mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
7ddf212f33
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73863 This PR fully aligns the convert function with the design: https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md and simplifies the implementation of convert function by always produce a reference quantized model (with reference patterns) first, and then lower the model to a quantized model that is runnable with PyTorch native backend (fbgemm/qnnpack). This PR makes the convert.py much easier to understand than the previous implementation, and we are able to remove majority of code in quantization_patterns.py as well (in followup PRs). Test Plan: ``` python test/test_quantization.py TestQuantizeFx python test/test_quantization.py TestQuantizeFxOps python test/test_quantization.py TestFXNumericSuiteCoreAPIs python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels ``` and other internal/oss regression tests Imported from OSS Reviewed By: andrewor14 Differential Revision: D34778506 fbshipit-source-id: 0678b66addf736039a8749b352f6f569caca962b (cherry picked from commit 33ec9caf23f3ab373d827117efbd9db0668b2437)
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
import torch
|
|
import torch.nn.quantized.functional
|
|
import torch.nn.intrinsic as nni
|
|
from torch import Tensor
|
|
|
|
class _BatchNorm(torch.nn.modules.batchnorm._BatchNorm):
|
|
def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(num_features, eps, momentum, True, True, **factory_kwargs)
|
|
self.register_buffer('scale', torch.tensor(1.0, **factory_kwargs))
|
|
self.register_buffer('zero_point', torch.tensor(0, **factory_kwargs))
|
|
|
|
@staticmethod
|
|
def from_float(cls, mod):
|
|
activation_post_process = mod.activation_post_process
|
|
if type(mod) == cls._NNI_BN_RELU_MODULE:
|
|
mod = mod[0]
|
|
scale, zero_point = activation_post_process.calculate_qparams()
|
|
new_mod = cls(mod.num_features, mod.eps)
|
|
new_mod.weight = mod.weight
|
|
new_mod.bias = mod.bias
|
|
new_mod.running_mean = mod.running_mean
|
|
new_mod.running_var = mod.running_var
|
|
new_mod.scale = scale
|
|
new_mod.zero_point = zero_point
|
|
return new_mod
|
|
|
|
@classmethod
|
|
def from_reference(cls, bn, output_scale, output_zero_point):
|
|
qbn = cls(
|
|
bn.num_features,
|
|
bn.eps,
|
|
bn.momentum,
|
|
device=bn.weight.device,
|
|
dtype=bn.weight.dtype
|
|
)
|
|
qbn.weight = bn.weight
|
|
qbn.bias = bn.bias
|
|
qbn.running_mean = bn.running_mean
|
|
qbn.running_var = bn.running_var
|
|
qbn.scale = output_scale
|
|
qbn.zero_point = output_zero_point
|
|
return qbn
|
|
|
|
class BatchNorm2d(_BatchNorm):
|
|
r"""This is the quantized version of :class:`~torch.nn.BatchNorm2d`.
|
|
"""
|
|
|
|
_NNI_BN_RELU_MODULE = nni.BNReLU2d
|
|
|
|
def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(num_features, eps, momentum, **factory_kwargs)
|
|
|
|
def _get_name(self):
|
|
return 'QuantizedBatchNorm2d'
|
|
|
|
def _check_input_dim(self, input):
|
|
# Temporarily using len(shape) instead of ndim due to JIT issue
|
|
# https://github.com/pytorch/pytorch/issues/23890
|
|
if len(input.shape) != 4:
|
|
raise ValueError("Input shape must be `(N, C, H, W)`!")
|
|
|
|
def forward(self, input: Tensor) -> Tensor:
|
|
# disabling this since this is not symbolically traceable
|
|
# self._check_input_dim(input)
|
|
return torch.ops.quantized.batch_norm2d(
|
|
input, self.weight, self.bias, self.running_mean,
|
|
self.running_var, self.eps, self.scale, self.zero_point)
|
|
|
|
@classmethod
|
|
def from_float(cls, mod):
|
|
return _BatchNorm.from_float(cls, mod)
|
|
|
|
class BatchNorm3d(_BatchNorm):
|
|
r"""This is the quantized version of :class:`~torch.nn.BatchNorm3d`.
|
|
"""
|
|
|
|
_NNI_BN_RELU_MODULE = nni.BNReLU3d
|
|
|
|
def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(num_features, eps, momentum, **factory_kwargs)
|
|
|
|
def _get_name(self):
|
|
return 'QuantizedBatchNorm3d'
|
|
|
|
def _check_input_dim(self, input):
|
|
# Temporarily using len(shape) instead of ndim due to JIT issue
|
|
# https://github.com/pytorch/pytorch/issues/23890
|
|
if len(input.shape) != 5:
|
|
raise ValueError("Input shape must be `(N, C, H, W)`!")
|
|
|
|
def forward(self, input: Tensor) -> Tensor:
|
|
# disabling this since this is not symbolically traceable
|
|
# self._check_input_dim(input)
|
|
return torch.ops.quantized.batch_norm3d(
|
|
input, self.weight, self.bias, self.running_mean,
|
|
self.running_var, self.eps, self.scale, self.zero_point)
|
|
|
|
@classmethod
|
|
def from_float(cls, mod):
|
|
return _BatchNorm.from_float(cls, mod)
|