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https://github.com/NVIDIA/TensorRT-LLM.git
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f7eca56161
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
215 lines
8.1 KiB
Python
215 lines
8.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 typing import Tuple
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from ..functional import conv1d, conv2d, conv_transpose2d
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from ..module import Module
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from ..parameter import Parameter
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class Conv2d(Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Tuple[int, int],
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stride: Tuple[int, int] = (1, 1),
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padding: Tuple[int, int] = (0, 0),
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dilation: Tuple[int, int] = (1, 1),
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros', # TODO: refine this type
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dtype=None) -> None:
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super().__init__()
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if groups <= 0:
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raise ValueError('groups must be a positive integer')
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if in_channels % groups != 0:
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raise ValueError('in_channels must be divisible by groups')
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if out_channels % groups != 0:
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raise ValueError('out_channels must be divisible by groups')
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.groups = groups
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self.padding_mode = padding_mode
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self.weight = Parameter(shape=(out_channels, in_channels // groups,
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*kernel_size),
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dtype=dtype)
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if bias:
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self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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def forward(self, input):
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return conv2d(input, self.weight.value,
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None if self.bias is None else self.bias.value,
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self.stride, self.padding, self.dilation, self.groups)
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class ConvTranspose2d(Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Tuple[int, int],
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stride: Tuple[int, int] = (1, 1),
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padding: Tuple[int, int] = (0, 0),
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output_padding: Tuple[int, int] = (0, 0),
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dilation: Tuple[int, int] = (1, 1),
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros', # TODO: refine this type
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dtype=None) -> None:
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super().__init__()
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if groups <= 0:
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raise ValueError('groups must be a positive integer')
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if in_channels % groups != 0:
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raise ValueError('in_channels must be divisible by groups')
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if out_channels % groups != 0:
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raise ValueError('out_channels must be divisible by groups')
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.output_padding = output_padding
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self.dilation = dilation
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self.groups = groups
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self.padding_mode = padding_mode
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self.weight = Parameter(shape=(in_channels, out_channels // groups,
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*kernel_size),
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dtype=dtype)
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if bias:
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self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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def _output_padding(self,
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input,
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output_size,
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stride,
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padding,
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kernel_size,
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num_spatial_dims: int,
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dilation=None):
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if output_size is None:
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ret = self.output_padding
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else:
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has_batch_dim = input.dim() == num_spatial_dims + 2
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num_non_spatial_dims = 2 if has_batch_dim else 1
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if len(output_size) == num_non_spatial_dims + num_spatial_dims:
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output_size = output_size[num_non_spatial_dims:]
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if len(output_size) != num_spatial_dims:
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raise ValueError(
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"ConvTranspose{}D: for {}D input, output_size must have {} or {} elements (got {})"
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.format(num_spatial_dims, input.dim(), num_spatial_dims,
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num_non_spatial_dims + num_spatial_dims,
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len(output_size)))
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min_sizes = []
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max_sizes = []
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for d in range(num_spatial_dims):
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dim_size = (
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(input.size(d + num_non_spatial_dims) - 1) * stride[d] -
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2 * padding[d] +
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(dilation[d] if dilation is not None else 1) *
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(kernel_size[d] - 1) + 1)
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min_sizes.append(dim_size)
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max_sizes.append(min_sizes[d] + stride[d] - 1)
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for i in range(len(output_size)):
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size = output_size[i]
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min_size = min_sizes[i]
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max_size = max_sizes[i]
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if size < min_size or size > max_size:
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raise ValueError((
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"requested an output size of {}, but valid sizes range "
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"from {} to {} (for an input of {})").format(
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output_size, min_sizes, max_sizes,
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input.size()[2:]))
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res = []
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for d in range(num_spatial_dims):
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res.append(output_size[d] - min_sizes[d])
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ret = res
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return ret
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def forward(self, input, output_size=None):
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num_spatial_dims = 2
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output_padding = self._output_padding(input, output_size, self.stride,
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self.padding, self.kernel_size,
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num_spatial_dims, self.dilation)
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return conv_transpose2d(input, self.weight.value,
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None if self.bias is None else self.bias.value,
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self.stride, self.padding, output_padding,
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self.dilation, self.groups)
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class Conv1d(Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride: int = 1,
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padding: int = 0,
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dilation: int = 1,
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros', # TODO: refine this type
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dtype=None) -> None:
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super().__init__()
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if groups <= 0:
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raise ValueError('groups must be a positive integer')
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if in_channels % groups != 0:
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raise ValueError('in_channels must be divisible by groups')
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if out_channels % groups != 0:
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raise ValueError('out_channels must be divisible by groups')
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.groups = groups
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self.padding_mode = padding_mode
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self.weight = Parameter(shape=(out_channels, in_channels // groups,
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kernel_size, 1),
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dtype=dtype)
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if bias:
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self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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def forward(self, input):
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return conv1d(input, self.weight.value,
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None if self.bias is None else self.bias.value,
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self.stride, self.padding, self.dilation, self.groups)
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