Transformers
PyTorch
code
custom_code
Inference Endpoints
File size: 16,218 Bytes
34e872f
 
 
 
 
 
 
 
 
 
 
 
 
2238d0b
 
 
 
 
34e872f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2238d0b
34e872f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2238d0b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
34e872f
2238d0b
34e872f
 
 
 
 
bd7c93d
34e872f
73f5acd
 
 
34e872f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bd7c93d
34e872f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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",
    "codesage/codesage-base",
    "codesage/codesage-large",
    # 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,
        )