mirror of https://github.com/hpcaitech/ColossalAI
115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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def forward_fn():
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, embed_dim = hidden_states.size()
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mixed_qkv = self.qkv(hidden_states)
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# modified from original code, which is:
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# mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute(
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# 2, 0, 3, 1, 4
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# )
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# to:
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mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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query_states, key_states, value_states = (
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mixed_qkv[0],
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mixed_qkv[1],
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mixed_qkv[2],
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)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
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attention_scores = attention_scores * self.scale
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)
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new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
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context_layer = context_layer.reshape(new_context_layer_shape)
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output = self.projection(context_layer)
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outputs = (output, attention_probs) if output_attentions else (output, None)
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return outputs
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return forward
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def get_blip2_flash_attention_forward():
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from transformers.models.blip_2.modeling_blip_2 import Blip2Attention
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from colossalai.kernel.cuda_native import ColoAttention
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def forward(
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self: Blip2Attention,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, embed_dim = hidden_states.size()
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mixed_qkv = self.qkv(hidden_states)
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mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, -1).permute(2, 0, 1, 3, 4)
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query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2]
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attention = ColoAttention(
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embed_dim=self.embed_dim, num_heads=self.num_heads, dropout=self.dropout.p, scale=self.scale
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)
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context_layer = attention(query_states, key_states, value_states)
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output = self.projection(context_layer)
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outputs = (output, None)
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return outputs
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return forward
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def get_jit_fused_blip2_QFormer_self_output_forward():
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from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerSelfOutput
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def forward(self: Blip2QFormerSelfOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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return forward
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def get_jit_fused_blip2_QFormer_output_forward():
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from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerOutput
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def forward(self: Blip2QFormerOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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return forward
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