any():
+ circuit.append(U1Gate(_mod2pi(phi+lam)), [0])
+ elif simplify and np.isclose(theta, np.pi/2, atol=atol, rtol=rtol):
+ circuit.append(U2Gate(phi, lam), [0])
+ else:
+ circuit.append(U3Gate(theta, phi, lam), [0])
+ return circuit
+
@staticmethod
def _circuit_u(theta,
phi,
@@ -335,7 +357,7 @@ def _circuit_u(theta,
atol=DEFAULT_ATOL):
# pylint: disable=unused-argument
circuit = QuantumCircuit(1, global_phase=phase)
- circuit.u(theta, phi, lam, 0)
+ circuit.append(UGate(theta, phi, lam), [0])
return circuit
@staticmethod
diff --git a/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py b/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py
--- a/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py
+++ b/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py
@@ -13,12 +13,14 @@
"""Optimize chains of single-qubit gates using Euler 1q decomposer"""
import logging
+import copy
import numpy as np
from qiskit.quantum_info import Operator
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.quantum_info.synthesis import one_qubit_decompose
+from qiskit.circuit.library.standard_gates import U3Gate
from qiskit.converters import circuit_to_dag
logger = logging.getLogger(__name__)
@@ -40,9 +42,22 @@ def __init__(self, basis=None):
if basis:
self.basis = []
basis_set = set(basis)
- for basis_name, gates in one_qubit_decompose.ONE_QUBIT_EULER_BASIS_GATES.items():
+ euler_basis_gates = one_qubit_decompose.ONE_QUBIT_EULER_BASIS_GATES
+ for euler_basis_name, gates in euler_basis_gates.items():
if set(gates).issubset(basis_set):
- self.basis.append(one_qubit_decompose.OneQubitEulerDecomposer(basis_name))
+ basis_copy = copy.copy(self.basis)
+ for base in basis_copy:
+ # check if gates are a superset of another basis
+ # and if so, remove that basis
+ if set(euler_basis_gates[base.basis]).issubset(set(gates)):
+ self.basis.remove(base)
+ # check if the gates are a subset of another basis
+ elif set(gates).issubset(set(euler_basis_gates[base.basis])):
+ break
+ # if not a subset, add it to the list
+ else:
+ self.basis.append(one_qubit_decompose.OneQubitEulerDecomposer(
+ euler_basis_name))
def run(self, dag):
"""Run the Optimize1qGatesDecomposition pass on `dag`.
@@ -59,14 +74,21 @@ def run(self, dag):
runs = dag.collect_1q_runs()
identity_matrix = np.eye(2)
for run in runs:
- # Don't try to optimize a single 1q gate
+ single_u3 = False
+ # Don't try to optimize a single 1q gate, except for U3
if len(run) <= 1:
params = run[0].op.params
# Remove single identity gates
if len(params) > 0 and np.array_equal(run[0].op.to_matrix(),
identity_matrix):
dag.remove_op_node(run[0])
- continue
+ continue
+ if (isinstance(run[0].op, U3Gate) and
+ np.isclose(float(params[0]), [0, np.pi/2],
+ atol=1e-12, rtol=0).any()):
+ single_u3 = True
+ else:
+ continue
new_circs = []
operator = Operator(run[0].op)
@@ -76,7 +98,8 @@ def run(self, dag):
new_circs.append(decomposer(operator))
if new_circs:
new_circ = min(new_circs, key=len)
- if len(run) > len(new_circ):
+ if (len(run) > len(new_circ) or (single_u3 and
+ new_circ.data[0][0].name != 'u3')):
new_dag = circuit_to_dag(new_circ)
dag.substitute_node_with_dag(run[0], new_dag)
# Delete the other nodes in the run
diff --git a/qiskit/transpiler/preset_passmanagers/level1.py b/qiskit/transpiler/preset_passmanagers/level1.py
--- a/qiskit/transpiler/preset_passmanagers/level1.py
+++ b/qiskit/transpiler/preset_passmanagers/level1.py
@@ -40,7 +40,6 @@
from qiskit.transpiler.passes import FixedPoint
from qiskit.transpiler.passes import Depth
from qiskit.transpiler.passes import RemoveResetInZeroState
-from qiskit.transpiler.passes import Optimize1qGates
from qiskit.transpiler.passes import Optimize1qGatesDecomposition
from qiskit.transpiler.passes import ApplyLayout
from qiskit.transpiler.passes import CheckCXDirection
@@ -179,11 +178,7 @@ def _direction_condition(property_set):
def _opt_control(property_set):
return not property_set['depth_fixed_point']
- if basis_gates and ('u1' in basis_gates or 'u2' in basis_gates or
- 'u3' in basis_gates):
- _opt = [Optimize1qGates(basis_gates), CXCancellation()]
- else:
- _opt = [Optimize1qGatesDecomposition(basis_gates), CXCancellation()]
+ _opt = [Optimize1qGatesDecomposition(basis_gates), CXCancellation()]
# 10. Schedule the circuit only when scheduling_method is supplied
if scheduling_method:
diff --git a/qiskit/transpiler/preset_passmanagers/level2.py b/qiskit/transpiler/preset_passmanagers/level2.py
--- a/qiskit/transpiler/preset_passmanagers/level2.py
+++ b/qiskit/transpiler/preset_passmanagers/level2.py
@@ -41,7 +41,6 @@
from qiskit.transpiler.passes import FixedPoint
from qiskit.transpiler.passes import Depth
from qiskit.transpiler.passes import RemoveResetInZeroState
-from qiskit.transpiler.passes import Optimize1qGates
from qiskit.transpiler.passes import Optimize1qGatesDecomposition
from qiskit.transpiler.passes import CommutativeCancellation
from qiskit.transpiler.passes import ApplyLayout
@@ -175,11 +174,7 @@ def _direction_condition(property_set):
def _opt_control(property_set):
return not property_set['depth_fixed_point']
- if basis_gates and ('u1' in basis_gates or 'u2' in basis_gates or
- 'u3' in basis_gates):
- _opt = [Optimize1qGates(basis_gates), CommutativeCancellation()]
- else:
- _opt = [Optimize1qGatesDecomposition(basis_gates), CommutativeCancellation()]
+ _opt = [Optimize1qGatesDecomposition(basis_gates), CommutativeCancellation()]
# 9. Schedule the circuit only when scheduling_method is supplied
if scheduling_method:
diff --git a/qiskit/transpiler/preset_passmanagers/level3.py b/qiskit/transpiler/preset_passmanagers/level3.py
--- a/qiskit/transpiler/preset_passmanagers/level3.py
+++ b/qiskit/transpiler/preset_passmanagers/level3.py
@@ -42,7 +42,6 @@
from qiskit.transpiler.passes import FixedPoint
from qiskit.transpiler.passes import Depth
from qiskit.transpiler.passes import RemoveResetInZeroState
-from qiskit.transpiler.passes import Optimize1qGates
from qiskit.transpiler.passes import Optimize1qGatesDecomposition
from qiskit.transpiler.passes import CommutativeCancellation
from qiskit.transpiler.passes import OptimizeSwapBeforeMeasure
@@ -180,23 +179,13 @@ def _opt_control(property_set):
_meas = [OptimizeSwapBeforeMeasure(), RemoveDiagonalGatesBeforeMeasure()]
- if basis_gates and ('u1' in basis_gates or 'u2' in basis_gates or
- 'u3' in basis_gates):
- _opt = [
- Collect2qBlocks(),
- ConsolidateBlocks(basis_gates=basis_gates),
- UnitarySynthesis(basis_gates),
- Optimize1qGates(basis_gates),
- CommutativeCancellation(),
- ]
- else:
- _opt = [
- Collect2qBlocks(),
- ConsolidateBlocks(basis_gates=basis_gates),
- UnitarySynthesis(basis_gates),
- Optimize1qGatesDecomposition(basis_gates),
- CommutativeCancellation(),
- ]
+ _opt = [
+ Collect2qBlocks(),
+ ConsolidateBlocks(basis_gates=basis_gates),
+ UnitarySynthesis(basis_gates),
+ Optimize1qGatesDecomposition(basis_gates),
+ CommutativeCancellation(),
+ ]
# Schedule the circuit only when scheduling_method is supplied
if scheduling_method:
| Transpilation fails with snapshot instruction
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master
- **Python version**: 3.8.5
- **Operating system**: Linux
### What is the current behavior?
Transpiling a simple circuit, which contains a snapshot, with basis gates `['u', 'cx']`, raises an error. The same happens with more sets of basis gates: `['r', 'cz'], ['rz', 'rx', 'cz'], ['p', 'sx', 'cx']`. However transpiling the same circuit with basis gates `['u3', 'cx']` is OK.
Note: this is working well with the stable version, the bug appears only in the master version.
### Steps to reproduce the problem
```
from qiskit import QuantumCircuit, transpile
from qiskit.providers.aer import QasmSimulator
from qiskit.providers.aer.extensions import snapshot_statevector
backend = QasmSimulator()
circ = QuantumCircuit(1)
circ.z(0)
circ.snapshot_statevector('final')
transpile(circ, backend, basis_gates=['u3', 'cx'])
print("Transpilation with ['u3', 'cx'] is fine")
transpile(circ, backend, basis_gates=['u', 'cx'])
print("Transpilation with ['u', 'cx'] is fine")
```
results with
```
(YaelEnv) yaelbh@iris-quantum2:~/work/not_qiskit$ python snapshot_invalid.py
/home/yaelbh/work/terra/System/qiskit/__init__.py:69: RuntimeWarning: Could not import the IBMQ provider from the qiskit-ibmq-provider package. Install qiskit-ibmq-provider or check your installation.
warnings.warn('Could not import the IBMQ provider from the '
/opt/anaconda3/envs/YaelEnv/lib/python3.8/site-packages/qiskit/aqua/operators/operator_globals.py:48: DeprecationWarning: `from_label` is deprecated and will be removed no earlier than 3 months after the release date. Use Pauli(label) instead.
X = make_immutable(PrimitiveOp(Pauli.from_label('X')))
Transpilation with ['u3', 'cx'] is fine
Traceback (most recent call last):
File "snapshot_invalid.py", line 14, in <module>
transpile(circ, backend, basis_gates=['u', 'cx'])
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 241, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/home/yaelbh/work/terra/System/qiskit/tools/parallel.py", line 112, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 324, in _transpile_circuit
result = pass_manager.run(circuit, callback=transpile_config['callback'],
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 225, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 288, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 144, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 156, in _run_this_pass
new_dag = pass_.run(dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py", line 87, in run
operator = Operator(qc)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 85, in __init__
self._data = self._init_instruction(data).data
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 496, in _init_instruction
op._append_instruction(instruction)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 554, in _append_instruction
self._append_instruction(instr, qargs=new_qargs)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 532, in _append_instruction
raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
qiskit.exceptions.QiskitError: 'Cannot apply Instruction: snapshot'
```
| 2020-12-21T18:51:00Z | [] | [] |
Traceback (most recent call last):
File "snapshot_invalid.py", line 14, in <module>
transpile(circ, backend, basis_gates=['u', 'cx'])
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 241, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/home/yaelbh/work/terra/System/qiskit/tools/parallel.py", line 112, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 324, in _transpile_circuit
result = pass_manager.run(circuit, callback=transpile_config['callback'],
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 225, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 288, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 144, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 156, in _run_this_pass
new_dag = pass_.run(dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py", line 87, in run
operator = Operator(qc)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 85, in __init__
self._data = self._init_instruction(data).data
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 496, in _init_instruction
op._append_instruction(instruction)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 554, in _append_instruction
self._append_instruction(instr, qargs=new_qargs)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 532, in _append_instruction
raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
qiskit.exceptions.QiskitError: 'Cannot apply Instruction: snapshot'
| 1,652 |
||||
Qiskit/qiskit | Qiskit__qiskit-5570 | 74ed881a9fcad89a5e8f41a0cfdeffb98a8e3051 | diff --git a/qiskit/dagcircuit/dagcircuit.py b/qiskit/dagcircuit/dagcircuit.py
--- a/qiskit/dagcircuit/dagcircuit.py
+++ b/qiskit/dagcircuit/dagcircuit.py
@@ -1384,6 +1384,8 @@ def filter_fn(node):
return node.type == 'op' and len(node.qargs) == 1 \
and len(node.cargs) == 0 and node.condition is None \
and not node.op.is_parameterized() \
+ and isinstance(node.op, Gate) \
+ and hasattr(node.op, '__array__')
group_list = rx.collect_runs(self._multi_graph, filter_fn)
return set(tuple(x) for x in group_list)
| Transpilation fails with snapshot instruction
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master
- **Python version**: 3.8.5
- **Operating system**: Linux
### What is the current behavior?
Transpiling a simple circuit, which contains a snapshot, with basis gates `['u', 'cx']`, raises an error. The same happens with more sets of basis gates: `['r', 'cz'], ['rz', 'rx', 'cz'], ['p', 'sx', 'cx']`. However transpiling the same circuit with basis gates `['u3', 'cx']` is OK.
Note: this is working well with the stable version, the bug appears only in the master version.
### Steps to reproduce the problem
```
from qiskit import QuantumCircuit, transpile
from qiskit.providers.aer import QasmSimulator
from qiskit.providers.aer.extensions import snapshot_statevector
backend = QasmSimulator()
circ = QuantumCircuit(1)
circ.z(0)
circ.snapshot_statevector('final')
transpile(circ, backend, basis_gates=['u3', 'cx'])
print("Transpilation with ['u3', 'cx'] is fine")
transpile(circ, backend, basis_gates=['u', 'cx'])
print("Transpilation with ['u', 'cx'] is fine")
```
results with
```
(YaelEnv) yaelbh@iris-quantum2:~/work/not_qiskit$ python snapshot_invalid.py
/home/yaelbh/work/terra/System/qiskit/__init__.py:69: RuntimeWarning: Could not import the IBMQ provider from the qiskit-ibmq-provider package. Install qiskit-ibmq-provider or check your installation.
warnings.warn('Could not import the IBMQ provider from the '
/opt/anaconda3/envs/YaelEnv/lib/python3.8/site-packages/qiskit/aqua/operators/operator_globals.py:48: DeprecationWarning: `from_label` is deprecated and will be removed no earlier than 3 months after the release date. Use Pauli(label) instead.
X = make_immutable(PrimitiveOp(Pauli.from_label('X')))
Transpilation with ['u3', 'cx'] is fine
Traceback (most recent call last):
File "snapshot_invalid.py", line 14, in <module>
transpile(circ, backend, basis_gates=['u', 'cx'])
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 241, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/home/yaelbh/work/terra/System/qiskit/tools/parallel.py", line 112, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 324, in _transpile_circuit
result = pass_manager.run(circuit, callback=transpile_config['callback'],
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 225, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 288, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 144, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 156, in _run_this_pass
new_dag = pass_.run(dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py", line 87, in run
operator = Operator(qc)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 85, in __init__
self._data = self._init_instruction(data).data
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 496, in _init_instruction
op._append_instruction(instruction)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 554, in _append_instruction
self._append_instruction(instr, qargs=new_qargs)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 532, in _append_instruction
raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
qiskit.exceptions.QiskitError: 'Cannot apply Instruction: snapshot'
```
| Oddly enough, I was just working on this as part of #5554. This is caused by `Optimize1qGatesDecomposition.run()` calling `Operator(qc)`. Operator will fail for 'snapshot', 'delay', and 'reset'. The reason it works with 'u3' in the basis is that if the basis has 'u1', 'u2', or 'u3', the optimizer calls `Optimize1qGates.run()` instead, which does not call `Operator()`.
It should run ok with `optimization_level=0` for transpile. | 2021-01-01T10:05:49Z | [] | [] |
Traceback (most recent call last):
File "snapshot_invalid.py", line 14, in <module>
transpile(circ, backend, basis_gates=['u', 'cx'])
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 241, in transpile
circuits = parallel_map(_transpile_circuit, list(zip(circuits, transpile_args)))
File "/home/yaelbh/work/terra/System/qiskit/tools/parallel.py", line 112, in parallel_map
return [task(values[0], *task_args, **task_kwargs)]
File "/home/yaelbh/work/terra/System/qiskit/compiler/transpile.py", line 324, in _transpile_circuit
result = pass_manager.run(circuit, callback=transpile_config['callback'],
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 225, in run
return self._run_single_circuit(circuits, output_name, callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passmanager.py", line 288, in _run_single_circuit
result = running_passmanager.run(circuit, output_name=output_name, callback=callback)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 113, in run
dag = self._do_pass(pass_, dag, passset.options)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 144, in _do_pass
dag = self._run_this_pass(pass_, dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/runningpassmanager.py", line 156, in _run_this_pass
new_dag = pass_.run(dag)
File "/home/yaelbh/work/terra/System/qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py", line 87, in run
operator = Operator(qc)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 85, in __init__
self._data = self._init_instruction(data).data
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 496, in _init_instruction
op._append_instruction(instruction)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 554, in _append_instruction
self._append_instruction(instr, qargs=new_qargs)
File "/home/yaelbh/work/terra/System/qiskit/quantum_info/operators/operator.py", line 532, in _append_instruction
raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
qiskit.exceptions.QiskitError: 'Cannot apply Instruction: snapshot'
| 1,656 |
|||
Qiskit/qiskit | Qiskit__qiskit-5755 | 123d829acb824ba906a10d2f06b92891a9f34221 | diff --git a/qiskit/pulse/schedule.py b/qiskit/pulse/schedule.py
--- a/qiskit/pulse/schedule.py
+++ b/qiskit/pulse/schedule.py
@@ -24,6 +24,8 @@
from collections import defaultdict
from typing import List, Tuple, Iterable, Union, Dict, Callable, Set, Optional, Any
+import numpy as np
+
from qiskit.circuit.parameter import Parameter
from qiskit.circuit.parameterexpression import ParameterExpression, ParameterValueType
from qiskit.pulse.channels import Channel
@@ -521,7 +523,7 @@ def _add_timeslots(self,
Raises:
PulseError: If timeslots overlap or an invalid start time is provided.
"""
- if not isinstance(time, int):
+ if not np.issubdtype(type(time), np.integer):
raise PulseError("Schedule start time must be an integer.")
other_timeslots = _get_timeslots(schedule)
| Pulse Schedule durations fail with numpy integers
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: 0.16.1
- **Python version**: 3.7.6
- **Operating system**: Linux via WSL
### What is the current behavior?
``PulseError`` (from https://github.com/Qiskit/qiskit-terra/blob/7710880167ddf6e11922ce608e6579e304c7eb04/qiskit/pulse/schedule.py#L520-L521) when a previous duration was a numpy integer.
### Steps to reproduce the problem
Works:
```python
import qiskit.pulse as qp
chan = qp.DriveChannel(0)
with qp.build() as sched:
qp.delay(5, chan)
qp.play(qp.library.Constant(50, 1.0), chan)
```
Doesn't work:
```python
import qiskit.pulse as qp
import numpy as np
chan = qp.DriveChannel(0)
with qp.build() as sched:
qp.delay(np.int32(5), chan)
qp.play(qp.library.Constant(50, 1.0), chan)
```
Error flags on the last call to ``qp.play()``:
```
>>> with qp.build() as bad_sched:
... qp.delay(np.int32(5), chan)
... qp.play(qp.library.Constant(50, 1.0), chan)
...
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 1384, in play
append_instruction(instructions.Play(pulse, channel))
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 630, in append_instruction
_active_builder().append_instruction(instruction)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 247, in wrapper
return function(self, *args, **kwargs)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 416, in append_instruction
self.context_schedule.append(instruction, inplace=True)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 308, in append
return self.insert(time, schedule, name=name, inplace=inplace)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 253, in insert
return self._mutable_insert(start_time, schedule)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 266, in _mutable_insert
self._add_timeslots(start_time, schedule)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 477, in _add_timeslots
raise PulseError("Schedule start time must be an integer.")
qiskit.pulse.exceptions.PulseError: 'Schedule start time must be an integer.'
```
### What is the expected behavior?
Qiskit schedules should accept numpy integers for durations. An example of why is for an easier time generating scans using e.g. ``np.linspace()``
### Suggested solutions
Change the ``isinstance(time, int)`` check to ``isinstance(time, (int, np.integer))``
| 2021-01-31T03:08:41Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 1384, in play
append_instruction(instructions.Play(pulse, channel))
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 630, in append_instruction
_active_builder().append_instruction(instruction)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 247, in wrapper
return function(self, *args, **kwargs)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/builder.py", line 416, in append_instruction
self.context_schedule.append(instruction, inplace=True)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 308, in append
return self.insert(time, schedule, name=name, inplace=inplace)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 253, in insert
return self._mutable_insert(start_time, schedule)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 266, in _mutable_insert
self._add_timeslots(start_time, schedule)
File "/nix/store/z8hw09q3vlpxgl01d6iwpimw7fdlvqsk-python3-3.7.6-env/lib/python3.7/site-packages/qiskit/pulse/schedule.py", line 477, in _add_timeslots
raise PulseError("Schedule start time must be an integer.")
qiskit.pulse.exceptions.PulseError: 'Schedule start time must be an integer.'
| 1,687 |
||||
Qiskit/qiskit | Qiskit__qiskit-5807 | 20a0c6d8bb9e3a858833522f2e291d7cb69ea4ee | diff --git a/qiskit/circuit/quantumcircuit.py b/qiskit/circuit/quantumcircuit.py
--- a/qiskit/circuit/quantumcircuit.py
+++ b/qiskit/circuit/quantumcircuit.py
@@ -83,7 +83,7 @@ class QuantumCircuit:
name (str): the name of the quantum circuit. If not set, an
automatically generated string will be assigned.
- global_phase (float): The global phase of the circuit in radians.
+ global_phase (float or ParameterExpression): The global phase of the circuit in radians.
metadata (dict): Arbitrary key value metadata to associate with the
circuit. This gets stored as free-form data in a dict in the
:attr:`~qiskit.circuit.QuantumCircuit.metadata` attribute. It will
@@ -1806,7 +1806,14 @@ def global_phase(self, angle):
@property
def parameters(self):
"""Convenience function to get the parameters defined in the parameter table."""
- return self._parameter_table.get_keys()
+ # parameters from gates
+ params = self._parameter_table.get_keys()
+
+ # parameters in global phase
+ if isinstance(self.global_phase, ParameterExpression):
+ return params.union(self.global_phase.parameters)
+
+ return params
@property
def num_parameters(self):
@@ -1882,7 +1889,7 @@ def assign_parameters(self, param_dict, inplace=False):
# check that all param_dict items are in the _parameter_table for this circuit
params_not_in_circuit = [param_key for param_key in unrolled_param_dict
- if param_key not in self._parameter_table.keys()]
+ if param_key not in self.parameters]
if len(params_not_in_circuit) > 0:
raise CircuitError('Cannot bind parameters ({}) not present in the circuit.'.format(
', '.join(map(str, params_not_in_circuit))))
@@ -1936,25 +1943,27 @@ def _assign_parameter(self, parameter, value):
value (Union(ParameterExpression, float, int)): A numeric or parametric expression to
replace instances of ``parameter``.
"""
- for instr, param_index in self._parameter_table[parameter]:
- new_param = instr.params[param_index].assign(parameter, value)
- # if fully bound, validate
- if len(new_param.parameters) == 0:
- instr.params[param_index] = instr.validate_parameter(new_param)
- else:
- instr.params[param_index] = new_param
+ # parameter might be in global phase only
+ if parameter in self._parameter_table.keys():
+ for instr, param_index in self._parameter_table[parameter]:
+ new_param = instr.params[param_index].assign(parameter, value)
+ # if fully bound, validate
+ if len(new_param.parameters) == 0:
+ instr.params[param_index] = instr.validate_parameter(new_param)
+ else:
+ instr.params[param_index] = new_param
- self._rebind_definition(instr, parameter, value)
+ self._rebind_definition(instr, parameter, value)
- if isinstance(value, ParameterExpression):
- entry = self._parameter_table.pop(parameter)
- for new_parameter in value.parameters:
- if new_parameter in self._parameter_table:
- self._parameter_table[new_parameter].extend(entry)
- else:
- self._parameter_table[new_parameter] = entry
- else:
- del self._parameter_table[parameter] # clear evaluated expressions
+ if isinstance(value, ParameterExpression):
+ entry = self._parameter_table.pop(parameter)
+ for new_parameter in value.parameters:
+ if new_parameter in self._parameter_table:
+ self._parameter_table[new_parameter].extend(entry)
+ else:
+ self._parameter_table[new_parameter] = entry
+ else:
+ del self._parameter_table[parameter] # clear evaluated expressions
if (isinstance(self.global_phase, ParameterExpression) and
parameter in self.global_phase.parameters):
| Unable bind parameters in global phase
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Information
- **Qiskit Terra version**: master @ c3b2d7ac
- **Python version**: 3.7.9
- **Operating system**: macOS Big Sur
### What is the current behavior?
If the global phase of a circuit contains a parameter it can currently not be bound:
```python
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> x = Parameter('x')
>>> circuit = QuantumCircuit(1, global_phase=x)
>>> circuit.bind_parameters({x: 2}).draw()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1912, in bind_parameters
return self.assign_parameters(value_dict)
File "/Users/jul/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1886, in assign_parameters
', '.join(map(str, params_not_in_circuit))))
qiskit.circuit.exceptions.CircuitError: 'Cannot bind parameters (x) not present in the circuit.'
```
The above bug might be due to #5648, since the low-level `_assign_parameter` seems to support global phase binding.
Also, they are not listed when calling `circuit.parameters`:
```python
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> from qiskit.opflow import StateFn
>>> x = Parameter('x')
>>> circuit = QuantumCircuit(1, global_phase=x)
>>> circuit.parameters
set()
```
| 2021-02-07T10:00:28Z | [] | [] |
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jul/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1912, in bind_parameters
return self.assign_parameters(value_dict)
File "/Users/jul/Qiskit/qiskit-terra/qiskit/circuit/quantumcircuit.py", line 1886, in assign_parameters
', '.join(map(str, params_not_in_circuit))))
qiskit.circuit.exceptions.CircuitError: 'Cannot bind parameters (x) not present in the circuit.'
| 1,695 |