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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", |
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"codesage/codesage-base", |
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"codesage/codesage-large", |
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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) |
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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 |
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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] |
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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 |
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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) |
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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 |
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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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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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