Open
Description
Hi,
I have been trying to understand and use both static quantization and quantization-aware training (QAT) by following the documentation/tutorial, and I have been failing with the following error message:
NotImplementedError: Could not run 'aten::quantize_per_tensor' with arguments from the 'QuantizedCPU' backend.
This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build
process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit
https://fburl.com/ptmfixes for possible resolutions. 'aten::quantize_per_tensor' is only available for these backends: [CPU,
CUDA, BackendSelect, Named, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA,
UNKNOWN_TENSOR_TYPE_ID, AutogradMLC, AutogradHPU, AutogradNestedTensor, AutogradPrivateUse1,
AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].
CPU: registered at /pytorch/build/aten/src/ATen/RegisterCPU.cpp:16286 [kernel]
CUDA: registered at /pytorch/build/aten/src/ATen/RegisterCUDA.cpp:20674 [kernel]
BackendSelect: fallthrough registered at /pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Named: registered at /pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
ADInplaceOrView: fallthrough registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:60 [backend fallback]
AutogradOther: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradCPU: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradCUDA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradXLA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
UNKNOWN_TENSOR_TYPE_ID: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradMLC: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradHPU: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradNestedTensor: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradPrivateUse1: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradPrivateUse2: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
AutogradPrivateUse3: registered at /pytorch/torch/csrc/autograd/generated/VariableType_0.cpp:9848 [autograd kernel]
Tracer: registered at /pytorch/torch/csrc/autograd/generated/TraceType_0.cpp:9750 [kernel]
Autocast: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:255 [backend fallback]
Batched: registered at /pytorch/aten/src/ATen/BatchingRegistrations.cpp:1019 [backend fallback]
VmapMode: fallthrough registered at /pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]in both situations when I would like to do inferencing using the quantized models. Moreover, when/if I try to save the model(s) in onnx format (via torch.onnx.export), I get the following error:
AttributeError: 'torch.dtype' object has no attribute 'detach'I am not sure if my problem is related to #1235 somehow (because the issue does not mention which errors the missing statement results in); but, I have also tried the proposed solution/workaround in the aforementioned issue, which has not helped me at all.
To reproduce the problem, I share the details of my setup:
System Information
$ cat /etc/os-release
NAME="SLES"
VERSION="15-SP3"
VERSION_ID="15.3"
PRETTY_NAME="SUSE Linux Enterprise Server 15 SP3"
ID="sles"
ID_LIKE="suse"
ANSI_COLOR="0;32"
CPE_NAME="cpe:/o:suse:sles:15:sp3"
DOCUMENTATION_URL="https://documentation.suse.com/"
$ uname -a
Linux 669d648493bf 5.10.47-linuxkit #1 SMP Sat Jul 3 21:51:47 UTC 2021 x86_64 x86_64 x86_64 GNU/LinuxBasically, I am running the example inside a Docker container based on SUSE Linux Enterprise Server (SLES).
Pipfile
Below is my environment's Pipfile:
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[[source]]
url = "https://download.pytorch.org/whl/cpu/torch_stable.html"
verify_ssl = true
name = "torch-cpu"
[packages]
dvc = "~=2.6.0"
onnx = "~=1.10.0"
onnxruntime = "~=1.8.0"
pandas = "~=1.3.0"
pyyaml = "~=5.4.0"
scikit-learn = "~=0.24.0"
skl2onnx = "~=1.9.0"
torch = {version = "~=1.9.0", index = "torch-cpu"}
[dev-packages]
autopep8 = "*"
ipykernel = "*"
mypy = "*"
pylint = "*"
types-pyyaml = "*"
[requires]
python_version = "3.8"Pipfile.lock
As of today, the above Pipfile results in the following Pipfile.lock file:
{
"_meta": {
"hash": {
"sha256": "512950fde5402d5912d4d02b96e8d5bb9a3326660b122cd7f7db4da470af3bcf"
},
"pipfile-spec": 6,
"requires": {
"python_version": "3.8"
},
"sources": [
{
"name": "pypi",
"url": "https://pypi.org/simple",
"verify_ssl": true
},
{
"name": "torch-cpu",
"url": "https://download.pytorch.org/whl/cpu/torch_stable.html",
"verify_ssl": true
}
]
},
"default": {
"appdirs": {
"hashes": [
"sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41",
"sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"
],
"version": "==1.4.4"
},
"atpublic": {
"hashes": [
"sha256:d6b9167fc3e09a2de2d2adcfc9a1b48d84eab70753c97de3800362e1703e3367"
],
"markers": "python_version >= '3.6'",
"version": "==2.3"
},
"certifi": {
"hashes": [
"sha256:2bbf76fd432960138b3ef6dda3dde0544f27cbf8546c458e60baf371917ba9ee",
"sha256:50b1e4f8446b06f41be7dd6338db18e0990601dce795c2b1686458aa7e8fa7d8"
],
"version": "==2021.5.30"
},
"cffi": {
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"sha256:ed38b924ce794e505647f7c331b22a693bee1538fdf46b0222c4717b42f744e7",
"sha256:f0010c6f9d1a4011e429109fda55a225921e3206e7f62a0c22a35344bfd13cca",
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"sha256:f10afb1004f102c7868ebfe91c28f4a712227fe4cb24974350ace1f90e1febbf",
"sha256:f174135f5609428cc6e1b9090f9268f5c8935fddb1b25ccb8255a2d50de6789e",
"sha256:f3ebe6e73c319340830a9b2825d32eb6d8475c1dac020b4f0aa774ee3b898d1c",
"sha256:f627688813d0a4140153ff532537fbe4afea5a3dffce1f9deb7f91f848a832b5",
"sha256:fd4305f86f53dfd8cd3522269ed7fc34856a8ee3709a5e28b2836b2db9d4cd69"
],
"version": "==1.14.6"
},
"charset-normalizer": {
"hashes": [
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"sha256:fa471a601dfea0f492e4f4fca035cd82155e65dc45c9b83bf4322dfab63755dd"
],
"markers": "python_version >= '3'",
"version": "==2.0.5"
},
"colorama": {
"hashes": [
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],
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
"version": "==0.4.4"
},
"commonmark": {
"hashes": [
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},
"configobj": {
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"dictdiffer": {
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"diskcache": {
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"distro": {
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"dpath": {
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"dulwich": {
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"dvc": {
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"nanotime": {
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"networkx": {
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}Quantization Example
Originally, I am using Quantization.ipynb. However, instead of sharing the file as an attachment, I have chosen to share the cells as Python excerpts below:
import random
import numpy as np
import torch
def set_random_state(random_state:int):
torch.manual_seed(random_state)
random.seed(random_state)
np.random.seed(random_state)
random_state = 42
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=random_state)
X_train = X_train.to_numpy()
y_train = y_train.to_numpy().reshape(-1, 1)
X_test = X_test.to_numpy()
y_test = y_test.to_numpy().reshape(-1, 1)
input_train = torch.as_tensor(X_train, dtype=torch.float32)
input_test = torch.as_tensor(X_test, dtype=torch.float32)
output_train = torch.as_tensor(y_train, dtype=torch.float32)
output_test = torch.as_tensor(y_test, dtype=torch.float32)
from collections import OrderedDict
from typing import Iterable
from torch.nn import Hardsigmoid, Linear, ReLU, Sequential
class MyModel(Sequential):
def __init__(self, input_dim: int, output_dim: int, hidden_layer_dims: Iterable[int]):
layer_sizes = [input_dim, *hidden_layer_dims, output_dim]
modules = OrderedDict()
for idx, size in enumerate(layer_sizes):
if idx == len(layer_sizes) - 1:
break
modules[f"linear-{idx}"] = Linear(size, layer_sizes[idx+1])
if idx < len(layer_sizes) - 2:
modules[f"relu-{idx}"] = ReLU()
modules[f"sigmoid-{len(layer_sizes)}"] = Hardsigmoid()
super().__init__(modules)
class MyQuantizedModel(MyModel):
def __init__(self, input_dim: int, output_dim: int, hidden_layer_dims: Iterable[int]):
super().__init__(input_dim, output_dim, hidden_layer_dims)
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()
def forward(self, x):
x = self.quant(x)
x = super().forward(x)
x = self.dequant(x)
return x
model = MyModel(input_dim=X_train.shape[1], output_dim=y_train.shape[1],
hidden_layer_dims=(10, 20, 30))
from torch.nn import BCELoss
from torch.optim import Adam
set_random_state(random_state)
optimizer = Adam(model.parameters())
criterion = BCELoss(reduction="sum")
max_iter = 1000
for iter in range(max_iter):
output = model(input_train)
loss = criterion(output, output_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
from torch.onnx import export as torch_onnx_export
from os.path import join as path_join
model.eval()
output_actual = model(input_test).detach().apply_(lambda x: 1 if x > 0.5 else 0)
accuracy = 1 - (output_test - output_actual).abs().mean().item()
torch.save(model, path_join("models", "model-pytorch-float.pth"))
torch_onnx_export(model, input_test, path_join("models", "model-pytorch-float.onnx"),
example_outputs=output_test,
input_names=['input'], output_names=['target'],
dynamic_axes={'input': {0: 'batch_size'},
'target': {0: 'batch_size'}
}
)
model = MyQuantizedModel(input_dim=X_train.shape[1], output_dim=y_train.shape[1],
hidden_layer_dims=(10, 20, 30))
set_random_state(random_state)
optimizer = Adam(model.parameters())
criterion = BCELoss(reduction="sum")
max_iter = 1000
for iter in range(max_iter):
output = model(input_train)
loss = criterion(output, output_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
model.qconfig = torch.quantization.get_default_qconfig()
model_fused = torch.quantization.fuse_modules(model, [[f'linear-{n}', f'relu-{n}'] for n in range(3)])
model_prepared = torch.quantization.prepare(model_fused)
model_prepared(input_test)
model_int8 = torch.quantization.convert(model_prepared)
# below line fails with the `NotImplementedError` exception
output_actual = model_int8(input_test).detach().apply_(lambda x: 1 if x > 0.5 else 0)
accuracy = 1 - (output_test - output_actual).abs().mean().item()
torch.save(model_int8, path_join("models", "model-pytorch-int8.pth"))
# below line fails with the `AttributeError` exception
torch_onnx_export(model_int8, input_test, path_join("models", "model-pytorch-int8.onnx"),
example_outputs=output_test,
input_names=['input'], output_names=['target'],
dynamic_axes={'input': {0: 'batch_size'},
'target': {0: 'batch_size'}
}
)
model_qat = MyQuantizedModel(input_dim=X_train.shape[1], output_dim=y_train.shape[1],
hidden_layer_dims=(10, 20, 30))
model_qat.train()
model_qat.qconfig = torch.quantization.get_default_qat_qconfig()
model_qat_fused = torch.quantization.fuse_modules(model_qat, [[f'linear-{n}', f'relu-{n}'] for n in range(3)])
model_qat_prepared = torch.quantization.prepare_qat(model_qat_fused)
set_random_state(random_state)
optimizer = Adam(model_qat_prepared.parameters())
criterion = BCELoss(reduction="sum")
max_iter = 1000
for iter in range(max_iter):
output = model_qat_prepared(input_train)
loss = criterion(output, output_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
model_qat_prepared.eval()
model_qat_int8 = torch.quantization.convert(model_qat_prepared)
# below line fails with the `NotImplementedError` exception
output_actual = model_qat_int8(input_test).detach().apply_(lambda x: 1 if x > 0.5 else 0)
accuracy = 1 - (output_test - output_actual).abs().mean().item()
torch.save(model_qat_int8, path_join("models", "model-pytorch-qat-int8.pth"))
# below line fails with the `AttributeError` exception
torch_onnx_export(model_qat_int8, input_test, path_join("models", "model-pytorch-qat-int8.onnx"),
example_outputs=output_test,
input_names=['input'], output_names=['target'],
dynamic_axes={'input': {0: 'batch_size'},
'target': {0: 'batch_size'}
}
)By the way, it's worth mentioning that I do obtain the following (trained) quantized models for both static (i.e., post-training) quantization (model_int8) and QAT (model_qat_int8), respectively:
>>> model_int8
MyQuantizedModel(
(linear-0): QuantizedLinearReLU(in_features=30, out_features=10, scale=4.299503803253174, zero_point=0, qscheme=torch.per_channel_affine)
(relu-0): Identity()
(linear-1): QuantizedLinearReLU(in_features=10, out_features=20, scale=2.4122533798217773, zero_point=0, qscheme=torch.per_channel_affine)
(relu-1): Identity()
(linear-2): QuantizedLinearReLU(in_features=20, out_features=30, scale=0.8285157084465027, zero_point=0, qscheme=torch.per_channel_affine)
(relu-2): Identity()
(linear-3): QuantizedLinear(in_features=30, out_features=1, scale=0.2541472017765045, zero_point=109, qscheme=torch.per_channel_affine)
(sigmoid-5): Hardsigmoid()
(quant): Quantize(scale=tensor([27.0104]), zero_point=tensor([0]), dtype=torch.quint8)
(dequant): DeQuantize()
)
>>> model_qat_int8
MyQuantizedModel(
(linear-0): QuantizedLinearReLU(in_features=30, out_features=10, scale=8.171445846557617, zero_point=0, qscheme=torch.per_channel_affine)
(relu-0): Identity()
(linear-1): QuantizedLinearReLU(in_features=10, out_features=20, scale=3.8092589378356934, zero_point=0, qscheme=torch.per_channel_affine)
(relu-1): Identity()
(linear-2): QuantizedLinearReLU(in_features=20, out_features=30, scale=1.669329047203064, zero_point=0, qscheme=torch.per_channel_affine)
(relu-2): Identity()
(linear-3): QuantizedLinear(in_features=30, out_features=1, scale=0.545098066329956, zero_point=0, qscheme=torch.per_channel_affine)
(sigmoid-5): Hardsigmoid()
(quant): Quantize(scale=tensor([16.6679]), zero_point=tensor([0]), dtype=torch.quint8)
(dequant): DeQuantize()
)They seem different, which is the expected result, but I cannot get them to work with the test data (i.e., I cannot do inferencing with them).
I would appreciate it if you helped me solve the issue.
cc @jerryzh168 @jianyuh @sekyondaMeta @svekars @carljparker @NicolasHug @kit1980 @subramen