ColossalAI/colossalai/fx/tracer/meta_patch/patched_function/convolution.py

186 lines
5.6 KiB
Python

import collections
import math
from itertools import repeat
import torch
from ...registry import meta_patched_function
def _ntuple(n, name="parse"):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return tuple(x)
return tuple(repeat(x, n))
parse.__name__ = name
return parse
_single = _ntuple(1, "_single")
_pair = _ntuple(2, "_pair")
_triple = _ntuple(3, "_triple")
def _extract_kwargs(kwargs):
if "stride" in kwargs:
stride = kwargs["stride"]
else:
stride = 1
# TODO: process str type padding
if "padding" in kwargs:
padding = kwargs["padding"]
else:
padding = 0
if "dilation" in kwargs:
dilation = kwargs["dilation"]
else:
dilation = 1
if "output_padding" in kwargs:
output_padding = kwargs["output_padding"]
else:
output_padding = 0
return stride, padding, dilation, output_padding
@meta_patched_function.register(torch.nn.functional.conv1d)
def torch_nn_functional_conv1d(input, weight, **kwargs):
stride, padding, dilation, _ = _extract_kwargs(kwargs)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
kernel_size = weight.shape[2:]
l_in = input.shape[-1]
c_out = weight.shape[0]
l_out = math.floor((l_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
result_shape = input.shape[:-2] + (
c_out,
l_out,
)
return torch.empty(result_shape, device="meta")
@meta_patched_function.register(torch.nn.functional.conv2d)
def torch_nn_functional_conv2d(input, weight, **kwargs):
stride, padding, dilation, _ = _extract_kwargs(kwargs)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
kernel_size = weight.shape[2:]
h_in, w_in = input.shape[-2:]
c_out = weight.shape[0]
h_out = math.floor((h_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
w_out = math.floor((w_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
result_shape = input.shape[:-3] + (
c_out,
h_out,
w_out,
)
return torch.empty(result_shape, device="meta")
@meta_patched_function.register(torch.nn.functional.conv3d)
def torch_nn_functional_conv3d(input, weight, **kwargs):
stride, padding, dilation, _ = _extract_kwargs(kwargs)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
kernel_size = weight.shape[2:]
d_in, h_in, w_in = input.shape[-3:]
c_out = weight.shape[0]
d_out = math.floor((d_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
h_out = math.floor((h_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
w_out = math.floor((w_in + 2 * padding[2] - dilation[2] * (kernel_size[2] - 1) - 1) / stride[2] + 1)
result_shape = input.shape[:-4] + (
c_out,
d_out,
h_out,
w_out,
)
return torch.empty(result_shape, device="meta")
@meta_patched_function.register(torch.nn.functional.conv_transpose1d)
def torch_nn_functional_convtranspose1d(input, weight, **kwargs):
stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
output_padding = _single(output_padding)
kernel_size = weight.shape[2:]
l_in = input.shape[-1]
c_out = weight.shape[1]
l_out = math.floor(
(l_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) + output_padding[0] + 1
)
result_shape = input.shape[:-2] + (
c_out,
l_out,
)
return torch.empty(result_shape, device="meta")
@meta_patched_function.register(torch.nn.functional.conv_transpose2d)
def torch_nn_functional_convtranspose2d(input, weight, **kwargs):
stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
output_padding = _pair(output_padding)
kernel_size = weight.shape[2:]
h_in, w_in = input.shape[-2:]
c_out = weight.shape[1]
h_out = math.floor(
(h_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) + output_padding[0] + 1
)
w_out = math.floor(
(w_in - 1) * stride[1] - 2 * padding[1] + dilation[1] * (kernel_size[1] - 1) + output_padding[1] + 1
)
result_shape = input.shape[:-3] + (
c_out,
h_out,
w_out,
)
return torch.empty(result_shape, device="meta")
@meta_patched_function.register(torch.nn.functional.conv_transpose3d)
def torch_nn_functional_convtranspose3d(input, weight, **kwargs):
stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
output_padding = _triple(output_padding)
kernel_size = weight.shape[2:]
d_in, h_in, w_in = input.shape[-3:]
c_out = weight.shape[1]
d_out = math.floor(
(d_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) + output_padding[0] + 1
)
h_out = math.floor(
(h_in - 1) * stride[1] - 2 * padding[1] + dilation[1] * (kernel_size[1] - 1) + output_padding[1] + 1
)
w_out = math.floor(
(w_in - 1) * stride[2] - 2 * padding[2] + dilation[2] * (kernel_size[2] - 1) + output_padding[2] + 1
)
result_shape = input.shape[:-4] + (
c_out,
d_out,
h_out,
w_out,
)
return torch.empty(result_shape, device="meta")