mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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108 lines
3.5 KiB
Python
Executable File
108 lines
3.5 KiB
Python
Executable File
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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from ....functional import allgather, split
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from ....mapping import Mapping
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from ....module import Module
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from ..attention import CrossAttention, SelfAttention, _attention
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class DistriSelfAttentionPP(Module):
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def __init__(self, module: SelfAttention, mapping: Mapping = Mapping()):
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super().__init__()
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self.mapping = mapping
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self.module = module
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def forward(self, hidden_states):
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mapping = self.mapping
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attn = self.module
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batch_size, sequence_length, _ = hidden_states.shape
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qkv = attn.to_qkv(hidden_states)
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query, kv = split(qkv, [attn.inner_dim, attn.inner_dim * 2], dim=2)
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if mapping.tp_size == 1:
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full_kv = kv
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else:
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full_kv = allgather(kv, group=mapping.tp_group, gather_dim=1)
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key, value = split(full_kv, full_kv.shape[-1] // 2, dim=-1)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view([batch_size, -1, attn.heads,
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head_dim]).transpose(1, 2)
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key = key.view([batch_size, -1, attn.heads, head_dim]).transpose(1, 2)
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value = value.view([batch_size, -1, attn.heads,
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head_dim]).transpose(1, 2)
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hidden_states = _attention(query, key, value, attn.scale)
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hidden_states = hidden_states.view(
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[batch_size, -1, attn.heads * head_dim])
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# linear proj
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hidden_states = attn.to_out(hidden_states)
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return hidden_states
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class DistriCrossAttentionPP(Module):
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def __init__(self, module: CrossAttention, mapping: Mapping = Mapping()):
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super().__init__()
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self.mapping = mapping
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self.module = module
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self.kv_cache = None
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def forward(self, hidden_states, context):
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attn = self.module
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recompute_kv = self.kv_cache is None
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if context is None:
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context = hidden_states
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batch_size, sequence_length, _ = context.shape
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query = attn.to_q(hidden_states)
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if recompute_kv or self.kv_cache is None:
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kv = attn.to_kv(context)
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self.kv_cache = kv
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else:
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kv = self.kv_cache
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key, value = split(kv, kv.shape[-1] // 2, dim=-1)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view([batch_size, -1, attn.heads,
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head_dim]).transpose(1, 2)
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key = key.view([batch_size, -1, attn.heads, head_dim]).transpose(1, 2)
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value = value.view([batch_size, -1, attn.heads,
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head_dim]).transpose(1, 2)
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hidden_states = _attention(query, key, value, scale=attn.scale)
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hidden_states = hidden_states.view(
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[batch_size, -1, attn.heads * head_dim])
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# linear proj
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hidden_states = attn.to_out(hidden_states)
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return hidden_states
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