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2024-07-25 21:41:45
*Starting idea: 20240725_214144_rl_lr_adaptation*
2024-07-25 21:41:45
*Starting Experiments*
Experiment Plan
We will conduct the following experiments to evaluate the effectiveness of using
Q-learning for dynamic learning rate adaptation in transformer training:
1 Run 1: Q-learning with initial learning rate 1e-3
• Dataset: shakespeare_char
• Initial learning rate: 1e-3
• Q-learning parameters:
• Learning rate for Q-learning: 0.1
• Discount factor: 0.9
• Exploration rate: 0.1
2 Run 2: Q-learning with initial learning rate 5e-4
• Dataset: enwik8
• Initial learning rate: 5e-4
• Q-learning parameters:
• Learning rate for Q-learning: 0.1
• Discount factor: 0.9
• Exploration rate: 0.1
3 Run 3: Q-learning with initial learning rate 1e-3
• Dataset: text8
• Initial learning rate: 1e-3
• Q-learning parameters:
• Learning rate for Q-learning: 0.1
• Discount factor: 0.9
• Exploration rate: 0.1
4 Run 4: Q-learning with initial learning rate 5e-4
• Dataset: shakespeare_char
• Initial learning rate: 5e-4
• Q-learning parameters:
• Learning rate for Q-learning: 0.1
• Discount factor: 0.9
• Exploration rate: 0.1
5 Run 5: Q-learning with initial learning rate 1e-3
• Dataset: enwik8
• Initial learning rate: 1e-3
• Q-learning parameters:
• Learning rate for Q-learning: 0.1
• Discount factor: 0.9
• Exploration rate: 0.1
Implementation Steps
1 Implement Q-learning agent: Create a Q-learning agent that will adjust the
learning rate based on the current state (validation loss, current learning
rate).
2 Integrate Q-learning agent into training loop: Modify the training loop to
incorporate the Q-learning agent's adjustments to the learning rate at each
evaluation interval.
3 Run experiments: Execute the planned experiments and compare the results with
the baseline.
Step 1: Implement Q-learning Agent
Let's start by implementing the Q-learning agent.
q_learning_agent.py
<<<<<<< SEARCH
=======
import numpy as np
class QLearningAgent:
def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1):
self.lr = lr
self.gamma = gamma
self.epsilon = epsilon
self.q_table = {}
def get_state(self, val_loss, current_lr):
return (round(val_loss, 2), round(current_lr, 5))
def choose_action(self, state):
if np.random.rand() < self.epsilon:
return np.random.choice([-1, 0, 1])
if state not in self.q_table:
self.q_table[state] = [0, 0, 0]
return np.argmax(self.q_table[state]) - 1
def update_q_values(self, state, action, reward, next_state):
if state not in self.q_table:
self.q_table[state] = [0, 0, 0]
if next_state not in self.q_table:
self.q_table[next_state] = [0, 0, 0]
best_next_action = np.argmax(self.q_table[next_state])
td_target = reward + self.gamma *
self.q_table[next_state][best_next_action]
td_error = td_target - self.q_table[state][action + 1]
self.q_table[state][action + 1] += self.lr * td_error
>>>>>>> REPLACE
Step 2: Integrate Q-learning Agent into Training Loop
Next, we will modify the training loop in experiment.py to incorporate the
Q-learning agent's adjustments to the learning rate at each evaluation interval.
experiment.py
<<<<<<< SEARCH
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
import argparse
=======
from q_learning_agent import QLearningAgent
>>>>>>> REPLACE
experiment.py
<<<<<<< SEARCH
# model init
=======
# Q-learning agent init
q_agent = QLearningAgent(lr=0.1, gamma=0.9, epsilon=0.1)
>>>>>>> REPLACE
experiment.py
<<<<<<< SEARCH
val_log_info.append(
=======
# Q-learning: Update learning rate based on Q-learning agent
state = q_agent.get_state(losses["val"], lr)
action = q_agent.choose_action(state)
lr = max(min_lr, lr * (1 + action * 0.1)) # Adjust learning rate
next_state = q_agent.get_state(losses["val"], lr)
reward = -losses["val"] # Reward is negative validation loss
q_agent.update_q_values(state, action, reward, next_state)
val_log_info.append(
>>>>>>> REPLACE
Step 3: Run Experiments
After implementing the Q-learning agent and integrating it into the training
loop, we will run the experiments using the command python experiment.py
--out_dir=run_i where i is the run number.
Please proceed to run the first experiment using the command:
python experiment.py --out_dir=run_1
Once the first experiment is complete, we can proceed with the subsequent
experiments.
10207 prompt tokens, 1220 completion tokens, $0.069335 cost
Applied edit to q_learning_agent.py
Applied edit to experiment.py
# Fix any errors below, if possible.
## Running: /home/anon/ai_scientist/venv/bin/python3 -m flake8
--select=E9,F821,F823,F831,F406,F407,F701,F702,F704,F706 --show-source
--isolated experiment.py
experiment.py:12:17: F821 undefined name 'nn'
class LayerNorm(nn.Module):
^
experiment.py:17:23: F821 undefined name 'nn'
self.weight = nn.Parameter(torch.ones(ndim))
^
experiment.py:17:36: F821 undefined name 'torch'
self.weight = nn.Parameter(torch.ones(ndim))
^
experiment.py:18:21: F821 undefined name 'nn'
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
^
experiment.py:18:34: F821 undefined name 'torch'
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
^
experiment.py:21:16: F821 undefined name 'F'
return F.layer_norm(input, self.weight.shape, self.weight, self.bias,
1e-5)
^
experiment.py:24:27: F821 undefined name 'nn'
class CausalSelfAttention(nn.Module):
^
experiment.py:30:23: F821 undefined name 'nn'
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd,
bias=config.bias)
^
experiment.py:32:23: F821 undefined name 'nn'
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
^
experiment.py:34:29: F821 undefined name 'nn'
self.attn_dropout = nn.Dropout(config.dropout)
^
experiment.py:35:30: F821 undefined name 'nn'
self.resid_dropout = nn.Dropout(config.dropout)
^
experiment.py:40:30: F821 undefined name 'torch'
self.flash = hasattr(torch.nn.functional,
"scaled_dot_product_attention")
^
experiment.py:48:17: F821 undefined name 'torch'
torch.tril(torch.ones(config.block_size,
config.block_size)).view(
^
experiment.py:48:28: F821 undefined name 'torch'
torch.tril(torch.ones(config.block_size,
config.block_size)).view(
^
experiment.py:73:17: F821 undefined name 'torch'
y = torch.nn.functional.scaled_dot_product_attention(
^
experiment.py:85:19: F821 undefined name 'F'
att = F.softmax(att, dim=-1)
^
experiment.py:97:11: F821 undefined name 'nn'
class MLP(nn.Module):
^
experiment.py:101:21: F821 undefined name 'nn'
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd,
bias=config.bias)
^
experiment.py:102:21: F821 undefined name 'nn'
self.gelu = nn.GELU()
^
experiment.py:103:23: F821 undefined name 'nn'
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd,
bias=config.bias)
^
experiment.py:104:24: F821 undefined name 'nn'
self.dropout = nn.Dropout(config.dropout)
^
experiment.py:114:13: F821 undefined name 'nn'
class Block(nn.Module):
^
experiment.py:144:11: F821 undefined name 'nn'
class GPT(nn.Module):
^
experiment.py:152:28: F821 undefined name 'nn'
self.transformer = nn.ModuleDict(
^
experiment.py:154:21: F821 undefined name 'nn'
wte=nn.Embedding(config.vocab_size, config.n_embd),
^
experiment.py:155:21: F821 undefined name 'nn'
wpe=nn.Embedding(config.block_size, config.n_embd),
^
experiment.py:156:22: F821 undefined name 'nn'
drop=nn.Dropout(config.dropout),
^
experiment.py:157:19: F821 undefined name 'nn'
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
^
experiment.py:161:24: F821 undefined name 'nn'
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
^
experiment.py:175:17: F821 undefined name 'torch'
torch.nn.init.normal_(
^
experiment.py:195:31: F821 undefined name 'nn'
if isinstance(module, nn.Linear):
^
experiment.py:196:13: F821 undefined name 'torch'
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
^
experiment.py:198:17: F821 undefined name 'torch'
torch.nn.init.zeros_(module.bias)
^
experiment.py:199:33: F821 undefined name 'nn'
elif isinstance(module, nn.Embedding):
^
experiment.py:200:13: F821 undefined name 'torch'
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
^
experiment.py:208:15: F821 undefined name 'torch'
pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)
^
experiment.py:208:40: F821 undefined name 'torch'
pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)
^
experiment.py:221:20: F821 undefined name 'F'
loss = F.cross_entropy(
^
experiment.py:239:39: F821 undefined name 'nn'
self.transformer.wpe.weight = nn.Parameter(
^
experiment.py:268:56: F821 undefined name 'torch'
fused_available = "fused" in
inspect.signature(torch.optim.AdamW).parameters
^
experiment.py:271:21: F821 undefined name 'torch'
optimizer = torch.optim.AdamW(
^
experiment.py:278:6: F821 undefined name 'torch'
@torch.no_grad()
^
experiment.py:298:24: F821 undefined name 'torch'
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
^
experiment.py:301:21: F821 undefined name 'F'
probs = F.softmax(logits, dim=-1)
^
experiment.py:303:24: F821 undefined name 'torch'
idx_next = torch.multinomial(probs, num_samples=1)
^
experiment.py:305:19: F821 undefined name 'torch'
idx = torch.cat((idx, idx_next), dim=1)
^
experiment.py:351:27: F821 undefined name 'torch'
dtype = 'bfloat16' if torch.cuda.is_available() and
torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or
'float16', the latter will auto implement a GradScaler
^
experiment.py:351:57: F821 undefined name 'torch'
dtype = 'bfloat16' if torch.cuda.is_available() and
torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or
'float16', the latter will auto implement a GradScaler
^
experiment.py:363:5: F821 undefined name 'torch'
torch.manual_seed(1337 + seed_offset)
^
experiment.py:364:5: F821 undefined name 'torch'
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
^
experiment.py:365:5: F821 undefined name 'torch'
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
^
experiment.py:369:20: F821 undefined name 'torch'
"float32": torch.float32,
^
experiment.py:370:21: F821 undefined name 'torch'
"bfloat16": torch.bfloat16,
^
experiment.py:371:20: F821 undefined name 'torch'
"float16": torch.float16,
^
experiment.py:376:14: F821 undefined name 'torch'
else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
^
experiment.py:387:20: F821 undefined name 'np'
data = np.memmap(os.path.join(data_dir, "train.bin"),
dtype=np.uint16, mode="r")
^
experiment.py:387:73: F821 undefined name 'np'
data = np.memmap(os.path.join(data_dir, "train.bin"),
dtype=np.uint16, mode="r")
^
experiment.py:389:20: F821 undefined name 'np'
data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16,
mode="r")
^
experiment.py:389:71: F821 undefined name 'np'
data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16,
mode="r")
^
experiment.py:390:14: F821 undefined name 'torch'
ix = torch.randint(len(data) - block_size, (batch_size,))
^
experiment.py:391:13: F821 undefined name 'torch'
x = torch.stack(
^
experiment.py:392:14: F821 undefined name 'torch'
[torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i
in ix]
^
experiment.py:392:65: F821 undefined name 'np'
[torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i
in ix]
^
experiment.py:394:13: F821 undefined name 'torch'
y = torch.stack(
^
experiment.py:396:17: F821 undefined name 'torch'
torch.from_numpy((data[i + 1 : i + 1 +
block_size]).astype(np.int64))
^
experiment.py:396:76: F821 undefined name 'np'
torch.from_numpy((data[i + 1 : i + 1 +
block_size]).astype(np.int64))
^
experiment.py:451:14: F821 undefined name 'torch'
scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))
^
experiment.py:463:17: F821 undefined name 'torch'
model = torch.compile(model) # requires PyTorch 2.0
^
experiment.py:467:6: F821 undefined name 'torch'
@torch.no_grad()
^
experiment.py:472:22: F821 undefined name 'torch'
losses = torch.zeros(eval_iters)
^
experiment.py:548:21: F821 undefined name 'torch'
torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt"))
^
experiment.py:567:13: F821 undefined name 'torch'
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
^
experiment.py:632:10: F821 undefined name 'torch'
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
^
experiment.py:632:40: F821 undefined name 'torch'
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
^
experiment.py:637:10: F821 undefined name 'torch'
with torch.no_grad():
^
experiment.py:671:10: F821 undefined name 'argparse'
parser = argparse.ArgumentParser(description='Run experiment')
^
experiment.py:694:31: F821 undefined name 'np'
means = {f"{k}_mean": np.mean(v) for k, v in final_info_dict.items()}
^
experiment.py:695:35: F821 undefined name 'np'
stderrs = {f"{k}_stderr": np.std(v) / len(v) for k, v in
final_info_dict.items()}
^
experiment.py:706:9: F821 undefined name 'np'
np.save(f, all_results)
^
## See relevant lines below marked with █.
experiment.py:
...⋮...
9│from q_learning_agent import QLearningAgent
10│
11│# --- BEGIN model.py ---
12█class LayerNorm(nn.Module):
13│ """LayerNorm but with an optional bias. PyTorch doesn't support simply
bias=False"""
14│
15│ def __init__(self, ndim, bias):
16│ super().__init__()
17█ self.weight = nn.Parameter(torch.ones(ndim))
18█ self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
19│
20│ def forward(self, input):
21█ return F.layer_norm(input, self.weight.shape, self.weight,
self.bias, 1e-5)
22│
23│
24█class CausalSelfAttention(nn.Module):
25│
26│ def __init__(self, config):
27│ super().__init__()
28│ assert config.n_embd % config.n_head == 0
29│ # key, query, value projections for all heads, but in a batch
30█ self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd,
bias=config.bias)
31│ # output projection
32█ self.c_proj = nn.Linear(config.n_embd, config.n_embd,
bias=config.bias)
33│ # regularization
34█ self.attn_dropout = nn.Dropout(config.dropout)
35█ self.resid_dropout = nn.Dropout(config.dropout)
36│ self.n_head = config.n_head
37│ self.n_embd = config.n_embd
38│ self.dropout = config.dropout
39│ # flash attention make GPU go brrrrr but support is only in PyTorch
>= 2.0
40█ self.flash = hasattr(torch.nn.functional,
"scaled_dot_product_attention")
41│ if not self.flash:
42│ print(
43│ "WARNING: using slow attention. Flash Attention requires
PyTorch >= 2.0"
44│ )
45│ # causal mask to ensure that attention is only applied to the
left in the input sequence
46│ self.register_buffer(
47│ "bias",
48█ torch.tril(torch.ones(config.block_size,
config.block_size)).view(
49│ 1, 1, config.block_size, config.block_size
50│ ),
51│ )
52│
53│ def forward(self, x):
54│ B, T, C = (
55│ x.size()
...⋮...
70│ # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs,
T) -> (B, nh, T, T)
71│ if self.flash:
72│ # efficient attention using Flash Attention CUDA kernels
73█ y = torch.nn.functional.scaled_dot_product_attention(
74│ q,
75│ k,
76│ v,
77│ attn_mask=None,
78│ dropout_p=self.dropout if self.training else 0,
79│ is_causal=True,
80│ )
81│ else:
82│ # manual implementation of attention
83│ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
84│ att = att.masked_fill(self.bias[:, :, :T, :T] == 0,
float("-inf"))
85█ att = F.softmax(att, dim=-1)
86│ att = self.attn_dropout(att)
87│ y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
88│ y = (
...⋮...
94│ return y
95│
96│
97█class MLP(nn.Module):
98│
99│ def __init__(self, config):
100│ super().__init__()
101█ self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd,
bias=config.bias)
102█ self.gelu = nn.GELU()
103█ self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd,
bias=config.bias)
104█ self.dropout = nn.Dropout(config.dropout)
105│
106│ def forward(self, x):
107│ x = self.c_fc(x)
...⋮...
111│ return x
112│
113│
114█class Block(nn.Module):
115│
116│ def __init__(self, config):
117│ super().__init__()
...⋮...
141│ )
142│
143│
144█class GPT(nn.Module):
145│
146│ def __init__(self, config):
147│ super().__init__()
148│ assert config.vocab_size is not None
149│ assert config.block_size is not None
150│ self.config = config
151│
152█ self.transformer = nn.ModuleDict(
153│ dict(
154█ wte=nn.Embedding(config.vocab_size, config.n_embd),
155█ wpe=nn.Embedding(config.block_size, config.n_embd),
156█ drop=nn.Dropout(config.dropout),
157█ h=nn.ModuleList([Block(config) for _ in
range(config.n_layer)]),
158│ ln_f=LayerNorm(config.n_embd, bias=config.bias),
159│ )
160│ )
161█ self.lm_head = nn.Linear(config.n_embd, config.vocab_size,
bias=False)
162│ # with weight tying when using torch.compile() some warnings get
generated:
163│ # "UserWarning: functional_call was passed multiple values for tied
weights.
164│ # This behavior is deprecated and will be an error in future
versions"
...⋮...
172│ # apply special scaled init to the residual projections, per GPT-2
paper
173│ for pn, p in self.named_parameters():
174│ if pn.endswith("c_proj.weight"):
175█ torch.nn.init.normal_(
176│ p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)
177│ )
178│
...⋮...
192│ return n_params
193│
194│ def _init_weights(self, module):
195█ if isinstance(module, nn.Linear):
196█ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
197│ if module.bias is not None:
198█ torch.nn.init.zeros_(module.bias)
199█ elif isinstance(module, nn.Embedding):
200█ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
201│
202│ def forward(self, idx, targets=None):
203│ device = idx.device
204│ b, t = idx.size()
205│ assert (
206│ t <= self.config.block_size
207│ ), f"Cannot forward sequence of length {t}, block size is only
{self.config.block_size}"
208█ pos = torch.arange(0, t, dtype=torch.long, device=device) # shape
(t)
209│
210│ # forward the GPT model itself
211│ tok_emb = self.transformer.wte(idx) # token embeddings of shape (b,
t, n_embd)
...⋮...
218│ if targets is not None:
219│ # if we are given some desired targets also calculate the loss
220│ logits = self.lm_head(x)
221█ loss = F.cross_entropy(
222│ logits.view(-1, logits.size(-1)), targets.view(-1),
ignore_index=-1
223│ )
224│ else:
...⋮...
233│ def crop_block_size(self, block_size):
...⋮...
236│ # but want to use a smaller block size for some smaller, simpler
model
237│ assert block_size <= self.config.block_size
238│ self.config.block_size = block_size
239█ self.transformer.wpe.weight = nn.Parameter(
240│ self.transformer.wpe.weight[:block_size]
241│ )
242│ for block in self.transformer.h:
...⋮...
246│ def configure_optimizers(self, weight_decay, learning_rate, betas,
device_type):
247│ # start with all of the candidate parameters
248│ param_dict = {pn: p for pn, p in self.named_parameters()}
...⋮...
265│ f"num non-decayed parameter tensors: {len(nodecay_params)}, with
{num_nodecay_params:,} parameters"
266│ )
267│ # Create AdamW optimizer and use the fused version if it is
available
268█ fused_available = "fused" in
inspect.signature(torch.optim.AdamW).parameters
269│ use_fused = fused_available and device_type == "cuda"
270│ extra_args = dict(fused=True) if use_fused else dict()
271█ optimizer = torch.optim.AdamW(
272│ optim_groups, lr=learning_rate, betas=betas, **extra_args
273│ )
274│ print(f"using fused AdamW: {use_fused}")
275│
276│ return optimizer
277│
278█ @torch.no_grad()
279│ def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
280│ """
281│ Take a conditioning sequence of indices idx (LongTensor of shape
(b,t)) and complete
282│ the sequence max_new_tokens times, feeding the predictions back into
the model each time.
283│ Most likely you'll want to make sure to be in model.eval() mode of
operation for this.
284│ """
285│ for _ in range(max_new_tokens):
286│ # if the sequence context is growing too long we must crop it at
block_size
287│ idx_cond = (
288│ idx
289│ if idx.size(1) <= self.config.block_size
290│ else idx[:, -self.config.block_size :]
...⋮...
295│ logits = logits[:, -1, :] / temperature
296│ # optionally crop the logits to only the top k options
297│ if top_k is not None:
298█ v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
299│ logits[logits < v[:, [-1]]] = -float("Inf")
300│ # apply softmax to convert logits to (normalized) probabilities
301█ probs = F.softmax(logits, dim=-1)
302│ # sample from the distribution
303█ idx_next = torch.multinomial(probs, num_samples=1)
304│ # append sampled index to the running sequence and continue
305█ idx = torch.cat((idx, idx_next), dim=1)
306│
307│ return idx
308│
...⋮...
311│def train(dataset="shakespeare_char", out_dir="run_0", seed_offset=0):
...⋮...
315│ gradient_accumulation_steps = 1
...⋮...
348│ backend = "nccl" # 'nccl', 'gloo', etc.
349│ # system
350│ device = "cuda" # Always use CUDA
351█ dtype = 'bfloat16' if torch.cuda.is_available() and
torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or
'float16', the latter will auto implement a GradScaler
352│ compile = True # do not torch compile the model on macbooks
353│
354│
...⋮...
360│
361│ if master_process:
362│ os.makedirs(out_dir, exist_ok=True)
363█ torch.manual_seed(1337 + seed_offset)
364█ torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
365█ torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
366│ device_type = "cuda" if "cuda" in device else "cpu" # for later use in
torch.autocast
367│ # note: float16 data type will automatically use a GradScaler
368│ ptdtype = {
369█ "float32": torch.float32,
370█ "bfloat16": torch.bfloat16,
371█ "float16": torch.float16,
372│ }[dtype]
373│ ctx = (
374│ nullcontext()
375│ if device_type == "cpu"
376█ else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
377│ )
378│
379│ # poor man's data loader
...⋮...
383│ def get_batch(split):
384│ # We recreate np.memmap every batch to avoid a memory leak, as per
385│ #
https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-i
terate-once/61472122#61472122
386│ if split == "train":
387█ data = np.memmap(os.path.join(data_dir, "train.bin"),
dtype=np.uint16, mode="r")
388│ else:
389█ data = np.memmap(os.path.join(data_dir, "val.bin"),
dtype=np.uint16, mode="r")
390█ ix = torch.randint(len(data) - block_size, (batch_size,))
391█ x = torch.stack(
392█ [torch.from_numpy((data[i : i + block_size]).astype(np.int64))
for i in ix]
393│ )
394█ y = torch.stack(
395│ [
396█ torch.from_numpy((data[i + 1 : i + 1 +
block_size]).astype(np.int64))
397│ for i in ix
398│ ]
399│ )
...⋮...
448│ model.to(device)
449│
450│ # initialize a GradScaler. If enabled=False scaler is a no-op
451█ scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))
452│
453│ # optimizer
454│ optimizer = model.configure_optimizers(
...⋮...
460│ if compile:
461│ print("compiling the model... (takes a ~minute)")
462│ unoptimized_model = model
463█ model = torch.compile(model) # requires PyTorch 2.0
464│
465│
466│ # helps estimate an arbitrarily accurate loss over either split using
many batches
467█ @torch.no_grad()
468│ def estimate_loss():
469│ out = {}
470│ model.eval()
471│ for split in ["train", "val"]:
472█ losses = torch.zeros(eval_iters)
473│ for k in range(eval_iters):
474│ X, Y = get_batch(split)
475│ with ctx:
...⋮...
508│ while True:
509│
...⋮...
511│ lr = get_lr(iter_num) if decay_lr else learning_rate
...⋮...
516│ if iter_num % eval_interval == 0 and master_process:
517│ losses = estimate_loss()
...⋮...
537│ if losses["val"] < best_val_loss or always_save_checkpoint:
538│ best_val_loss = losses["val"]
539│ if iter_num > 0 and not never_save_checkpoint:
540│ checkpoint = {
541│ "model": raw_model.state_dict(),
542│ "optimizer": optimizer.state_dict(),
543│ "model_args": model_args,
544│ "iter_num": iter_num,
545│ "best_val_loss": best_val_loss,
546│ }
547│ print(f"saving checkpoint to {out_dir}")
548█ torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt"))
549│ if iter_num == 0 and eval_only:
550│ break
551│
...⋮...
564│ # clip the gradient
565│ if grad_clip != 0.0:
566│ scaler.unscale_(optimizer)
567█ torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
568│ # step the optimizer and scaler if training in fp16
569│ scaler.step(optimizer)
570│ scaler.update()
...⋮...
629│ with open(start[5:], 'r', encoding='utf-8') as f:
630│ start = f.read()
631│ start_ids = encode(start)
632█ x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None,
...])
633│
634│ # Run generation
635│ model.eval()
636│ results = []
637█ with torch.no_grad():
638│ with ctx:
639│ for k in range(num_samples):
640│ start_time = time.time()
...⋮...
668│ json.dump(final_info, f)
669│ return final_info, train_log_info, val_log_info
670│
671█parser = argparse.ArgumentParser(description='Run experiment')
672│parser.add_argument('--out_dir', type=str, default='run_0', help='Output
directory')
673│args = parser.parse_args()
674│
675│if __name__ == "__main__":
676│ num_seeds = {
677│ "shakespeare_char": 3,
678│ "enwik8": 1,
679│ "text8": 1,
...⋮...
685│ for dataset in ["shakespeare_char", "enwik8", "text8"]:
686│ final_info_list = []
...⋮...
691│ all_results[f"{dataset}_{seed_offset}_val_info"] = val_info
692│ final_info_list.append(final_info)
693│ final_info_dict = {k: [d[k] for d in final_info_list] for k in
final_info_list[0].keys()}
694█ means = {f"{k}_mean": np.mean(v) for k, v in
final_info_dict.items()}
695█ stderrs = {f"{k}_stderr": np.std(v) / len(v) for k, v in
final_info_dict.items()}
696│ final_infos[dataset] = {
697│ "means": means,
698│ "stderrs": stderrs,
...⋮...
703│ json.dump(final_infos, f)
704│
705│ with open(os.path.join(out_dir, "all_results.npy"), "wb") as f:
706█ np.save(f, all_results)
To fix the errors, we need to ensure that the necessary imports for torch,
torch.nn, torch.nn.functional, numpy, and argparse are included in
experiment.py.
Here are the SEARCH/REPLACE blocks to fix the errors:
experiment.py
<<<<<<< SEARCH
import os
import time
import math
import pickle
import inspect
import json
from contextlib import nullcontext
from dataclasses import dataclass
from q_learning_agent import QLearningAgent
=======
import os
import time
import math
import pickle
import inspect
import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
from contextlib import nullcontext
from dataclasses import dataclass
from q_learning_agent import QLearningAgent
>>>>>>> REPLACE
Please apply these changes and then re-run the command python experiment.py
--out_dir=run_1.
18550 prompt tokens, 200 completion tokens, $0.095750 cost
Applied edit to experiment.py
To fix the errors, we need to ensure that the necessary imports for `torch`, `torch.nn`, `torch.nn.functional`, `numpy`, and `argparse` are included in `experiment.py`.
Here are the *SEARCH/REPLACE* blocks to fix the errors:
experiment.py
```python
<<<<<<< SEARCH
import os
import time
import math
import pickle
import inspect
import json
from contextlib import nullcontext
from dataclasses import dataclass
from q_learning_agent import QLearningAgent
=======
import os
import time
import math
import pickle
import inspect
import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
from contextlib import nullcontext
from dataclasses import dataclass
from q_learning_agent import QLearningAgent
>>>>>>> REPLACE
```
Please apply these changes and then re-run the command `python experiment.py --out_dir=run_1`.
Traceback (most recent call last):
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 693, in <module>
final_info, train_info, val_info = train(dataset, out_dir, seed_offset)
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 527, in train
state = q_agent.get_state(losses["val"], lr)
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/q_learning_agent.py", line 11, in get_state
return (round(val_loss, 2), round(current_lr, 5))
TypeError: type Tensor doesn't define __round__ method
Run 1 failed with return code 1
Run failed with the following error Traceback (most recent call last):
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 693, in <module>
final_info, train_info, val_info = train(dataset, out_dir, seed_offset)
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/experiment.py", line 527, in train
state = q_agent.get_state(losses["val"], lr)
File "/home/anon/ai_scientist/results/nanoGPT/20240725_214144_rl_lr_adaptation/q_learning_agent.py", line 11, in get_state
return (round(val_loss, 2), round(current_lr, 5))
TypeError: type Tensor doesn't define __round__ method
To fix the error, we need to convert the val_loss tensor to a Python float
before rounding it. We can do this by calling the .item() method on the tensor.
Here are the necessary changes:
q_learning_agent.py
<<<<<<< SEARCH
return (round(val_loss, 2), round(current_lr, 5))
=======
return (round(val_loss.item(), 2), round(current_lr, 5))
>>>>>>> REPLACE
This change will ensure that val_loss is converted to a float before rounding,
which should resolve the TypeError.
10543 prompt tokens, 122 completion tokens, $0.054545 cost
Applied edit to q_learning_agent.py
To fix the error, we need to convert the `val_loss` tensor to a Python float before rounding it. We can do this by calling the `.item()` method on the tensor.
Here are the necessary changes:
q_learning_agent.py
```python
<<<<<<< SEARCH
return (round(val_loss, 2), round(current_lr, 5))
=======
return (round(val_loss.item(), 2), round(current_lr, 5))
>>>>>>> REPLACE
```
This change will ensure that `val_loss` is converted to a float before rounding, which should resolve the `TypeError`.
First, let's update the notes.txt file to include the results and description
for Run 1.
notes.txt
<<<<<<< SEARCH
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437,
'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean':
77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527},
'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean':
1.0055421590805054, 'total_train_time_mean': 819.4551751613617,
'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8':
{'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean':
0.979989230632782, 'total_train_time_mean': 801.224205493927,
'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
=======
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437,
'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean':
77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527},
'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean':
1.0055421590805054, 'total_train_time_mean': 819.4551751613617,
'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8':
{'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean':
0.979989230632782, 'total_train_time_mean': 801.224205493927,
'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046,
'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean':
76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138},
'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean':
1.0050768852233887, 'total_train_time_mean': 799.199625492096,
'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8':
{'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean':
0.9795507192611694, 'total_train_time_mean': 796.1075961589813,
'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation. The Q-learning agent adjusted the learning rate based on the curre
state (validation loss, current learning rate) and used a reward signal derive
from validation performance to update the Q-values. The training loop was
modified to incorporate the RL agent's adjustments to the learning rate at eac
evaluation interval. The results were compared with the baseline model using
static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
Next, let's implement the next experiment. For this experiment, we will use a
different initial learning rate to see how it affects the Q-learning adaptation.
We will set the initial learning rate to 2e-3 for shakespeare_char and 1e-3 for
enwik8 and text8.
experiment.py
<<<<<<< SEARCH
learning_rate = (
1e-3 if dataset == "shakespeare_char" else 5e-4
)
=======
learning_rate = (
2e-3 if dataset == "shakespeare_char" else 1e-3
)
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_2
11043 prompt tokens, 973 completion tokens, $0.069810 cost
Applied edit to experiment.py
Applied edit to notes.txt
First, let's update the `notes.txt` file to include the results and description for Run 1.
notes.txt
```python
<<<<<<< SEARCH
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
=======
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437, 'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean': 77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527}, 'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean': 1.0055421590805054, 'total_train_time_mean': 819.4551751613617, 'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8': {'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean': 0.979989230632782, 'total_train_time_mean': 801.224205493927, 'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
```
Next, let's implement the next experiment. For this experiment, we will use a different initial learning rate to see how it affects the Q-learning adaptation. We will set the initial learning rate to `2e-3` for `shakespeare_char` and `1e-3` for `enwik8` and `text8`.
experiment.py
```python
<<<<<<< SEARCH
learning_rate = (
1e-3 if dataset == "shakespeare_char" else 5e-4
)
=======
learning_rate = (
2e-3 if dataset == "shakespeare_char" else 1e-3
)
>>>>>>> REPLACE
```
Now, you can run the next experiment with the command:
```sh
python experiment.py --out_dir=run_2
```
First, let's update the notes.txt file to include the results and description
for Run 2.
notes.txt
<<<<<<< SEARCH
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046,
'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean':
76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138},
'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean':
1.0050768852233887, 'total_train_time_mean': 799.199625492096,
'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8':
{'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean':
0.9795507192611694, 'total_train_time_mean': 796.1075961589813,
'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation. The Q-learning agent adjusted the learning rate based on the curre
state (validation loss, current learning rate) and used a reward signal derive
from validation performance to update the Q-values. The training loop was
modified to incorporate the RL agent's adjustments to the learning rate at eac
evaluation interval. The results were compared with the baseline model using
static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
=======
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046,
'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean':
76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138},
'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean':
1.0050768852233887, 'total_train_time_mean': 799.199625492096,
'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8':
{'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean':
0.9795507192611694, 'total_train_time_mean': 796.1075961589813,
'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation. The Q-learning agent adjusted the learning rate based on the curre
state (validation loss, current learning rate) and used a reward signal derive
from validation performance to update the Q-values. The training loop was
modified to incorporate the RL agent's adjustments to the learning rate at eac
evaluation interval. The results were compared with the baseline model using
static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812,
'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean':
76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302},
'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean':
0.9933806657791138, 'total_train_time_mean': 806.1875951290131,
'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8':
{'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean':
0.9613448977470398, 'total_train_time_mean': 807.7686207294464,
'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with different initial learning rates. The initial learning rate wa
set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning
agent adjusted the learning rate based on the current state (validation loss,
current learning rate) and used a reward signal derived from validation
performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char
enwik8, text8).
>>>>>>> REPLACE
Next, let's implement the next experiment. For this experiment, we will use a
different reward signal for the Q-learning agent. Instead of using the negative
validation loss as the reward, we will use the improvement in validation loss as
the reward.
q_learning_agent.py
<<<<<<< SEARCH
reward = -losses["val"] # Reward is negative validation loss
=======
reward = best_val_loss - losses["val"] # Reward is improvement in
validation loss
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_3
12718 prompt tokens, 1173 completion tokens, $0.081185 cost
Applied edit to q_learning_agent.py
Applied edit to notes.txt
First, let's update the `notes.txt` file to include the results and description for Run 2.
notes.txt
```python
<<<<<<< SEARCH
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
=======
## Run 1: Q-learning with initial learning rate adaptation
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8112714489301046, 'best_val_loss_mean': 1.4664853016535442, 'total_train_time_mean': 76.33582202593486, 'avg_inference_tokens_per_second_mean': 680.220956113138}, 'enwik8': {'final_train_loss_mean': 0.9324554204940796, 'best_val_loss_mean': 1.0050768852233887, 'total_train_time_mean': 799.199625492096, 'avg_inference_tokens_per_second_mean': 690.1664700419294}, 'text8': {'final_train_loss_mean': 0.9926028251647949, 'best_val_loss_mean': 0.9795507192611694, 'total_train_time_mean': 796.1075961589813, 'avg_inference_tokens_per_second_mean': 691.9504174462957}}
Description: This run implemented Q-learning for dynamic learning rate adaptation. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
```
Next, let's implement the next experiment. For this experiment, we will use a different reward signal for the Q-learning agent. Instead of using the negative validation loss as the reward, we will use the improvement in validation loss as the reward.
q_learning_agent.py
```python
<<<<<<< SEARCH
reward = -losses["val"] # Reward is negative validation loss
=======
reward = best_val_loss - losses["val"] # Reward is improvement in validation loss
>>>>>>> REPLACE
```
Now, you can run the next experiment with the command:
```sh
python experiment.py --out_dir=run_3
```
Let's first update the notes.txt file with the results and description for Run
3.
notes.txt
<<<<<<< SEARCH
Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run
implemented Q-learning for dynamic learning rate adaptation with a different
reward signal. Instead of using the negative validation loss as the reward, the
improvement in validation loss was used. The Q-learning agent adjusted the
learning rate based on the current state (validation loss, current learning
rate) and used the reward signal derived from the improvement in validation
performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char,
enwik8, text8).
▌ ▌ ▌ ▌ ▌ ▌ ▌ REPLACE
Next, let's propose the next experiment. For Run 4, we will use a different
exploration strategy for the Q-learning agent. Instead of using a fixed epsilo
value for exploration, we will use an epsilon decay strategy where epsilon
decreases over time.
Here are the changes needed:
q_learning_agent.py
```python
<<<<<<< SEARCH
self.epsilon = epsilon
=======
self.epsilon = epsilon
self.initial_epsilon = epsilon
self.epsilon_decay = 0.99 # Decay rate for epsilon
>>>>>>> REPLACE
q_learning_agent.py
<<<<<<< SEARCH
if np.random.rand() < self.epsilon:
=======
if np.random.rand() < self.epsilon:
self.epsilon *= self.epsilon_decay # Decay epsilon
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_4
11638 prompt tokens, 584 completion tokens, $0.066950 cost
The LLM did not conform to the edit format.
https://aider.chat/docs/troubleshooting/edit-errors.html
Let's first update the `notes.txt` file with the results and description for Run
3.
notes.txt
```python
<<<<<<< SEARCH
```
## Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with a different reward signal. Instead of using the negative
validation loss as the reward, the improvement in validation loss was used. The
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used the reward signal derived from
the improvement in validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning rate
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
^^^ Expected `=======` not >>>>>>> REPLACE
Let's update the notes.txt file with the results and description for Run 3
correctly.
notes.txt
<<<<<<< SEARCH
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812,
'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean':
76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302},
'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean':
0.9933806657791138, 'total_train_time_mean': 806.1875951290131,
'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8':
{'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean':
0.9613448977470398, 'total_train_time_mean': 807.7686207294464,
'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with different initial learning rates. The initial learning rate wa
set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning
agent adjusted the learning rate based on the current state (validation loss,
current learning rate) and used a reward signal derived from validation
performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char
enwik8, text8).
=======
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812,
'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean':
76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302},
'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean':
0.9933806657791138, 'total_train_time_mean': 806.1875951290131,
'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8':
{'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean':
0.9613448977470398, 'total_train_time_mean': 807.7686207294464,
'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with different initial learning rates. The initial learning rate wa
set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning
agent adjusted the learning rate based on the current state (validation loss,
current learning rate) and used a reward signal derived from validation
performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char
enwik8, text8).
## Run 3: Q-learning with reward signal based on improvement in validation los
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with a different reward signal. Instead of using the negative
validation loss as the reward, the improvement in validation loss was used. Th
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used the reward signal derived fr
the improvement in validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning ra
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
Next, let's propose the next experiment. For Run 4, we will use a different
exploration strategy for the Q-learning agent. Instead of using a fixed epsilon
value for exploration, we will use an epsilon decay strategy where epsilon
decreases over time.
Here are the changes needed:
q_learning_agent.py
<<<<<<< SEARCH
self.epsilon = epsilon
=======
self.epsilon = epsilon
self.initial_epsilon = epsilon
self.epsilon_decay = 0.99 # Decay rate for epsilon
>>>>>>> REPLACE
q_learning_agent.py
<<<<<<< SEARCH
if np.random.rand() < self.epsilon:
=======
if np.random.rand() < self.epsilon:
self.epsilon *= self.epsilon_decay # Decay epsilon
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_4
12629 prompt tokens, 1303 completion tokens, $0.082690 cost
Applied edit to q_learning_agent.py
Applied edit to notes.txt
Let's update the `notes.txt` file with the results and description for Run 3 correctly.
notes.txt
```python
<<<<<<< SEARCH
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
=======
## Run 2: Q-learning with different initial learning rates
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8047561645507812, 'best_val_loss_mean': 1.4602874914805095, 'total_train_time_mean': 76.26222737630208, 'avg_inference_tokens_per_second_mean': 675.5019470493302}, 'enwik8': {'final_train_loss_mean': 0.9224221706390381, 'best_val_loss_mean': 0.9933806657791138, 'total_train_time_mean': 806.1875951290131, 'avg_inference_tokens_per_second_mean': 682.6881990162254}, 'text8': {'final_train_loss_mean': 0.9798105955123901, 'best_val_loss_mean': 0.9613448977470398, 'total_train_time_mean': 807.7686207294464, 'avg_inference_tokens_per_second_mean': 652.3187905322042}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with different initial learning rates. The initial learning rate was set to 2e-3 for shakespeare_char and 1e-3 for enwik8 and text8. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
## Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
```
Next, let's propose the next experiment. For Run 4, we will use a different exploration strategy for the Q-learning agent. Instead of using a fixed epsilon value for exploration, we will use an epsilon decay strategy where epsilon decreases over time.
Here are the changes needed:
q_learning_agent.py
```python
<<<<<<< SEARCH
self.epsilon = epsilon
=======
self.epsilon = epsilon
self.initial_epsilon = epsilon
self.epsilon_decay = 0.99 # Decay rate for epsilon
>>>>>>> REPLACE
```
q_learning_agent.py
```python
<<<<<<< SEARCH
if np.random.rand() < self.epsilon:
=======
if np.random.rand() < self.epsilon:
self.epsilon *= self.epsilon_decay # Decay epsilon
>>>>>>> REPLACE
```
Now, you can run the next experiment with the command:
```bash
python experiment.py --out_dir=run_4
```
Let's first update the notes.txt file with the results and description for Run
4.
notes.txt
<<<<<<< SEARCH
Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}} Description: This run
implemented Q-learning for dynamic learning rate adaptation with a different
reward signal. Instead of using the negative validation loss as the reward, the
improvement in validation loss was used. The Q-learning agent adjusted the
learning rate based on the current state (validation loss, current learning
rate) and used the reward signal derived from the improvement in validation
performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char,
enwik8, text8).
Run 4: Q-learning with epsilon decay strategy
Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894,
'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean':
79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749},
'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean':
0.9917866587638855, 'total_train_time_mean': 852.1484353542328,
'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8':
{'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean':
0.9615200161933899, 'total_train_time_mean': 846.4471461772919,
'avg_inference_tokens_per_second_mean': 613.2623906747798}} Description: This
run implemented Q-learning for dynamic learning rate adaptation with an epsilon
decay strategy. Instead of using a fixed epsilon value for exploration, an
epsilon decay strategy was used where epsilon decreases over time. The
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used a reward signal derived from
validation performance to update the Q-values. The training loop was modified to
incorporate the RL agent's adjustments to the learning rate at each evaluation
interval. The results were compared with the baseline model using static or
heuristic-based learning rate schedules on multiple datasets (shakespeare_char,
enwik8, text8).
Next, let's propose the next experiment. We will use a different discount factor
(gamma) for the Q-learning agent to see how it affects the learning rate
adaptation.
q_learning_agent.py
<<<<<<< SEARCH
def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1):
=======
def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1):
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_5
12168 prompt tokens, 864 completion tokens, $0.073800 cost
The LLM did not conform to the edit format.
https://aider.chat/docs/troubleshooting/edit-errors.html
Let's first update the `notes.txt` file with the results and description for Run
4.
notes.txt
```python
<<<<<<< SEARCH
```
## Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with a different reward signal. Instead of using the negative
validation loss as the reward, the improvement in validation loss was used. The
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used the reward signal derived from
the improvement in validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning rate
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
## Run 4: Q-learning with epsilon decay strategy
Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894,
'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean':
79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749},
'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean':
0.9917866587638855, 'total_train_time_mean': 852.1484353542328,
'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8':
{'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean':
0.9615200161933899, 'total_train_time_mean': 846.4471461772919,
'avg_inference_tokens_per_second_mean': 613.2623906747798}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with an epsilon decay strategy. Instead of using a fixed epsilon
value for exploration, an epsilon decay strategy was used where epsilon
decreases over time. The Q-learning agent adjusted the learning rate based on
the current state (validation loss, current learning rate) and used a reward
signal derived from validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning rate
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
```
```
Next, let's propose the next experiment. We will use a different discount factor
(gamma) for the Q-learning agent to see how it affects the learning rate
adaptation.
q_learning_agent.py
```python
<<<<<<< SEARCH
^^^ Expected `=======` not <<<<<<< SEARCH
Let's first update the notes.txt file with the results and description for Run
4.
notes.txt
<<<<<<< SEARCH
## Run 3: Q-learning with reward signal based on improvement in validation los
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with a different reward signal. Instead of using the negative
validation loss as the reward, the improvement in validation loss was used. Th
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used the reward signal derived fr
the improvement in validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning ra
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
=======
## Run 3: Q-learning with reward signal based on improvement in validation los
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496,
'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean':
75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188},
'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean':
0.9944368004798889, 'total_train_time_mean': 796.9592888355255,
'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8':
{'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean':
0.961367666721344, 'total_train_time_mean': 791.6123127937317,
'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with a different reward signal. Instead of using the negative
validation loss as the reward, the improvement in validation loss was used. Th
Q-learning agent adjusted the learning rate based on the current state
(validation loss, current learning rate) and used the reward signal derived fr
the improvement in validation performance to update the Q-values. The training
loop was modified to incorporate the RL agent's adjustments to the learning ra
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
## Run 4: Q-learning with epsilon decay strategy
Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894,
'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean':
79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749},
'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean':
0.9917866587638855, 'total_train_time_mean': 852.1484353542328,
'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8':
{'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean':
0.9615200161933899, 'total_train_time_mean': 846.4471461772919,
'avg_inference_tokens_per_second_mean': 613.2623906747798}}
Description: This run implemented Q-learning for dynamic learning rate
adaptation with an epsilon decay strategy. Instead of using a fixed epsilon
value for exploration, an epsilon decay strategy was used where epsilon
decreases over time. The Q-learning agent adjusted the learning rate based on
the current state (validation loss, current learning rate) and used a reward
signal derived from validation performance to update the Q-values. The trainin
loop was modified to incorporate the RL agent's adjustments to the learning ra
at each evaluation interval. The results were compared with the baseline model
using static or heuristic-based learning rate schedules on multiple datasets
(shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
Next, let's propose the next experiment. We will use a different discount factor
(gamma) for the Q-learning agent to see how it affects the learning rate
adaptation.
q_learning_agent.py
<<<<<<< SEARCH
def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1):
=======
def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1):
>>>>>>> REPLACE
Now, you can run the next experiment with the command:
python experiment.py --out_dir=run_5
13832 prompt tokens, 1218 completion tokens, $0.087430 cost
Applied edit to q_learning_agent.py
Applied edit to notes.txt
Let's first update the `notes.txt` file with the results and description for Run 4.
notes.txt
```python
<<<<<<< SEARCH
## Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
=======
## Run 3: Q-learning with reward signal based on improvement in validation loss
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8062439958254496, 'best_val_loss_mean': 1.461962143580119, 'total_train_time_mean': 75.80110216140747, 'avg_inference_tokens_per_second_mean': 668.3102066342188}, 'enwik8': {'final_train_loss_mean': 0.9246289730072021, 'best_val_loss_mean': 0.9944368004798889, 'total_train_time_mean': 796.9592888355255, 'avg_inference_tokens_per_second_mean': 688.6266631351763}, 'text8': {'final_train_loss_mean': 0.9843199849128723, 'best_val_loss_mean': 0.961367666721344, 'total_train_time_mean': 791.6123127937317, 'avg_inference_tokens_per_second_mean': 658.961942825521}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with a different reward signal. Instead of using the negative validation loss as the reward, the improvement in validation loss was used. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used the reward signal derived from the improvement in validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
## Run 4: Q-learning with epsilon decay strategy
Results: {'shakespeare_char': {'final_train_loss_mean': 0.7984780073165894, 'best_val_loss_mean': 1.463551680246989, 'total_train_time_mean': 79.24612506230672, 'avg_inference_tokens_per_second_mean': 617.9132836431749}, 'enwik8': {'final_train_loss_mean': 0.925983190536499, 'best_val_loss_mean': 0.9917866587638855, 'total_train_time_mean': 852.1484353542328, 'avg_inference_tokens_per_second_mean': 605.0617699125265}, 'text8': {'final_train_loss_mean': 0.9827583432197571, 'best_val_loss_mean': 0.9615200161933899, 'total_train_time_mean': 846.4471461772919, 'avg_inference_tokens_per_second_mean': 613.2623906747798}}
Description: This run implemented Q-learning for dynamic learning rate adaptation with an epsilon decay strategy. Instead of using a fixed epsilon value for exploration, an epsilon decay strategy was used where epsilon decreases over time. The Q-learning agent adjusted the learning rate based on the current state (validation loss, current learning rate) and used a reward signal derived from validation performance to update the Q-values. The training loop was modified to incorporate the RL agent's adjustments to the learning rate at each evaluation interval. The results were compared with the baseline model using static or heuristic-based learning rate schedules on multiple datasets (shakespeare_char, enwik8, text8).
>>>>>>> REPLACE
```
Next, let's propose the next experiment. We will use a different discount factor (gamma) for the Q-learning agent to see how it affects the learning rate adaptation.
q_learning_agent.py
```python
<<<<<<< SEARCH
def __init__(self, lr=0.1, gamma=0.9, epsilon=0.1):
=======
def __init__(self, lr=0.1, gamma=0.95, epsilon=0.1):
>>>>>>> REPLACE
```
Now, you can run the next experiment with the command:
```sh
python experiment.py --out_dir=run_5
```
To modify plot.py to generate the most relevant plots for the final writeup and
fill in the "labels" dictionary with the correct names for each run, we will:
1 Update the labels dictionary to include all relevant runs.
2 Ensure that the plots are generated for each dataset and saved with
appropriate filenames.
Here are the SEARCH/REPLACE blocks:
plot.py
<<<<<<< SEARCH
labels = {
"run_0": "Baselines",
=======
labels = {
"run_0": "Baseline",
"run_1": "Q-learning with initial learning rate adaptation",
"run_2": "Q-learning with different initial learning rates",
"run_3": "Q-learning with reward signal based on improvement in validation
loss",
"run_4": "Q-learning with epsilon decay strategy",
>>>>>>> REPLACE
}
plot.py
<<<<<<< SEARCH
# Plot 1: Line plot of training loss for each dataset across the runs with
labels
for dataset in datasets:
plt.figure(figsize=(10, 6))
for i, run in enumerate(runs):
iters = results_info[run][dataset]["iters"]
mean = results_info[run][dataset]["train_loss"]
sterr = results_info[run][dataset]["train_loss_sterr"]
plt.plot(iters, mean, label=labels[run], color=colors[i])
plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i],
alpha=0.2)
plt.title(f"Training Loss Across Runs for {dataset} Dataset")
plt.xlabel("Iteration")
plt.ylabel("Training Loss")
plt.legend()
plt.grid(True, which="both", ls="-", alpha=0.2)
plt.tight_layout()
plt.savefig(f"train_loss_{dataset}.png")
plt.close()
# Plot 2: Line plot of validation loss for each dataset across the runs with
labels
for dataset in datasets:
plt.figure(figsize=(10, 6))
for i, run in enumerate(runs):
iters = results_info[run][dataset]["iters"]
mean = results_info[run][dataset]["val_loss"]
sterr = results_info[run][dataset]["val_loss_sterr"]
plt.plot(iters, mean, label=labels[run], color=colors[i])
plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i],
alpha=0.2)
plt.title(f"Validation Loss Across Runs for {dataset} Dataset")
plt.xlabel("Iteration")
plt.ylabel("Validation Loss")
plt.legend()
plt.grid(True, which="both", ls="-", alpha=0.2)
plt.tight_layout()
plt.savefig(f"val_loss_{dataset}.png")
plt.close()
=======
# Plot 1: Line plot of training loss for each dataset across the runs with
labels
for dataset in datasets:
plt.figure(figsize=(10, 6))
for i, run in enumerate(runs):
iters = results_info[run][dataset]["iters"]
mean = results_info[run][dataset]["train_loss"]
sterr = results_info[run][dataset]["train_loss_sterr"]
plt.plot(iters, mean, label=labels[run], color=colors[i])
plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i],
alpha=0.2)
plt.title(f"Training Loss Across Runs for {dataset} Dataset")
plt.xlabel("Iteration")
plt.ylabel("Training Loss")
plt.legend()
plt.grid(True, which="both", ls="-", alpha=0.2)
plt.tight_layout()
plt.savefig(f"train_loss_{dataset}.png")
plt.close()
# Plot 2: Line plot of validation loss for each dataset across the runs with
labels
for dataset in datasets:
plt.figure(figsize=(10, 6))
for i, run in enumerate(runs):
iters = results_info[run][dataset]["iters"]
mean = results_info[run][dataset]["val_loss"]
sterr = results_info[run][dataset]["val_loss_sterr"]
plt.plot(iters, mean, label=labels[run], color=colors[i])
plt.fill_between(iters, mean - sterr, mean + sterr, color=colors[i],
alpha=0.2)
plt.title(f"Validation Loss Across Runs for {dataset} Dataset")
plt.xlabel("Iteration")
plt.ylabel("Validation Loss")
plt.legend()
plt.grid(True, which="both", ls="-", alpha=0.2)
plt.tight_layout()
plt.savefig(f"val_loss_{dataset}.png")
plt.close()
>>>>>>> REPLACE
These changes will ensure that the plots are generated for each dataset and
saved with appropriate filenames, and the labels dictionary will include all
relevant runs.
12063 prompt tokens, 1043 completion tokens, $0.075960 cost
Applied edit to plot.py
/home/anon/ai_scientist/venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
return _methods._mean(a, axis=axis, dtype=dtype,
/home/anon/ai_scientist/venv/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
ret = ret.dtype.type(ret / rcount)
Here are the changes to notes.txt to include descriptions of what each plot
shows along with the filename of the figure:
notes.txt
<<<<<<< SEARCH
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437,
'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean':
77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527},
'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean':
1.0055421590805054, 'total_train_time_mean': 819.4551751613617,
'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8':
{'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean':
0.979989230632782, 'total_train_time_mean': 801.224205493927,
'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
=======
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8186181902885437,
'best_val_loss_mean': 1.4654763221740723, 'total_train_time_mean':
77.26942734718322, 'avg_inference_tokens_per_second_mean': 666.5076153519527},
'enwik8': {'final_train_loss_mean': 0.930223822593689, 'best_val_loss_mean':
1.0055421590805054, 'total_train_time_mean': 819.4551751613617,
'avg_inference_tokens_per_second_mean': 671.9918599180683}, 'text8':
{'final_train_loss_mean': 1.0013301372528076, 'best_val_loss_mean':
0.979989230632782, 'total_train_time_mean': 801.224205493927,
'avg_inference_tokens_per_second_mean': 671.5678332249411}}
Description: Baseline results.
Plot Descriptions:
1. Training Loss Across Runs for shakespeare_char Dataset: This plot shows the
training loss over iterations for the shakespeare_char dataset across differen
runs. It helps in understanding how the training loss decreases over time for
each run. Filename: train_loss_shakespeare_char.png
2. Validation Loss Across Runs for shakespeare_char Dataset: This plot shows t
validation loss over iterations for the shakespeare_char dataset across
different runs. It helps in understanding how the validation loss decreases ov
time for each run. Filename: val_loss_shakespeare_char.png
3. Training Loss Across Runs for enwik8 Dataset: This plot shows the training
loss over iterations for the enwik8 dataset across different runs. It helps in
understanding how the training loss decreases over time for each run. Filename
train_loss_enwik8.png
4. Validation Loss Across Runs for enwik8 Dataset: This plot shows the
validation loss over iterations for the enwik8 dataset across different runs.
helps in understanding how the validation loss decreases over time for each ru
Filename: val_loss_enwik8.png
5. Training Loss Across Runs for text8 Dataset: This plot shows the training
loss over iterations for the text8 dataset across different runs. It helps in
understanding how the training loss decreases over time for each run. Filename
train_loss_text8.png
6. Validation Loss Across Runs for text8 Dataset: This plot shows the validati
loss over iterations for the text8 dataset across different runs. It helps in
understanding how the validation loss decreases over time for each run.
Filename: val_loss_text8.png
>>>>>>> REPLACE
This update will ensure that the notes.txt file contains detailed descriptions
of what each plot shows along with the filenames of the figures.
13240 prompt tokens, 834 completion tokens, $0.078710 cost
Applied edit to notes.txt
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