blob: 8b172d6b29d6d46fd2422f9d02252f16b74fcc8b [file] [log] [blame]
import unittest
import math
import torch
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nniq
import torch.nn.intrinsic.qat as nniqat
from torch.quantization import \
QConfig, QConfigDynamic, default_observer, default_weight_observer, get_observer_dict,\
quantize, prepare, convert, prepare_qat, quantize_qat, fuse_modules, \
quantize_dynamic, default_qconfig, default_debug_qconfig, default_qat_qconfig, \
default_dynamic_qconfig, HistogramObserver, MinMaxObserver, PerChannelMinMaxObserver,\
RecordingObserver, MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver, \
QuantWrapper
from torch.quantization._quantize_script import quantize_script
from common_utils import run_tests
from common_quantization import QuantizationTestCase, \
AnnotatedSingleLayerLinearModel, SingleLayerLinearModel, \
SkipQuantModel, QuantStubModel, \
ModelForFusion, ModelWithSequentialFusion, ManualLinearQATModel, ManualConvLinearQATModel, \
ModelWithFunctionals, \
test_only_eval_fn, test_only_train_fn, \
prepare_dynamic, convert_dynamic, SingleLayerLinearDynamicModel, \
TwoLayerLinearModel, NestedModel, ResNetBase, LSTMDynamicModel, \
ModelWithNoQconfigPropagation
from common_quantization import AnnotatedTwoLayerLinearModel, AnnotatedNestedModel, \
AnnotatedSubNestedModel, AnnotatedCustomConfigNestedModel
from jit_utils import _tmp_donotuse_dont_inline_everything
from hypothesis import given
from hypothesis import strategies as st
from hypothesis_utils import no_deadline
import io
import copy
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.")
class EagerModePostTrainingQuantTest(QuantizationTestCase):
@no_deadline
@given(qconfig=st.sampled_from((torch.quantization.default_qconfig, torch.quantization.default_per_channel_qconfig)))
def test_single_layer(self, qconfig):
r"""Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped
to nnq.Linear which is the quantized version of the module
"""
model = AnnotatedSingleLayerLinearModel()
model.qconfig = qconfig
model = prepare(model)
# Check if observers and quant/dequant nodes are inserted
self.checkNoPrepModules(model)
self.checkHasPrepModules(model.fc1)
self.checkObservers(model)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
self.checkNoPrepModules(model)
self.checkHasPrepModules(model.fc1)
self.checkWrappedQuantizedLinear(model.fc1)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API - out of place version
base = AnnotatedSingleLayerLinearModel()
base.qconfig = qconfig
keys_before = set(list(base.state_dict().keys()))
model = quantize(base, test_only_eval_fn, self.calib_data)
checkQuantized(model)
keys_after = set(list(base.state_dict().keys()))
self.assertEqual(keys_before, keys_after) # simple check that nothing changed
# in-place version
model = AnnotatedSingleLayerLinearModel()
model.qconfig = qconfig
quantize(model, test_only_eval_fn, self.calib_data, inplace=True)
checkQuantized(model)
def test_two_layers(self):
r"""TwoLayerLinearModel has two Linear modules but we only quantize the second one
`fc2`, and `fc1`is not quantized
"""
model = AnnotatedTwoLayerLinearModel()
model = prepare(model)
self.checkNoPrepModules(model)
self.checkObservers(model)
self.checkNoPrepModules(model.fc1)
self.checkHasPrepModules(model.fc2)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
self.checkNoPrepModules(model)
self.checkNoPrepModules(model.fc1)
self.checkHasPrepModules(model.fc2)
self.assertEqual(type(model.fc1), torch.nn.Linear)
self.checkWrappedQuantizedLinear(model.fc2)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(AnnotatedTwoLayerLinearModel(), test_only_eval_fn,
self.calib_data)
checkQuantized(model)
def test_nested1(self):
r"""Test quantization for nested model, top level 'fc3' and
'fc1' of submodule 'sub2', 'sub2.fc2' is not quantized
"""
model = AnnotatedNestedModel()
def checkPrepModules(model, before_calib=False):
if before_calib:
self.checkObservers(model)
self.checkNoPrepModules(model)
self.checkNoPrepModules(model.sub1)
self.checkNoPrepModules(model.sub1.fc)
self.checkNoPrepModules(model.sub1.relu)
self.checkNoPrepModules(model.sub2)
self.checkHasPrepModules(model.sub2.fc1)
self.checkNoPrepModules(model.sub2.fc2)
self.checkHasPrepModules(model.fc3)
model = prepare(model)
checkPrepModules(model, True)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
checkPrepModules(model)
self.checkLinear(model.sub1.fc)
self.checkWrappedQuantizedLinear(model.fc3)
self.checkWrappedQuantizedLinear(model.sub2.fc1)
self.checkLinear(model.sub2.fc2)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(AnnotatedNestedModel(), test_only_eval_fn,
self.calib_data)
checkQuantized(model)
def test_nested2(self):
model = AnnotatedSubNestedModel()
model = prepare(model)
def checkPrepModules(model, before_calib=False):
if before_calib:
self.checkObservers(model)
self.checkNoPrepModules(model)
self.checkNoPrepModules(model.sub1)
self.checkNoPrepModules(model.sub1.fc)
self.checkNoPrepModules(model.sub1.relu)
self.checkHasPrepModules(model.sub2)
self.checkNoPrepModules(model.sub2.module.fc1)
self.checkNoPrepModules(model.sub2.module.fc2)
self.checkHasPrepModules(model.fc3)
checkPrepModules(model, True)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
checkPrepModules(model)
self.checkLinear(model.sub1.fc)
self.assertEqual(type(model.sub1.relu), torch.nn.ReLU)
self.checkQuantizedLinear(model.sub2.module.fc1)
self.checkQuantizedLinear(model.sub2.module.fc2)
self.checkWrappedQuantizedLinear(model.fc3)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(AnnotatedSubNestedModel(), test_only_eval_fn,
self.calib_data)
checkQuantized(model)
def test_nested3(self):
r"""More complicated nested test case with child qconfig overrides
parent qconfig
"""
model = AnnotatedCustomConfigNestedModel()
model = prepare(model)
def checkPrepModules(model, before_calib=False):
if before_calib:
self.checkObservers(model)
self.checkNoPrepModules(model)
self.checkNoPrepModules(model.sub1)
self.checkNoPrepModules(model.sub1.fc)
self.checkNoPrepModules(model.sub1.relu)
self.checkNoPrepModules(model.sub2)
self.checkHasPrepModules(model.sub2.fc1)
self.checkHasPrepModules(model.sub2.fc2)
self.checkHasPrepModules(model.fc3)
checkPrepModules(model, True)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
checkPrepModules(model)
self.checkWrappedQuantizedLinear(model.sub2.fc1)
self.checkWrappedQuantizedLinear(model.sub2.fc2)
self.checkWrappedQuantizedLinear(model.fc3)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(AnnotatedCustomConfigNestedModel(), test_only_eval_fn,
self.calib_data)
checkQuantized(model)
def test_skip_quant(self):
r"""The case when we want to skip quantizing some layers
"""
model = SkipQuantModel()
model = prepare(model)
self.checkObservers(model)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
self.checkLinear(model.fc)
self.checkQuantDequant(model.sub)
self.checkQuantizedLinear(model.sub.module.fc1)
self.checkQuantizedLinear(model.sub.module.fc2)
self.assertEqual(type(model.sub.module.relu), nnq.ReLU)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(SkipQuantModel(), test_only_eval_fn, self.calib_data)
checkQuantized(model)
def test_manual(self):
r"""User inserts QuantStub and DeQuantStub in model code
and call the quantization utility functions.
"""
model = QuantStubModel()
# propagate the qconfig of parents to children, model is changed
# inplace
model = prepare(model)
self.checkObservers(model)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.fc), nnq.Linear)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(QuantStubModel(), test_only_eval_fn, self.calib_data)
checkQuantized(model)
@given(qconfig=st.sampled_from((torch.quantization.default_qconfig, torch.quantization.default_per_channel_qconfig)))
def test_resnet_base(self, qconfig):
r"""Test quantization for bottleneck topology used in resnet/resnext
and add coverage for conversion of average pool and float functional
"""
model = ResNetBase().float().eval()
model = QuantWrapper(model)
model.qconfig = qconfig
fuse_list = ['module.conv1', 'module.bn1', 'module.relu1']
fuse_modules(model, fuse_list, inplace=True)
model = prepare(model)
self.checkObservers(model)
test_only_eval_fn(model, self.img_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.module.conv1), nn.intrinsic.quantized.ConvReLU2d)
self.assertEqual(type(model.module.myop), nn.quantized.QFunctional)
self.assertEqual(type(model.module.avgpool), nn.AdaptiveAvgPool2d)
test_only_eval_fn(model, self.img_data)
checkQuantized(model)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.")
class PostTrainingDynamicQuantTest(QuantizationTestCase):
def test_single_layer(self):
r"""Dynamic Quantize SingleLayerLinearDynamicModel which has one Linear module,
make sure it is swapped to nnqd.Linear which is the quantized version of
the module
"""
model = SingleLayerLinearDynamicModel().eval()
qconfig_dict = {
'': default_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dict)
convert_dynamic(model)
def checkQuantized(model):
self.checkDynamicQuantizedLinear(model.fc1)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API - out of place version
base = SingleLayerLinearDynamicModel()
keys_before = set(list(base.state_dict().keys()))
model = quantize_dynamic(base, qconfig_dict)
checkQuantized(model)
keys_after = set(list(base.state_dict().keys()))
self.assertEqual(keys_before, keys_after) # simple check that nothing changed
# in-place version
model = SingleLayerLinearDynamicModel()
quantize_dynamic(model, qconfig_dict, inplace=True)
checkQuantized(model)
# Test set qconfig
model = SingleLayerLinearDynamicModel()
quantize_dynamic(model, set([nn.Linear]), inplace=True)
checkQuantized(model)
def test_two_layers(self):
r"""TwoLayerLinearModel has two Linear modules but we only quantize the second one
`fc2`, and `fc1`is not quantized
"""
model = TwoLayerLinearModel().eval()
qconfig_dict = {
'fc2': default_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dict)
convert_dynamic(model)
def checkQuantized(model):
self.assertEqual(type(model.fc1), torch.nn.Linear)
self.checkDynamicQuantizedLinear(model.fc2)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API
model = quantize_dynamic(TwoLayerLinearModel().eval(), qconfig_dict)
checkQuantized(model)
# Test set API
model = quantize_dynamic(TwoLayerLinearModel().eval(), {'fc2'})
checkQuantized(model)
def test_nested1(self):
r"""Test quantization for nested model, top level 'fc3' and
'fc1' of submodule 'sub2', 'sub2.fc2' is not quantized
"""
model = NestedModel().eval()
qconfig_dict = {
'fc3': default_dynamic_qconfig,
'sub2.fc1': default_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dict)
convert_dynamic(model)
def checkQuantized(model):
self.checkLinear(model.sub1.fc)
self.checkDynamicQuantizedLinear(model.fc3)
self.checkDynamicQuantizedLinear(model.sub2.fc1)
self.checkLinear(model.sub2.fc2)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API
model = quantize_dynamic(NestedModel().eval(), qconfig_dict)
checkQuantized(model)
model = quantize_dynamic(NestedModel().eval(), {'fc3', 'sub2.fc1'})
checkQuantized(model)
def test_nested2(self):
r"""Another test case for quantized, we will quantize all submodules
of submodule sub2
"""
model = NestedModel().eval()
qconfig_dict = {
'fc3': default_dynamic_qconfig,
'sub2': default_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dict)
convert_dynamic(model)
def checkQuantized(model):
self.checkLinear(model.sub1.fc)
self.assertEqual(type(model.sub1.relu), torch.nn.ReLU)
self.checkDynamicQuantizedLinear(model.sub2.fc1)
self.checkDynamicQuantizedLinear(model.sub2.fc2)
self.checkDynamicQuantizedLinear(model.fc3)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API
model = quantize_dynamic(NestedModel().eval(), qconfig_dict)
checkQuantized(model)
# Test set API
model = quantize_dynamic(NestedModel().eval(), {'fc3', 'sub2'})
checkQuantized(model)
def test_nested3(self):
r"""More complicated nested test case with child qconfig overrides
parent qconfig
"""
model = NestedModel().eval()
custum_options = {
'dtype': torch.quint8,
'qscheme': torch.per_tensor_affine
}
custom_dynamic_qconfig = QConfigDynamic(weight=default_weight_observer)
qconfig_dynamic_dict = {
'fc3': default_dynamic_qconfig,
'sub2': default_dynamic_qconfig,
'sub2.fc1': custom_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dynamic_dict)
convert_dynamic(model)
def checkQuantized(model):
self.checkDynamicQuantizedLinear(model.sub2.fc1)
self.checkDynamicQuantizedLinear(model.sub2.fc2)
self.checkDynamicQuantizedLinear(model.fc3)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API
model = quantize_dynamic(NestedModel().eval(), qconfig_dynamic_dict)
checkQuantized(model)
# Test set API
model = quantize_dynamic(NestedModel().eval(), {'fc3', 'sub2', 'sub2.fc1'})
checkQuantized(model)
def test_type_match_rule(self):
r"""Test quantization for nested model, top level 'fc3' and
'fc1' of submodule 'sub2', All 'torch.nn.Linear' modules are quantized
"""
model = NestedModel().eval()
qconfig_dict = {
'fc3': None,
'sub2.fc1': None,
torch.nn.Linear: default_dynamic_qconfig
}
prepare_dynamic(model, qconfig_dict)
test_only_eval_fn(model, self.calib_data)
convert_dynamic(model)
def checkQuantized(model):
self.checkDynamicQuantizedLinear(model.sub1.fc)
self.checkLinear(model.fc3)
self.checkLinear(model.sub2.fc1)
self.checkDynamicQuantizedLinear(model.sub2.fc2)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data, check_save_load=True)
checkQuantized(model)
# test one line API
model = quantize_dynamic(NestedModel().eval(), qconfig_dict)
checkQuantized(model)
def test_quantized_rnn(self):
d_in, d_hid = 2, 2
model = LSTMDynamicModel().eval()
cell = model.lstm
# Replace parameter values s.t. the range of values is exactly
# 255, thus we will have 0 quantization error in the quantized
# GEMM call. This i s for testing purposes.
#
# Note that the current implementation does not support
# accumulation values outside of the range representable by a
# 16 bit integer, instead resulting in a saturated value. We
# must take care that in our test we do not end up with a dot
# product that overflows the int16 range, e.g.
# (255*127+255*127) = 64770. So, we hardcode the test values
# here and ensure a mix of signedness.
vals = [[100, -155],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155]]
if isinstance(cell, torch.nn.LSTM):
num_chunks = 4
vals = vals[:d_hid * num_chunks]
cell.weight_ih_l0 = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float),
requires_grad=False)
cell.weight_hh_l0 = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float),
requires_grad=False)
ref = copy.deepcopy(cell)
model_int8 = quantize_dynamic(model=model, dtype=torch.qint8)
model_fp16 = quantize_dynamic(model=model, dtype=torch.float16)
# Smoke test extra reprs
self.assertTrue('DynamicQuantizedLSTM' in str(model_int8))
self.assertTrue('DynamicQuantizedLSTM' in str(model_fp16))
cell_int8 = model_int8.lstm
cell_fp16 = model_fp16.lstm
assert type(cell_int8) == torch.nn.quantized.dynamic.LSTM, \
'torch.nn.LSTM should be converted to torch.nn.quantized.dynamic.LSTM after quantize_dynamic'
assert type(cell_fp16) == torch.nn.quantized.dynamic.LSTM, \
'torch.nn.LSTM should be converted to torch.nn.quantized.dynamic.LSTM after quantize_dynamic'
niter = 10
x = torch.tensor([[100, -155],
[-155, 100],
[100, -155]], dtype=torch.float).unsqueeze(0).repeat(niter, 1, 1)
h0_vals = [[-155, 100],
[-155, 155],
[100, -155]]
hx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)
cx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)
if isinstance(ref, torch.nn.LSTM):
hiddens = (hx, cx)
ref_out, ref_hid = ref(x, hiddens)
# Compare int8 quantized to unquantized
output_int8, final_hiddens_int8 = cell_int8(x, hiddens)
torch.testing.assert_allclose(output_int8, ref_out)
self.assertEqual(output_int8, ref_out)
for out_val, ref_val in zip(final_hiddens_int8, ref_hid):
torch.testing.assert_allclose(out_val, ref_val)
class ScriptWrapper(torch.nn.Module):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
def forward(self, x, hiddens):
# type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
return self.cell(x, hiddens)
# TODO: TorchScript overloads don't work without this wrapper
cell_script = torch.jit.script(ScriptWrapper(cell_int8))
out_script, hid_script = cell_script(x, hiddens)
self.assertEqual(len(out_script), len(ref_out))
for out_val, ref_val in zip(out_script, ref_out):
torch.testing.assert_allclose(out_val, ref_val)
# Test save/load
b = io.BytesIO()
torch.jit.save(cell_script, b)
b.seek(0)
loaded = torch.jit.load(b)
out_loaded, hid_loaded = loaded(x, hiddens)
for loaded_val, ref_val in zip(out_loaded, ref_out):
torch.testing.assert_allclose(loaded_val, ref_val)
# Compare fp16 quantized to unquantized
output_fp16, final_hiddens_fp16 = cell_fp16(x, hiddens)
torch.testing.assert_allclose(output_fp16, ref_out)
self.assertEqual(output_fp16, ref_out)
for out, ref in zip(final_hiddens_fp16, ref_hid):
torch.testing.assert_allclose(out, ref)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.")
class EagerModeQuantizationAwareTrainingTest(QuantizationTestCase):
def test_manual(self):
model = ManualLinearQATModel()
model = prepare_qat(model)
self.checkObservers(model)
test_only_train_fn(model, self.train_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.fc1), nnq.Linear)
self.assertEqual(type(model.fc2), nnq.Linear)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
model = quantize_qat(ManualLinearQATModel(), test_only_train_fn,
self.train_data)
checkQuantized(model)
def test_eval_only_fake_quant(self):
r"""Using FakeQuant in evaluation only mode,
this is useful for estimating accuracy loss when we quantize the
network
"""
model = ManualLinearQATModel()
model = prepare_qat(model)
self.checkObservers(model)
model.eval()
test_only_eval_fn(model, self.calib_data)
def test_conv_linear(self):
model = ManualConvLinearQATModel()
model = prepare_qat(model)
self.checkObservers(model)
test_only_train_fn(model, self.img_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.conv), nnq.Conv2d)
self.assertEqual(type(model.fc1), nnq.Linear)
self.assertEqual(type(model.fc2), nnq.Linear)
test_only_eval_fn(model, self.img_data)
self.checkScriptable(model, self.img_data)
checkQuantized(model)
model = ManualConvLinearQATModel()
model = quantize_qat(model, test_only_train_fn, self.img_data)
checkQuantized(model)
@unittest.skipUnless(
'fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.",
)
class GraphModePostTrainingQuantTest(QuantizationTestCase):
@_tmp_donotuse_dont_inline_everything
def test_single_layer(self):
r"""Compare the result of quantizing single linear layer in
eager mode and graph mode
"""
# eager mode
annotated_linear_model = AnnotatedSingleLayerLinearModel()
linear_model = SingleLayerLinearModel()
# copy the weight from eager mode so that we can
# compare the result of the two quantized models later
linear_model.fc1.weight = torch.nn.Parameter(annotated_linear_model.fc1.module.weight.detach())
linear_model.fc1.bias = torch.nn.Parameter(annotated_linear_model.fc1.module.bias.detach())
model_eager = quantize(annotated_linear_model, test_only_eval_fn,
self.calib_data)
qconfig_dict = {
'': QConfig(
activation=default_observer,
weight=default_weight_observer)
}
model_script = quantize_script(
torch.jit.script(linear_model),
qconfig_dict,
test_only_eval_fn,
[self.calib_data],
inplace=False)
result_eager = model_eager(self.calib_data[0][0])
torch._C._jit_pass_quant_fusion(model_script._c._get_module('fc1')._get_method('forward').graph)
result_script = model_script._c._get_method('forward')(self.calib_data[0][0])
self.assertEqual(result_eager, result_script)
class FunctionalModuleTest(QuantizationTestCase):
# Histogram Observers are slow, so have no-deadline to ensure test doesn't time out
@no_deadline
@given(train_mode=st.booleans())
def test_functional_module(self, train_mode):
model = ModelWithFunctionals()
x = torch.rand(10, 1, dtype=torch.float)
xq = torch.quantize_per_tensor(x, 0.01, 30, torch.quint8)
self.checkScriptable(model, [(x, x)], check_save_load=True)
if train_mode:
model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
model = prepare_qat(model)
else:
model.qconfig = torch.quantization.get_default_qconfig('qnnpack')
model = prepare(model)
# Check if observers and quant/dequant nodes are inserted
self.checkNoPrepModules(model)
self.checkObservers(model)
# Calibrate
model(xq.dequantize())
model = convert(model)
def checkQuantized(model):
self.checkNoPrepModules(model)
self.assertEquals(type(model.myadd), torch.nn.quantized.QFunctional)
self.assertEquals(type(model.mycat), torch.nn.quantized.QFunctional)
self.assertEquals(type(model.myadd_relu), torch.nn.quantized.QFunctional)
checkQuantized(model)
self.checkScriptable(model, [(xq, xq)], check_save_load=True)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.")
class FusionTest(QuantizationTestCase):
def test_fuse_module_train(self):
model = ModelForFusion(default_qat_qconfig).train()
# Test step by step fusion
model = fuse_modules(model, ['conv1', 'bn1', 'relu1'])
model = fuse_modules(model, ['sub1.conv', 'sub1.bn'])
self.assertEqual(type(model.conv1), nni.ConvBnReLU2d,
"Fused Conv + BN + Relu first layer")
self.assertEqual(type(model.bn1), torch.nn.Identity,
"Fused Conv + BN + Relu (skipped BN)")
self.assertEqual(type(model.relu1), torch.nn.Identity,
"Fused Conv + BN + Relu (skipped Relu)")
self.assertEqual(type(model.sub1.conv), nni.ConvBn2d,
"Fused submodule Conv + BN")
self.assertEqual(type(model.sub1.bn), torch.nn.Identity,
"Fused submodule Conv + BN (skipped BN)")
self.assertEqual(type(model.sub2.conv), torch.nn.Conv2d,
"Non-fused submodule Conv")
self.assertEqual(type(model.sub2.relu), torch.nn.ReLU,
"Non-fused submodule ReLU")
model = prepare_qat(model)
self.checkObservers(model)
def checkQAT(model):
self.assertEqual(type(model.conv1), nniqat.ConvBnReLU2d)
self.assertEqual(type(model.bn1), nn.Identity)
self.assertEqual(type(model.relu1), nn.Identity)
self.assertEqual(type(model.sub1.conv), nniqat.ConvBn2d)
self.assertEqual(type(model.sub1.bn), nn.Identity)
self.assertEqual(type(model.sub2.conv), nn.Conv2d)
self.assertEqual(type(model.sub2.relu), nn.ReLU)
checkQAT(model)
test_only_train_fn(model, self.img_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.conv1), nniq.ConvReLU2d)
self.assertEqual(type(model.bn1), nn.Identity)
self.assertEqual(type(model.relu1), nn.Identity)
self.assertEqual(type(model.sub1.conv), nnq.Conv2d)
self.assertEqual(type(model.sub1.bn), nn.Identity)
self.assertEqual(type(model.sub2.conv), nn.Conv2d)
self.assertEqual(type(model.sub2.relu), nn.ReLU)
test_only_eval_fn(model, self.img_data)
checkQuantized(model)
model = ModelForFusion(default_qat_qconfig).train()
model = fuse_modules(model, [['conv1', 'bn1', 'relu1'],
['sub1.conv', 'sub1.bn']])
model = quantize_qat(model, test_only_train_fn, self.img_data)
checkQuantized(model)
def test_fuse_module_eval(self):
model = ModelForFusion(default_qconfig)
model.eval()
model = fuse_modules(model, [['conv1', 'bn1', 'relu1'] ,
['sub1.conv', 'sub1.bn']])
self.assertEqual(type(model.conv1), nni.ConvReLU2d,
"Fused Conv + BN + Relu first layer (BN is folded)")
self.assertEqual(type(model.conv1[0]), nn.Conv2d,
"Fused Conv + BN + Relu (Conv + folded BN only)")
self.assertEqual(type(model.conv1[1]), nn.ReLU,
"Fused Conv + BN + Relu second layer (Relu only)")
self.assertEqual(type(model.bn1), nn.Identity,
"Fused Conv + BN + Relu second layer (Skipped BN)")
self.assertEqual(type(model.relu1), nn.Identity,
"Fused Conv + BN + Relu second layer (Skipped Relu)")
self.assertEqual(type(model.sub1.conv), nn.Conv2d,
"Fused submodule Conv + folded BN")
self.assertEqual(type(model.sub1.bn), nn.Identity,
"Fused submodule (skipped BN)")
self.assertEqual(type(model.sub2.conv), nn.Conv2d,
"Non-fused submodule Conv")
self.assertEqual(type(model.sub2.relu), torch.nn.ReLU,
"Non-fused submodule ReLU")
model = prepare(model)
self.checkObservers(model)
test_only_eval_fn(model, self.img_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.conv1), nniq.ConvReLU2d)
self.assertEqual(type(model.bn1), nn.Identity)
self.assertEqual(type(model.relu1), nn.Identity)
self.assertEqual(type(model.sub1.conv), nnq.Conv2d)
self.assertEqual(type(model.sub1.bn), nn.Identity)
self.assertEqual(type(model.sub2.conv), nn.Conv2d)
self.assertEqual(type(model.sub2.relu), nn.ReLU)
test_only_eval_fn(model, self.img_data)
checkQuantized(model)
model = ModelForFusion(default_qconfig).eval()
model = fuse_modules(model, [['conv1', 'bn1', 'relu1'],
['sub1.conv', 'sub1.bn']])
model = quantize(model, test_only_eval_fn, self.img_data)
checkQuantized(model)
def test_fusion_sequential_model_train(self):
model = ModelWithSequentialFusion().train()
model.to(torch.float)
fuse_modules(model, [['conv1', 'relu1'] ,
['features.0.0', 'features.0.1', 'features.0.2'],
['features.1.0', 'features.1.1', 'features.1.2'],
['features.2.0', 'features.2.1', 'features.2.2'],
['classifier.0', 'classifier.1']], inplace=True)
self.assertEqual(type(model.conv1), nni.ConvReLU2d,
"Fused Conv + Relu: nni.ConvReLU2d")
self.assertEqual(type(model.conv1[0]), nn.Conv2d,
"Fused Conv + Relu: Conv2d")
self.assertEqual(type(model.conv1[1]), nn.ReLU,
"Fused Conv + Relu: Relu")
self.assertEqual(type(model.relu1), nn.Identity,
"Fused Conv + Relu: Identity")
for i in range(3):
self.assertEqual(type(model.features[i][0]), nni.ConvBnReLU2d,
"Fused submodule Conv + folded BN")
self.assertEqual(type(model.features[i][1]), nn.Identity,
"Fused submodule (skipped BN)")
self.assertEqual(type(model.features[i][2]), nn.Identity,
"Non-fused submodule Conv")
self.assertEqual(type(model.classifier[0]), nni.LinearReLU)
self.assertEqual(type(model.classifier[1]), nn.Identity)
model.qconfig = default_qat_qconfig
prepare_qat(model, inplace=True)
self.checkObservers(model)
model(self.img_data[0][0])
def checkQAT(model):
self.assertEqual(type(model.conv1), nniqat.ConvReLU2d)
self.assertEqual(type(model.relu1), nn.Identity)
for i in range(3):
self.assertEqual(type(model.features[i][0]), nniqat.ConvBnReLU2d,
"Fused submodule Conv + folded BN")
self.assertEqual(type(model.features[i][1]), nn.Identity,
"Fused submodule (skipped BN)")
self.assertEqual(type(model.features[i][2]), nn.Identity,
"Non-fused submodule Conv")
self.assertEqual(type(model.classifier[0]), nniqat.LinearReLU)
self.assertEqual(type(model.classifier[1]), nn.Identity)
checkQAT(model)
model(self.img_data[1][0])
convert(model, inplace=True)
model(self.img_data[1][0])
self.checkModelWithSequentialQuantized(model)
def test_fusion_sequential_model_eval(self):
model = ModelWithSequentialFusion().eval()
model.to(torch.float)
fuse_modules(model, [['conv1', 'relu1'] ,
['features.0.0', 'features.0.1', 'features.0.2'],
['features.1.0', 'features.1.1', 'features.1.2'],
['features.2.0', 'features.2.1', 'features.2.2'],
['classifier.0', 'classifier.1']], inplace=True)
self.assertEqual(type(model.conv1), nni.ConvReLU2d,
"Fused Conv + Relu: nni.ConvReLU2d")
self.assertEqual(type(model.conv1[0]), nn.Conv2d,
"Fused Conv + Relu: Conv2d")
self.assertEqual(type(model.conv1[1]), nn.ReLU,
"Fused Conv + Relu: Relu")
self.assertEqual(type(model.relu1), nn.Identity,
"Fused Conv + Relu: Identity")
for i in range(3):
self.assertEqual(type(model.features[i][0]), nni.ConvReLU2d,
"Fused submodule Conv + folded BN")
self.assertEqual(type(model.features[i][1]), nn.Identity,
"Fused submodule (skipped BN)")
self.assertEqual(type(model.features[i][2]), nn.Identity,
"Non-fused submodule Conv")
self.assertEqual(type(model.classifier[0]), nni.LinearReLU)
self.assertEqual(type(model.classifier[1]), nn.Identity)
model.qconfig = default_qconfig
prepare(model, inplace=True)
self.checkObservers(model)
model(self.img_data[0][0])
convert(model, inplace=True)
model(self.img_data[1][0])
self.checkModelWithSequentialQuantized(model)
def checkModelWithSequentialQuantized(self, model):
self.assertEqual(type(model.conv1), nniq.ConvReLU2d)
self.assertEqual(type(model.relu1), nn.Identity)
for i in range(3):
self.assertEqual(type(model.features[i][0]), nniq.ConvReLU2d)
self.assertEqual(type(model.features[i][1]), nn.Identity)
self.assertEqual(type(model.features[i][2]), nn.Identity)
self.assertEqual(type(model.classifier[0]), nniq.LinearReLU)
self.assertEqual(type(model.classifier[1]), nn.Identity)
class ObserverTest(QuantizationTestCase):
@given(qdtype=st.sampled_from((torch.qint8, torch.quint8)),
qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)),
reduce_range=st.booleans())
def test_per_tensor_observers(self, qdtype, qscheme, reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qdtype == torch.quint8 and qscheme == torch.per_tensor_symmetric:
reduce_range = False
ObserverList = [MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range),
MovingAverageMinMaxObserver(averaging_constant=0.5,
dtype=qdtype,
qscheme=qscheme,
reduce_range=reduce_range)]
for myobs in ObserverList:
# Calculate Qparams should return with a warning for observers with no data
qparams = myobs.calculate_qparams()
if type(myobs) == MinMaxObserver:
x = torch.tensor([1.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0])
y = torch.tensor([4.0, 5.0, 5.0, 6.0, 7.0, 8.0])
else:
# Moving average of min/max for x and y matches that of
# extreme values for x/y used for minmax observer
x = torch.tensor([0.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0])
y = torch.tensor([2.0, 5.0, 5.0, 6.0, 7.0, 10.0])
result = myobs(x)
result = myobs(y)
self.assertEqual(result, y)
self.assertEqual(myobs.min_val, 1.0)
self.assertEqual(myobs.max_val, 8.0)
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.062745
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0313725
ref_zero_point = -128 if qdtype is torch.qint8 else 0
self.assertEqual(qparams[1].item(), ref_zero_point)
self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5)
state_dict = myobs.state_dict()
b = io.BytesIO()
torch.save(state_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in state_dict:
self.assertEqual(state_dict[key], loaded_dict[key])
loaded_obs = MinMaxObserver(dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_val, loaded_obs.min_val)
self.assertEqual(myobs.max_val, loaded_obs.max_val)
self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams())
@given(qdtype=st.sampled_from((torch.qint8, torch.quint8)),
qscheme=st.sampled_from((torch.per_channel_affine, torch.per_channel_symmetric)),
ch_axis=st.sampled_from((0, 1, 2, 3)), reduce_range=st.booleans())
def test_per_channel_observers(self, qdtype, qscheme, ch_axis, reduce_range):
# reduce_range cannot be true for symmetric quantization with uint8
if qdtype == torch.quint8 and qscheme == torch.per_channel_symmetric:
reduce_range = False
ObserverList = [PerChannelMinMaxObserver(reduce_range=reduce_range,
ch_axis=ch_axis,
dtype=qdtype,
qscheme=qscheme),
MovingAveragePerChannelMinMaxObserver(averaging_constant=0.5,
reduce_range=reduce_range,
ch_axis=ch_axis,
dtype=qdtype,
qscheme=qscheme)]
for myobs in ObserverList:
# Calculate qparams should work for empty observers
qparams = myobs.calculate_qparams()
x = torch.tensor(
[
[[[1.0, 2.0], [2.0, 2.5]], [[3.0, 4.0], [4.5, 6.0]]],
[[[-4.0, -3.0], [5.0, 5.0]], [[6.0, 3.0], [7.0, 8.0]]],
]
)
if type(myobs) == MovingAveragePerChannelMinMaxObserver:
# Scaling the input tensor to model change in min/max values
# across batches
result = myobs(0.5 * x)
result = myobs(1.5 * x)
self.assertEqual(result, 1.5 * x)
else:
result = myobs(x)
self.assertEqual(result, x)
qparams = myobs.calculate_qparams()
ref_min_vals = [[1.0, -4.0], [-4.0, 3.0], [-4.0, 2.0], [-4.0, -3.0]]
ref_max_vals = [[6.0, 8.0], [5.0, 8.0], [6.0, 8.0], [7.0, 8.0]]
per_channel_symmetric_ref_scales = [
[0.04705882, 0.06274509],
[0.03921569, 0.0627451],
[0.04705882, 0.0627451],
[0.05490196, 0.0627451],
]
per_channel_affine_ref_scales = [
[0.02352941, 0.04705882],
[0.03529412, 0.03137255],
[0.03921569, 0.03137255],
[0.04313726, 0.04313726],
]
per_channel_affine_qint8_zp = [
[-128, -43],
[-15, -128],
[-26, -128],
[-35, -58],
]
per_channel_affine_quint8_zp = [[0, 85], [113, 0], [102, 0], [93, 70]]
self.assertEqual(myobs.min_vals, ref_min_vals[ch_axis])
self.assertEqual(myobs.max_vals, ref_max_vals[ch_axis])
if qscheme == torch.per_channel_symmetric:
ref_scales = per_channel_symmetric_ref_scales[ch_axis]
ref_zero_points = [0, 0] if qdtype is torch.qint8 else [128, 128]
else:
ref_scales = per_channel_affine_ref_scales[ch_axis]
ref_zero_points = (
per_channel_affine_qint8_zp[ch_axis]
if qdtype is torch.qint8
else per_channel_affine_quint8_zp[ch_axis]
)
if reduce_range:
ref_scales = [s * 255 / 127 for s in ref_scales]
ref_zero_points = [math.floor(z / 2) for z in ref_zero_points]
self.assertTrue(torch.allclose(qparams[0], torch.tensor(ref_scales, dtype=qparams[0].dtype)))
self.assertTrue(torch.allclose(qparams[1], torch.tensor(ref_zero_points, dtype=qparams[1].dtype)))
# Test for serializability
state_dict = myobs.state_dict()
b = io.BytesIO()
torch.save(state_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in state_dict:
self.assertEqual(state_dict[key], loaded_dict[key])
loaded_obs = PerChannelMinMaxObserver(reduce_range=reduce_range, ch_axis=ch_axis, dtype=qdtype, qscheme=qscheme)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_vals, loaded_obs.min_vals)
self.assertEqual(myobs.max_vals, loaded_obs.max_vals)
self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams())
def test_observer_scriptable(self):
obs_list = [MinMaxObserver(), MovingAverageMinMaxObserver()]
for obs in obs_list:
scripted = torch.jit.script(obs)
x = torch.rand(3, 4)
obs(x)
scripted(x)
self.assertEqual(obs.calculate_qparams(), scripted.calculate_qparams())
buf = io.BytesIO()
torch.jit.save(scripted, buf)
buf.seek(0)
loaded = torch.jit.load(buf)
self.assertEqual(obs.calculate_qparams(), loaded.calculate_qparams())
def test_no_qconfig_propagation(self):
model = ModelWithNoQconfigPropagation()
model.qconfig = torch.quantization.default_qconfig
model = prepare(model)
self.assertTrue(hasattr(model.fc1, 'qconfig'),
"QConfig is expected to propagate")
self.assertFalse(hasattr(model.no_quant_module, 'qconfig'),
"QConfig is expected to NOT propagate")
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
" with instruction set support avx2 or newer.")
class RecordHistogramObserverTest(QuantizationTestCase):
def test_record_observer(self):
model = AnnotatedSingleLayerLinearModel()
model.qconfig = default_debug_qconfig
model = prepare(model)
# run the evaluation and dump all tensors
test_only_eval_fn(model, self.calib_data)
test_only_eval_fn(model, self.calib_data)
observer_dict = {}
get_observer_dict(model, observer_dict)
self.assertTrue('fc1.module.activation_post_process' in observer_dict.keys(),
'observer is not recorded in the dict')
self.assertEqual(len(observer_dict['fc1.module.activation_post_process'].get_tensor_value()), 2 * len(self.calib_data))
self.assertEqual(observer_dict['fc1.module.activation_post_process'].get_tensor_value()[0], model(self.calib_data[0][0]))
@no_deadline
@given(qdtype=st.sampled_from((torch.qint8, torch.quint8)),
qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)))
def test_observer_scriptable(self, qdtype, qscheme):
obs = RecordingObserver(dtype=qdtype, qscheme=qscheme)
scripted = torch.jit.script(obs)
x = torch.rand(3, 4)
obs(x)
scripted(x)
self.assertTrue(torch.equal(obs.get_tensor_value()[0], scripted.get_tensor_value()[0]))
buf = io.BytesIO()
torch.jit.save(scripted, buf)
buf.seek(0)
loaded = torch.jit.load(buf)
self.assertTrue(torch.equal(obs.get_tensor_value()[0], loaded.get_tensor_value()[0]))
@no_deadline
@given(qdtype=st.sampled_from((torch.qint8, torch.quint8)),
qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric)),
reduce_range=st.booleans())
def test_histogram_observer(self, qdtype, qscheme, reduce_range):
myobs = HistogramObserver(bins=3, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
# Calculate qparams should work for empty observers
qparams = myobs.calculate_qparams()
x = torch.tensor([2.0, 3.0, 4.0, 5.0])
y = torch.tensor([5.0, 6.0, 7.0, 8.0])
myobs(x)
myobs(y)
self.assertEqual(myobs.min_val, 2.0)
self.assertEqual(myobs.max_val, 8.0)
self.assertEqual(myobs.histogram, [2., 3., 3.])
qparams = myobs.calculate_qparams()
if reduce_range:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588 * 255 / 127
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294 * 255 / 127
ref_zero_point = -64 if qdtype is torch.qint8 else 0
else:
if qscheme == torch.per_tensor_symmetric:
ref_scale = 0.0470588
ref_zero_point = 0 if qdtype is torch.qint8 else 128
else:
ref_scale = 0.0235294
ref_zero_point = -128 if qdtype is torch.qint8 else 0
self.assertEqual(qparams[1].item(), ref_zero_point)
self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5)
# Test for serializability
state_dict = myobs.state_dict()
b = io.BytesIO()
torch.save(state_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in state_dict:
self.assertEqual(state_dict[key], loaded_dict[key])
loaded_obs = HistogramObserver(bins=3, dtype=qdtype, qscheme=qscheme, reduce_range=reduce_range)
loaded_obs.load_state_dict(loaded_dict)
loaded_qparams = loaded_obs.calculate_qparams()
self.assertEqual(myobs.min_val, loaded_obs.min_val)
self.assertEqual(myobs.max_val, loaded_obs.max_val)
self.assertEqual(myobs.histogram, loaded_obs.histogram)
self.assertEqual(myobs.bins, loaded_obs.bins)
self.assertEqual(myobs.calculate_qparams(), loaded_obs.calculate_qparams())
if __name__ == '__main__':
run_tests()