427 lines
16 KiB
Python
427 lines
16 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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import math
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import Conv1D, PreTrainedModel
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from transformers.utils import logging
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from .config_codesage import CodeSageConfig
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from transformers.modeling_outputs import (
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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SequenceClassifierOutput
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)
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logger = logging.get_logger(__name__)
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CODESAGE_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"codesage/codesage-small-v2",
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"codesage/codesage-base-v2",
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"codesage/codesage-large-v2",
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# See all CodeSage models at https://huggingface.co/models?filter=codesage
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]
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class CodeSageAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = config.hidden_size // self.num_heads
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if self.head_dim * self.num_heads != config.hidden_size:
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raise ValueError(
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f"`hidden_size` must be divisible by num_heads "
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f"(got `hidden_size`: {config.hidden_size} and `num_heads`: {self.num_heads})."
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)
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self.c_attn = Conv1D(3 * self.hidden_size, self.hidden_size)
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self.c_proj = Conv1D(self.hidden_size, self.hidden_size)
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self.attention_dropout = nn.Dropout(config.attention_dropout_prob)
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self.residual_dropout = nn.Dropout(config.residual_dropout_prob)
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def attn(self, query, key, value, attention_mask=None, head_mask=None):
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attn_weights = torch.matmul(query, key.transpose(-1, -2))
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.Softmax(dim=-1)(attn_weights)
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attn_weights = self.attention_dropout(attn_weights)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask
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attn_output = torch.matmul(attn_weights, value)
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return attn_output, attn_weights
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def split_heads(self, tensor, num_heads, attn_head_size):
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"""
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Splits hidden_size dim into attn_head_size and num_heads
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"""
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new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
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tensor = tensor.view(*new_shape)
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return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
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def merge_heads(self, tensor, num_heads, attn_head_size):
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"""
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Merges attn_head_size dim and num_attn_heads dim into hidden_size
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"""
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tensor = tensor.permute(0, 2, 1, 3).contiguous()
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new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
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return tensor.view(new_shape)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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):
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query, key, value = self.c_attn(hidden_states).split(self.hidden_size, dim=2)
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query = self.split_heads(query, self.num_heads, self.head_dim)
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key = self.split_heads(key, self.num_heads, self.head_dim)
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value = self.split_heads(value, self.num_heads, self.head_dim)
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attn_output, attn_weights = self.attn(query, key, value, attention_mask, head_mask)
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attn_output = self.merge_heads(attn_output, self.num_heads, self.head_dim)
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attn_output = self.c_proj(attn_output)
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attn_output = self.residual_dropout(attn_output)
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outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
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return outputs # a, present, (attentions)
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class CodeSageMLP(nn.Module):
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def __init__(self, intermediate_size, config):
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super().__init__()
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self.c_fc = Conv1D(intermediate_size, config.hidden_size)
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self.act = ACT2FN[config.activation_function]
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self.c_proj = Conv1D(config.hidden_size, intermediate_size)
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self.dropout = nn.Dropout(config.residual_dropout_prob)
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def forward(self, hidden_states):
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hidden_states = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.c_proj(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class CodeSageBlock(nn.Module):
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def __init__(self, config):
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super().__init__()
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hidden_size = config.hidden_size
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inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
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self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.attn = CodeSageAttention(config)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = CodeSageMLP(inner_dim, config)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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):
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_outputs = self.attn(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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output_attentions=output_attentions
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)
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attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
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outputs = attn_outputs[1:]
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hidden_states = attn_output + residual
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = residual + feed_forward_hidden_states
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outputs = (hidden_states,) + outputs[1:]
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return outputs # hidden_states, present, (attentions)
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class CodeSagePreTrainedModel(PreTrainedModel):
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config_class = CodeSageConfig
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base_model_prefix = "transformer"
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, (nn.Linear, Conv1D)):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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class CodeSageModel(CodeSagePreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
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self.wpe = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.drop = nn.Dropout(config.embedding_dropout_prob)
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self.h = nn.ModuleList([CodeSageBlock(config) for _ in range(config.num_hidden_layers)])
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self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.init_weights()
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def get_input_embeddings(self):
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return self.wte
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def set_input_embeddings(self, new_embeddings: torch.Tensor):
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self.wte = new_embeddings
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None
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):
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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if input_ids is not None:
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input_shape = input_ids.size()
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if position_ids is None:
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position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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else:
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position_ids = position_ids.view(-1, input_shape[-1])
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extended_attention_mask = None
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if attention_mask is not None:
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assert attention_mask.dim() == 2
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extended_attention_mask = attention_mask[:, None, None, :]
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extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
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extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
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if inputs_embeds is None:
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inputs_embeds = self.wte(input_ids)
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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hidden_states = self.drop(hidden_states)
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output_shape = input_shape + (hidden_states.size(-1),)
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all_self_attentions = () if output_attentions else None
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all_hidden_states = () if output_hidden_states else None
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for i, block in enumerate(self.h):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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outputs = block(
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hidden_states,
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attention_mask=extended_attention_mask,
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head_mask=head_mask[i],
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output_attentions=output_attentions,
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)
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hidden_states = outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (outputs[1],)
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(*output_shape)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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pooled_output = None # max-pooled output
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if attention_mask is not None:
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pooled_output = (hidden_states * attention_mask[:, :, None]).sum(1) / attention_mask.sum(1)[:, None]
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if not return_dict:
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return tuple(
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v
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for v in [hidden_states, pooled_output, all_hidden_states, all_self_attentions]
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if v is not None
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)
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return BaseModelOutputWithPooling(
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last_hidden_state=hidden_states,
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pooler_output=pooled_output,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions
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)
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class CodeSageForMaskedLM(CodeSagePreTrainedModel):
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(self, config):
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super().__init__(config)
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self.transformer = CodeSageModel(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.init_weights()
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def get_output_embeddings(self):
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return self.lm_head
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def set_output_embeddings(self, new_embeddings):
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self.lm_head = new_embeddings
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None
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):
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.transformer(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict
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)
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hidden_states = transformer_outputs[0]
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lm_logits = self.lm_head(hidden_states)
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masked_lm_loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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masked_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
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if not return_dict:
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output = (lm_logits,) + transformer_outputs[1:]
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return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
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return MaskedLMOutput(
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loss=masked_lm_loss,
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logits=lm_logits,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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)
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class CodeSageForSequenceClassification(CodeSagePreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.config = config
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self.transformer = CodeSageModel(config)
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classifier_dropout = (
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config.classifier_dropout
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if hasattr(config, 'classifier_dropout') and config.classifier_dropout is not None
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else config.residual_dropout_prob
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)
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self.dropout = nn.Dropout(classifier_dropout)
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self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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assert attention_mask is not None, "attention_mask is needed to perform max-pooling"
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outputs = self.transformer(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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pooled_output = outputs[1]
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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loss = None
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if labels is not None:
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(logits, labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == "multi_label_classification":
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(logits, labels)
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if not return_dict:
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output = (logits,) + outputs[2:]
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return ((loss,) + output) if loss is not None else output
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return SequenceClassifierOutput(
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loss=loss,
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logits=logits,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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