File size: 16,227 Bytes
67cadad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 |
#!/usr/bin/env python
# coding=utf-8
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
from transformers.activations import ACT2FN
from transformers.modeling_utils import Conv1D, PreTrainedModel
from transformers.utils import logging
from .config_codesage import CodeSageConfig
from transformers.modeling_outputs import (
BaseModelOutputWithPooling,
MaskedLMOutput,
SequenceClassifierOutput
)
logger = logging.get_logger(__name__)
CODESAGE_PRETRAINED_MODEL_ARCHIVE_LIST = [
"codesage/codesage-small-v2",
"codesage/codesage-base-v2",
"codesage/codesage-large-v2",
# See all CodeSage models at https://huggingface.co/models?filter=codesage
]
class CodeSageAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // self.num_heads
if self.head_dim * self.num_heads != config.hidden_size:
raise ValueError(
f"`hidden_size` must be divisible by num_heads "
f"(got `hidden_size`: {config.hidden_size} and `num_heads`: {self.num_heads})."
)
self.c_attn = Conv1D(3 * self.hidden_size, self.hidden_size)
self.c_proj = Conv1D(self.hidden_size, self.hidden_size)
self.attention_dropout = nn.Dropout(config.attention_dropout_prob)
self.residual_dropout = nn.Dropout(config.residual_dropout_prob)
def attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
attn_weights = attn_weights / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
attn_weights = self.attention_dropout(attn_weights)
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(*new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
query, key, value = self.c_attn(hidden_states).split(self.hidden_size, dim=2)
query = self.split_heads(query, self.num_heads, self.head_dim)
key = self.split_heads(key, self.num_heads, self.head_dim)
value = self.split_heads(value, self.num_heads, self.head_dim)
attn_output, attn_weights = self.attn(query, key, value, attention_mask, head_mask)
attn_output = self.merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.residual_dropout(attn_output)
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs # a, present, (attentions)
class CodeSageMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
self.c_fc = Conv1D(intermediate_size, config.hidden_size)
self.act = ACT2FN[config.activation_function]
self.c_proj = Conv1D(config.hidden_size, intermediate_size)
self.dropout = nn.Dropout(config.residual_dropout_prob)
def forward(self, hidden_states):
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class CodeSageBlock(nn.Module):
def __init__(self, config):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = CodeSageAttention(config)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = CodeSageMLP(inner_dim, config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
class CodeSagePreTrainedModel(PreTrainedModel):
config_class = CodeSageConfig
base_model_prefix = "transformer"
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class CodeSageModel(CodeSagePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
self.wpe = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.drop = nn.Dropout(config.embedding_dropout_prob)
self.h = nn.ModuleList([CodeSageBlock(config) for _ in range(config.num_hidden_layers)])
self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.init_weights()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.wte = new_embeddings
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
if input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
else:
position_ids = position_ids.view(-1, input_shape[-1])
extended_attention_mask = None
if attention_mask is not None:
assert attention_mask.dim() == 2
extended_attention_mask = attention_mask[:, None, None, :]
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = block(
hidden_states,
attention_mask=extended_attention_mask,
head_mask=head_mask[i],
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
pooled_output = None # max-pooled output
if attention_mask is not None:
pooled_output = (hidden_states * attention_mask[:, :, None]).sum(1) / attention_mask.sum(1)[:, None]
if not return_dict:
return tuple(
v
for v in [hidden_states, pooled_output, all_hidden_states, all_self_attentions]
if v is not None
)
return BaseModelOutputWithPooling(
last_hidden_state=hidden_states,
pooler_output=pooled_output,
hidden_states=all_hidden_states,
attentions=all_self_attentions
)
class CodeSageForMaskedLM(CodeSagePreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = CodeSageModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
class CodeSageForSequenceClassification(CodeSagePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = CodeSageModel(config)
classifier_dropout = (
config.classifier_dropout
if hasattr(config, 'classifier_dropout') and config.classifier_dropout is not None
else config.residual_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
assert attention_mask is not None, "attention_mask is needed to perform max-pooling"
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
|