Upload folder using huggingface_hub
Browse files- README.md +69 -0
- all_results.json +14 -0
- config.json +37 -0
- eval_results.json +8 -0
- generation_config.json +6 -0
- interpretation/embeddings-0.pt +3 -0
- interpretation/embeddings-1.pt +3 -0
- interpretation/embeddings-2.pt +3 -0
- interpretation/embeddings-3.pt +3 -0
- interpretation/embeddings-4.pt +3 -0
- interpretation/embeddings-5.pt +3 -0
- interpretation/examples-0.pkl +3 -0
- interpretation/examples-1.pkl +3 -0
- interpretation/examples-2.pkl +3 -0
- interpretation/examples-3.pkl +3 -0
- interpretation/examples-4.pkl +3 -0
- interpretation/examples-5.pkl +3 -0
- interpretation/inputs.pt +3 -0
- interpretation/routings-0.pkl +3 -0
- interpretation/routings-1.pkl +3 -0
- interpretation/routings-2.pkl +3 -0
- interpretation/routings-3.pkl +3 -0
- interpretation/routings-4.pkl +3 -0
- interpretation/routings-5.pkl +3 -0
- model.safetensors +3 -0
- modeling_monet.py +663 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +44 -0
- train_results.json +9 -0
- trainer_state.json +757 -0
README.md
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---
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library_name: transformers
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tags:
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- alignment-handbook
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- generated_from_trainer
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datasets:
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- HuggingFaceTB/Magpie-Pro-300K-Filtered-H4
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- HuggingFaceTB/self-oss-instruct-sc2-H4
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- HuggingFaceTB/OpenHermes-2.5-H4
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- HuggingFaceTB/everyday-conversations-llama3.1-2k
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- HuggingFaceTB/instruct-data-basics-smollm-H4
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model-index:
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- name: monet-vd-1.4B-100BT-chat-hf
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# monet-vd-1.4B-100BT-chat-hf
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This model is a fine-tuned version of [monet-vd-1.4B-100BT-hf](https://huggingface.co/MonetLLM/monet-vd-1.4B-100BT-hf) on the HuggingFaceTB/Magpie-Pro-300K-Filtered-H4, the HuggingFaceTB/self-oss-instruct-sc2-H4, the HuggingFaceTB/OpenHermes-2.5-H4, the HuggingFaceTB/everyday-conversations-llama3.1-2k and the HuggingFaceTB/instruct-data-basics-smollm-H4 datasets.
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It achieves the following results on the evaluation set:
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- Loss: 1.1664
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.8032 | 0.9988 | 502 | 1.1664 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 0.9987565282268093,
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"eval_loss": 1.1664291620254517,
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"eval_runtime": 1405.1859,
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"eval_samples": 82730,
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"eval_samples_per_second": 16.588,
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"eval_steps_per_second": 1.037,
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"total_flos": 234414617395200.0,
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"train_loss": 0.8937448220423968,
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"train_runtime": 13768.4166,
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"train_samples": 186330,
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"train_samples_per_second": 4.672,
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"train_steps_per_second": 0.036
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}
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config.json
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{
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"architectures": ["MonetForCausalLM"],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "modeling_monet.MonetConfig",
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"AutoModelForCausalLM": "modeling_monet.MonetForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "relu2",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": null,
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"max_position_embeddings": 2048,
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"mlp_bias": null,
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"model_type": "monet",
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"moe_decompose": "vertical",
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"moe_dim": 16,
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"moe_experts": 512,
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"moe_groups": 4,
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"moe_heads": 8,
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"moe_topk": 8,
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"output_router_probs": false,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-6,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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eval_results.json
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{
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"epoch": 0.9987565282268093,
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"eval_loss": 1.1664291620254517,
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"eval_runtime": 1405.1859,
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"eval_samples": 82730,
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"eval_samples_per_second": 16.588,
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"eval_steps_per_second": 1.037
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}
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generation_config.json
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}
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modeling_monet.py
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|
1 |
+
# fmt: off
|
2 |
+
from __future__ import annotations
|
3 |
+
|
4 |
+
from dataclasses import dataclass
|
5 |
+
|
6 |
+
import torch
|
7 |
+
import torch.utils.checkpoint
|
8 |
+
from scipy.stats import norm
|
9 |
+
from torch import nn
|
10 |
+
from torch.nn import CrossEntropyLoss
|
11 |
+
from transformers.activations import ACT2FN
|
12 |
+
from transformers.cache_utils import Cache, DynamicCache, StaticCache
|
13 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
14 |
+
from transformers.modeling_utils import PreTrainedModel
|
15 |
+
from transformers.models.llama.configuration_llama import LlamaConfig
|
16 |
+
from transformers.models.llama.modeling_llama import (
|
17 |
+
LLAMA_ATTENTION_CLASSES,
|
18 |
+
LlamaRMSNorm,
|
19 |
+
)
|
20 |
+
from transformers.utils import ModelOutput, logging
|
21 |
+
|
22 |
+
logger = logging.get_logger(__name__)
|
23 |
+
|
24 |
+
|
25 |
+
@dataclass
|
26 |
+
class MonetModelOutputWithPast(ModelOutput):
|
27 |
+
last_hidden_state: torch.FloatTensor = None
|
28 |
+
past_key_values: tuple[tuple[torch.FloatTensor]] | None = None
|
29 |
+
hidden_states: tuple[torch.FloatTensor, ...] | None = None
|
30 |
+
attentions: tuple[torch.FloatTensor, ...] | None = None
|
31 |
+
router_probs: tuple[tuple[torch.FloatTensor, ...], ...] | None = None
|
32 |
+
|
33 |
+
|
34 |
+
@dataclass
|
35 |
+
class MonetCausalLMOutputWithPast(ModelOutput):
|
36 |
+
loss: torch.FloatTensor | None = None
|
37 |
+
aux_loss: torch.FloatTensor | None = None
|
38 |
+
logits: torch.FloatTensor = None
|
39 |
+
past_key_values: tuple[tuple[torch.FloatTensor]] | None = None
|
40 |
+
hidden_states: tuple[torch.FloatTensor, ...] | None = None
|
41 |
+
attentions: tuple[torch.FloatTensor, ...] | None = None
|
42 |
+
router_probs: tuple[tuple[torch.FloatTensor, ...], ...] | None = None
|
43 |
+
|
44 |
+
|
45 |
+
class MonetConfig(LlamaConfig):
|
46 |
+
model_type = "monet"
|
47 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
48 |
+
|
49 |
+
def __init__(
|
50 |
+
self,
|
51 |
+
vocab_size=32000,
|
52 |
+
hidden_size=4096,
|
53 |
+
intermediate_size=None,
|
54 |
+
num_hidden_layers=32,
|
55 |
+
num_attention_heads=32,
|
56 |
+
num_key_value_heads=None,
|
57 |
+
hidden_act="relu2",
|
58 |
+
max_position_embeddings=2048,
|
59 |
+
initializer_range=0.02,
|
60 |
+
rms_norm_eps=1e-6,
|
61 |
+
use_cache=True,
|
62 |
+
pad_token_id=None,
|
63 |
+
bos_token_id=1,
|
64 |
+
eos_token_id=2,
|
65 |
+
pretraining_tp=1,
|
66 |
+
tie_word_embeddings=False,
|
67 |
+
rope_theta=10000.0,
|
68 |
+
rope_scaling=None,
|
69 |
+
attention_bias=False,
|
70 |
+
attention_dropout=0.0,
|
71 |
+
mlp_bias=None,
|
72 |
+
moe_dim=8,
|
73 |
+
moe_heads=8,
|
74 |
+
moe_experts=512,
|
75 |
+
moe_topk=32,
|
76 |
+
moe_groups=4,
|
77 |
+
moe_decompose="vertical",
|
78 |
+
output_router_probs=False,
|
79 |
+
**kwargs,
|
80 |
+
):
|
81 |
+
self.moe_dim = moe_dim
|
82 |
+
self.moe_heads = moe_heads
|
83 |
+
self.moe_experts = moe_experts
|
84 |
+
self.moe_topk = moe_topk
|
85 |
+
self.moe_groups = moe_groups
|
86 |
+
self.moe_decompose = moe_decompose
|
87 |
+
self.output_router_probs = output_router_probs
|
88 |
+
|
89 |
+
super().__init__(
|
90 |
+
vocab_size=vocab_size,
|
91 |
+
hidden_size=hidden_size,
|
92 |
+
intermediate_size=intermediate_size,
|
93 |
+
num_hidden_layers=num_hidden_layers,
|
94 |
+
num_attention_heads=num_attention_heads,
|
95 |
+
num_key_value_heads=num_key_value_heads,
|
96 |
+
hidden_act=hidden_act,
|
97 |
+
max_position_embeddings=max_position_embeddings,
|
98 |
+
initializer_range=initializer_range,
|
99 |
+
rms_norm_eps=rms_norm_eps,
|
100 |
+
use_cache=use_cache,
|
101 |
+
pad_token_id=pad_token_id,
|
102 |
+
bos_token_id=bos_token_id,
|
103 |
+
eos_token_id=eos_token_id,
|
104 |
+
pretraining_tp=pretraining_tp,
|
105 |
+
tie_word_embeddings=tie_word_embeddings,
|
106 |
+
rope_theta=rope_theta,
|
107 |
+
rope_scaling=rope_scaling,
|
108 |
+
attention_bias=attention_bias,
|
109 |
+
attention_dropout=attention_dropout,
|
110 |
+
mlp_bias=mlp_bias,
|
111 |
+
**kwargs,
|
112 |
+
)
|
113 |
+
|
114 |
+
|
115 |
+
class MonetRouter(nn.Module):
|
116 |
+
def __init__(self, config: MonetConfig):
|
117 |
+
super().__init__()
|
118 |
+
self.config = config
|
119 |
+
flatten_shape = config.moe_heads * config.moe_experts
|
120 |
+
|
121 |
+
self.w1 = nn.Linear(config.hidden_size, flatten_shape, bias=False)
|
122 |
+
self.w2 = nn.Linear(config.hidden_size, flatten_shape, bias=False)
|
123 |
+
self.norm1 = nn.BatchNorm1d(config.moe_heads, affine=False)
|
124 |
+
self.norm2 = nn.BatchNorm1d(config.moe_heads, affine=False)
|
125 |
+
|
126 |
+
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
127 |
+
g1z = self.w1(x).unflatten(-1, (self.config.moe_heads, -1)).float()
|
128 |
+
g2z = self.w2(x).unflatten(-1, (self.config.moe_heads, -1)).float()
|
129 |
+
|
130 |
+
g1n = self.norm1(g1z.transpose(2, 3).flatten(0, -2))
|
131 |
+
g2n = self.norm2(g2z.transpose(2, 3).flatten(0, -2))
|
132 |
+
g1n = g1n.view(g1z.size(0), g1z.size(1), g1z.size(3), -1).transpose(2, 3)
|
133 |
+
g2n = g2n.view(g2z.size(0), g2z.size(1), g2z.size(3), -1).transpose(2, 3)
|
134 |
+
|
135 |
+
sigma = float(norm.ppf(1 - self.config.moe_topk / self.config.moe_experts))
|
136 |
+
g1s = g1n.amax(-1, keepdim=True).clamp_max_(sigma)
|
137 |
+
g2s = g2n.amax(-1, keepdim=True).clamp_max_(sigma)
|
138 |
+
|
139 |
+
g1 = nn.functional.softmax(torch.where(g1n >= g1s, g1z, -1e10), dim=-1)
|
140 |
+
g2 = nn.functional.softmax(torch.where(g2n >= g2s, g2z, -1e10), dim=-1)
|
141 |
+
return g1, g2
|
142 |
+
|
143 |
+
|
144 |
+
class MonetMoVDE(nn.Module):
|
145 |
+
def __init__(self, config: MonetConfig):
|
146 |
+
super().__init__()
|
147 |
+
self.config = config
|
148 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
149 |
+
flatten_shape = config.moe_experts * config.moe_dim // 2
|
150 |
+
|
151 |
+
self.u1 = nn.Linear(config.hidden_size, flatten_shape)
|
152 |
+
self.u2 = nn.Linear(config.hidden_size, flatten_shape)
|
153 |
+
|
154 |
+
self.v11 = nn.Linear(flatten_shape, config.hidden_size // 2, bias=False)
|
155 |
+
self.v12 = nn.Linear(flatten_shape, config.hidden_size // 2, bias=False)
|
156 |
+
self.v21 = nn.Linear(flatten_shape, config.hidden_size // 2, bias=False)
|
157 |
+
self.v22 = nn.Linear(flatten_shape, config.hidden_size // 2, bias=False)
|
158 |
+
|
159 |
+
self.b1 = nn.Parameter(torch.zeros(config.moe_experts, config.hidden_size // 2))
|
160 |
+
self.b2 = nn.Parameter(torch.zeros(config.moe_experts, config.hidden_size // 2))
|
161 |
+
|
162 |
+
def forward(
|
163 |
+
self, x: torch.Tensor, g1: torch.Tensor, g2: torch.Tensor
|
164 |
+
) -> torch.Tensor:
|
165 |
+
g1, g2 = g1.type_as(x), g2.type_as(x)
|
166 |
+
x1 = self.act_fn(self.u1(x).unflatten(-1, (self.config.moe_experts, -1)))
|
167 |
+
x2 = self.act_fn(self.u2(x).unflatten(-1, (self.config.moe_experts, -1)))
|
168 |
+
|
169 |
+
x11 = self.v11(torch.einsum("btim,bthi->btim", x1, g1).flatten(-2))
|
170 |
+
x12 = self.v12(torch.einsum("btjm,bthj,bthi->btim", x2, g2, g1).flatten(-2))
|
171 |
+
x13 = torch.einsum("bthi,id->btd", g1, self.b1.type_as(x))
|
172 |
+
|
173 |
+
x21 = self.v21(torch.einsum("btim,bthi,bthj->btjm", x1, g1, g2).flatten(-2))
|
174 |
+
x22 = self.v22(torch.einsum("btjm,bthj->btjm", x2, g2).flatten(-2))
|
175 |
+
x23 = torch.einsum("bthj,jd->btd", g2, self.b2.type_as(x))
|
176 |
+
|
177 |
+
return torch.cat((x11 + x12 + x13, x21 + x22 + x23), dim=-1)
|
178 |
+
|
179 |
+
|
180 |
+
class MonetMoHDE(nn.Module):
|
181 |
+
def __init__(self, config: MonetConfig):
|
182 |
+
super().__init__()
|
183 |
+
self.config = config
|
184 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
185 |
+
flatten_shape = config.moe_experts * config.moe_dim
|
186 |
+
|
187 |
+
self.u = nn.Linear(config.hidden_size, flatten_shape)
|
188 |
+
self.v = nn.Linear(flatten_shape, config.hidden_size, bias=False)
|
189 |
+
self.b = nn.Parameter(torch.zeros(config.moe_experts, config.hidden_size))
|
190 |
+
|
191 |
+
def forward(
|
192 |
+
self, x: torch.Tensor, g1: torch.Tensor, g2: torch.Tensor
|
193 |
+
) -> torch.Tensor:
|
194 |
+
g1, g2 = g1.type_as(x), g2.type_as(x)
|
195 |
+
x = self.act_fn(self.u(x).unflatten(-1, (self.config.moe_experts, -1)))
|
196 |
+
x = self.v(torch.einsum("btim,bthi,bthj->btjm", x, g1, g2).flatten(-2))
|
197 |
+
return x + torch.einsum("bthj,jd->btd", g2, self.b)
|
198 |
+
|
199 |
+
|
200 |
+
class MonetDecoderLayer(nn.Module):
|
201 |
+
def __init__(self, config: MonetConfig, layer_idx: int):
|
202 |
+
super().__init__()
|
203 |
+
self.hidden_size = config.hidden_size
|
204 |
+
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
|
205 |
+
config=config, layer_idx=layer_idx
|
206 |
+
)
|
207 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
208 |
+
self.post_attention_layernorm = LlamaRMSNorm(
|
209 |
+
config.hidden_size, eps=config.rms_norm_eps
|
210 |
+
)
|
211 |
+
|
212 |
+
if config.moe_decompose == "vertical":
|
213 |
+
self.moe = MonetMoVDE(config)
|
214 |
+
elif config.moe_decompose == "horizontal":
|
215 |
+
self.moe = MonetMoHDE(config)
|
216 |
+
if layer_idx % config.moe_groups == 0:
|
217 |
+
self.router = MonetRouter(config).requires_grad_(False)
|
218 |
+
|
219 |
+
def forward(
|
220 |
+
self,
|
221 |
+
hidden_states: torch.Tensor,
|
222 |
+
attention_mask: torch.Tensor | None = None,
|
223 |
+
position_ids: torch.LongTensor | None = None,
|
224 |
+
past_key_value: Cache | None = None,
|
225 |
+
previous_router_probs: tuple[torch.Tensor, torch.Tensor] | None = None,
|
226 |
+
output_attentions: bool | None = False,
|
227 |
+
use_cache: bool | None = False,
|
228 |
+
cache_position: torch.LongTensor | None = None,
|
229 |
+
**kwargs,
|
230 |
+
) -> tuple[torch.FloatTensor, ...]:
|
231 |
+
residual = hidden_states
|
232 |
+
|
233 |
+
hidden_states = self.input_layernorm(hidden_states)
|
234 |
+
|
235 |
+
# Self Attention
|
236 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
237 |
+
hidden_states=hidden_states,
|
238 |
+
attention_mask=attention_mask,
|
239 |
+
position_ids=position_ids,
|
240 |
+
past_key_value=past_key_value,
|
241 |
+
output_attentions=output_attentions,
|
242 |
+
use_cache=use_cache,
|
243 |
+
cache_position=cache_position,
|
244 |
+
)
|
245 |
+
hidden_states = residual + hidden_states
|
246 |
+
|
247 |
+
# Fully Connected
|
248 |
+
residual = hidden_states
|
249 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
250 |
+
g1, g2 = (
|
251 |
+
self.router(hidden_states)
|
252 |
+
if hasattr(self, "router")
|
253 |
+
else previous_router_probs
|
254 |
+
)
|
255 |
+
hidden_states = self.moe(hidden_states, g1, g2)
|
256 |
+
hidden_states = residual + hidden_states
|
257 |
+
|
258 |
+
outputs = (hidden_states,)
|
259 |
+
|
260 |
+
if output_attentions:
|
261 |
+
outputs += (self_attn_weights,)
|
262 |
+
|
263 |
+
if use_cache:
|
264 |
+
outputs += (present_key_value,)
|
265 |
+
|
266 |
+
return outputs + ((g1, g2) if hasattr(self, "router") else None,)
|
267 |
+
|
268 |
+
|
269 |
+
class MonetPreTrainedModel(PreTrainedModel):
|
270 |
+
config_class = MonetConfig
|
271 |
+
base_model_prefix = "model"
|
272 |
+
supports_gradient_checkpointing = True
|
273 |
+
_no_split_modules = ["MonetDecoderLayer"]
|
274 |
+
_skip_keys_device_placement = ["past_key_values"]
|
275 |
+
_supports_flash_attn_2 = True
|
276 |
+
_supports_sdpa = True
|
277 |
+
_supports_cache_class = True
|
278 |
+
_supports_quantized_cache = True
|
279 |
+
_supports_static_cache = True
|
280 |
+
|
281 |
+
def _init_weights(self, module):
|
282 |
+
std = self.config.initializer_range
|
283 |
+
if isinstance(module, nn.Linear):
|
284 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
285 |
+
if module.bias is not None:
|
286 |
+
module.bias.data.zero_()
|
287 |
+
elif isinstance(module, nn.Embedding):
|
288 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
289 |
+
if module.padding_idx is not None:
|
290 |
+
module.weight.data[module.padding_idx].zero_()
|
291 |
+
|
292 |
+
|
293 |
+
class MonetModel(MonetPreTrainedModel):
|
294 |
+
def __init__(self, config: MonetConfig):
|
295 |
+
super().__init__(config)
|
296 |
+
self.padding_idx = config.pad_token_id
|
297 |
+
self.vocab_size = config.vocab_size
|
298 |
+
|
299 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) # noqa
|
300 |
+
self.layers = nn.ModuleList([MonetDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) # noqa
|
301 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
302 |
+
self.gradient_checkpointing = False
|
303 |
+
|
304 |
+
# Initialize weights and apply final processing
|
305 |
+
self.post_init()
|
306 |
+
|
307 |
+
def get_input_embeddings(self):
|
308 |
+
return self.embed_tokens
|
309 |
+
|
310 |
+
def set_input_embeddings(self, value):
|
311 |
+
self.embed_tokens = value
|
312 |
+
|
313 |
+
def forward(
|
314 |
+
self,
|
315 |
+
input_ids: torch.LongTensor = None,
|
316 |
+
attention_mask: torch.Tensor | None = None,
|
317 |
+
position_ids: torch.LongTensor | None = None,
|
318 |
+
past_key_values: Cache | list[torch.FloatTensor] | None = None,
|
319 |
+
inputs_embeds: torch.FloatTensor | None = None,
|
320 |
+
use_cache: bool | None = None,
|
321 |
+
output_attentions: bool | None = None,
|
322 |
+
output_hidden_states: bool | None = None,
|
323 |
+
output_router_probs: bool | None = None,
|
324 |
+
return_dict: bool | None = None,
|
325 |
+
cache_position: torch.LongTensor | None = None,
|
326 |
+
) -> tuple[torch.Tensor, ...] | MonetModelOutputWithPast:
|
327 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions # noqa
|
328 |
+
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states # noqa
|
329 |
+
output_router_probs = output_router_probs if output_router_probs is not None else self.config.output_router_probs # noqa
|
330 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
331 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict # noqa
|
332 |
+
|
333 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
334 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one") # noqa
|
335 |
+
|
336 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
337 |
+
logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.") # noqa
|
338 |
+
use_cache = False
|
339 |
+
|
340 |
+
if inputs_embeds is None:
|
341 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
342 |
+
|
343 |
+
return_legacy_cache = False
|
344 |
+
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs) # noqa
|
345 |
+
return_legacy_cache = True
|
346 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
347 |
+
logger.warning_once(
|
348 |
+
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. " # noqa
|
349 |
+
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" # noqa
|
350 |
+
)
|
351 |
+
|
352 |
+
if cache_position is None:
|
353 |
+
past_seen_tokens = (
|
354 |
+
past_key_values.get_seq_length() if past_key_values is not None else 0
|
355 |
+
)
|
356 |
+
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device) # noqa
|
357 |
+
if position_ids is None:
|
358 |
+
position_ids = cache_position.unsqueeze(0)
|
359 |
+
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions) # noqa
|
360 |
+
|
361 |
+
# embed positions
|
362 |
+
hidden_states = inputs_embeds
|
363 |
+
|
364 |
+
# decoder layers
|
365 |
+
all_hidden_states = () if output_hidden_states else None
|
366 |
+
all_self_attns = () if output_attentions else None
|
367 |
+
all_router_probs = () if output_router_probs else None
|
368 |
+
previous_router_probs, next_decoder_cache = None, None
|
369 |
+
|
370 |
+
for decoder_layer in self.layers:
|
371 |
+
if output_hidden_states:
|
372 |
+
all_hidden_states += (hidden_states,)
|
373 |
+
|
374 |
+
if self.gradient_checkpointing and self.training:
|
375 |
+
layer_outputs = self._gradient_checkpointing_func(
|
376 |
+
decoder_layer.__call__,
|
377 |
+
hidden_states,
|
378 |
+
causal_mask,
|
379 |
+
position_ids,
|
380 |
+
past_key_values,
|
381 |
+
previous_router_probs,
|
382 |
+
output_attentions,
|
383 |
+
use_cache,
|
384 |
+
cache_position,
|
385 |
+
)
|
386 |
+
else:
|
387 |
+
layer_outputs = decoder_layer(
|
388 |
+
hidden_states,
|
389 |
+
attention_mask=causal_mask,
|
390 |
+
position_ids=position_ids,
|
391 |
+
past_key_value=past_key_values,
|
392 |
+
previous_router_probs=previous_router_probs,
|
393 |
+
output_attentions=output_attentions,
|
394 |
+
use_cache=use_cache,
|
395 |
+
cache_position=cache_position,
|
396 |
+
)
|
397 |
+
|
398 |
+
hidden_states = layer_outputs[0]
|
399 |
+
if use_cache:
|
400 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
401 |
+
if output_attentions:
|
402 |
+
all_self_attns += (layer_outputs[1],)
|
403 |
+
if output_router_probs:
|
404 |
+
all_router_probs += (layer_outputs[-1],)
|
405 |
+
previous_router_probs = (
|
406 |
+
layer_outputs[-1]
|
407 |
+
if layer_outputs[-1] is not None
|
408 |
+
else previous_router_probs
|
409 |
+
)
|
410 |
+
|
411 |
+
hidden_states = self.norm(hidden_states)
|
412 |
+
|
413 |
+
# add hidden states from the last decoder layer
|
414 |
+
if output_hidden_states:
|
415 |
+
all_hidden_states += (hidden_states,)
|
416 |
+
|
417 |
+
next_cache = next_decoder_cache if use_cache else None
|
418 |
+
if return_legacy_cache:
|
419 |
+
next_cache = next_cache.to_legacy_cache()
|
420 |
+
|
421 |
+
if not return_dict:
|
422 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_probs] if v is not None) # noqa
|
423 |
+
return MonetModelOutputWithPast(
|
424 |
+
last_hidden_state=hidden_states,
|
425 |
+
past_key_values=next_cache,
|
426 |
+
hidden_states=all_hidden_states,
|
427 |
+
attentions=all_self_attns,
|
428 |
+
router_probs=all_router_probs,
|
429 |
+
)
|
430 |
+
|
431 |
+
def _update_causal_mask(
|
432 |
+
self,
|
433 |
+
attention_mask: torch.Tensor,
|
434 |
+
input_tensor: torch.Tensor,
|
435 |
+
cache_position: torch.Tensor,
|
436 |
+
past_key_values: Cache,
|
437 |
+
output_attentions: bool,
|
438 |
+
):
|
439 |
+
if self.config._attn_implementation == "flash_attention_2":
|
440 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
441 |
+
return attention_mask
|
442 |
+
return None
|
443 |
+
|
444 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 # noqa
|
445 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
446 |
+
|
447 |
+
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: # noqa
|
448 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
449 |
+
attention_mask,
|
450 |
+
inputs_embeds=input_tensor,
|
451 |
+
past_key_values_length=past_seen_tokens,
|
452 |
+
is_training=self.training,
|
453 |
+
):
|
454 |
+
return None
|
455 |
+
|
456 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
457 |
+
min_dtype = torch.finfo(dtype).min
|
458 |
+
sequence_length = input_tensor.shape[1]
|
459 |
+
if using_static_cache:
|
460 |
+
target_length = past_key_values.get_max_length()
|
461 |
+
else:
|
462 |
+
target_length = (
|
463 |
+
attention_mask.shape[-1]
|
464 |
+
if isinstance(attention_mask, torch.Tensor)
|
465 |
+
else past_seen_tokens + sequence_length + 1
|
466 |
+
)
|
467 |
+
|
468 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
469 |
+
if attention_mask.max() != 0:
|
470 |
+
raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`") # noqa
|
471 |
+
causal_mask = attention_mask
|
472 |
+
else:
|
473 |
+
causal_mask = torch.full(
|
474 |
+
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device # noqa
|
475 |
+
)
|
476 |
+
if sequence_length != 1:
|
477 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
478 |
+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) # noqa
|
479 |
+
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) # noqa
|
480 |
+
if attention_mask is not None:
|
481 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit # noqa
|
482 |
+
mask_length = attention_mask.shape[-1]
|
483 |
+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] # noqa
|
484 |
+
padding_mask = padding_mask == 0
|
485 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype) # noqa
|
486 |
+
if (
|
487 |
+
self.config._attn_implementation == "sdpa"
|
488 |
+
and attention_mask is not None
|
489 |
+
and attention_mask.device.type == "cuda"
|
490 |
+
and not output_attentions
|
491 |
+
):
|
492 |
+
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) # noqa
|
493 |
+
|
494 |
+
return causal_mask
|
495 |
+
|
496 |
+
|
497 |
+
class MonetForCausalLM(MonetPreTrainedModel):
|
498 |
+
_tied_weights_keys = ["lm_head.weight"]
|
499 |
+
|
500 |
+
def __init__(self, config):
|
501 |
+
super().__init__(config)
|
502 |
+
self.model = MonetModel(config)
|
503 |
+
self.vocab_size = config.vocab_size
|
504 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
505 |
+
|
506 |
+
# Initialize weights and apply final processing
|
507 |
+
self.post_init()
|
508 |
+
|
509 |
+
def get_input_embeddings(self):
|
510 |
+
return self.model.embed_tokens
|
511 |
+
|
512 |
+
def set_input_embeddings(self, value):
|
513 |
+
self.model.embed_tokens = value
|
514 |
+
|
515 |
+
def get_output_embeddings(self):
|
516 |
+
return self.lm_head
|
517 |
+
|
518 |
+
def set_output_embeddings(self, new_embeddings):
|
519 |
+
self.lm_head = new_embeddings
|
520 |
+
|
521 |
+
def set_decoder(self, decoder):
|
522 |
+
self.model = decoder
|
523 |
+
|
524 |
+
def get_decoder(self):
|
525 |
+
return self.model
|
526 |
+
|
527 |
+
def forward(
|
528 |
+
self,
|
529 |
+
input_ids: torch.LongTensor = None,
|
530 |
+
attention_mask: torch.Tensor | None = None,
|
531 |
+
position_ids: torch.LongTensor | None = None,
|
532 |
+
past_key_values: Cache | list[torch.FloatTensor] | None = None,
|
533 |
+
inputs_embeds: torch.FloatTensor | None = None,
|
534 |
+
labels: torch.LongTensor | None = None,
|
535 |
+
use_cache: bool | None = None,
|
536 |
+
output_attentions: bool | None = None,
|
537 |
+
output_hidden_states: bool | None = None,
|
538 |
+
output_router_probs: bool | None = None,
|
539 |
+
return_dict: bool | None = None,
|
540 |
+
cache_position: torch.LongTensor | None = None,
|
541 |
+
) -> tuple[torch.Tensor, ...] | MonetCausalLMOutputWithPast:
|
542 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions # noqa
|
543 |
+
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states # noqa
|
544 |
+
output_router_probs = output_router_probs if output_router_probs is not None else self.config.output_router_probs # noqa
|
545 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict # noqa
|
546 |
+
|
547 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
548 |
+
outputs = self.model(
|
549 |
+
input_ids=input_ids,
|
550 |
+
attention_mask=attention_mask,
|
551 |
+
position_ids=position_ids,
|
552 |
+
past_key_values=past_key_values,
|
553 |
+
inputs_embeds=inputs_embeds,
|
554 |
+
use_cache=use_cache,
|
555 |
+
output_attentions=output_attentions,
|
556 |
+
output_hidden_states=output_hidden_states,
|
557 |
+
output_router_probs=output_router_probs,
|
558 |
+
return_dict=return_dict,
|
559 |
+
cache_position=cache_position,
|
560 |
+
)
|
561 |
+
|
562 |
+
hidden_states = outputs[0]
|
563 |
+
logits = self.lm_head(hidden_states)
|
564 |
+
logits = logits.float()
|
565 |
+
|
566 |
+
loss = None
|
567 |
+
if labels is not None:
|
568 |
+
# Shift so that tokens < n predict n
|
569 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
570 |
+
shift_labels = labels[..., 1:].contiguous()
|
571 |
+
# Flatten the tokens
|
572 |
+
loss_fct = CrossEntropyLoss()
|
573 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
574 |
+
shift_labels = shift_labels.view(-1)
|
575 |
+
# Enable model parallelism
|
576 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
577 |
+
loss = loss_fct(shift_logits, shift_labels)
|
578 |
+
|
579 |
+
if not return_dict:
|
580 |
+
output = (logits,) + outputs[1:]
|
581 |
+
return (loss,) + output if loss is not None else output
|
582 |
+
|
583 |
+
return MonetCausalLMOutputWithPast(
|
584 |
+
loss=loss,
|
585 |
+
logits=logits,
|
586 |
+
past_key_values=outputs.past_key_values,
|
587 |
+
hidden_states=outputs.hidden_states,
|
588 |
+
attentions=outputs.attentions,
|
589 |
+
router_probs=outputs.router_probs,
|
590 |
+
)
|
591 |
+
|
592 |
+
def prepare_inputs_for_generation(
|
593 |
+
self,
|
594 |
+
input_ids,
|
595 |
+
past_key_values=None,
|
596 |
+
attention_mask=None,
|
597 |
+
inputs_embeds=None,
|
598 |
+
cache_position=None,
|
599 |
+
use_cache=True,
|
600 |
+
**kwargs,
|
601 |
+
):
|
602 |
+
past_length = 0
|
603 |
+
if past_key_values is not None:
|
604 |
+
past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length() # noqa
|
605 |
+
max_cache_length = (
|
606 |
+
torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
|
607 |
+
if past_key_values.get_max_length() is not None
|
608 |
+
else None
|
609 |
+
)
|
610 |
+
cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length) # noqa
|
611 |
+
|
612 |
+
# Keep only the unprocessed tokens:
|
613 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: # noqa
|
614 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
615 |
+
# input_ids based on the past_length.
|
616 |
+
elif past_length < input_ids.shape[1]:
|
617 |
+
input_ids = input_ids[:, past_length:]
|
618 |
+
|
619 |
+
if (
|
620 |
+
max_cache_length is not None
|
621 |
+
and attention_mask is not None
|
622 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
623 |
+
):
|
624 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
625 |
+
|
626 |
+
position_ids = kwargs.get("position_ids", None)
|
627 |
+
if attention_mask is not None and position_ids is None:
|
628 |
+
# create position_ids on the fly for batch generation
|
629 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
630 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
631 |
+
if past_key_values:
|
632 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
633 |
+
|
634 |
+
if inputs_embeds is not None and past_length == 0:
|
635 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
636 |
+
else:
|
637 |
+
model_inputs = {"input_ids": input_ids.contiguous()}
|
638 |
+
|
639 |
+
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] # noqa
|
640 |
+
if cache_position is None:
|
641 |
+
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) # noqa
|
642 |
+
elif use_cache:
|
643 |
+
cache_position = cache_position[-input_length:]
|
644 |
+
|
645 |
+
model_inputs.update(
|
646 |
+
{
|
647 |
+
"position_ids": position_ids,
|
648 |
+
"cache_position": cache_position,
|
649 |
+
"past_key_values": past_key_values,
|
650 |
+
"use_cache": use_cache,
|
651 |
+
"attention_mask": attention_mask,
|
652 |
+
}
|
653 |
+
)
|
654 |
+
return model_inputs
|
655 |
+
|
656 |
+
@staticmethod
|
657 |
+
def _reorder_cache(past_key_values, beam_idx):
|
658 |
+
reordered_past = ()
|
659 |
+
for layer_past in past_key_values:
|
660 |
+
reordered_past += (
|
661 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), # noqa
|
662 |
+
)
|
663 |
+
return reordered_past
|
special_tokens_map.json
ADDED
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<s>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"eos_token": {
|
10 |
+
"content": "</s>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": "</s>",
|
17 |
+
"unk_token": {
|
18 |
+
"content": "<unk>",
|
19 |
+
"lstrip": false,
|
20 |
+
"normalized": false,
|
21 |
+
"rstrip": false,
|
22 |
+
"single_word": false
|
23 |
+
}
|
24 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
3 |
+
size 499723
|
tokenizer_config.json
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"add_bos_token": true,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"add_prefix_space": true,
|
5 |
+
"added_tokens_decoder": {
|
6 |
+
"0": {
|
7 |
+
"content": "<unk>",
|
8 |
+
"lstrip": false,
|
9 |
+
"normalized": false,
|
10 |
+
"rstrip": false,
|
11 |
+
"single_word": false,
|
12 |
+
"special": true
|
13 |
+
},
|
14 |
+
"1": {
|
15 |
+
"content": "<s>",
|
16 |
+
"lstrip": false,
|
17 |
+
"normalized": false,
|
18 |
+
"rstrip": false,
|
19 |
+
"single_word": false,
|
20 |
+
"special": true
|
21 |
+
},
|
22 |
+
"2": {
|
23 |
+
"content": "</s>",
|
24 |
+
"lstrip": false,
|
25 |
+
"normalized": false,
|
26 |
+
"rstrip": false,
|
27 |
+
"single_word": false,
|
28 |
+
"special": true
|
29 |
+
}
|
30 |
+
},
|
31 |
+
"bos_token": "<s>",
|
32 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
|
33 |
+
"clean_up_tokenization_spaces": false,
|
34 |
+
"eos_token": "</s>",
|
35 |
+
"legacy": false,
|
36 |
+
"model_max_length": 2048,
|
37 |
+
"pad_token": "</s>",
|
38 |
+
"padding_side": "right",
|
39 |
+
"sp_model_kwargs": {},
|
40 |
+
"spaces_between_special_tokens": false,
|
41 |
+
"tokenizer_class": "LlamaTokenizer",
|
42 |
+
"unk_token": "<unk>",
|
43 |
+
"use_default_system_prompt": false
|
44 |
+
}
|
train_results.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 0.9987565282268093,
|
3 |
+
"total_flos": 234414617395200.0,
|
4 |
+
"train_loss": 0.8937448220423968,
|
5 |
+
"train_runtime": 13768.4166,
|
6 |
+
"train_samples": 186330,
|
7 |
+
"train_samples_per_second": 4.672,
|
8 |
+
"train_steps_per_second": 0.036
|
9 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,757 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
|
|
|
|
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|
|
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|
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|
|
|
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|
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