mirror of
https://github.com/datawhalechina/llms-from-scratch-cn.git
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550 lines
22 KiB
Python
550 lines
22 KiB
Python
"""PyTorch PanguAlpha GPT2 Model"""
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from configuration_gptpangu import GPTPanguConfig
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from typing import Tuple
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import math
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import torch
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import PreTrainedModel
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class GPTPanguAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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max_positions = config.max_position_embeddings
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self.register_buffer(
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"bias",
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torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
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1, 1, max_positions, max_positions
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),
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)
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self.register_buffer("masked_bias", torch.tensor(-1e4))
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
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)
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self.scale_attn_weights = config.scale_attn_weights
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
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self.c_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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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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if self.scale_attn_weights:
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attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)
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query_length, key_length = query.size(-2), key.size(-2)
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causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
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attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
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if attention_mask is not None:
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# Apply the attention mask
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
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attn_weights = attn_weights.type(value.dtype)
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attn_weights = self.attn_dropout(attn_weights)
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# Mask heads if we want to
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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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layer_past=None,
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attention_mask=None,
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head_mask=None,
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custom_query=None,
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use_cache=False,
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output_attentions=False,
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):
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query = self.q_proj(custom_query) if custom_query is not None else self.q_proj(hidden_states)
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key = self.k_proj(hidden_states)
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value = self.v_proj(hidden_states)
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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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if layer_past is not None:
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past_key, past_value = layer_past
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key = torch.cat((past_key, key), dim=-2)
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value = torch.cat((past_value, value), dim=-2)
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if use_cache is True:
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present = (key, value)
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else:
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present = None
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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.resid_dropout(attn_output)
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outputs = (attn_output, present)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs # a, present, (attentions)
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class GPTPanguMLP(nn.Module):
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def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * hidden_size
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super().__init__()
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embed_dim = config.hidden_size
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self.c_fc = nn.Linear(embed_dim, intermediate_size)
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self.c_proj = nn.Linear(intermediate_size, embed_dim)
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self.act = ACT2FN[config.activation_function]
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self.dropout = nn.Dropout(config.resid_pdrop)
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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 GPTPanguBlock(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 = GPTPanguAttention(config)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = GPTPanguMLP(inner_dim, config)
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def forward(
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self,
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hidden_states,
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layer_past=None,
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attention_mask=None,
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head_mask=None,
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custom_query=None,
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use_cache=False,
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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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layer_past=layer_past,
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attention_mask=attention_mask,
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head_mask=head_mask,
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custom_query=custom_query,
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use_cache=use_cache,
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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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# residual connection
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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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# residual connection
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hidden_states = residual + feed_forward_hidden_states
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if use_cache:
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outputs = (hidden_states,) + outputs
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else:
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outputs = (hidden_states,) + outputs[1:]
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return outputs # hidden_states, present, (attentions, cross_attentions)
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class GPTPanguPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = GPTPanguConfig
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base_model_prefix = "transformer"
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supports_gradient_checkpointing = True
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def __init__(self, *inputs, **kwargs):
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super().__init__(*inputs, **kwargs)
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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,)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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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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# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
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# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
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# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
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# > -- GPT-2 :: https://openai.com/blog/better-language-models/
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#
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# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
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for name, p in module.named_parameters():
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if "c_proj" in name and "weight" in name:
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# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
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p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.num_layers)))
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def _set_gradient_checkpointing(self, module, value=False):
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if isinstance(module, GPTPanguModel):
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module.gradient_checkpointing = value
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class GPTPanguModel(GPTPanguPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.embed_dim = config.hidden_size
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self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.wqe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.drop = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([GPTPanguBlock(config) for _ in range(config.num_layers)])
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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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):
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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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past_key_values=None,
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attention_mask=None,
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token_type_ids=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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use_cache=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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use_cache = use_cache if use_cache is not None else self.config.use_cache
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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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elif input_ids is not None:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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batch_size = input_ids.shape[0]
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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batch_size = inputs_embeds.shape[0]
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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 token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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if position_ids is not None:
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position_ids = position_ids.view(-1, input_shape[-1])
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if past_key_values is None:
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past_length = 0
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past_key_values = tuple([None] * len(self.h))
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else:
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past_length = past_key_values[0][0].size(-2)
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if position_ids is None:
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position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# GPT2Attention mask.
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if attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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attention_mask = attention_mask[:, None, None, :]
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and -10000.0 for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * -10000.0
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape bsz x num_heads x N x N
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# head_mask has shape n_layer x batch x num_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.num_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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if token_type_ids is not None:
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token_type_embeds = self.wte(token_type_ids)
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hidden_states = hidden_states + token_type_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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# top attention custom query
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last_layer_id = len(self.h) - 1
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query_embeds = self.wqe(position_ids)
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presents = () if use_cache else None
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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, layer_past) in enumerate(zip(self.h, past_key_values)):
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# Final LayerNorm before last query layer
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if i == last_layer_id:
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hidden_states = self.ln_f(hidden_states)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, use_cache, output_attentions)
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return custom_forward
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outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(block),
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hidden_states=hidden_states,
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layer_past=None,
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attention_mask=attention_mask,
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head_mask=head_mask[i],
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# custom query
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custom_query=query_embeds if i == last_layer_id else None,
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)
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else:
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outputs = block(
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hidden_states,
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layer_past=layer_past,
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attention_mask=attention_mask,
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head_mask=head_mask[i],
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# custom query
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custom_query=query_embeds if i == last_layer_id else None,
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use_cache=use_cache,
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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 use_cache is True:
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presents = presents + (outputs[1],)
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if output_attentions:
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all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
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hidden_states = hidden_states.view(*output_shape)
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# Add last hidden state
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=presents,
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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 GPTPanguForCausalLM(GPTPanguPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.transformer = GPTPanguModel(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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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 prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
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token_type_ids = kwargs.get("token_type_ids", None)
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# only last token for inputs_ids if past is defined in kwargs
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if past:
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input_ids = input_ids[:, -1].unsqueeze(-1)
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if token_type_ids is not None:
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token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
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attention_mask = kwargs.get("attention_mask", None)
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position_ids = kwargs.get("position_ids", None)
|
|
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.int().cumsum(-1).long() - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past:
|
|
position_ids = position_ids[:, -1].unsqueeze(-1)
|
|
else:
|
|
position_ids = None
|
|
return {
|
|
"input_ids": input_ids,
|
|
"past_key_values": past,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"position_ids": position_ids,
|
|
"attention_mask": attention_mask,
|
|
"token_type_ids": token_type_ids,
|
|
}
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
|
|
``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
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)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
|
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
|
|
"""
|
|
This function is used to re-order the :obj:`past_key_values` cache if
|
|
:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
|
|
called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
|
|
"""
|
|
return tuple(
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
|
|
for layer_past in past
|
|
)
|