wehub-resource-sync
217 lines
8.2 KiB
Python
217 lines
8.2 KiB
Python
#
|
|
# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
|
|
"""tests of integrating Quant layers into a network"""
|
|
|
|
import pytest
|
|
import io
|
|
|
|
import numpy as np
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torch.optim as optim
|
|
|
|
from pytorch_quantization import tensor_quant
|
|
from pytorch_quantization import quant_modules
|
|
from pytorch_quantization import nn as quant_nn
|
|
from pytorch_quantization.tensor_quant import QuantDescriptor
|
|
from pytorch_quantization.nn.modules import tensor_quantizer
|
|
from tests.fixtures.models import LeNet, QuantLeNet
|
|
from tests.fixtures import verbose
|
|
|
|
np.random.seed(12345) # seed 1234 causes 1 number mismatch at 6th decimal in one of the tests
|
|
|
|
# make everything run on the GPU
|
|
torch.set_default_tensor_type('torch.cuda.FloatTensor')
|
|
|
|
# pylint:disable=missing-docstring, no-self-use
|
|
|
|
class TestNetwork():
|
|
"""test basic operations of quantized network"""
|
|
|
|
def test_simple_build(self):
|
|
"""test instantiation"""
|
|
quant_model = QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor())
|
|
for name, module in quant_model.named_modules():
|
|
if "quantizer" in name:
|
|
module.disable()
|
|
|
|
input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc)
|
|
|
|
input_desc = QuantDescriptor(amax=6.)
|
|
weight_desc = QuantDescriptor(amax=1.)
|
|
quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc)
|
|
|
|
|
|
def test_forward(self):
|
|
"""test forward pass with random data"""
|
|
input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc)
|
|
output = quant_model(torch.empty(16, 1, 28, 28))
|
|
|
|
def test_backward(self):
|
|
"""test one iteration with random data and labels"""
|
|
input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc)
|
|
optimizer = optim.SGD(quant_model.parameters(), lr=0.01)
|
|
optimizer.zero_grad()
|
|
output = quant_model(torch.empty(16, 1, 28, 28))
|
|
loss = F.nll_loss(output, torch.randint(10, (16,), dtype=torch.int64))
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
def test_native_amp_fp16(self):
|
|
"""test one iteration with random data and labels"""
|
|
input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR
|
|
model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc)
|
|
optimizer = optim.SGD(model.parameters(), lr=0.01)
|
|
optimizer.zero_grad()
|
|
with torch.cuda.amp.autocast():
|
|
output = model(torch.empty(16, 1, 28, 28))
|
|
loss = F.nll_loss(output, torch.randint(10, (16,), dtype=torch.int64))
|
|
loss.backward()
|
|
optimizer.step()
|
|
assert loss.dtype == torch.float32
|
|
|
|
def test_asp(self):
|
|
"""test Sparsity (ASP) and QAT toolkits together"""
|
|
try:
|
|
from apex.contrib.sparsity import ASP
|
|
except ImportError:
|
|
pytest.skip("ASP is not available.")
|
|
|
|
quant_modules.initialize()
|
|
model = LeNet()
|
|
quant_modules.deactivate()
|
|
|
|
optimizer = optim.SGD(model.parameters(), lr=0.01)
|
|
|
|
ASP.init_model_for_pruning(
|
|
model,
|
|
mask_calculator="m4n2_1d",
|
|
verbosity=2,
|
|
whitelist=[torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv3d, quant_nn.modules.quant_linear.QuantLinear],
|
|
allow_recompute_mask=False,
|
|
custom_layer_dict={
|
|
quant_nn.QuantConv1d: ['weight'],
|
|
quant_nn.QuantConv2d: ['weight'],
|
|
quant_nn.QuantConv3d: ['weight'],
|
|
quant_nn.QuantConvTranspose1d: ['weight'],
|
|
quant_nn.QuantConvTranspose2d: ['weight'],
|
|
quant_nn.QuantConvTranspose3d: ['weight'],
|
|
quant_nn.QuantLinear: ['weight']
|
|
},
|
|
allow_permutation=False)
|
|
ASP.init_optimizer_for_pruning(optimizer)
|
|
ASP.compute_sparse_masks()
|
|
|
|
model = model.to('cuda')
|
|
output = model(torch.empty(16, 1, 28, 28).to('cuda'))
|
|
optimizer.zero_grad()
|
|
loss = F.nll_loss(output, torch.randint(10, (16,), dtype=torch.int64))
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
def test_quant_module_replacement(self):
|
|
"""test monkey patching of modules with their quantized versions"""
|
|
lenet = LeNet()
|
|
qlenet = QuantLeNet()
|
|
|
|
mod_list = [type(mod) for name, mod in lenet.named_modules()]
|
|
mod_list = mod_list[1:]
|
|
qmod_list = [type(mod) for name, mod in qlenet.named_modules()]
|
|
qmod_list = qmod_list[1:]
|
|
|
|
# Before any monkey patching, the networks should be different
|
|
assert(mod_list != qmod_list)
|
|
|
|
# Monkey patch the modules
|
|
no_replace_list = ["Linear"]
|
|
custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
|
|
|
|
quant_modules.initialize(no_replace_list, custom_quant_modules)
|
|
|
|
lenet = LeNet()
|
|
qlenet = QuantLeNet()
|
|
|
|
mod_list = [type(mod) for name, mod in lenet.named_modules()]
|
|
mod_list = mod_list[1:]
|
|
qmod_list = [type(mod) for name, mod in qlenet.named_modules()]
|
|
qmod_list = qmod_list[1:]
|
|
|
|
# After monkey patching, the networks should be same
|
|
assert(mod_list == qmod_list)
|
|
|
|
# Reverse monkey patching
|
|
quant_modules.deactivate()
|
|
|
|
lenet = LeNet()
|
|
qlenet = QuantLeNet()
|
|
|
|
mod_list = [type(mod) for name, mod in lenet.named_modules()]
|
|
mod_list = mod_list[1:]
|
|
qmod_list = [type(mod) for name, mod in qlenet.named_modules()]
|
|
qmod_list = qmod_list[1:]
|
|
|
|
# After reversing monkey patching, the networks should again be different
|
|
assert(mod_list != qmod_list)
|
|
|
|
def test_calibration(self):
|
|
quant_model = QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor()).cuda()
|
|
|
|
for name, module in quant_model.named_modules():
|
|
if name.endswith("_quantizer"):
|
|
if module._calibrator is not None:
|
|
module.disable_quant()
|
|
module.enable_calib()
|
|
else:
|
|
module.disable()
|
|
print(F"{name:40}: {module}")
|
|
|
|
quant_model(torch.rand(16, 1, 224, 224, device="cuda"))
|
|
|
|
# Load calib result and disable calibration
|
|
for name, module in quant_model.named_modules():
|
|
if name.endswith("_quantizer"):
|
|
if module._calibrator is not None:
|
|
module.load_calib_amax()
|
|
module.enable_quant()
|
|
module.disable_calib()
|
|
else:
|
|
module.enable()
|
|
quant_model.cuda()
|
|
|
|
def test_state_load(self):
|
|
quant_desc = tensor_quant.QuantDescriptor(axis=1, num_bits=8, amax=127.0)
|
|
quantizer = tensor_quantizer.TensorQuantizer(quant_desc).cuda()
|
|
quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc).cuda()
|
|
quantizer2.pre_quant_scale = torch.Tensor([[1.0, 2.0, 3.0, 4.0]]).cuda()
|
|
buffer = io.BytesIO()
|
|
torch.save(quantizer2.state_dict(), buffer)
|
|
|
|
buffer.seek(0)
|
|
quantizer.load_state_dict(torch.load(buffer))
|
|
|
|
assert torch.allclose(quantizer.pre_quant_scale, quantizer2.pre_quant_scale)
|