Training in progress, step 200
Browse files- README.md +64 -0
- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- config.json +69 -0
- generation_config.json +6 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +298 -0
- sparsification_sftt.py +898 -0
- tokenizer.json +6 -1
- training_args.bin +1 -1
README.md
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-v0.1
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tags:
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- generated_from_trainer
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model-index:
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- name: Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-13
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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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# Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-13
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 8.6108
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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: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 0
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- distributed_type: multi-GPU
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- num_devices: 2
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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- total_eval_batch_size: 2
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 2.1687 | 0.0 | 10 | 2.4190 |
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| 9.3886 | 0.0 | 25 | 9.1091 |
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| 8.3891 | 0.01 | 50 | 8.3752 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.1.1+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"gate_proj",
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"up_proj",
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"
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"
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],
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"task_type": "CAUSAL_LM"
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}
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"q_proj",
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"up_proj",
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"v_proj",
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"gate_proj"
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"task_type": "CAUSAL_LM"
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}
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adapter_model.safetensors
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config.json
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{
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"_name_or_path": "mistralai/Mistral-7B-v0.1",
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"architectures": [
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"SparseMistralforCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "sparsification_sftt.SparseMistralConfig",
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"AutoModelForCausalLM": "sparsification_sftt.SparseMistralforCausalLM"
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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": "silu",
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"hidden_size": 4096,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "sparse_mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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generation_config.json
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model-00001-of-00003.safetensors
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model.safetensors.index.json
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|
sparsification_sftt.py
ADDED
@@ -0,0 +1,898 @@
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|
1 |
+
from transformers import TrainerCallback, Trainer
|
2 |
+
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
|
3 |
+
from peft import PeftModel
|
4 |
+
from datasets import Dataset
|
5 |
+
from transformers.utils import is_sagemaker_mp_enabled, is_sagemaker_dp_enabled
|
6 |
+
from typing import Any, Dict, Union, Optional, Tuple
|
7 |
+
from torch.nn import MSELoss
|
8 |
+
|
9 |
+
import warnings
|
10 |
+
import torch
|
11 |
+
import torch.nn as nn
|
12 |
+
import torch.nn.functional as F
|
13 |
+
import matplotlib.pyplot as plt
|
14 |
+
import numpy as np
|
15 |
+
import time
|
16 |
+
import os
|
17 |
+
import copy
|
18 |
+
|
19 |
+
from transformers.models.mistral.modeling_mistral import (
|
20 |
+
MistralMLP,
|
21 |
+
MistralAttention,
|
22 |
+
MistralModel,
|
23 |
+
MistralDecoderLayer,
|
24 |
+
MistralConfig,
|
25 |
+
MISTRAL_ATTENTION_CLASSES,
|
26 |
+
MistralRMSNorm,
|
27 |
+
MistralForCausalLM,
|
28 |
+
)
|
29 |
+
from experiments.models.sparse_mistral.svd_router import (
|
30 |
+
low_rank_approximation,
|
31 |
+
SparsePredictor,
|
32 |
+
)
|
33 |
+
from utils.utils import (
|
34 |
+
print_size_of_model,
|
35 |
+
is_running_deepspeed,
|
36 |
+
is_mainprocess,
|
37 |
+
get_datetime,
|
38 |
+
ds_print,
|
39 |
+
)
|
40 |
+
|
41 |
+
|
42 |
+
class SparseSFTTTrainer(SFTTrainer):
|
43 |
+
def __init__(self, *args, **kwargs):
|
44 |
+
self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
|
45 |
+
self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
|
46 |
+
self.use_spm_loss = False
|
47 |
+
self.freeze_original_weights = False
|
48 |
+
self.regularization_type = kwargs.pop("regularization_type", "L1 positive activation")
|
49 |
+
assert self.regularization_type in [
|
50 |
+
"L2 activation",
|
51 |
+
"L1 positive activation",
|
52 |
+
], f"Invalid regularization type: {self.regularization_type}"
|
53 |
+
self.sparse_layers = []
|
54 |
+
self.sparse_decoder_layers = []
|
55 |
+
super(SparseSFTTTrainer, self).__init__(*args, **kwargs)
|
56 |
+
|
57 |
+
def initialize_sparse_silu_layers(self, model):
|
58 |
+
self.sparse_layers = [m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)]
|
59 |
+
|
60 |
+
def initialize_sparse_decoder_layers(self, model):
|
61 |
+
self.sparse_decoder_layers = [m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)]
|
62 |
+
|
63 |
+
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
|
64 |
+
"""
|
65 |
+
Override the huggingface's training_step function to add a regularization term.
|
66 |
+
A regularization term is computed with intermediate values, which are freed after "backward()."
|
67 |
+
You need to set `retain_graph=True` inside `backward` function to keep the values.
|
68 |
+
"""
|
69 |
+
model.train()
|
70 |
+
inputs = self._prepare_inputs(inputs)
|
71 |
+
|
72 |
+
with self.compute_loss_context_manager():
|
73 |
+
loss = self.compute_loss(model, inputs)
|
74 |
+
|
75 |
+
if self.args.n_gpu > 1:
|
76 |
+
loss = loss.mean() # mean() to average on multi-gpu parallel training
|
77 |
+
if not self.freeze_original_weights:
|
78 |
+
if loss is not None:
|
79 |
+
self.accelerator.backward(loss, retain_graph=False)
|
80 |
+
|
81 |
+
if self.use_sparse_regularization:
|
82 |
+
regularization_loss = self.compute_regularization(model)
|
83 |
+
if self.args.n_gpu > 1:
|
84 |
+
regularization_loss = regularization_loss.mean()
|
85 |
+
if regularization_loss is not None:
|
86 |
+
self.accelerator.backward(regularization_loss, retain_graph=True)
|
87 |
+
loss += regularization_loss
|
88 |
+
|
89 |
+
if self.use_spm_loss:
|
90 |
+
spm_loss = self.compute_spm_loss(model)
|
91 |
+
if self.args.n_gpu > 1:
|
92 |
+
spm_loss = spm_loss.mean()
|
93 |
+
if spm_loss is not None:
|
94 |
+
self.accelerator.backward(spm_loss, retain_graph=False)
|
95 |
+
loss += spm_loss
|
96 |
+
|
97 |
+
return loss.detach() / self.args.gradient_accumulation_steps
|
98 |
+
|
99 |
+
def compute_regularization(self, model):
|
100 |
+
"""
|
101 |
+
Compute a sparse regularization loss for SiLU
|
102 |
+
"""
|
103 |
+
loss = 0
|
104 |
+
if len(self.sparse_layers) == 0:
|
105 |
+
self.initialize_sparse_silu_layers(model)
|
106 |
+
num_layers = len(self.sparse_layers)
|
107 |
+
|
108 |
+
for module in self.sparse_layers:
|
109 |
+
if module.activation_norm is not None:
|
110 |
+
loss += module.activation_norm
|
111 |
+
|
112 |
+
loss /= num_layers
|
113 |
+
loss *= self.regularization_coefficient
|
114 |
+
|
115 |
+
if self.state.global_step % 20 == 0 and loss != 0:
|
116 |
+
print("Negative relularizer loss: ", loss.item())
|
117 |
+
return loss
|
118 |
+
|
119 |
+
def compute_spm_loss(self, model):
|
120 |
+
loss = 0
|
121 |
+
if len(self.sparse_decoder_layers) == 0:
|
122 |
+
self.initialize_sparse_decoder_layers(model)
|
123 |
+
for module in self.sparse_decoder_layers:
|
124 |
+
if module.distill_loss != None:
|
125 |
+
loss += module.distill_loss
|
126 |
+
if self.state.global_step % 20 == 0 and loss != 0:
|
127 |
+
print("Sparse Predictor Distillation loss: ", loss.item())
|
128 |
+
return loss
|
129 |
+
|
130 |
+
# def compute_loss(self, model, inputs, return_outputs=False):
|
131 |
+
# loss = super().compute_loss(model, inputs, return_outputs)
|
132 |
+
#
|
133 |
+
# if is_sagemaker_mp_enabled():
|
134 |
+
# import smdistributed.modelparallel.torch as smp
|
135 |
+
# @smp.step()
|
136 |
+
# def smp_forward_backward(model, inputs, gradient_accumulation_steps=1):
|
137 |
+
# outputs = model(**inputs)
|
138 |
+
# loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
|
139 |
+
# loss /= gradient_accumulation_steps
|
140 |
+
# model.backward(loss)
|
141 |
+
# return loss
|
142 |
+
#
|
143 |
+
# loss_mb = smp_forward_backward(
|
144 |
+
# model, inputs, self.args.gradient_accumulation_steps
|
145 |
+
# )
|
146 |
+
# if self.use_sparse_regularization:
|
147 |
+
# return loss_mb.reduce_mean().detach().to(
|
148 |
+
# self.args.device
|
149 |
+
# ) + self.regularization_coefficient * self.compute_regularization(model)
|
150 |
+
# else:
|
151 |
+
# return loss_mb.reduce_mean().detach().to(self)
|
152 |
+
#
|
153 |
+
# if return_outputs:
|
154 |
+
# classification_loss, outputs = loss
|
155 |
+
# else:
|
156 |
+
# classification_loss = loss
|
157 |
+
#
|
158 |
+
# loss = classification_loss
|
159 |
+
# if self.use_sparse_regularization:
|
160 |
+
# regularization_loss = self.compute_regularization(model)
|
161 |
+
# loss += self.regularization_coefficient * regularization_loss
|
162 |
+
#
|
163 |
+
# return (loss, outputs) if return_outputs else loss
|
164 |
+
|
165 |
+
|
166 |
+
class SparseTrainer(Trainer):
|
167 |
+
def __init__(self, *args, **kwargs):
|
168 |
+
self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
|
169 |
+
self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
|
170 |
+
self.use_spm_loss = False
|
171 |
+
self.freeze_original_weights = False
|
172 |
+
self.regularization_type = kwargs.pop("regularization_type", "L1 positive activation")
|
173 |
+
assert self.regularization_type in [
|
174 |
+
"L2 activation",
|
175 |
+
"L1 positive activation",
|
176 |
+
], f"Invalid regularization type: {self.regularization_type}"
|
177 |
+
self.sparse_layers = []
|
178 |
+
self.sparse_decoder_layers = []
|
179 |
+
super(SparseTrainer, self).__init__(*args, **kwargs)
|
180 |
+
|
181 |
+
def initialize_sparse_silu_layers(self, model):
|
182 |
+
self.sparse_layers = [m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)]
|
183 |
+
|
184 |
+
def initialize_sparse_decoder_layers(self, model):
|
185 |
+
self.sparse_decoder_layers = [m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)]
|
186 |
+
|
187 |
+
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
|
188 |
+
"""
|
189 |
+
Override the huggingface's training_step function to add a regularization term.
|
190 |
+
A regularization term is computed with intermediate values, which are freed after "backward()."
|
191 |
+
You need to set `retain_graph=True` inside `backward` function to keep the values.
|
192 |
+
"""
|
193 |
+
model.train()
|
194 |
+
inputs = self._prepare_inputs(inputs)
|
195 |
+
|
196 |
+
with self.compute_loss_context_manager():
|
197 |
+
loss = self.compute_loss(model, inputs)
|
198 |
+
|
199 |
+
if self.args.n_gpu > 1:
|
200 |
+
loss = loss.mean() # mean() to average on multi-gpu parallel training
|
201 |
+
if not self.freeze_original_weights:
|
202 |
+
if loss is not None:
|
203 |
+
self.accelerator.backward(loss, retain_graph=False)
|
204 |
+
|
205 |
+
if self.use_sparse_regularization:
|
206 |
+
regularization_loss = self.compute_regularization(model)
|
207 |
+
if self.args.n_gpu > 1:
|
208 |
+
regularization_loss = regularization_loss.mean()
|
209 |
+
if regularization_loss is not None:
|
210 |
+
self.accelerator.backward(regularization_loss, retain_graph=True)
|
211 |
+
loss += regularization_loss
|
212 |
+
|
213 |
+
if self.use_spm_loss:
|
214 |
+
spm_loss = self.compute_spm_loss(model)
|
215 |
+
if self.args.n_gpu > 1:
|
216 |
+
spm_loss = spm_loss.mean()
|
217 |
+
if spm_loss is not None:
|
218 |
+
self.accelerator.backward(spm_loss, retain_graph=False)
|
219 |
+
loss += spm_loss
|
220 |
+
|
221 |
+
return loss.detach() / self.args.gradient_accumulation_steps
|
222 |
+
|
223 |
+
def compute_regularization(self, model):
|
224 |
+
"""
|
225 |
+
Compute a sparse regularization loss for SiLU
|
226 |
+
"""
|
227 |
+
loss = 0
|
228 |
+
if len(self.sparse_layers) == 0:
|
229 |
+
self.initialize_sparse_silu_layers(model)
|
230 |
+
num_layers = len(self.sparse_layers)
|
231 |
+
|
232 |
+
for module in self.sparse_layers:
|
233 |
+
if module.activation_norm is not None:
|
234 |
+
loss += module.activation_norm
|
235 |
+
|
236 |
+
loss /= num_layers
|
237 |
+
loss *= self.regularization_coefficient
|
238 |
+
|
239 |
+
if self.state.global_step % 20 == 0 and loss != 0:
|
240 |
+
print("Negative relularizer loss: ", loss.item())
|
241 |
+
return loss
|
242 |
+
|
243 |
+
def compute_spm_loss(self, model):
|
244 |
+
loss = 0
|
245 |
+
if len(self.sparse_decoder_layers) == 0:
|
246 |
+
self.initialize_sparse_decoder_layers(model)
|
247 |
+
for module in self.sparse_decoder_layers:
|
248 |
+
if module.distill_loss != None:
|
249 |
+
loss += module.distill_loss
|
250 |
+
if self.state.global_step % 20 == 0 and loss != 0:
|
251 |
+
print("Sparse Predictor Distillation loss: ", loss.item())
|
252 |
+
return loss
|
253 |
+
|
254 |
+
|
255 |
+
class SparseSiLU(nn.SiLU):
|
256 |
+
def __init__(self, threshold):
|
257 |
+
super(SparseSiLU, self).__init__()
|
258 |
+
self.threshold = threshold
|
259 |
+
self.m = nn.Threshold(self.threshold, 0)
|
260 |
+
|
261 |
+
def set_new_threshold(self, threshold):
|
262 |
+
self.threshold = threshold
|
263 |
+
self.m = nn.Threshold(threshold, 0)
|
264 |
+
|
265 |
+
def forward(self, x):
|
266 |
+
act = super(SparseSiLU, self).forward(x)
|
267 |
+
return self.m(act) - self.m(-act)
|
268 |
+
|
269 |
+
|
270 |
+
class MistralSparseSiluMLP(MistralMLP):
|
271 |
+
def __init__(self, config, *args, **kwargs):
|
272 |
+
super().__init__(config)
|
273 |
+
self.swish_outputs = None
|
274 |
+
self.relu = nn.ReLU()
|
275 |
+
|
276 |
+
self.kill_sparse_swish_outputs = False
|
277 |
+
self.dead_percentage = 0
|
278 |
+
self.is_stats = False
|
279 |
+
self.visit_counts = 0
|
280 |
+
|
281 |
+
# Hyperparameters to tune
|
282 |
+
self.dead_threshold = kwargs.pop("dead_threshold", 0)
|
283 |
+
self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", True)
|
284 |
+
self.regularization_type = kwargs.pop("regularization_type", "L1 regularization")
|
285 |
+
self.regularization_threshold = kwargs.pop("regularization_threshold", 0.5)
|
286 |
+
self.use_relu = kwargs.pop("use_relu", False)
|
287 |
+
self.activation_norm = None
|
288 |
+
|
289 |
+
# Activation Histograms
|
290 |
+
self.is_collect_histogram = False
|
291 |
+
num_bins = 1000
|
292 |
+
self.histogram_bins = torch.linspace(-1, 1, num_bins - 2)
|
293 |
+
self.histogram_bins = torch.cat([torch.tensor([-torch.inf]), self.histogram_bins, torch.tensor([torch.inf])])
|
294 |
+
self.pre_act_hist_counts = torch.zeros(num_bins - 1)
|
295 |
+
self.post_act_hist_counts = torch.zeros(num_bins - 1)
|
296 |
+
self.t = 0
|
297 |
+
self.count = 0
|
298 |
+
self.agg_sparsity = 0
|
299 |
+
|
300 |
+
# Sparse activation function
|
301 |
+
self.sparse_act_fn = SparseSiLU(threshold=self.dead_threshold)
|
302 |
+
|
303 |
+
def activate_stats(self, is_collect_histogram: bool = True):
|
304 |
+
self.is_stats = True
|
305 |
+
self.dead_percentage = 0
|
306 |
+
self.visit_counts = 0
|
307 |
+
self.is_collect_histogram = is_collect_histogram
|
308 |
+
self.histogram_counts = torch.zeros(2000) # .to(self.down_proj.weight.device)
|
309 |
+
|
310 |
+
def deactivate_stats(self):
|
311 |
+
self.is_stats = False
|
312 |
+
|
313 |
+
def collect_stats(self, pre_activation, post_activation):
|
314 |
+
start_time = time.time()
|
315 |
+
pre_activation = pre_activation.float().cpu().detach()
|
316 |
+
post_activation = post_activation.float().cpu().detach()
|
317 |
+
# self.histogram_bins=self.histogram_bins.to(pre_activation.device).type(pre_activation.dtype)
|
318 |
+
self.pre_act_hist_counts += torch.histogram(pre_activation, bins=self.histogram_bins)[0]
|
319 |
+
self.post_act_hist_counts += torch.histogram(torch.abs(post_activation), bins=self.histogram_bins)[0]
|
320 |
+
self.t += time.time() - start_time
|
321 |
+
if self.visit_counts % 30 == 0:
|
322 |
+
print(f"Time taken to collect stats: {self.t}s.")
|
323 |
+
|
324 |
+
def forward(
|
325 |
+
self,
|
326 |
+
x,
|
327 |
+
sp_mask: torch.tensor = None,
|
328 |
+
):
|
329 |
+
"""
|
330 |
+
If kill_sparse_swish_outputs is set to False, this layer functions exactly like a normal MLP layer.
|
331 |
+
"""
|
332 |
+
if sp_mask != None: # When sparse mask is given
|
333 |
+
return self.down_proj(
|
334 |
+
self.sparse_act_fn(self.gate_proj(x) * sp_mask) * self.up_proj(x)
|
335 |
+
) # Todo: This doesn't accelerate runtime (instead slowing down)
|
336 |
+
|
337 |
+
elif self.use_relu:
|
338 |
+
post_act = self.relu(self.gate_proj(x))
|
339 |
+
self.count += 1
|
340 |
+
if self.count <= 1:
|
341 |
+
print("USING RELU!!!!")
|
342 |
+
|
343 |
+
if self.is_stats:
|
344 |
+
dead_neurons = post_act == 0
|
345 |
+
dead_percentage = dead_neurons.float().mean()
|
346 |
+
agg_sparsity = dead_neurons.all(dim=0).float().mean()
|
347 |
+
|
348 |
+
self.dead_percentage = (self.dead_percentage * self.visit_counts + dead_percentage) / (self.visit_counts + 1)
|
349 |
+
self.agg_sparsity = (self.agg_sparsity * self.visit_counts + agg_sparsity) / (self.visit_counts + 1)
|
350 |
+
self.visit_counts += 1
|
351 |
+
|
352 |
+
return self.down_proj(post_act * self.up_proj(x))
|
353 |
+
|
354 |
+
else:
|
355 |
+
self.count += 1
|
356 |
+
if self.count <= 1:
|
357 |
+
print("USING SparseSILU!!!!")
|
358 |
+
pre_act = self.gate_proj(x)
|
359 |
+
post_act = self.act_fn(pre_act)
|
360 |
+
if self.kill_sparse_swish_outputs:
|
361 |
+
dead_neurons = post_act.abs() <= self.dead_threshold
|
362 |
+
# print("pre act sparsity: ", (pre_act==0).float().mean())
|
363 |
+
|
364 |
+
dead_percentage = dead_neurons.float().mean()
|
365 |
+
agg_sparsity = dead_neurons.all(dim=0).float().mean()
|
366 |
+
|
367 |
+
if self.is_stats:
|
368 |
+
self.dead_percentage = (self.dead_percentage * self.visit_counts + dead_percentage) / (self.visit_counts + 1)
|
369 |
+
self.agg_sparsity = (self.agg_sparsity * self.visit_counts + agg_sparsity) / (self.visit_counts + 1)
|
370 |
+
self.visit_counts += 1
|
371 |
+
|
372 |
+
self.a = dead_percentage
|
373 |
+
|
374 |
+
# Collect histogram stats
|
375 |
+
if self.is_collect_histogram and pre_act.eq(0).float().mean() < 0.99: # Padded dataset
|
376 |
+
self.collect_stats(pre_act, post_act)
|
377 |
+
|
378 |
+
if self.count <= 1:
|
379 |
+
print("KILL!")
|
380 |
+
post_act[dead_neurons] = 0
|
381 |
+
|
382 |
+
out = self.down_proj(post_act * self.up_proj(x))
|
383 |
+
if self.use_sparse_regularization:
|
384 |
+
if self.regularization_type == "L1 regularization":
|
385 |
+
self.activation_norm = torch.abs(post_act)[torch.abs(post_act) < self.regularization_threshold].mean()
|
386 |
+
elif self.regularization_type == "L2 regularization":
|
387 |
+
self.activation_norm = torch.sqrt(torch.square(post_act)[torch.abs(post_act) < self.regularization_threshold]).mean()
|
388 |
+
|
389 |
+
return out
|
390 |
+
|
391 |
+
|
392 |
+
class SparseMistralDecoderLayer(MistralDecoderLayer):
|
393 |
+
def __init__(
|
394 |
+
self,
|
395 |
+
config: MistralConfig,
|
396 |
+
layer_idx: int,
|
397 |
+
decoder_layer: MistralDecoderLayer,
|
398 |
+
init_svd: bool = True,
|
399 |
+
*args,
|
400 |
+
**kwargs,
|
401 |
+
):
|
402 |
+
assert isinstance(decoder_layer.mlp, MistralSparseSiluMLP), f"{type(decoder_layer.mlp)} should MistralSparseSiluMLP."
|
403 |
+
|
404 |
+
super().__init__(config, layer_idx)
|
405 |
+
self.hidden_size = config.hidden_size
|
406 |
+
self.intermediate_size = config.intermediate_size
|
407 |
+
|
408 |
+
self.init_svd = init_svd
|
409 |
+
self.self_attn = decoder_layer.self_attn
|
410 |
+
|
411 |
+
self.mlp = decoder_layer.mlp
|
412 |
+
self.input_layernorm = decoder_layer.input_layernorm
|
413 |
+
self.post_attention_layernorm = decoder_layer.post_attention_layernorm
|
414 |
+
|
415 |
+
# Sparse predictor for mlp (initialized with SVD decomposed matrix)
|
416 |
+
self.low_rank = kwargs.pop("low_rank", 64)
|
417 |
+
self.sparse_act_func = decoder_layer.mlp.sparse_act_fn
|
418 |
+
|
419 |
+
print(f"Setting {layer_idx}th mlp layer's sparse predictor... svd init: {init_svd}")
|
420 |
+
self.sp_mlp = low_rank_approximation(
|
421 |
+
decoder_layer.mlp.gate_proj,
|
422 |
+
act_func=self.sparse_act_func,
|
423 |
+
init_svd=init_svd,
|
424 |
+
)
|
425 |
+
self.use_async = kwargs.pop("use_async", False)
|
426 |
+
self.use_sparse_predictor = False
|
427 |
+
self.distill_loss = None
|
428 |
+
|
429 |
+
def forward(
|
430 |
+
self,
|
431 |
+
hidden_states: torch.Tensor,
|
432 |
+
attention_mask: Optional[torch.Tensor] = None,
|
433 |
+
position_ids: Optional[torch.LongTensor] = None,
|
434 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
435 |
+
output_attentions: Optional[bool] = False,
|
436 |
+
use_cache: Optional[bool] = False,
|
437 |
+
**kwargs,
|
438 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
439 |
+
print("hidden_states shape: ", hidden_states.shape)
|
440 |
+
if "padding_mask" in kwargs:
|
441 |
+
warnings.warn(
|
442 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
443 |
+
)
|
444 |
+
|
445 |
+
residual = hidden_states
|
446 |
+
sp_mask = None
|
447 |
+
|
448 |
+
if self.use_async:
|
449 |
+
sp_mask = self.sp_mlp(hidden_states)
|
450 |
+
|
451 |
+
hidden_states = self.input_layernorm(hidden_states)
|
452 |
+
|
453 |
+
# Self Attention
|
454 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
455 |
+
hidden_states=hidden_states,
|
456 |
+
attention_mask=attention_mask,
|
457 |
+
position_ids=position_ids,
|
458 |
+
past_key_value=past_key_value,
|
459 |
+
output_attentions=output_attentions,
|
460 |
+
use_cache=use_cache,
|
461 |
+
)
|
462 |
+
hidden_states = residual + hidden_states
|
463 |
+
|
464 |
+
# Fully Connected
|
465 |
+
residual = hidden_states
|
466 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
467 |
+
|
468 |
+
if not self.use_async:
|
469 |
+
sp_mask = self.sp_mlp(hidden_states)
|
470 |
+
|
471 |
+
# Compute distillation loss
|
472 |
+
gating_output = self.mlp.sparse_act_fn(self.mlp.gate_proj(hidden_states))
|
473 |
+
loss_func = MSELoss()
|
474 |
+
self.distill_loss = loss_func(sp_mask, gating_output)
|
475 |
+
|
476 |
+
# Convert sp mask into binary form
|
477 |
+
sp_mask = sp_mask > 0
|
478 |
+
|
479 |
+
if self.training:
|
480 |
+
sp_mask = None
|
481 |
+
# if not self.use_sparse_predictor:
|
482 |
+
# sp_mask = None
|
483 |
+
|
484 |
+
hidden_states = self.mlp(hidden_states, sp_mask)
|
485 |
+
hidden_states = residual + hidden_states
|
486 |
+
|
487 |
+
outputs = (hidden_states,)
|
488 |
+
|
489 |
+
if output_attentions:
|
490 |
+
outputs += (self_attn_weights,)
|
491 |
+
|
492 |
+
if use_cache:
|
493 |
+
outputs += (present_key_value,)
|
494 |
+
|
495 |
+
return outputs
|
496 |
+
|
497 |
+
|
498 |
+
class SparseMistralConfig(MistralConfig):
|
499 |
+
model_type = "sparse_mistral"
|
500 |
+
|
501 |
+
def __init__(self, **kwargs):
|
502 |
+
super().__init__(**kwargs)
|
503 |
+
|
504 |
+
|
505 |
+
class SparseMistralforCausalLM(MistralForCausalLM):
|
506 |
+
config_class = SparseMistralConfig
|
507 |
+
|
508 |
+
def __init__(self, config):
|
509 |
+
super().__init__(config)
|
510 |
+
self.config = config
|
511 |
+
if config.use_sparse_model:
|
512 |
+
self.apply_sparse_mlp()
|
513 |
+
if config.thresholds is not None:
|
514 |
+
for idx, m in enumerate(self.model.layers):
|
515 |
+
if isinstance(m.mlp, MistralSparseSiluMLP):
|
516 |
+
m.mlp.dead_threshold = config.thresholds[idx]
|
517 |
+
m.mlp.sparse_act_fn.set_new_threshold(m.mlp.dead_threshold)
|
518 |
+
m.mlp.kill_sparse_swish_outputs = True
|
519 |
+
m.mlp.use_relu = config.use_relu
|
520 |
+
if config.use_sparse_predictor:
|
521 |
+
self.apply_sparse_predictor(init_svd=config.init_svd)
|
522 |
+
|
523 |
+
def apply_sparse_mlp(self):
|
524 |
+
apply_mistral_sparse_silu_mlp(
|
525 |
+
self,
|
526 |
+
config=self.config,
|
527 |
+
use_sparse_regularization=self.config.use_sparse_regularization,
|
528 |
+
)
|
529 |
+
|
530 |
+
def apply_sparse_predictor(self, init_svd: bool = True):
|
531 |
+
apply_mistral_sparse_decoder_layer(self, config=self.config, init_svd=init_svd)
|
532 |
+
|
533 |
+
|
534 |
+
class GracefulRegularizationScheduler(TrainerCallback):
|
535 |
+
def __init__(
|
536 |
+
self,
|
537 |
+
num_warmup_steps=40,
|
538 |
+
is_enabled: bool = False,
|
539 |
+
model_name: str = "mistral",
|
540 |
+
test_dataset: Dataset = None,
|
541 |
+
targeted_sparsity: float = 0.5,
|
542 |
+
keep_regularization_with_kill: bool = False,
|
543 |
+
):
|
544 |
+
"""Scheduler for regularizing the model first before applying the dead threshold.
|
545 |
+
|
546 |
+
:param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
|
547 |
+
:param increment_ratio: by how much to increase the dead threshold.
|
548 |
+
For example, 0.5 means "increase the threshold by 0.5 * desired threshold
|
549 |
+
"""
|
550 |
+
self.num_warmup_steps = num_warmup_steps
|
551 |
+
self.is_enabled = is_enabled
|
552 |
+
self.model_name = model_name
|
553 |
+
self.test_dataset = test_dataset
|
554 |
+
self.targeted_sparsity = targeted_sparsity
|
555 |
+
self.keep_regularization_with_kill = keep_regularization_with_kill
|
556 |
+
self.act_hist_path = f"/scr/lukeai/histograms/warm_up_reg_{targeted_sparsity}/act_hist.pt"
|
557 |
+
if self.is_enabled:
|
558 |
+
print("GracefulRegularizationScheduler is enabled.")
|
559 |
+
self.trainer = None
|
560 |
+
|
561 |
+
def set_trainer(self, trainer):
|
562 |
+
self.trainer = trainer
|
563 |
+
|
564 |
+
def on_step_end(self, args, state, control, **kwargs):
|
565 |
+
if not self.is_enabled:
|
566 |
+
return
|
567 |
+
|
568 |
+
model = kwargs["model"]
|
569 |
+
if isinstance(model, PeftModel):
|
570 |
+
base_model = model.get_base_model()
|
571 |
+
else:
|
572 |
+
base_model = model
|
573 |
+
|
574 |
+
if state.global_step == 1:
|
575 |
+
ds_print("Setting an initial reg threshold to 0.1")
|
576 |
+
set_regularization_threshold(base_model, 0.1)
|
577 |
+
disable_sparse_silu(base_model)
|
578 |
+
|
579 |
+
if state.global_step == self.num_warmup_steps:
|
580 |
+
activate_stats(base_model)
|
581 |
+
enable_sparse_silu(base_model)
|
582 |
+
self.trainer.evaluate()
|
583 |
+
save_act_hist(base_model, self.act_hist_path)
|
584 |
+
set_sparse_threshold(base_model, self.targeted_sparsity, True)
|
585 |
+
deactivate_stats(base_model)
|
586 |
+
self.trainer.use_sparse_regularization = self.keep_regularization_with_kill
|
587 |
+
# set_layer_specific_regularization(model.get_base_model())
|
588 |
+
print_dead_neuron_stats(model.get_base_model())
|
589 |
+
|
590 |
+
|
591 |
+
class GradualSparsificationScheduler(TrainerCallback):
|
592 |
+
def __init__(
|
593 |
+
self,
|
594 |
+
num_warmup_steps=40,
|
595 |
+
increment_ratio=0.5,
|
596 |
+
is_enabled: bool = False,
|
597 |
+
model_name: str = "mistral",
|
598 |
+
):
|
599 |
+
"""Scheduler for gradually increasing a dead threshold until it reaches the desired threshold.
|
600 |
+
|
601 |
+
:param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
|
602 |
+
:param increment_ratio: by how much to increase the dead threshold.
|
603 |
+
For example, 0.5 means "increase the threshold by 0.5 * desired threshold
|
604 |
+
"""
|
605 |
+
self.num_warmup_steps = num_warmup_steps
|
606 |
+
self.increment_ratio = increment_ratio
|
607 |
+
self.step_size = int(num_warmup_steps * increment_ratio)
|
608 |
+
self.is_enabled = is_enabled
|
609 |
+
self.model_name = model_name
|
610 |
+
|
611 |
+
def on_step_end(self, args, state, control, **kwargs):
|
612 |
+
model = kwargs["model"]
|
613 |
+
|
614 |
+
if not self.is_enabled:
|
615 |
+
if state.global_step <= 10:
|
616 |
+
for module in model.modules():
|
617 |
+
if isinstance(module, MistralSparseSiluMLP):
|
618 |
+
module.current_dead_threshold = module.dead_threshold
|
619 |
+
return
|
620 |
+
|
621 |
+
current_dead_threshold = 0
|
622 |
+
desired_dead_threshold = 0
|
623 |
+
|
624 |
+
if is_mainprocess():
|
625 |
+
ds_print(state.global_step)
|
626 |
+
|
627 |
+
if state.global_step % self.step_size == 2:
|
628 |
+
for module in model.modules():
|
629 |
+
if isinstance(module, MistralSparseSiluMLP):
|
630 |
+
desired_dead_threshold = copy.deepcopy(module.dead_threshold)
|
631 |
+
current_dead_threshold = module.current_dead_threshold
|
632 |
+
current_dead_threshold += self.increment_ratio * desired_dead_threshold
|
633 |
+
module.current_dead_threshold = min(desired_dead_threshold, current_dead_threshold)
|
634 |
+
|
635 |
+
if is_running_deepspeed and is_mainprocess():
|
636 |
+
ds_print(
|
637 |
+
state.global_step,
|
638 |
+
current_dead_threshold,
|
639 |
+
desired_dead_threshold,
|
640 |
+
)
|
641 |
+
|
642 |
+
if state.global_step % 2000 == 0:
|
643 |
+
if is_running_deepspeed and is_mainprocess():
|
644 |
+
ds_print(
|
645 |
+
f"Saving to /matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
|
646 |
+
)
|
647 |
+
torch.save(
|
648 |
+
model.state_dict(),
|
649 |
+
f"/matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
|
650 |
+
)
|
651 |
+
|
652 |
+
|
653 |
+
def get_sparse_mistral_config(
|
654 |
+
config: MistralConfig,
|
655 |
+
use_sparse_model=False,
|
656 |
+
use_sparse_predictor=False,
|
657 |
+
use_sparse_regularization=False,
|
658 |
+
use_graceful_regularization=False,
|
659 |
+
thresholds=None,
|
660 |
+
):
|
661 |
+
new_config = SparseMistralConfig()
|
662 |
+
new_config.__dict__.update(config.__dict__)
|
663 |
+
config = new_config
|
664 |
+
config.use_sparse_model = use_sparse_model
|
665 |
+
config.use_sparse_predictor = use_sparse_predictor
|
666 |
+
config.use_sparse_regularization = use_sparse_regularization
|
667 |
+
config.use_graceful_regularization = use_graceful_regularization
|
668 |
+
config.thresholds = thresholds
|
669 |
+
|
670 |
+
return config
|
671 |
+
|
672 |
+
|
673 |
+
def apply_mistral_sparse_silu_mlp(
|
674 |
+
model,
|
675 |
+
config,
|
676 |
+
use_sparse_regularization: bool = False,
|
677 |
+
):
|
678 |
+
# counts = 0
|
679 |
+
for layer in model.model.layers:
|
680 |
+
# counts += 1
|
681 |
+
# if counts < 4:
|
682 |
+
# continue
|
683 |
+
original_mlp = layer.mlp
|
684 |
+
new_mlp = MistralSparseSiluMLP(config, use_sparse_regularization=use_sparse_regularization)
|
685 |
+
new_mlp.gate_proj = original_mlp.gate_proj
|
686 |
+
new_mlp.up_proj = original_mlp.up_proj
|
687 |
+
new_mlp.down_proj = original_mlp.down_proj
|
688 |
+
layer.mlp = new_mlp
|
689 |
+
|
690 |
+
|
691 |
+
def apply_mistral_sparse_decoder_layer(
|
692 |
+
model,
|
693 |
+
config,
|
694 |
+
init_svd: bool = True,
|
695 |
+
):
|
696 |
+
assert isinstance(model.model, MistralModel), "model.model must be a MistralModel."
|
697 |
+
new_layers = []
|
698 |
+
for layer_idx, layer in enumerate(model.model.layers):
|
699 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
700 |
+
new_layers.append(
|
701 |
+
SparseMistralDecoderLayer(
|
702 |
+
config=config,
|
703 |
+
layer_idx=layer_idx,
|
704 |
+
decoder_layer=layer,
|
705 |
+
init_svd=init_svd,
|
706 |
+
)
|
707 |
+
)
|
708 |
+
print(f"{layer_idx}th mlp layer activation: {layer.mlp.sparse_act_fn}")
|
709 |
+
else:
|
710 |
+
new_layers.append(layer)
|
711 |
+
model.model.layers = nn.ModuleList(new_layers)
|
712 |
+
|
713 |
+
|
714 |
+
def enable_sparse_predictor(
|
715 |
+
model,
|
716 |
+
):
|
717 |
+
for layer_idx, layer in enumerate(model.model.layers):
|
718 |
+
if isinstance(layer, MistralDecoderLayer):
|
719 |
+
layer.use_sparse_predictor = True
|
720 |
+
|
721 |
+
|
722 |
+
def disable_sparse_predictor(
|
723 |
+
model,
|
724 |
+
):
|
725 |
+
for layer_idx, layer in enumerate(model.model.layers):
|
726 |
+
if isinstance(layer, MistralDecoderLayer):
|
727 |
+
layer.use_sparse_predictor = False
|
728 |
+
|
729 |
+
|
730 |
+
def activate_stats(model, is_collect_histogram: bool = True):
|
731 |
+
for layer in model.model.layers:
|
732 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
733 |
+
layer.mlp.activate_stats(is_collect_histogram=is_collect_histogram)
|
734 |
+
|
735 |
+
|
736 |
+
def deactivate_stats(model):
|
737 |
+
for layer in model.model.layers:
|
738 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
739 |
+
layer.mlp.deactivate_stats()
|
740 |
+
|
741 |
+
|
742 |
+
def enable_sparse_silu(model):
|
743 |
+
print("Enabling SparseSilu")
|
744 |
+
for i, layer in enumerate(model.model.layers):
|
745 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
746 |
+
layer.mlp.kill_sparse_swish_outputs = True
|
747 |
+
|
748 |
+
|
749 |
+
def disable_sparse_silu(model):
|
750 |
+
print("Enabling SparseSilu")
|
751 |
+
for i, layer in enumerate(model.model.layers):
|
752 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
753 |
+
layer.mlp.kill_sparse_swish_outputs = False
|
754 |
+
|
755 |
+
|
756 |
+
def print_dead_neuron_stats(model):
|
757 |
+
total_sparsity = 0
|
758 |
+
counts = 0
|
759 |
+
for i, layer in enumerate(model.model.layers):
|
760 |
+
if isinstance(layer.mlp, MistralSparseSiluMLP):
|
761 |
+
dead_percentage = layer.mlp.dead_percentage * 100
|
762 |
+
agg_sparsity = layer.mlp.agg_sparsity * 100
|
763 |
+
print(f"layer {i} sparsity: {dead_percentage:.3f}%")
|
764 |
+
print(f"layer {i} agg sparsity: {agg_sparsity:.3f}%")
|
765 |
+
total_sparsity += dead_percentage
|
766 |
+
counts += 1
|
767 |
+
|
768 |
+
print(f"Total sparsity: {total_sparsity/counts: .3f}%")
|
769 |
+
return total_sparsity / counts
|
770 |
+
|
771 |
+
|
772 |
+
def get_sparse_layers(model: MistralModel):
|
773 |
+
sparse_layers = [m.mlp for m in model.layers() if isinstance(m.mlp, MistralSparseSiluMLP)]
|
774 |
+
return sparse_layers
|
775 |
+
|
776 |
+
|
777 |
+
def get_threshold(bin_edges: torch.tensor, histogram_counts: torch.tensor, sparsity_level: float): # Only for L1 Regularization
|
778 |
+
assert len(bin_edges.shape) == len(histogram_counts.shape) == 1, "bin_edges and histogram are expected to be 1-dimensional."
|
779 |
+
histogram_counts /= histogram_counts.sum()
|
780 |
+
threshold_idx = torch.searchsorted(histogram_counts.cumsum(0), sparsity_level, side="right")
|
781 |
+
|
782 |
+
return bin_edges[threshold_idx]
|
783 |
+
|
784 |
+
|
785 |
+
def set_regularization_threshold(model, threshold: float = 0.1):
|
786 |
+
for i, layer in enumerate(model.model.layers):
|
787 |
+
if (
|
788 |
+
isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
|
789 |
+
): # Can set the threshold only the relevant statistics is collected.
|
790 |
+
layer.mlp.regularization_threshold = threshold # TODO: find better param
|
791 |
+
|
792 |
+
|
793 |
+
def set_sparse_threshold(model, sparsity_level: float, use_relu: bool = False):
|
794 |
+
for i, layer in enumerate(model.model.layers):
|
795 |
+
if (
|
796 |
+
isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
|
797 |
+
): # Can set the threshold only the relevant statistics is collected.
|
798 |
+
if use_relu:
|
799 |
+
layer.mlp.sparse_act_fn = nn.ReLU()
|
800 |
+
layer.mlp.use_relu = True
|
801 |
+
else:
|
802 |
+
layer.mlp.dead_threshold = get_threshold(
|
803 |
+
layer.mlp.histogram_bins,
|
804 |
+
layer.mlp.post_act_hist_counts,
|
805 |
+
sparsity_level,
|
806 |
+
)
|
807 |
+
layer.mlp.sparse_act_fn.set_new_threshold(layer.mlp.dead_threshold)
|
808 |
+
layer.mlp.regularization_threshold = layer.mlp.dead_threshold * 1.2 # TODO: find better param
|
809 |
+
|
810 |
+
|
811 |
+
def plot_histogram(
|
812 |
+
bin_edges,
|
813 |
+
histogram_counts: torch.tensor,
|
814 |
+
title: str = "Activation Distribution",
|
815 |
+
fig_dir: str = "figures",
|
816 |
+
):
|
817 |
+
plt.bar(bin_edges[:-1], histogram_counts, width=np.diff(bin_edges), edgecolor="black")
|
818 |
+
plt.title(title)
|
819 |
+
plt.xlabel("Activation Value")
|
820 |
+
plt.ylabel("Frequency")
|
821 |
+
os.makedirs(fig_dir, exist_ok=True)
|
822 |
+
plt.savefig(f"{fig_dir}/{title}.png")
|
823 |
+
# plt.show()
|
824 |
+
plt.clf()
|
825 |
+
|
826 |
+
|
827 |
+
def plot_act(model, fig_dir: str = "figures"):
|
828 |
+
for i, layer in enumerate(model.model.layers):
|
829 |
+
if (
|
830 |
+
isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
|
831 |
+
): # Can set the threshold only the relevant statistics is collected.
|
832 |
+
plot_title = f"Layer: {i} Pre-Activation Distribution"
|
833 |
+
plot_histogram(layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, plot_title)
|
834 |
+
|
835 |
+
plot_title = f"Layer: {i} Post-Activation Absolute Distribution"
|
836 |
+
plot_histogram(layer.mlp.histogram_bins, layer.mlp.post_act_hist_counts, plot_title)
|
837 |
+
|
838 |
+
|
839 |
+
def save_act_hist(model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"):
|
840 |
+
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
841 |
+
act_dict = {}
|
842 |
+
for i, layer in enumerate(model.model.layers):
|
843 |
+
if (
|
844 |
+
isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
|
845 |
+
): # Can set the threshold only the relevant statistics is collected.
|
846 |
+
act_dict[i] = (
|
847 |
+
layer.mlp.histogram_bins,
|
848 |
+
layer.mlp.pre_act_hist_counts,
|
849 |
+
layer.mlp.post_act_hist_counts,
|
850 |
+
)
|
851 |
+
print("Saving activation histograms...\n\n\n")
|
852 |
+
torch.save(act_dict, filename)
|
853 |
+
|
854 |
+
|
855 |
+
def load_act_hist(model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"):
|
856 |
+
assert os.path.exists(filename), f"{filename} does not exist when loading pre/post-activation histogram of SparseMistralSiluMLP."
|
857 |
+
print("Loading activation histograms...\n\n\n")
|
858 |
+
|
859 |
+
act_dict = torch.load(filename)
|
860 |
+
for i, layer in enumerate(model.model.layers):
|
861 |
+
if (
|
862 |
+
isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
|
863 |
+
): # Can set the threshold only the relevant statistics is collected.
|
864 |
+
(
|
865 |
+
layer.mlp.histogram_bins,
|
866 |
+
layer.mlp.pre_act_hist_counts,
|
867 |
+
layer.mlp.post_act_hist_counts,
|
868 |
+
) = act_dict[i]
|
869 |
+
|
870 |
+
|
871 |
+
def enable_last_k_modules(model, start_module_idx: int):
|
872 |
+
assert 32 > start_module_idx >= 0
|
873 |
+
new_modules = []
|
874 |
+
new_idx = 0
|
875 |
+
for idx in range(start_module_idx, len(model.model.original_layers)):
|
876 |
+
module = model.model.original_layers[idx]
|
877 |
+
module.layer_idx = new_idx
|
878 |
+
module.self_attn.layer_idx = new_idx
|
879 |
+
new_modules.append(module)
|
880 |
+
new_idx += 1
|
881 |
+
print(module.layer_idx)
|
882 |
+
|
883 |
+
model.model.layers = nn.ModuleList(new_modules)
|
884 |
+
|
885 |
+
|
886 |
+
def enable_first_k_modules(model, end_module_idx: int):
|
887 |
+
assert 32 > end_module_idx >= 0
|
888 |
+
new_modules = []
|
889 |
+
new_idx = 0
|
890 |
+
for idx in range(0, end_module_idx + 1):
|
891 |
+
module = model.model.original_layers[idx]
|
892 |
+
module.layer_idx = new_idx
|
893 |
+
module.self_attn.layer_idx = new_idx
|
894 |
+
new_modules.append(module)
|
895 |
+
new_idx += 1
|
896 |
+
print(module.layer_idx)
|
897 |
+
|
898 |
+
model.model.layers = nn.ModuleList(new_modules)
|
tokenizer.json
CHANGED
@@ -1,6 +1,11 @@
|
|
1 |
{
|
2 |
"version": "1.0",
|
3 |
-
"truncation":
|
|
|
|
|
|
|
|
|
|
|
4 |
"padding": null,
|
5 |
"added_tokens": [
|
6 |
{
|
|
|
1 |
{
|
2 |
"version": "1.0",
|
3 |
+
"truncation": {
|
4 |
+
"direction": "Right",
|
5 |
+
"max_length": 1024,
|
6 |
+
"strategy": "LongestFirst",
|
7 |
+
"stride": 0
|
8 |
+
},
|
9 |
"padding": null,
|
10 |
"added_tokens": [
|
11 |
{
|
training_args.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 6520
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:eadc042dd35567f0b6943d283cc49cdcfbf587c23e6dd61a09d098bf3753ca6d
|
3 |
size 6520
|