mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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110 lines
4.2 KiB
Python
Executable File
110 lines
4.2 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, concat, conv2d, slice, stack, unsqueeze
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from ....layers import Conv2d
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from ....mapping import Mapping
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from ....module import Module
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class DistriConv2dPP(Module):
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def __init__(self,
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conv: Conv2d,
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mapping: Mapping = Mapping(),
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is_first_layer: bool = False):
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super().__init__()
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self.mapping = mapping
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self.conv = conv
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self.is_first_layer = is_first_layer
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def sliced_forward(self, x):
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mapping = self.mapping
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b, c, h, w = x.shape
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assert h % mapping.tp_size == 0
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stride = self.conv.stride[0]
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padding = self.conv.padding[0]
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output_h = x.shape[2] // stride // mapping.tp_size
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idx = mapping.tp_rank
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h_begin = output_h * idx * stride - padding
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h_end = output_h * (idx + 1) * stride + padding
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pre_padding = [0, padding]
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post_padding = [0, padding]
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if h_begin < 0:
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h_begin = 0
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pre_padding[0] = padding
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if h_end > h:
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h_end = h
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post_padding[0] = padding
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sliced_input = slice(x, [0, 0, h_begin, 0], [b, c, h_end - h_begin, w])
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return conv2d(sliced_input,
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self.conv.weight.value,
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None if self.conv.bias is None else self.conv.bias.value,
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stride=self.conv.stride,
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padding=(0, 0),
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pre_padding=tuple(pre_padding),
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post_padding=tuple(post_padding))
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def forward(self, x, *args, **kwargs):
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mapping = self.mapping
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if self.is_first_layer:
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full_x = x
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output = self.sliced_forward(full_x)
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else:
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boundary_size = self.conv.padding[0]
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def create_padded_x(x, boundaries):
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preH = 0
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postH = 0
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if mapping.tp_rank == 0:
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b = boundaries.select(0, mapping.tp_rank + 1).select(0, 0)
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padded_x = concat([x, b], dim=2)
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preH = boundary_size
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elif mapping.tp_rank == mapping.tp_size - 1:
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b = boundaries.select(0, mapping.tp_rank - 1).select(0, 1)
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padded_x = concat([b, x], dim=2)
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postH = boundary_size
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else:
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b0 = boundaries.select(0, mapping.tp_rank - 1).select(0, 1)
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b1 = boundaries.select(0, mapping.tp_rank + 1).select(0, 0)
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padded_x = concat(
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[
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b0,
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x,
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b1,
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],
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dim=2,
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)
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return padded_x, preH, postH
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n, c, h, w = x.shape
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b0 = slice(x, [0, 0, 0, 0], [n, c, boundary_size, w])
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b1 = slice(x, [0, 0, h - boundary_size, 0],
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[n, c, boundary_size, w])
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boundary = stack([b0, b1], dim=0)
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boundaries = allgather(unsqueeze(boundary, 0),
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group=mapping.tp_group)
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padded_x, preH, postH = create_padded_x(x, boundaries)
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output = conv2d(padded_x,
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self.conv.weight.value,
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self.conv.bias.value,
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stride=self.conv.stride,
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pre_padding=(preH, self.conv.padding[1]),
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post_padding=(postH, self.conv.padding[1]))
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return output
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