92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.models.utils import use_sdp_non_causal
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def gpt2_attention_attn(
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self,
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query,
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key,
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value,
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attention_mask=None,
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head_mask=None
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):
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# ipex-llm changes start
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if (
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self.scale_attn_weights
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and not self.scale_attn_by_inverse_layer_idx
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and head_mask is None
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and query.size(-2) == key.size(-2)
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and use_sdp_non_causal(query.size(-1), query.device, query.dtype)
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):
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if not self.is_cross_attention:
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seq_len = query.size(-2)
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causal_mask = self.bias[:, :, :seq_len, :seq_len]
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mask_value = torch.finfo(query.dtype).min
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mask_value = torch.full([], mask_value, dtype=query.dtype, device=query.device)
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attention_mask = attention_mask.expand(-1, -1, seq_len, seq_len)
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attention_mask = torch.where(causal_mask, attention_mask, mask_value)
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else:
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attention_mask = attention_mask.expand(-1, -1, seq_len, seq_len)
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query, key.contiguous(), value.contiguous(),
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attention_mask, False
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)
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return attn_output, attn_weights
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# ipex-llm changes end
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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 / torch.full(
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[], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
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)
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# Layer-wise attention scaling
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if self.scale_attn_by_inverse_layer_idx:
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attn_weights = attn_weights / float(self.layer_idx + 1)
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if not self.is_cross_attention:
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# if only "normal" attention layer implements causal mask
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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]
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mask_value = torch.finfo(attn_weights.dtype).min
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mask_value = torch.full([], mask_value, dtype=attn_weights.dtype,
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device=attn_weights.device)
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attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
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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 = torch.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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