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[ao] disabling dynamic conv/convT ops (#71110)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71110

as mentioned in https://github.com/pytorch/pytorch/issues/70480 the dynamic conv ops are currently missing a key feature to bring their performance in line with other dynamic ops, this diff disables conv/convT from being automatically quantized with convert dynamic

Test Plan: buck test //caffe2/test:quantization --test-selectors test_quantized_module#TestDynamicQuantizedModule

Reviewed By: vkuzo

Differential Revision: D33511152

fbshipit-source-id: 50618fbe734c898664c390f896e70c68f1df3208
This commit is contained in:
Charles David Hernandez 2022-01-13 11:26:45 -08:00 committed by Facebook GitHub Bot
parent 37eaf7640f
commit 83b45fe166
2 changed files with 39 additions and 10 deletions
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14
torch/ao/quantization/quantization_mappings.py
View File

@ -112,12 +112,14 @@ DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
nni.LinearReLU: nniqd.LinearReLU,
nn.EmbeddingBag: nnq.EmbeddingBag,
nn.Embedding: nnq.Embedding,
nn.Conv1d: nnqd.Conv1d,
nn.Conv2d: nnqd.Conv2d,
nn.Conv3d: nnqd.Conv3d,
nn.ConvTranspose1d: nnqd.ConvTranspose1d,
nn.ConvTranspose2d: nnqd.ConvTranspose2d,
nn.ConvTranspose3d: nnqd.ConvTranspose3d,
# Don't want to enable these by default because the numerical
# accuracy is poor compared to other dynamic ops
# nn.Conv1d: nnqd.Conv1d,
# nn.Conv2d: nnqd.Conv2d,
# nn.Conv3d: nnqd.Conv3d,
# nn.ConvTranspose1d: nnqd.ConvTranspose1d,
# nn.ConvTranspose2d: nnqd.ConvTranspose2d,
# nn.ConvTranspose3d: nnqd.ConvTranspose3d,
}
# Allowlist for propagating the qconfig

35
torch/nn/quantized/dynamic/modules/conv.py
View File

@ -11,6 +11,7 @@ from torch.nn.common_types import _size_1_t
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.quantized.modules.conv import _reverse_repeat_padding
import torch.nn.quantized.modules as nnq
import warnings
class Conv1d(nnq.Conv1d):
@ -52,6 +53,11 @@ class Conv1d(nnq.Conv1d):
device=None,
dtype=None,
reduce_range=True):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
factory_kwargs = {'device': device, 'dtype': dtype}
kernel_size = _single(kernel_size)
stride = _single(stride)
@ -111,6 +117,11 @@ class Conv2d(nnq.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros', device=None, dtype=None):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
factory_kwargs = {'device': device, 'dtype': dtype}
kernel_size = _pair(kernel_size)
stride = _pair(stride)
@ -170,13 +181,17 @@ class Conv3d(nnq.Conv3d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros', device=None, dtype=None):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
assert padding_mode != 'reflect', "Conv3d does not support reflection padding"
factory_kwargs = {'device': device, 'dtype': dtype}
kernel_size = _triple(kernel_size)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
super(Conv3d, self)._init(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _triple(0), groups, bias, padding_mode, **factory_kwargs)
@ -235,8 +250,12 @@ class ConvTranspose1d(nnq.ConvTranspose1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True,
dilation=1, padding_mode='zeros', device=None, dtype=None):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
factory_kwargs = {'device': device, 'dtype': dtype}
super(ConvTranspose1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, **factory_kwargs)
@ -291,8 +310,12 @@ class ConvTranspose2d(nnq.ConvTranspose2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True,
dilation=1, padding_mode='zeros', device=None, dtype=None):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
factory_kwargs = {'device': device, 'dtype': dtype}
super(ConvTranspose2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, **factory_kwargs)
@ -347,8 +370,12 @@ class ConvTranspose3d(nnq.ConvTranspose3d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True,
dilation=1, padding_mode='zeros', device=None, dtype=None):
warnings.warn(
"The current implementation of the {} module has poor numerical accuracy and its use is not recommended".format(
self._get_name()
)
)
factory_kwargs = {'device': device, 'dtype': dtype}
super(ConvTranspose3d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode, **factory_kwargs)