#!/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 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() self.post_init() 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, )