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[hotfix] avoid conflict of meta registry with torch 1.13.0. #1530

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Merged
FrankLeeeee merged 2 commits into from
Sep 1, 2022
Merged

[hotfix] avoid conflict of meta registry with torch 1.13.0. #1530

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[hotfix] avoid conflict of meta registry with torch 1.13.0.
super-dainiu Aug 31, 2022
b689900
[hotfix] avoid conflict of meta registry with torch 1.13.0.
super-dainiu Aug 31, 2022
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74 changes: 29 additions & 45 deletions colossalai/fx/profiler/_meta_registrations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
import torch
from torch.utils._pytree import tree_map


aten = torch.ops.aten

meta_lib = torch.library.Library("aten", "IMPL", "Meta")
Expand All @@ -14,16 +13,17 @@


def register_meta(op, register_dispatcher=True):

def wrapper(f):

def add_func(op):
meta_table[op] = f
if register_dispatcher:
name = (
op.__name__
if op._overloadname != "default"
else op.overloadpacket.__name__
)
meta_lib.impl(name, f)
name = (op.__name__ if op._overloadname != "default" else op.overloadpacket.__name__)
try:
meta_lib.impl(name, f)
except:
pass
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@super-dainiu super-dainiu Aug 31, 2022

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Change happens only here. I don't know why but the pre-commit hook reformatted all of my code...

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@FrankLeeeee FrankLeeeee Sep 1, 2022

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No worry, pre-commit hook just standardized your code format.


tree_map(add_func, op)
return f
Expand All @@ -44,6 +44,7 @@ def meta_conv(
output_padding: List[int],
groups: int,
):

def _formula(ln: int, p: int, d: int, k: int, s: int) -> int:
"""
Formula to apply to calculate the length of some dimension of the output
Expand Down Expand Up @@ -120,14 +121,9 @@ def calc_conv_nd_return_shape(
kernel_size[i],
stride[i],
output_padding_list[i],
)
)
))
else:
ret_shape.append(
_formula(
dims[i], padding[i], dilation[i], kernel_size[i], stride[i]
)
)
ret_shape.append(_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i]))
return ret_shape

def pick_memory_format():
Expand Down Expand Up @@ -156,20 +152,16 @@ def pick_memory_format():
out_channels = weight.shape[0]
if weight.shape[1] != input_tensor.shape[1] / groups:
raise RuntimeError("Invalid channel dimensions")
shape_out = calc_conv_nd_return_shape(
dims, kernel_size, stride, padding, dilation
)
shape_out = calc_conv_nd_return_shape(dims, kernel_size, stride, padding, dilation)
out = input_tensor.new_empty((input_tensor.shape[0], out_channels, *shape_out))
mem_fmt = pick_memory_format()
out = out.to(memory_format=mem_fmt) # type: ignore[call-overload]
out = out.to(memory_format=mem_fmt) # type: ignore[call-overload]
return out


@register_meta(aten.convolution_backward.default)
def meta_conv_backward(
grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask
):
def meta_conv_backward(grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, bias_sizes, stride,
padding, dilation, transposed, output_padding, groups, output_mask):
return torch.empty_like(input), torch.empty_like(weight), torch.empty((bias_sizes), device='meta')


Expand All @@ -184,21 +176,18 @@ def meta_hardswish(input: torch.Tensor):


@register_meta(aten.hardswish_backward.default)
def meta_hardswish_backward(grad_out:torch.Tensor, input: torch.Tensor):
def meta_hardswish_backward(grad_out: torch.Tensor, input: torch.Tensor):
grad_in = torch.empty_like(input)
return grad_in


@register_meta([aten.roll.default, ])
def meta_roll(input:torch.Tensor, shifts, dims):
@register_meta(aten.roll.default)
def meta_roll(input: torch.Tensor, shifts, dims):
return torch.empty_like(input)


@register_meta(aten.native_batch_norm.default)
def meta_bn(
input: torch.Tensor,
weight, bias, running_mean, running_var, training, momentum, eps
):
def meta_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps):
n_input = input.size(1)

output = torch.empty_like(input)
Expand All @@ -208,21 +197,16 @@ def meta_bn(


@register_meta(aten.native_batch_norm_backward.default)
def meta_bn_backward(
dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
running_mean, running_var, save_mean, save_invstd, train, eps, output_mask
):
def meta_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, save_mean,
save_invstd, train, eps, output_mask):
dX = torch.empty_like(input)
dgamma = torch.empty_like(weight)
dbeta = torch.empty_like(weight)
return dX, dgamma, dbeta


@register_meta(aten.native_layer_norm.default)
def meta_ln(
input: torch.Tensor,
normalized_shape, weight, bias, eps
):
def meta_ln(input: torch.Tensor, normalized_shape, weight, bias, eps):
n_input = input.size(1)

output = torch.empty_like(input)
Expand All @@ -232,11 +216,8 @@ def meta_ln(


@register_meta(aten.native_layer_norm_backward.default)
def meta_ln_backward(
dY: torch.Tensor,
input: torch.Tensor,
normalized_shape, mean, rstd, weight, bias, grad_input_mask
):
def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, mean, rstd, weight, bias,
grad_input_mask):
dX = torch.empty_like(input)
dgamma = torch.empty_like(weight)
dbeta = torch.empty_like(bias)
Expand All @@ -245,7 +226,8 @@ def meta_ln_backward(

@register_meta(aten._adaptive_avg_pool2d_backward.default)
def meta_adaptive_avg_pool2d_backward(
grad_output: torch.Tensor, input: torch.Tensor,
grad_output: torch.Tensor,
input: torch.Tensor,
):
grad_input = torch.empty_like(input)
return torch.empty_like(input)
Expand All @@ -266,7 +248,9 @@ def meta_index_Tensor(self, indices):
k = len(result)
assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}"
for j in range(index.ndim):
assert index.shape[j] == self.shape[k + j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}"
assert index.shape[j] == self.shape[
k +
j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}"
result.append(nonzero.select(1, j))
else:
result.append(index)
Expand All @@ -275,7 +259,7 @@ def meta_index_Tensor(self, indices):
indices = result
assert len(indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})"
# expand_outplace
import torch._refs as refs # avoid import cycle in mypy
import torch._refs as refs # avoid import cycle in mypy

indices = list(refs._maybe_broadcast(*indices))
# add missing null tensors
Expand Down