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https://github.com/NVIDIA/TensorRT-LLM.git
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58 lines
2.2 KiB
Python
Executable File
58 lines
2.2 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 allreduce, pow, select, stack
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from ....layers import GroupNorm
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from ....mapping import Mapping
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from ....module import Module
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class DistriGroupNorm(Module):
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def __init__(self,
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module: GroupNorm,
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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.module = module
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def forward(self, x, *args, **kwargs):
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mapping = self.mapping
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module = self.module
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n, c, h, w = x.shape
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num_groups = module.num_groups
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group_size = c // num_groups
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x = x.view([n, num_groups, group_size, h, w])
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x_mean = x.mean(dim=4, keepdim=True).mean(dim=(3, 2), keepdim=True)
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x2_mean = pow(x, 2.0).mean(dim=4, keepdim=True).mean(dim=(3, 2),
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keepdim=True)
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mean = stack([x_mean, x2_mean], dim=0)
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mean = allreduce(mean, mapping.tp_group)
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mean = mean / (mapping.tp_size * 1.0)
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x_mean = select(mean, 0, 0)
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x2_mean = select(mean, 0, 1)
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var = x2_mean - pow(x_mean, 2.0)
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num_elements = group_size * h * w
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var = var * (num_elements / (num_elements - 1))
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std = (var + module.eps).sqrt()
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output = (x - x_mean) / std
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output = output.view([n, c, h, w])
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if module.affine:
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output = output * module.weight.value.view([1, -1, 1, 1])
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output = output + module.bias.value.view([1, -1, 1, 1])
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return output
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