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https://github.com/NVIDIA/TensorRT-LLM.git
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1730a587d8
* Update TensorRT-LLM --------- Co-authored-by: tonylek <137782967+tonylek@users.noreply.github.com>
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# 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 group_norm, layer_norm, rms_norm
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from ..module import Module
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from ..parameter import Parameter
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class LayerNorm(Module):
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def __init__(self,
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normalized_shape,
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eps=1e-05,
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elementwise_affine=True,
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bias=True,
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dtype=None,
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tp_size=1,
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tp_dim=-1):
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super().__init__()
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if isinstance(normalized_shape, int):
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normalized_shape = (normalized_shape, )
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self.normalized_shape = tuple(normalized_shape)
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self.elementwise_affine = elementwise_affine
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if self.elementwise_affine:
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self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
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if bias:
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self.bias = Parameter(shape=self.normalized_shape, dtype=dtype)
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else:
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self.register_parameter('bias', None)
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else:
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self.register_parameter('weight', None)
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self.register_parameter('bias', None)
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self.eps = eps
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self.dtype = dtype
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self.tp_size = tp_size
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self.tp_dim = tp_dim
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def forward(self, x, normalized_shape=None):
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weight = 1. if self.weight is None else self.weight.value
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bias = 0. if self.bias is None else self.bias.value
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if normalized_shape is None:
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normalized_shape = self.normalized_shape
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return layer_norm(x, normalized_shape, weight, bias, self.eps)
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class RmsNorm(Module):
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def __init__(self,
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normalized_shape,
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num_groups=1,
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eps=1e-06,
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elementwise_affine=True,
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dtype=None):
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super().__init__()
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if isinstance(normalized_shape, int):
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normalized_shape = (normalized_shape, )
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self.normalized_shape = tuple(normalized_shape)
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self.elementwise_affine = elementwise_affine
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self.num_groups = num_groups
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num_channels = normalized_shape[-1]
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if num_channels % num_groups != 0:
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raise ValueError('num_channels must be divisible by num_groups')
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if self.elementwise_affine:
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self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
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else:
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self.register_parameter('weight', None)
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self.eps = eps
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self.dtype = dtype
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def forward(self, x, normalized_shape=None):
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weight = None if self.weight is None else self.weight.value
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if normalized_shape is None:
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normalized_shape = self.normalized_shape
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return rms_norm(x, normalized_shape, self.num_groups, weight, self.eps)
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class GroupNorm(Module):
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def __init__(self,
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num_groups,
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num_channels,
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eps=1e-05,
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affine=True,
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dtype=None):
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super().__init__()
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if num_channels % num_groups != 0:
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raise ValueError('num_channels must be divisible by num_groups')
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self.num_groups = num_groups
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self.num_channels = num_channels
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self.affine = affine
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if self.affine:
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self.weight = Parameter(shape=(self.num_channels, ), dtype=dtype)
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self.bias = Parameter(shape=(self.num_channels, ), dtype=dtype)
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else:
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self.register_parameter('weight', None)
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self.register_parameter('bias', None)
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self.eps = eps
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def forward(self, x):
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weight = None if self.weight is None else self.weight.value
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bias = None if self.bias is None else self.bias.value
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return group_norm(x, self.num_groups, weight, bias, self.eps)
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