mirror of https://github.com/hpcaitech/ColossalAI
246 lines
9.6 KiB
Python
246 lines
9.6 KiB
Python
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from transformers.activations import ACT2FN
|
|
from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
|
|
from transformers.pytorch_utils import Conv1D
|
|
|
|
|
|
class GPT2MLP(nn.Module):
|
|
def __init__(self, intermediate_size, config):
|
|
super().__init__()
|
|
embed_dim = config.hidden_size
|
|
self.c_fc = Conv1D(intermediate_size, embed_dim)
|
|
self.c_proj = Conv1D(embed_dim, intermediate_size)
|
|
self.act = ACT2FN[config.activation_function]
|
|
self.dropout = nn.Dropout(config.resid_pdrop)
|
|
|
|
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
|
|
hidden_states = self.c_fc(hidden_states)
|
|
hidden_states = self.act(hidden_states)
|
|
hidden_states = self.c_proj(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# The reason Why we don't import GPT2Attention from transformers directly is that:
|
|
# 1. The tracer will not work correctly when we feed meta_args and concrete_args at same time,
|
|
# so we have to build the customized GPT2Attention class and remove the conditional branch manually.
|
|
# 2. The order of split and view op has been changed in the customized GPT2Attention class, the new
|
|
# order is same as megatron-lm gpt model.
|
|
class GPT2Attention(nn.Module):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
|
|
max_positions = config.max_position_embeddings
|
|
self.register_buffer(
|
|
"bias",
|
|
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
|
|
1, 1, max_positions, max_positions
|
|
),
|
|
)
|
|
self.register_buffer("masked_bias", torch.tensor(-1e4))
|
|
|
|
self.embed_dim = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = self.embed_dim // self.num_heads
|
|
self.split_size = self.embed_dim
|
|
self.scale_attn_weights = config.scale_attn_weights
|
|
|
|
# Layer-wise attention scaling, reordering, and upcasting
|
|
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
|
|
self.layer_idx = layer_idx
|
|
|
|
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
|
|
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
|
|
|
|
self.attn_dropout = nn.Dropout(config.attn_pdrop)
|
|
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
|
|
|
self.pruned_heads = set()
|
|
|
|
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
|
|
attn_weights = torch.matmul(query, key.transpose(-1, -2))
|
|
|
|
if self.scale_attn_weights:
|
|
attn_weights = attn_weights / (value.size(-1) ** 0.5)
|
|
|
|
# Layer-wise attention scaling
|
|
if self.scale_attn_by_inverse_layer_idx:
|
|
attn_weights = attn_weights / float(self.layer_idx + 1)
|
|
|
|
# if only "normal" attention layer implements causal mask
|
|
query_length, key_length = query.size(-2), key.size(-2)
|
|
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].to(torch.bool)
|
|
attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
|
|
|
|
if attention_mask is not None:
|
|
# Apply the attention mask
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_weights = attn_weights.type(value.dtype)
|
|
|
|
# Mask heads if we want to
|
|
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):
|
|
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):
|
|
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: Optional[Tuple[torch.FloatTensor]],
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
|
|
qkv = self.c_attn(hidden_states)
|
|
query, key, value = self._split_heads(qkv, self.num_heads, 3 * self.head_dim).split(self.head_dim, dim=3)
|
|
(key, value)
|
|
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)
|
|
return attn_output
|
|
|
|
|
|
class GPT2Block(nn.Module):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
hidden_size = config.hidden_size
|
|
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
|
|
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
|
self.attn = GPT2Attention(config, layer_idx=layer_idx)
|
|
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
|
self.mlp = GPT2MLP(inner_dim, config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
|
|
residual = hidden_states
|
|
hidden_states = self.ln_1(hidden_states)
|
|
attn_outputs = self.attn(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
)
|
|
# residual connection
|
|
hidden_states = attn_outputs + residual
|
|
residual = hidden_states
|
|
hidden_states = self.ln_2(hidden_states)
|
|
feed_forward_hidden_states = self.mlp(hidden_states)
|
|
# residual connection
|
|
hidden_states = residual + feed_forward_hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
class GPT2Model(GPT2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.embed_dim = config.hidden_size
|
|
|
|
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
|
|
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
|
|
|
|
self.drop = nn.Dropout(config.embd_pdrop)
|
|
self.h = nn.ModuleList([GPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)])
|
|
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
batch_size = input_ids.shape[0]
|
|
|
|
device = input_ids.device
|
|
|
|
past_length = 0
|
|
past_key_values = tuple([None] * len(self.h))
|
|
|
|
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
|
|
|
# GPT2Attention mask.
|
|
attention_mask = attention_mask.view(batch_size, -1)
|
|
attention_mask = attention_mask[:, None, None, :]
|
|
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
|
attention_mask = (1.0 - attention_mask) * -10000.0
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# head_mask has shape n_layer x batch x n_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
inputs_embeds = self.wte(input_ids)
|
|
position_embeds = self.wpe(position_ids)
|
|
|
|
hidden_states = inputs_embeds + position_embeds
|
|
|
|
output_shape = input_shape + (hidden_states.size(-1),)
|
|
|
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
|
outputs = block(hidden_states, attention_mask=attention_mask, head_mask=head_mask[i])
|
|
hidden_states = outputs
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
hidden_states = hidden_states.view(output_shape)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class GPT2LMHeadModel(GPT2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = GPT2Model(config)
|
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
):
|
|
transformer_outputs = self.transformer(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
lm_logits = self.lm_head(transformer_outputs)
|
|
|
|
return lm_logits
|
|
|
|
|
|
class GPTLMLoss(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.loss_fn = nn.CrossEntropyLoss()
|
|
|
|
def forward(self, logits, labels):
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|