modeling_codesage.py

#!/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,
        )