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ab8a99bd36
pytorch/test/quantization/test_quantize_jit.py
Vasiliy Kuznetsov ab8a99bd36 graph mode: add hardswish inplace handling (#40284) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40284

Adds graph mode handling for inplace hardswish, and test coverage for functional hardswish.

Test Plan:
```
python test/test_quantization.py TestQuantizeScriptPTSQOps.test_hardswish
```

Imported from OSS

Differential Revision: D22140628

fbshipit-source-id: 55a514f7dc1130d510f69ee4e611d7cb5e08d02e
2020-06-21 09:40:50 -07:00

3362 lines
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# -*- coding: utf-8 -*-
# torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.jit
import torch.jit.quantized
from torch._C import parse_ir
# torch.quantization
from torch.quantization import (
QConfig,
default_dynamic_qconfig,
default_observer,
per_channel_dynamic_qconfig,
default_per_channel_weight_observer,
default_qconfig,
get_default_qconfig,
quantize,
quantize_dynamic,
default_weight_observer,
default_histogram_observer,
default_eval_fn,
fuse_modules,
quantize_jit,
quantize_dynamic_jit,
)
# torch.quantization.quantize_jit
from torch.quantization.quantize_jit import (
convert_jit,
convert_dynamic_jit,
fuse_conv_bn_jit,
prepare_jit,
prepare_dynamic_jit,
script_qconfig,
)
# Testing utils
from torch.testing._internal.common_quantized import (
override_qengines,
qengine_is_fbgemm,
qengine_is_qnnpack,
)
from torch.testing._internal.common_quantization import (
QuantizationTestCase,
skipIfNoFBGEMM,
get_script_module,
SingleLayerLinearModel,
SkipQuantModel,
NestedModel,
ConvModel,
default_per_channel_qconfig,
test_only_eval_fn,
ConvBnModel,
)
# Annotated models
from torch.testing._internal.common_quantization import (
AnnotatedSingleLayerLinearModel,
AnnotatedSkipQuantModel,
AnnotatedNestedModel,
AnnotatedConvModel,
AnnotatedConvBnModel,
)
from torch.testing import FileCheck
from torch.testing._internal.common_utils import suppress_warnings
from torch.testing._internal.common_utils import TemporaryFileName
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing._internal.jit_utils import attrs_with_prefix
from torch.testing._internal.jit_utils import get_forward
from torch.testing._internal.jit_utils import get_forward_graph
from torch.jit._recursive import wrap_cpp_module
# Standard library
import io
import copy
import itertools
import unittest
import numpy as np
class TestQuantizeJitPasses(QuantizationTestCase):
""" Test graph mode quantization passes used by quantize_jit
"""
def test_foldbn_trivial(self):
# Test trivial case
class TestModule(torch.nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.conv = torch.nn.Conv2d(1, 20, 5, 1)
self.bn = torch.nn.BatchNorm2d(num_features=20)
self.bn.eps = 0.0023
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
# Check that the transformation doesn't change numerics
for tracing_mode in [True, False]:
eager = TestModule()
eager.eval()
if tracing_mode:
x = torch.rand(1, 1, 6, 6)
scripted_or_traced = torch.jit.trace(eager, x)
else:
scripted_or_traced = torch.jit.script(eager)
scripted_or_traced.eval()
# Check that in the original script module's forward we have two
# CallMethod nodes. One of them should be for conv.forward and the other
# for bn.forward.
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward(scripted_or_traced._c).graph))
# Run FoldConvBatchnorm2d pass.
scripted_or_traced = fuse_conv_bn_jit(scripted_or_traced)
# Check that after the pass one of the CallMethods is gone (supposedly,
# the bn.forward).
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced._c)))
# Check that the transformation doesn't change numerics
x = torch.rand(1, 1, 6, 6)
self.assertEqual(eager(x), scripted_or_traced(x))
def test_foldbn_trivial_nobias(self):
# Test trivial case
class TestModule(torch.nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.conv = torch.nn.Conv2d(1, 20, 5, 1, bias=False)
self.bn = torch.nn.BatchNorm2d(num_features=20)
# to make sure new bias is not zero
self.bn.eps = 0.0027
self.bn.bias = torch.nn.Parameter(torch.rand([20]))
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
for tracing_mode in [True, False]:
eager = TestModule()
eager.eval()
if tracing_mode:
x = torch.rand(1, 1, 6, 6)
scripted_or_traced = torch.jit.trace(eager, x)
else:
scripted_or_traced = torch.jit.script(eager)
scripted_or_traced.eval()
# Check that in the original script module's forward we have two
# CallMethod nodes. One of them should be for conv.forward and the other
# for bn.forward.
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced._c)))
# Run FoldConvBatchnorm2d pass.
scripted_or_traced = fuse_conv_bn_jit(scripted_or_traced)
# Check that after the pass one of the CallMethods is gone (supposedly,
# the bn.forward).
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced._c)))
# Check that the transformation doesn't change numerics
x = torch.rand(1, 1, 6, 6)
self.assertEqual(eager(x), scripted_or_traced(x))
def test_foldbn_in_submodule(self):
# Test that we find Conv-BN patterns in submodules
class SubModule(torch.nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.conv = torch.nn.Conv2d(1, 20, 5, 1)
self.bn = torch.nn.BatchNorm2d(num_features=20)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class TestModule(torch.nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub = SubModule()
def forward(self, x):
x = self.sub(x)
return x
for tracing_mode in [True, False]:
eager = TestModule()
eager.eval()
if tracing_mode:
x = torch.rand(1, 1, 10, 10)
scripted_or_traced = torch.jit.trace(eager, x)
else:
scripted_or_traced = torch.jit.script(eager)
scripted_or_traced.eval()
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub._c)))
scripted_or_traced = fuse_conv_bn_jit(scripted_or_traced)
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub._c)))
x = torch.rand(1, 1, 10, 10)
self.assertEqual(eager(x), scripted_or_traced(x))
def test_foldbn_in_customConv2D(self):
# Make sure a custom Conv2D class is not folded
# as we do not know it does.
class CustomConv2D(torch.nn.Module):
def __init__(self, a, b, c, d):
super(CustomConv2D, self).__init__()
def forward(self, x):
return F.relu(x)
class SubModule(torch.nn.Module):
def __init__(self):
super(SubModule, self).__init__()
self.conv = CustomConv2D(1, 20, 5, 1)
self.bn = torch.nn.BatchNorm2d(num_features=20)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class TestModule(torch.nn.Module):
def __init__(self):
super(TestModule, self).__init__()
self.sub = SubModule()
def forward(self, x):
x = self.sub(x)
return x
for tracing_mode in [True, False]:
eager = TestModule()
eager.eval()
if tracing_mode:
x = torch.rand(1, 20, 10, 10)
scripted_or_traced = torch.jit.trace(eager, x)
else:
scripted_or_traced = torch.jit.script(eager)
scripted_or_traced.eval()
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub._c)))
scripted_or_traced = fuse_conv_bn_jit(scripted_or_traced)
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub._c)))
x = torch.rand(1, 20, 10, 10)
self.assertEqual(eager(x), scripted_or_traced(x))
def test_foldbn_shared_classtype(self):
class TestModule(torch.nn.Module):
def __init__(self, bias=False):
super(TestModule, self).__init__()
self.conv1 = torch.nn.Conv2d(5, 5, 3, bias=bias)
self.bn1 = torch.nn.BatchNorm2d(num_features=5)
self.bn1.running_mean.fill_(-0.2)
self.bn1.bias = torch.nn.Parameter(torch.rand([5]))
# to make sure new bias is not zero
self.bn1.eps = 0.0023
self.conv2 = torch.nn.Conv2d(5, 5, 3, bias=bias)
self.bn2 = torch.nn.BatchNorm2d(num_features=5)
self.bn2.eps = 0.0029
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x
for tracing_mode in [True, False]:
for bias in [True, False]:
eager = TestModule(bias).eval()
if tracing_mode:
x = torch.rand(1, 5, 6, 6)
scripted_or_traced = torch.jit.trace(eager, x).copy()
else:
scripted_or_traced = torch.jit.script(eager).copy()
torch._C._jit_pass_dedup_module_uses(scripted_or_traced ._c)
folded = fuse_conv_bn_jit(scripted_or_traced)
x = torch.rand(1, 5, 6, 6)
self.assertEqual(eager(x), scripted_or_traced(x))
def test_foldbn_complex_cases(self):
# This test case attempt to try combinations of conv2d with bias/nobias
# as well as BatchNorm with affine/no-affine along with varying the
# number of layers.
# this only works when default dtype is double
torch.set_default_dtype(torch.double)
class SubModule(torch.nn.Module):
def __init__(self, num_blocks, enable_bias, enable_affine):
super(SubModule, self).__init__()
layers = []
for i in range(num_blocks):
layers.append(torch.nn.Conv2d(20, 20, 5, 1, bias=enable_bias))
bn_obj = torch.nn.BatchNorm2d(num_features=20, affine=enable_affine)
if enable_affine:
bn_obj.weight = torch.nn.Parameter(torch.rand_like(bn_obj.weight))
bn_obj.bias = torch.nn.Parameter(torch.rand_like(bn_obj.bias))
bn_obj.running_mean = torch.rand_like(bn_obj.running_mean)
bn_obj.running_var = torch.rand_like(bn_obj.running_var)
layers.append(bn_obj)
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class TestModule(torch.nn.Module):
def __init__(self, num_blocks, enable_bias, enable_affine):
super(TestModule, self).__init__()
self.sub = SubModule(num_blocks, enable_bias, enable_affine)
def forward(self, x):
x = self.sub(x)
return x
bias_affine_options = itertools.product([True, False], [True, False], [True, False], [1, 2])
for (tracing_mode, enable_bias, enable_bn_affine, num_layers) in bias_affine_options:
eager = TestModule(num_layers, enable_bias, enable_bn_affine)
eager.eval()
if tracing_mode:
x = torch.rand(1, 20, 10, 10)
scripted_or_traced = torch.jit.trace(eager, x)
else:
scripted_or_traced = torch.jit.script(eager)
scripted_or_traced.eval()
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", num_layers * 2, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub.layers._c)))
scripted_or_traced = fuse_conv_bn_jit(scripted_or_traced)
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", num_layers, exactly=True) \
.run(str(get_forward_graph(scripted_or_traced.sub.layers._c)))
x = torch.rand(1, 20, 10, 10)
self.assertEqual(eager(x), scripted_or_traced(x))
torch.set_default_dtype(torch.float)
def test_fuse_linear(self):
input_strs = ["""
graph(%input, %weight, %bias, %4):
# CHECK-NOT: aten::t
# CHECK-NOT: aten::addmm
# CHECK: aten::linear
%weight_t = aten::t(%weight)
%res = aten::addmm(%bias, %input, %weight_t, %4, %4)
return (%res)""", """
graph(%input, %weight, %bias, %4):
# CHECK-NOT: aten::t
# CHECK-NOT: aten::matmul
# CHECK-NOT: aten::add_
# CHECK: aten::linear
%weight_t = aten::t(%weight)
%output = aten::matmul(%input, %weight_t)
%res = aten::add_(%output, %bias, %4)
return (%res)""", """
graph(%input, %weight):
# CHECK-NOT: aten::t
# CHECK-NOT: aten::matmul
# CHECK: aten::linear
%weight_t = aten::t(%weight)
%output = aten::matmul(%input, %weight_t)
return (%output)"""]
for input_str in input_strs:
graph = parse_ir(input_str)
torch._C._jit_pass_fuse_linear(graph)
FileCheck().run(input_str, graph)
def test_insert_observers(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
def forward(self, x):
return self.conv(x)
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# for input and output of conv
assert len(attrs_with_prefix(m, '_observer_')) == 2
# for weight
assert len(attrs_with_prefix(m.conv, '_observer_')) == 1
def test_insert_observers_child_qconfig(self):
class Sub(torch.nn.Module):
def __init__(self):
super(Sub, self).__init__()
self.fc = torch.nn.Linear(5, 5)
def forward(self, x):
return self.fc(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
self.sub = Sub()
def forward(self, x):
return self.sub(self.conv(x))
m = torch.jit.script(M())
qconfig_dict = {'sub.fc': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# input and output of sub
assert len(attrs_with_prefix(m, '_observer_')) == 2
# not quantized
assert len(attrs_with_prefix(m.conv, '_observer_')) == 0
# no observers since we observe in the outer most call site
assert len(attrs_with_prefix(m.sub, '_observer_')) == 0
# weight of linear
assert len(attrs_with_prefix(m.sub.fc, '_observer_')) == 1
@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.")
def test_insert_observers_skip_values(self):
class ConvFunctionalReLU(torch.nn.Module):
def __init__(self):
super(ConvFunctionalReLU, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
def forward(self, x):
return F.relu(self.conv(x))
class ConvReLUModule(torch.nn.Module):
def __init__(self):
super(ConvReLUModule, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
self.relu = torch.nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
class AddReLUModule(torch.nn.Module):
def __init__(self):
super(AddReLUModule, self).__init__()
self.relu = torch.nn.ReLU()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
out = self.conv(x)
out += x
return self.relu(out)
class AddFunctionalReLU(torch.nn.Module):
def __init__(self):
super(AddFunctionalReLU, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
out = self.conv(x)
out += x
return F.relu(out)
def attrs_with_prefix(module, prefix):
return [x for x, _ in module._modules._c.items()
if x.startswith(prefix)]
qconfig_dict = {'': default_qconfig}
m = torch.jit.script(ConvFunctionalReLU())
m = prepare_jit(m, qconfig_dict)
# observer for weight of conv
assert len(attrs_with_prefix(m.conv, '_observer_')) == 1
# observer for input of conv and output of relu
assert len(attrs_with_prefix(m, '_observer_')) == 2
m = torch.jit.script(ConvReLUModule())
m = prepare_jit(m, qconfig_dict)
# observer for input of conv and output of relu
assert len(attrs_with_prefix(m, '_observer_')) == 2
# observer for weight of conv
assert len(attrs_with_prefix(m.conv, '_observer_')) == 1
# observer for output of relu
assert len(attrs_with_prefix(m.relu, '_observer_')) == 0
m = torch.jit.script(AddReLUModule())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
assert len(attrs_with_prefix(m, '_observer')) == 3
assert len(attrs_with_prefix(m.relu, '_observer')) == 0
FileCheck().check('aten::add_') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.check('ReLU = prim::GetAttr') \
.run(str(get_forward_graph(m._c)))
m = torch.jit.script(AddFunctionalReLU())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
assert len(attrs_with_prefix(m, '_observer')) == 3
FileCheck().check('aten::add_') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.check('CallFunction') \
.check('Observer = prim::GetAttr[name="_observer_') \
.run(str(get_forward_graph(m._c)))
def test_insert_observers_weight_dtype(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
def forward(self, x):
return F.relu(self.conv(x))
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
activation_dtypes = set(obs.getattr('dtype') for x, obs in m._modules._c.items()
if x.startswith('_observer_'))
weight_dtypes = set(obs.getattr('dtype') for x, obs in m.conv._modules._c.items()
if x.startswith('_observer_'))
assert len(activation_dtypes) == 1, 'Expected to have 1 activation dtype'
assert len(weight_dtypes) == 1, 'Expected to have 1 weight dtype'
assert list(activation_dtypes)[0] != list(weight_dtypes)[0], 'Expected activation dtype to '
' be different from wegiht dtype'
def test_insert_observers_for_reused_weight(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
def forward(self, x, y, weight):
x = F.conv2d(x, weight)
y = F.conv2d(y, weight)
return x + y
m = torch.jit.script(M()).eval()
m = prepare_jit(m, {'': default_qconfig})
# 3 for x, y, weight, one for output of each F.conv2d and one for output of add
assert len(attrs_with_prefix(m, '_observer')) == 6
def test_insert_observers_shared_class_type(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 5, 3).float()
self.conv2 = torch.nn.Conv2d(3, 5, 3).float()
def forward(self, x):
return self.conv2(self.conv1(x))
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# conv1 and conv2 shares the same type, we need to
# make sure we didn't quantize the type twice
conv1_observers = attrs_with_prefix(m.conv1, '_observer_')
conv2_observers = attrs_with_prefix(m.conv2, '_observer_')
assert len(conv1_observers) == 1, \
'Expected to have 1 observer submodules'
assert len(conv2_observers) == 1, \
'Expected to have 1 observer submodules'
assert conv1_observers == conv2_observers, \
'Expect conv1 and conv2 to have same observers since the class type is shared'
def test_insert_observers_for_general_ops(self):
""" Make sure we skip observers for ops that doesn't require
observation, e.g. flatten
"""
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
x = self.conv(x)
x = torch.flatten(x)
return x
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# input and output of conv
assert len(attrs_with_prefix(m, '_observer_')) == 2
FileCheck().check('Observer = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="conv"]') \
.check('prim::CallMethod') \
.check('Observer = prim::GetAttr[name="_observer_') \
.check('aten::flatten') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
# TODO: this is too long, split this to test_insert_observers.py and remove
# insrt_observers prefix
def test_insert_observers_propagate_observed(self):
""" Make sure we propagate observed property through general ops
"""
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3).float()
self.conv2 = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
x = self.conv1(x)
x = torch.flatten(x)
# we don't want to insert observer for input of self.conv2
# because output of self.conv1 is already observed
x = self.conv2(x)
return x
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# input and output of conv
assert len(attrs_with_prefix(m, '_observer_')) == 3
FileCheck().check('Observer = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="conv1"]') \
.check('prim::CallMethod') \
.check('Observer = prim::GetAttr[name="_observer_') \
.check('aten::flatten') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="conv2"]') \
.check('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
def test_insert_observers_propagate_observed_in_submodule(self):
""" Make sure we propagate observed property through general ops
"""
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3).float()
self.conv2 = torch.nn.Conv2d(3, 3, 3).float()
self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1))
def forward(self, x):
x = self.conv1(x)
x = self.avgpool(x)
# we don't want to insert observer for input of self.conv2
# because output of self.conv1 is already observed
x = self.conv2(x)
return x
m = torch.jit.script(M())
qconfig_dict = {'': default_qconfig}
m = prepare_jit(m, qconfig_dict)
# input and output of conv
assert len(attrs_with_prefix(m, '_observer_')) == 3
FileCheck().check('Observer = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="conv1"]') \
.check('prim::CallMethod') \
.check('Observer = prim::GetAttr[name="_observer_') \
.check('prim::CallMethod') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="conv2"]') \
.check('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
def test_insert_observers_propagate_observed_for_function(self):
def channel_shuffle(x, groups):
# type: (torch.Tensor, int) -> torch.Tensor
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups,
channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 1).float()
self.conv2 = torch.nn.Conv2d(3, 3, 1).float()
def forward(self, x):
x = self.conv1(x)
x = channel_shuffle(x, 1)
x = self.conv2(x)
return x
data = [(torch.rand((1, 3, 10, 10), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
m = torch.jit.script(M()).eval()
m = prepare_jit(m, {'': default_qconfig})
# we want to test that channel_shuffle is going to pass
# the observed property from the output of conv1 to input of conv2
# so that we don't insert observers for input of conv2
assert len(attrs_with_prefix(m, '_observer_',)) == 3
def test_insert_observers_for_if(self):
class QuantProp(torch.nn.Module):
def __init__(self, use_skip):
super(QuantProp, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
self.use_skip = use_skip
def forward(self, x):
if self.use_skip:
x = self.conv(x)
return torch.reshape(x, x.shape)
else:
x = self.conv(x)
return torch.reshape(x, x.shape)
class Res(torch.nn.Module):
def __init__(self, use_skip):
super(Res, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
self.use_skip = use_skip
def forward(self, x):
if self.use_skip:
return self.conv(x)
else:
return self.conv(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.quant_prop = QuantProp(True)
self.res = Res(False)
def forward(self, x):
x = self.quant_prop(x)
x = self.res(x)
return x
data = [torch.rand(1, 3, 10, 10, dtype=torch.float)]
result = {False : [1, 2, 2], True : [2, 1, 0]}
for tracing in [True, False]:
if tracing:
m = torch.jit.trace(M(), data).eval()
else:
m = torch.jit.script(M()).eval()
m = prepare_jit(m, {'': default_qconfig})
assert len(attrs_with_prefix(m, '_observer_',)) == result[tracing][0]
assert len(attrs_with_prefix(m.quant_prop, '_observer_',)) == result[tracing][1]
assert len(attrs_with_prefix(m.res, '_observer_',)) == result[tracing][2]
def test_insert_observers_for_nested_if(self):
class Res(torch.nn.Module):
def __init__(self, use_skip):
super(Res, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
self.cond = use_skip
self.use_skip = use_skip
def forward(self, x):
if self.use_skip:
if self.cond:
return self.conv(x)
else:
return self.conv(x)
else:
return self.conv(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.res1 = Res(True)
self.res2 = Res(False)
def forward(self, x):
x = self.res1(x)
x = self.res2(x)
return x
data = torch.rand((1, 3, 10, 10), dtype=torch.float)
result = {True : 3, False : 1}
for tracing in [True, False]:
if tracing:
m = torch.jit.trace(M(), data).eval()
else:
m = torch.jit.script(M()).eval()
m = prepare_jit(m, {'': default_qconfig})
assert len(attrs_with_prefix(m, '_observer_')) == result[tracing]
def test_insert_observers_for_if_consistent_observation(self):
""" check quantization for if works as long as
output of all branches are quantized/observed consistently
"""
class M(torch.nn.Module):
def __init__(self, cond):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
self.cond = cond
def forward(self, x):
x = self.conv(x)
# x is already observed
if self.cond:
x = torch.flatten(x)
return x
class M2(torch.nn.Module):
def __init__(self, cond):
super(M2, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3).float()
self.conv2 = torch.nn.Conv2d(3, 3, 3).float()
self.cond = cond
def forward(self, x):
x = self.conv1(x)
if self.cond:
x = self.conv2(x)
# x will be observed in the branch
else:
x = torch.flatten(x)
# since output for both branch are quantized
# the if node is quantized consistently
return x
data = torch.rand((1, 3, 5, 5), dtype=torch.float)
options = list(itertools.product([True, False], [True, False]))
for cond, tracing in options:
if tracing:
m = torch.jit.trace(M(cond), data)
else:
m = torch.jit.script(M(cond))
m = prepare_jit(m, {'': default_qconfig})
assert len(attrs_with_prefix(m, '_observer_')) == 2
for cond, tracing in options:
if tracing:
m = torch.jit.trace(M2(cond), data)
else:
m = torch.jit.script(M2(cond))
m = prepare_jit(m, {'': default_qconfig})
num_observers = 2 if tracing and not cond else 3
assert len(attrs_with_prefix(m, '_observer_')) == num_observers
def test_insert_quant_dequant(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3).float()
def forward(self, x):
return self.conv(x)
for is_per_channel in [True, False]:
m = torch.jit.script(M())
observer = default_per_channel_weight_observer.with_args(ch_axis=1) \
if is_per_channel else default_observer
qconfig_dict = {'': QConfig(activation=observer, weight=observer)}
m = prepare_jit(m, qconfig_dict)
data = torch.randn(1, 3, 10, 10, dtype=torch.float)
m(data)
m = convert_jit(m, debug=True)
assert len(m._modules._c.items()) == 1, \
'Expected to have single submodule of conv'
# make sure the quantized model is executable
m(data)
quant_func = "aten::quantize_per_channel" if is_per_channel \
else "aten::quantize_per_tensor"
FileCheck().check_count(quant_func, 3, exactly=True) \
.run(m.graph)
def test_insert_quant_dequant_shared_class_type(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3).float()
self.conv2 = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
return self.conv2(self.conv1(x))
for is_per_channel in [True, False]:
m = torch.jit.script(M())
observer = default_per_channel_weight_observer.with_args(ch_axis=1) \
if is_per_channel else default_observer
qconfig = QConfig(activation=observer, weight=observer)
qconfig_dict = {'': qconfig}
m = prepare_jit(m, qconfig_dict)
# observers for input, output and value between conv1/conv2
assert len(attrs_with_prefix(m, '_observer_')) == 3, \
'Expected to have 3 obervers'
# observer for weight
assert len(attrs_with_prefix(m.conv1, '_observer_')) == 1, \
'Expected to have 1 obervers'
# observer for weight
assert len(attrs_with_prefix(m.conv2, '_observer_')) == 1, \
'Expected to have 1 obervers'
data = torch.randn(1, 3, 10, 10, dtype=torch.float)
m(data)
m = convert_jit(m, debug=True)
m(data)
assert m.conv1._c._type() == m.conv2._c._type()
# check all observers have been removed
assert len(attrs_with_prefix(m, '_observer_')) == 0, \
'Expected to have 0 obervers'
assert len(attrs_with_prefix(m.conv1, '_observer_')) == 0, \
'Expected to have 0 obervers'
assert len(attrs_with_prefix(m.conv2, '_observer_')) == 0, \
'Expected to have 0 obervers'
quant_func = "aten::quantize_per_channel" if is_per_channel \
else "aten::quantize_per_tensor"
for module in ['conv1', 'conv2']:
conv = m._c.getattr(module)
# quantize weight
FileCheck().check(quant_func) \
.check_next("aten::dequantize") \
.check("prim::CallMethod[name=\"_conv_forward\"]") \
.check("return") \
.run(get_forward_graph(conv))
# no quantize node in _conv_forward
FileCheck().check_not(quant_func) \
.check("aten::conv2d") \
.check_not(quant_func) \
.check("return") \
.run(conv._get_method('_conv_forward').graph)
def test_dedup_module_uses(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.relu(x)
x -= 0.5
return self.relu(x)
data = torch.randn((2, 2))
m = torch.jit.script(M())
ref_res = m(data)
assert len([x for x, _ in m._modules._c.items()
if x.startswith('relu')]) == 1, \
"Expected to have 1 relu modules after dedup module uses"
torch._C._jit_pass_dedup_module_uses(m._c)
m = torch.jit._recursive.wrap_cpp_module(m._c)
res = m(data)
assert len([x for x, _ in m._modules._c.items()
if x.startswith('relu')]) == 2, \
"Expected to have 2 relu modules after dedup module uses"
self.assertEqual(res, ref_res)
def test_replicate_dequantize(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
def forward(self, x):
x = torch.dequantize(x)
r = self.conv(x)
r += x
return r
x = torch.randn([1, 3, 10, 10], dtype=torch.float)
x = torch.quantize_per_tensor(x, 0.5, 1, torch.quint8)
m = torch.jit.script(M())
ref_res = m(x)
FileCheck().check_count("aten::dequantize", 1, exactly=True) \
.run(m.graph)
torch._C._jit_pass_replicate_dequantize(m.graph)
FileCheck().check_count("aten::dequantize", 2, exactly=True) \
.run(m.graph)
res = get_forward(m._c)(x)
self.assertEqual(res, ref_res)
def test_replicate_dequantize_in_block(self):
class M(torch.nn.Module):
def __init__(self, cond):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
self.cond = cond
def forward(self, x):
x = torch.dequantize(x)
if self.cond:
x = self.conv(x)
else:
x = x + 3
return x
x = torch.randn([1, 3, 10, 10], dtype=torch.float)
x = torch.quantize_per_tensor(x, 0.5, 1, torch.quint8)
m = torch.jit.script(M(True))
ref_res = m(x)
FileCheck().check_count("aten::dequantize", 1, exactly=True) \
.run(m.graph)
torch._C._jit_pass_replicate_dequantize(m.graph)
FileCheck().check_count("aten::dequantize", 2, exactly=True) \
.run(m.graph)
# check dequantize is right before CallMethod of conv
FileCheck().check("aten::dequantize") \
.check_next("CallMethod") \
.run(m.graph)
# check dequantize is right before add
FileCheck().check("aten::dequantize") \
.check("aten::dequantize") \
.check_next("aten::add") \
.run(m.graph)
res = get_forward(m._c)(x)
self.assertEqual(res, ref_res)
def test_swap_functional_linear(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
def forward(self, x, weight, bias):
x = torch.dequantize(x)
weight = torch.dequantize(weight)
x = F.linear(x, weight, bias)
x = torch.quantize_per_tensor(x, scale=1.0, zero_point=0, dtype=torch.quint8)
return x
x = torch.rand((10, 5), dtype=torch.float)
x = torch.quantize_per_tensor(x, scale=0.5, zero_point=1, dtype=torch.quint8)
weight = torch.rand((5, 5), dtype=torch.float)
weight = torch.quantize_per_tensor(weight, scale=0.5, zero_point=1, dtype=torch.qint8)
bias = torch.rand((5), dtype=torch.float)
m = torch.jit.script(M())
ref_res = m(x, weight, bias)
FileCheck().check("CallFunction") \
.run(m.graph)
torch._C._jit_pass_swap_functional_linear(m.graph)
FileCheck().check("aten::linear") \
.check_not("CallFunction") \
.run(m.graph)
res = m(x, weight, bias)
self.assertEqual(res, ref_res)
def test_replicate_quantize_for_if(self):
""" We want to move quantize nodes for output of prim::If
inside the prim::If blocks so that we can match quantization
patterns.
"""
class Res(torch.nn.Module):
def __init__(self):
super(Res, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 1).float()
self.use_skip = True
def forward(self, x, cond):
# type: (Tensor, bool) -> Tensor
# to avoid being frozen
self.use_skip = cond
if self.use_skip:
return self.conv(x)
else:
return self.conv(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.res1 = Res()
self.res2 = Res()
def forward(self, x):
x = self.res1(x, True)
x = self.res2(x, False)
return x
data = [(torch.rand((1, 3, 10, 10), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
qconfig_dict = {'': default_qconfig}
m = torch.jit.script(M()).eval()
m = quantize_jit(m, qconfig_dict, test_only_eval_fn, [data])
# make sure patterns in both branches are fused
FileCheck().check_count("quantized::conv2d(", 4, exactly=True) \
.run(m.graph)
def test_finalize_for_linear(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5).float()
def forward(self, x):
return self.fc(x)
data = [(torch.rand((1, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
qconfig_dict = {'': default_qconfig}
model = torch.jit.script(M()).eval()
model = quantize_jit(model, qconfig_dict, test_only_eval_fn, [data])
# make sure there is only one quantize_per_tensor for input
# and linear_prepack is folded
FileCheck().check_count("aten::quantize_per_tensor", 1, exactly=True) \
.check_not("quantized::linear_prepack") \
.check("quantized::linear") \
.run(model.graph)
def test_finalize_debug(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
self.avgpool = torch.nn.AvgPool2d(3)
def forward(self, x):
x = self.conv(x)
x = self.avgpool(x)
return x
data = [(torch.rand((1, 3, 10, 10), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
qconfig_dict = {'': default_qconfig}
model = torch.jit.script(M()).eval()
model = quantize_jit(model, qconfig_dict, test_only_eval_fn, [data], debug=True)
FileCheck().check_not("quantized::conv2d") \
.check("aten::conv2d") \
.check("aten::avg_pool2d") \
.check("aten::q_scale") \
.check_next("aten::q_zero_point") \
.check_next("prim::dtype") \
.check_next("aten::quantize_per_tensor") \
.check("aten::dequantize") \
.run(model.graph)
def test_finalize_no_extra_dequantize(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
x = self.conv(x)
return x.size(0) * x
model = torch.jit.script(M()).eval()
model = quantize_jit(model, {'': default_qconfig}, test_only_eval_fn, [self.img_data])
FileCheck().check_not("aten::dequantize(") \
.run(model.graph)
def test_module_list(self):
class SimpleLinearLayer(torch.nn.Module):
def __init__(self):
super(SimpleLinearLayer, self).__init__()
self.fc = torch.nn.Linear(5, 5).float()
def forward(self, x):
return self.fc(x)
class ComplexModel(torch.nn.Module):
def __init__(self):
super(ComplexModel, self).__init__()
self.layers = torch.nn.ModuleList([SimpleLinearLayer() for i in range(2)])
def forward(self, x):
# type: (torch.Tensor) -> List[torch.Tensor]
states = []
for layer in self.layers:
val = layer(x)
states.append(val)
return states
data = torch.rand((1, 5), dtype=torch.float)
qconfig_dict = {'': default_qconfig}
model = torch.jit.script(ComplexModel()).eval()
model = prepare_jit(model, qconfig_dict)
assert len(attrs_with_prefix(model, '_observer')) == 3
model(data)
model = convert_jit(model, debug=False)
FileCheck().check("quantized::linear") \
.check("quantized::linear") \
.run(model.graph)
def test_conv_trace(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1d = torch.nn.Conv1d(3, 3, 3).float()
self.conv2d = torch.nn.Conv2d(3, 3, 3).float()
self.conv3d = torch.nn.Conv3d(3, 3, 3).float()
def forward(self, x, y, z):
a = self.conv1d(x)
b = self.conv2d(y)
c = self.conv3d(z)
return (a, b, c)
qconfig_dict = {'': default_qconfig}
inputs = (torch.rand((1, 3, 10), dtype=torch.float),
torch.rand((1, 3, 10, 10), dtype=torch.float),
torch.rand((1, 3, 10, 10, 10), dtype=torch.float))
model = torch.jit.trace(M(), inputs).eval()
m = prepare_jit(model, qconfig_dict)
FileCheck().check('aten::conv1d') \
.check_not("aten::_convolution") \
.run(str(get_forward_graph(m.conv1d._c)))
FileCheck().check('aten::conv2d') \
.check_not("aten::_convolution") \
.run(str(get_forward_graph(m.conv2d._c)))
FileCheck().check('aten::conv3d') \
.check_not("aten::_convolution") \
.run(str(get_forward_graph(m.conv3d._c)))
@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.")
def test_replicate_dequant_same_value(self):
class Mul(torch.nn.Module):
def __init__(self):
super(Mul, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3).float()
def forward(self, x):
x = self.conv(x)
return x * x
data = [(torch.rand((1, 3, 10, 10), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
qconfig_dict = {'': default_qconfig}
model = torch.jit.script(Mul()).eval()
m = quantize_jit(model, qconfig_dict, test_only_eval_fn, [data])
FileCheck().check("quantized::mul(") \
.check_not("aten::mul") \
.run(m.graph)
class TestQuantizeJitOps(QuantizationTestCase):
""" Test graph mode post training static quantization works
for individual ops end to end.
"""
@skipIfNoFBGEMM
def test_linear(self):
class ModuleLinear(torch.nn.Module):
def __init__(self, has_relu=False, f_relu=False):
super(ModuleLinear, self).__init__()
self.linear = torch.nn.Linear(30, 4).float()
if has_relu:
if f_relu:
self.relu = F.relu
else:
self.relu = torch.nn.ReLU()
else:
self.relu = torch.nn.Identity()
def forward(self, x):
return self.relu(self.linear(x))
class FuncLinear(torch.nn.Module):
def __init__(self, has_relu=False, f_relu=False):
super(FuncLinear, self).__init__()
self.w = torch.randn(4, 30)
self.b = torch.randn(4)
if has_relu:
if f_relu:
self.relu = F.relu
else:
self.relu = torch.nn.ReLU()
else:
self.relu = torch.nn.Identity()
def forward(self, x):
return self.relu(F.linear(x, self.w, self.b))
data = [(torch.rand((1, 30), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for model in [ModuleLinear(has_relu=False),
FuncLinear(has_relu=False)]:
model = self.checkGraphModeOp(model, data, "quantized::linear",
tracing=False)
FileCheck() \
.check_count("aten::quantize_per_tensor", 1, exactly=True) \
.run(model.graph)
FileCheck().check_not("quantized::linear_prepack") \
.run(model.graph)
for f_relu in [True, False]:
for model in [ModuleLinear(has_relu=True, f_relu=f_relu),
FuncLinear(has_relu=True, f_relu=f_relu)]:
model = self.checkGraphModeOp(model, data,
"quantized::linear_relu",
tracing=False)
checker = FileCheck().check_not("aten::linear") \
.check_not("aten::relu") \
.check_not("quantized::linear(") \
.check_not("quantized::relu(") \
.run(model.graph)
@skipIfNoFBGEMM
def test_quantized_conv(self):
conv_module = {1 : torch.nn.Conv1d, 2 : torch.nn.Conv2d, 3 : torch.nn.Conv3d}
class Conv(torch.nn.Module):
def __init__(self, dim):
super(Conv, self).__init__()
self.conv = conv_module[dim](3, 3, 3).float()
def forward(self, x):
return self.conv(x)
options = itertools.product([1, 2, 3], [True, False])
for dim, tracing in options:
model = self.checkGraphModeOp(
Conv(dim), self.img_data_dict[dim],
"quantized::conv{}d".format(dim), tracing)
# make sure there is only one quantize_per_tensor for input
# and conv2d_prepack is folded
FileCheck().check_count("aten::quantize_per_tensor", 1, exactly=True) \
.run(model.graph)
FileCheck().check_not("quantized::conv{}d_prepack".format(dim)) \
.run(model.graph)
@skipIfNoFBGEMM
def test_quantized_conv_relu(self):
"""tests for conv1d_relu/conv2d_relu/conv3d_relu"""
conv_module = {1 : torch.nn.Conv1d, 2 : torch.nn.Conv2d, 3 : torch.nn.Conv3d}
class ConvNdRelu(torch.nn.Module):
def __init__(self, dim, inplace):
super(ConvNdRelu, self).__init__()
self.conv = conv_module[dim](3, 3, 3).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x):
return self.relu(self.conv(x))
class ConvNdFunctionalRelu(torch.nn.Module):
def __init__(self, dim):
super(ConvNdFunctionalRelu, self).__init__()
self.conv = conv_module[dim](3, 3, 3).float()
def forward(self, x):
return F.relu(self.conv(x))
class ConvNdInplaceFunctionalRelu(torch.nn.Module):
def __init__(self, dim):
super(ConvNdInplaceFunctionalRelu, self).__init__()
self.conv = conv_module[dim](3, 3, 3).float()
def forward(self, x):
return F.relu(self.conv(x), True)
options = itertools.product([1, 2, 3], [True, False])
for dim, tracing in options:
for orig_m in [ConvNdRelu(dim, True),
ConvNdRelu(dim, False),
ConvNdFunctionalRelu(dim),
ConvNdInplaceFunctionalRelu(dim)]:
conv_name = "conv{}d".format(dim)
m = self.checkGraphModeOp(
orig_m, self.img_data_dict[dim], "quantized::conv{}d_relu(".format(dim), tracing=tracing)
FileCheck().check_not("aten::conv{}d(".format(dim)) \
.check_not("aten::relu") \
.check_not("quantized::conv{}d(".format(dim)) \
.check_not("quantized::relu(") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add_alpha(self):
""" Test quant fusion for multiple aten::add using same
constant alpha as the third argument
"""
class QuantizedAdd(torch.nn.Module):
def __init__(self):
super(QuantizedAdd, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
z = x + y
w = y + z
return z + w
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for tracing in [True, False]:
m = self.checkGraphModeOp(QuantizedAdd(), data, "quantized::add", tracing)
FileCheck().check_count("quantized::add", 3, exactly=True) \
.run(m.graph)
FileCheck().check_not("aten::add") \
.check_not("aten::add_") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add_relu_alpha(self):
""" Test quant fusion for multiple aten::add using same
constant alpha as the third argument in add_relu pattern
"""
class AddRelu(torch.nn.Module):
def __init__(self, inplace):
super(AddRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
x = self.relu(x)
x = x + y
return self.relu(x)
class InplaceAddRelu(torch.nn.Module):
def __init__(self, inplace):
super(InplaceAddRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
x = self.relu(x)
x += y
return self.relu(x)
class AddFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
x = F.relu(x)
x = x + y
return F.relu(x)
class InplaceAddFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
x = F.relu(x)
x += y
return F.relu(x)
class AddInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
x = F.relu(x, True)
x = x + y
return F.relu(x, True)
class InplaceAddInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
x = F.relu(x, True)
x += y
return F.relu(x, True)
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m_orig in [AddRelu(True), AddRelu(False),
InplaceAddRelu(True), InplaceAddRelu(False),
AddFunctionalRelu(), InplaceAddFunctionalRelu(),
AddInplaceFunctionalRelu(), InplaceAddInplaceFunctionalRelu()]:
for tracing in [True, False]:
m = self.checkGraphModeOp(m_orig, data, "quantized::add_relu(", tracing=tracing)
FileCheck().check_count("quantized::add_relu(", 2, exactly=True) \
.run(m.graph)
FileCheck().check_not("aten::add(") \
.check_not("aten::add_(") \
.check_not("aten::relu(") \
.check_not("aten::relu_(") \
.check_not("quantized::add(") \
.check_not("quantized::relu(") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add(self):
class QuantizedAdd(torch.nn.Module):
def __init__(self):
super(QuantizedAdd, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
return x + y
class QuantizedInplaceAdd(torch.nn.Module):
def __init__(self):
super(QuantizedInplaceAdd, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
return x
class NonQuantizedAdd(torch.nn.Module):
def __init__(self):
super(NonQuantizedAdd, self).__init__()
def forward(self, x, y):
return x + y
class NonQuantizedInplaceAdd(torch.nn.Module):
def __init__(self):
super(NonQuantizedInplaceAdd, self).__init__()
def forward(self, x, y):
x += y
return x
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m, quantized in [(QuantizedAdd(), True),
(QuantizedInplaceAdd(), True),
(NonQuantizedAdd(), False),
(NonQuantizedInplaceAdd(), False)]:
for tracing in [True, False]:
op = "quantized::add" if quantized else "aten::add"
m = self.checkGraphModeOp(m, data, op, tracing)
# TODO: remove after refactor of checkGraphModeOp
if quantized:
FileCheck().check_not("aten::add") \
.check_not("aten::add_") \
.run(m.graph)
else:
FileCheck().check_not("quantized::add") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add_scalar(self):
class QuantizedAddScalar(torch.nn.Module):
def __init__(self):
super(QuantizedAddScalar, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return x + 3
class QuantizedInplaceAddScalar(torch.nn.Module):
def __init__(self):
super(QuantizedInplaceAddScalar, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x += 3
return x
class NonQuantizedAddScalar(torch.nn.Module):
def __init__(self):
super(NonQuantizedAddScalar, self).__init__()
def forward(self, x):
return x + 3
class NonQuantizedInplaceAddScalar(torch.nn.Module):
def __init__(self):
super(NonQuantizedInplaceAddScalar, self).__init__()
def forward(self, x):
x += 3
return x
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m, quantized in [(QuantizedAddScalar(), True),
(QuantizedInplaceAddScalar(), True),
(NonQuantizedAddScalar(), False),
(NonQuantizedInplaceAddScalar(), False)]:
for tracing in [True, False]:
op = "quantized::add_scalar" if quantized else "aten::add"
# TODO: fix debug=True numerics
m = self.checkGraphModeOp(m, data, op, tracing, check=False)
# TODO: remove after refactor of checkGraphModeOp
if quantized:
FileCheck().check_not("aten::add") \
.check_not("aten::add_") \
.run(m.graph)
else:
FileCheck().check_not("quantized::add_scalar") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add_relu(self):
class AddRelu(torch.nn.Module):
def __init__(self, inplace):
super(AddRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
return self.relu(x)
class InplaceAddRelu(torch.nn.Module):
def __init__(self, inplace):
super(InplaceAddRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
return self.relu(x)
class AddFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
return F.relu(x)
class InplaceAddFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
return F.relu(x)
class AddInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x + y
return F.relu(x, True)
class InplaceAddInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x += y
return F.relu(x, True)
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m in [AddRelu(True), AddRelu(False),
InplaceAddRelu(True), InplaceAddRelu(False),
AddFunctionalRelu(), InplaceAddFunctionalRelu(),
AddInplaceFunctionalRelu(), InplaceAddInplaceFunctionalRelu()]:
for tracing in [True, False]:
m = self.checkGraphModeOp(m, data, "quantized::add_relu(", tracing)
FileCheck().check_not("aten::add(") \
.check_not("aten::add_(") \
.check_not("aten::relu(") \
.check_not("aten::relu_(") \
.check_not("quantized::add(") \
.check_not("quantized::relu(") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_add_scalar_relu(self):
class AddScalarRelu(torch.nn.Module):
def __init__(self, inplace):
super(AddScalarRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x):
x = self.conv(x)
return self.relu(x + 3)
class InplaceAddScalarRelu(torch.nn.Module):
def __init__(self, inplace):
super(InplaceAddScalarRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x):
x = self.conv(x)
x += 3
return self.relu(x)
class AddScalarFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddScalarFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return F.relu(x + 3)
class InplaceAddScalarFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddScalarFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x += 3
return F.relu(x)
class AddScalarInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(AddScalarInplaceFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return F.relu(x + 3, True)
class InplaceAddScalarInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceAddScalarInplaceFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x += 3
return F.relu(x, True)
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m in [AddScalarRelu(True), AddScalarRelu(False),
InplaceAddScalarRelu(True), InplaceAddScalarRelu(False),
AddScalarFunctionalRelu(),
InplaceAddScalarFunctionalRelu(),
AddScalarInplaceFunctionalRelu(),
InplaceAddScalarInplaceFunctionalRelu()]:
for tracing in [True, False]:
# quantized::add_scalar_relu or quantized::add_scalar_relu_out
# TODO: split this after refactor of checkGraphModeOp
# TODO: fix debug=True numerics
m = self.checkGraphModeOp(m, data, "quantized::add_scalar_relu", tracing, check=False)
FileCheck().check_not("aten::add(") \
.check_not("aten::add_(") \
.check_not("aten::relu(") \
.check_not("aten::relu_(") \
.check_not("quantized::add_scalar(") \
.check_not("quantized::relu(") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_cat(self):
""" quantization of the output of cat will be depend on the
input of cat. we only quantize the output of cat when its inputs are quantized.
"""
class QuantizedCat(torch.nn.Module):
def __init__(self):
super(QuantizedCat, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
return torch.cat([x, y], 1)
class NonQuantizedCat(torch.nn.Module):
def __init__(self):
super(NonQuantizedCat, self).__init__()
def forward(self, x, y):
return torch.cat([x, y], 1)
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for tracing in [True, False]:
m = self.checkGraphModeOp(QuantizedCat(), data, "quantized::cat", tracing)
FileCheck().check_not("aten::cat") \
.run(m.graph)
m = self.checkGraphModeOp(NonQuantizedCat(), data, "aten::cat", tracing)
FileCheck().check_not("quantized::cat") \
.run(m.graph)
@skipIfNoFBGEMM
def test_qbatch_norm(self):
bn_module = {2 : torch.nn.BatchNorm2d, 3 : torch.nn.BatchNorm3d}
class M(torch.nn.Module):
def __init__(self, dim):
super(M, self).__init__()
self.bn = bn_module[dim](3).to(torch.float)
def forward(self, x):
return self.bn(x)
options = itertools.product([True, False], [2, 3])
for tracing, dim in options:
model = self.checkGraphModeOp(M(dim), self.img_data_dict[dim], "quantized::batch_norm", tracing)
FileCheck().check_not("aten::batch_norm") \
.run(model.graph)
@skipIfNoFBGEMM
def test_qbatch_norm_relu(self):
bn_module = {2 : torch.nn.BatchNorm2d, 3 : torch.nn.BatchNorm3d}
class BNRelu(torch.nn.Module):
def __init__(self, dim, inplace):
super(BNRelu, self).__init__()
self.bn = bn_module[dim](3).to(torch.float)
self.relu = torch.nn.ReLU(inplace=inplace)
def forward(self, x):
return self.relu(self.bn(x))
class BNFuncRelu(torch.nn.Module):
def __init__(self, dim):
super(BNFuncRelu, self).__init__()
self.bn = bn_module[dim](3).to(torch.float)
def forward(self, x):
return F.relu(self.bn(x), False)
class BNFuncInplaceRelu(torch.nn.Module):
def __init__(self, dim):
super(BNFuncInplaceRelu, self).__init__()
self.bn = bn_module[dim](3).to(torch.float)
def forward(self, x):
return F.relu(self.bn(x), True)
options = itertools.product([True, False], [2, 3])
for tracing, dim in options:
for instance in [BNRelu(dim, True), BNRelu(dim, False),
BNFuncRelu(dim), BNFuncInplaceRelu(dim)]:
model = self.checkGraphModeOp(instance, self.img_data_dict[dim],
"quantized::batch_norm_relu", tracing)
FileCheck().check_not("aten::batch_norm") \
.check_not("aten::relu") \
.check_not("aten::relu_") \
.run(model.graph)
@skipIfNoFBGEMM
def test_quantized_mul(self):
class QuantizedMul(torch.nn.Module):
def __init__(self):
super(QuantizedMul, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
return x * y
class QuantizedInplaceMul(torch.nn.Module):
def __init__(self):
super(QuantizedInplaceMul, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x *= y
return x
class NonQuantizedMul(torch.nn.Module):
def __init__(self):
super(NonQuantizedMul, self).__init__()
def forward(self, x, y):
return x * y
class NonQuantizedInplaceMul(torch.nn.Module):
def __init__(self):
super(NonQuantizedInplaceMul, self).__init__()
def forward(self, x, y):
x *= y
return x
data = [(torch.randn(1, 2, 10, 10, dtype=torch.float),
torch.randn(1, 2, 10, 10, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m, quantized in [(QuantizedMul(), True),
(QuantizedInplaceMul(), True),
(NonQuantizedMul(), False),
(NonQuantizedInplaceMul(), False)]:
for tracing in [True, False]:
op = "quantized::mul" if quantized else "aten::mul"
m = self.checkGraphModeOp(m, data, op, tracing)
# TODO: remove after refactor of checkGraphModeOp
if quantized:
FileCheck().check_not("aten::mul") \
.check_not("aten::mul_") \
.run(m.graph)
else:
FileCheck().check_not("quantized::mul") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_mul_scalar(self):
class QuantizedMulScalar(torch.nn.Module):
def __init__(self):
super(QuantizedMulScalar, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return x * 3
class QuantizedInplaceMulScalar(torch.nn.Module):
def __init__(self):
super(QuantizedInplaceMulScalar, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x *= 3
return x
class NonQuantizedMulScalar(torch.nn.Module):
def __init__(self):
super(NonQuantizedMulScalar, self).__init__()
def forward(self, x):
return x * 3
class NonQuantizedInplaceMulScalar(torch.nn.Module):
def __init__(self):
super(NonQuantizedInplaceMulScalar, self).__init__()
def forward(self, x):
x *= 3
return x
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m, quantized in [(QuantizedMulScalar(), True),
(QuantizedInplaceMulScalar(), True),
(NonQuantizedMulScalar(), False),
(NonQuantizedInplaceMulScalar(), False)]:
for tracing in [True, False]:
op = "quantized::mul_scalar" if quantized else "aten::mul"
# TODO: fix debug=True numerics
m = self.checkGraphModeOp(m, data, op, tracing, check=False)
# TODO: remove after refactor of checkGraphModeOp
if quantized:
FileCheck().check_not("aten::mul") \
.check_not("aten::mul_") \
.run(m.graph)
else:
FileCheck().check_not("quantized::mul_scalar") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_mul_relu(self):
class MulRelu(torch.nn.Module):
def __init__(self, inplace):
super(MulRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x * y
return self.relu(x)
class InplaceMulRelu(torch.nn.Module):
def __init__(self, inplace):
super(InplaceMulRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x *= y
return self.relu(x)
class MulFunctionalRelu(torch.nn.Module):
def __init__(self):
super(MulFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x * y
return F.relu(x)
class InplaceMulFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceMulFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x *= y
return F.relu(x)
class MulInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(MulInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x = x * y
return F.relu(x, True)
class InplaceMulInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceMulInplaceFunctionalRelu, self).__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 2).float()
self.conv2 = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x, y):
x = self.conv1(x)
y = self.conv2(y)
x *= y
return F.relu(x, True)
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.rand((1, 2, 5, 5), dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m in [MulRelu(True), MulRelu(False),
InplaceMulRelu(True), InplaceMulRelu(False),
MulFunctionalRelu(), InplaceMulFunctionalRelu(),
MulInplaceFunctionalRelu(), InplaceMulInplaceFunctionalRelu()]:
for tracing in [True, False]:
m = self.checkGraphModeOp(m, data, "quantized::mul_relu(", tracing)
FileCheck().check_not("aten::mul(") \
.check_not("aten::mul_(") \
.check_not("aten::relu(") \
.check_not("aten::relu_(") \
.check_not("quantized::mul(") \
.check_not("quantized::relu(") \
.run(m.graph)
@skipIfNoFBGEMM
def test_quantized_mul_scalar_relu(self):
class MulScalarRelu(torch.nn.Module):
def __init__(self, inplace):
super(MulScalarRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x):
x = self.conv(x)
return self.relu(x * 3)
class InplaceMulScalarRelu(torch.nn.Module):
def __init__(self, inplace):
super(InplaceMulScalarRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
self.relu = torch.nn.ReLU(inplace)
def forward(self, x):
x = self.conv(x)
x *= 3
return self.relu(x)
class MulScalarFunctionalRelu(torch.nn.Module):
def __init__(self):
super(MulScalarFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return F.relu(x * 3)
class InplaceMulScalarFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceMulScalarFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x *= 3
return F.relu(x)
class MulScalarInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(MulScalarInplaceFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
return F.relu(x * 3, True)
class InplaceMulScalarInplaceFunctionalRelu(torch.nn.Module):
def __init__(self):
super(InplaceMulScalarInplaceFunctionalRelu, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
def forward(self, x):
x = self.conv(x)
x *= 3
return F.relu(x, True)
data = [(torch.randn(1, 2, 5, 5, dtype=torch.float),
torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
for m in [MulScalarRelu(True), MulScalarRelu(False),
InplaceMulScalarRelu(True), InplaceMulScalarRelu(False),
MulScalarFunctionalRelu(),
InplaceMulScalarFunctionalRelu(),
MulScalarInplaceFunctionalRelu(),
InplaceMulScalarInplaceFunctionalRelu()]:
for tracing in [True, False]:
# quantized::mul_scalar_relu or quantized::mul_scalar_relu_out
# TODO: fix debug=True numerics
m = self.checkGraphModeOp(m, data, "quantized::mul_scalar_relu", tracing, check=False)
FileCheck().check_not("aten::mul(") \
.check_not("aten::mul_(") \
.check_not("aten::relu(") \
.check_not("aten::relu_(") \
.check_not("quantized::mul_scalar(") \
.check_not("quantized::relu(") \
.run(m.graph)
def test_hardswish(self):
class FunctionalHardswish(torch.nn.Module):
def __init__(self, inplace):
super(FunctionalHardswish, self).__init__()
self.inplace = inplace
def forward(self, input):
return torch.nn.functional.hardswish(input, inplace=self.inplace)
modules = [torch.nn.Hardswish(), FunctionalHardswish(True),
FunctionalHardswish(False)]
for test_case in itertools.product([True, False], modules):
tracing, m = test_case
m = self.checkGraphModeOp(
m, self.img_data, "quantized::hardswish", tracing)
FileCheck().check_not("aten::hardswish") \
.check_not("aten::hardswish_") \
.run(m.graph)
def test_elu(self):
class FunctionalELU(torch.nn.Module):
def __init__(self, inplace=False):
super(FunctionalELU, self).__init__()
self.inplace = inplace
def forward(self, input):
return torch.nn.functional.elu(input, inplace=self.inplace)
modules = [torch.nn.ELU, FunctionalELU]
for test_case in itertools.product([True, False], [True, False], modules):
tracing, inplace, mod_class = test_case
m = mod_class(inplace=inplace)
m = self.checkGraphModeOp(m, self.img_data, "quantized::elu", tracing)
FileCheck().check_not("aten::elu") \
.check_not("aten::elu_") \
.run(m.graph)
def test_layer_norm(self):
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
layer_norm = torch.nn.LayerNorm([2, 5, 5])
for tracing in [True, False]:
m = self.checkGraphModeOp(layer_norm, data, "quantized::layer_norm", tracing)
FileCheck().check_not("aten::layer_norm") \
.run(m.graph)
def test_group_norm(self):
data = [(torch.rand((1, 4, 5, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
group_norm = torch.nn.GroupNorm(2, 4)
for tracing in [True, False]:
m = self.checkGraphModeOp(group_norm, data, "quantized::group_norm", tracing)
FileCheck().check_not("aten::group_norm") \
.run(m.graph)
def test_instance_norm(self):
data_1d = [(torch.rand((1, 4, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
data_2d = [(torch.rand((1, 4, 5, 1), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
data_3d = [(torch.rand((1, 4, 5, 1, 1), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
data = {1 : data_1d, 2 : data_2d, 3 : data_3d}
instance_norm_modules = {1 : torch.nn.InstanceNorm1d,
2 : torch.nn.InstanceNorm2d,
3 : torch.nn.InstanceNorm3d}
options = itertools.product([1, 2, 3], [True, False])
for dim, tracing in options:
instance_norm = instance_norm_modules[dim](4)
m = self.checkGraphModeOp(
instance_norm, data[dim], "quantized::instance_norm", tracing)
FileCheck().check_not("aten::instance_norm") \
.run(m.graph)
@skipIfNoFBGEMM
def test_clamp(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(2, 2, 2).float()
self.relu6 = torch.nn.ReLU6()
self.relu6_ = torch.nn.ReLU6(True)
self.hardtanh = torch.nn.Hardtanh()
self.hardtanh_ = torch.nn.Hardtanh(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.relu6(x)
self.relu6_(x)
x = F.relu6(x)
x = torch.clamp(x, -3, 3)
x = x.clamp(-2.5, 2.5)
# x = x.clamp_(-2, 2) # Enable when quantized `clamp_` is ready
x = self.hardtanh(x)
self.hardtanh_(x)
x = F.hardtanh(x)
F.hardtanh_(x)
return x
data = [(torch.rand((1, 2, 5, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
options = itertools.product(["aten::clamp", "aten::hardtanh", "aten::hardtanh_"], [True, False])
for op, tracing in options:
m = self.checkGraphModeOp(M(), data, op, tracing)
FileCheck().check_count("aten::quantize_per_tensor", 1, exactly=True) \
.run(m.graph)
FileCheck().check_count("aten::dequantize", 1, exactly=True) \
.run(m.graph)
def test_general_shape_ops(self):
""" A test that checks dequantize will be swapped for
all supported general shape ops like aten::flatten
without actually checking for execution of these ops
"""
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.maxpool1d = torch.nn.MaxPool1d(kernel_size=3)
self.maxpool2d = torch.nn.MaxPool2d(kernel_size=3)
self.maxpool3d = torch.nn.MaxPool3d(kernel_size=3)
self.dropout = torch.nn.Dropout()
self.conv = torch.nn.Conv2d(3, 3, 3)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.maxpool1d(x)
x = self.maxpool2d(x)
x = self.maxpool3d(x)
x = torch.flatten(x)
x = torch.max(x)
x = torch.min(x)
x = x.reshape([-1])
x = x.resize_(1, 1, x.numel())
x = x.view(-1)
# prim::ListConstruct
xs = [x, x]
# prim::ListUnpack
x, y = xs
# prim::TupleConstruct
xs = (x, x)
# prim::TupleUnpack
x, y = xs
x = x.transpose(1, 2)
x = x.contiguous()
x, y = torch.chunk(x, 2)
x = F.dropout(x)
x = self.dropout(x)
x, _ = torch.sort(x)
x = x.permute(0, 2, 3, 1)
x = torch.repeat_interleave(x, 3, 1)
x = self.relu(x)
x = F.relu(x)
x.relu_()
x = x.squeeze(0)
x.squeeze_(0)
x = torch.squeeze(x, 0)
x = x.unsqueeze(0)
x.unsqueeze_(0)
x = torch.unsqueeze(x, 0)
x = x.detach()
x.detach_()
x = x.repeat(4, 2)
y = []
y.append(x)
x, _ = y
x = self.conv(x)
return x
data = torch.rand(1, 3, 10, 10)
# This model is not executable since we just put all ops
# in the same forward, therefore we only test scripting
m = torch.jit.script(M())
qconfig = script_qconfig(default_qconfig)
# dummy data to suppress warning
get_forward(qconfig.activation)(data)
get_forward(qconfig.weight)(data)
m = wrap_cpp_module(torch._C._jit_pass_insert_observers(
m._c, 'forward', {'': qconfig}, inplace=False))
m = convert_jit(m)
# This checks that the dequantize from the output of first conv
# is being propagated to the end, so that we don't insert extra
# observers and also successfully fused two quantized::conv2d
# patterns
# one quantize_per_tensor for input
FileCheck().check_count("aten::quantize_per_tensor", 1, exactly=True) \
.check_count("quantized::conv2d", 2, exactly=True) \
.check("aten::dequantize") \
.run(m.graph)
def test_general_value_ops(self):
""" A test that checks correct patterns are produced for
all supported general value ops like aten::avg_pool2d \
without actually checking for execution of these ops
"""
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 3, 3)
self.avg_pool1d = torch.nn.AvgPool1d(3)
self.avg_pool2d = torch.nn.AvgPool2d(3)
self.avg_pool3d = torch.nn.AvgPool3d(3)
self.adaptive_avg_pool1d = torch.nn.AdaptiveAvgPool1d((1))
self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
self.adaptive_avg_pool3d = torch.nn.AdaptiveAvgPool3d((1, 1, 1))
self.leaky_relu = torch.nn.LeakyReLU()
self.hardsigmoid = torch.nn.Hardsigmoid()
self.sigmoid = torch.nn.Sigmoid()
self.tanh = torch.nn.Tanh()
def forward(self, x):
x = self.conv(x)
x = self.avg_pool1d(x)
x = self.avg_pool2d(x)
x = self.avg_pool3d(x)
x = self.adaptive_avg_pool1d(x)
x = self.adaptive_avg_pool2d(x)
x = self.adaptive_avg_pool3d(x)
x = F.avg_pool1d(x, 3)
x = F.avg_pool2d(x, 3)
x = F.avg_pool3d(x, 3)
x = F.adaptive_avg_pool1d(x, (1))
x = F.adaptive_avg_pool2d(x, (1, 1))
x = F.adaptive_avg_pool3d(x, (1, 1, 1))
x = torch.mean(x)
x = torch.mean(x, [2, 3], False)
x = x.mean()
x = x.mean([2, 3], True)
# interpolate node will introduce 3 quantize_per_tensor ops
x = F.interpolate(x, 4, mode='nearest') # interpolate node
x = F.upsample(x, (32, 32)) # interpolate node
x = F.upsample_nearest(x, (32, 32)) # interpolate node
x = F.interpolate(x, 4, mode='linear') # common node
x = F.upsample_bilinear(x, (32, 32)) # common node
x = self.leaky_relu(x)
x = F.leaky_relu(x)
x.leaky_relu_()
x = self.hardsigmoid(x)
x = F.hardsigmoid(x)
x.hardsigmoid_()
x = self.sigmoid(x)
x = torch.sigmoid(x)
# F.sigmoid is deprecated
x = x.sigmoid()
x.sigmoid_()
x = self.tanh(x)
# F.tanh is deprecated
x = torch.tanh(x)
x = x.tanh()
x.tanh_()
x = self.conv(x)
return x
# This model is not executable since we just put all ops
# in the same forward, therefore we only test scripting
m = torch.jit.script(M())
qconfig = script_qconfig(default_qconfig)
# dummy data to suppress warning
data = torch.rand(1, 3, 10, 10)
get_forward(qconfig.activation)(data)
get_forward(qconfig.weight)(data)
m = wrap_cpp_module(torch._C._jit_pass_insert_observers(
m._c, 'forward', {'': qconfig}, inplace=False))
# Checking the model before fianlize contain unfused patterns
# that numerically matches the model after quantize by checking
# number of aten::quantize_per_tensor functions
# conv has 3 quantize_per_tensor for activations and 1 for weight
# and for N general value op between conv we should have
# N + 1 quantize_per_tensor between these ops
m1 = convert_jit(m, debug=True)
# NB: This Needs to be updated when we add more ops to test
# mapping from number of quant for the op to the number of these ops
# for example, for `3` in the key means for this type of op
# we'll have 3 quantize_per_tensor
num_op_by_num_quant = {1: 32, 2: 2, 3: 3}
num_quantize_per_tensor = 1 # for output
for num_quant, num_op in num_op_by_num_quant.items():
num_quantize_per_tensor += num_op * num_quant
FileCheck().check_count("aten::quantize_per_tensor(", num_quantize_per_tensor, exactly=True) \
.run(m1.graph)
# This checks that the dequantize from the output of first conv
# is being propagated to the end, so that we don't insert extra
# observers and also successfully fused two quantized::conv2d
# patterns
# one quantize_per_tensor for input
m2 = convert_jit(m, debug=False)
FileCheck().check_count("aten::quantize_per_tensor(", 1, exactly=True) \
.run(m2.graph)
FileCheck().check_count("quantized::conv2d(", 2, exactly=True) \
.check("aten::dequantize(") \
.run(m2.graph)
class TestQuantizeDynamicJitPasses(QuantizationTestCase):
def test_prepare_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5)
def forward(self, x):
return self.fc(x)
m = torch.jit.script(M())
m = prepare_dynamic_jit(m, {'': default_dynamic_qconfig})
# for input of FC for dynamic quant
assert len(attrs_with_prefix(m, '_observer_')) == 1
# for weight
assert len(attrs_with_prefix(m.fc, '_observer_')) == 1
FileCheck().check('DynamicQuantObserver = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="fc"]') \
.check('prim::CallMethod') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
def test_prepare_dynamic_child_qconfig(self):
class Sub(torch.nn.Module):
def __init__(self):
super(Sub, self).__init__()
self.fc = torch.nn.Linear(5, 5)
def forward(self, x):
return self.fc(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
self.sub = Sub()
def forward(self, x):
return self.sub(self.conv(x))
m = torch.jit.script(M())
# only quantize child module.
m = prepare_dynamic_jit(m, {'sub.fc': default_dynamic_qconfig})
# input of sub for dynamic quant
assert len(attrs_with_prefix(m, '_observer_')) == 1
# not quantized
assert len(attrs_with_prefix(m.conv, '_observer_')) == 0
# no observers since we observe in the outer most call site
assert len(attrs_with_prefix(m.sub, '_observer_')) == 0
# weight of linear
assert len(attrs_with_prefix(m.sub.fc, '_observer_')) == 1
FileCheck().check('prim::GetAttr[name="sub') \
.check('prim::CallMethod') \
.check('DynamicQuantObserver = prim::GetAttr[name="_observer_') \
.check('prim::CallMethod') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
def test_insert_quant_dequant_linear_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc1 = torch.nn.Linear(5, 5).float()
self.fc2 = torch.nn.Linear(5, 5).float()
def forward(self, x):
x = self.fc1(x)
return self.fc2(x)
for is_per_channel in [True, False]:
m = torch.jit.script(M())
qconfig = per_channel_dynamic_qconfig if is_per_channel is True else default_dynamic_qconfig
m = quantize_dynamic_jit(m, {'': qconfig}, debug=True)
assert len(m._modules._c.items()) == 2, \
'Expected to have two submodule of linear'
wt_quant_func = "aten::quantize_per_channel" if is_per_channel \
else "aten::quantize_per_tensor"
act_quant_func = "aten::quantize_per_tensor"
# quantizing activations
FileCheck().check("aten::_choose_qparams_per_tensor") \
.check_next(act_quant_func) \
.check_next("aten::dequantize") \
.check("aten::_choose_qparams_per_tensor") \
.check_next(act_quant_func) \
.check_next("aten::dequantize") \
.check(wt_quant_func) \
.check_next("aten::dequantize") \
.check_not(wt_quant_func) \
.check("return") \
.run(m.graph)
@override_qengines
def test_dynamic_multi_op(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float)
def forward(self, x):
x = x + 5
return self.fc1(x)
x = torch.randn(5, 5)
for tracing in [True, False]:
model = self.checkGraphModeOp(M(), x, "quantized::linear_dynamic", tracing=tracing, dynamic=True)
# add op is not dynamically quantized.
FileCheck().check("aten::add") \
.run(model.graph)
@override_qengines
def test_dynamic_quant_multi_uses(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5).float()
def forward(self, x):
size1 = x.size()
size2 = x.size()
return self.fc(x), size1, size2
x = torch.randn(5, 5)
for tracing in [True, False]:
model = self.checkGraphModeOp(M(), x, "quantized::linear_dynamic", tracing=tracing, dynamic=True)
FileCheck().check_not("aten::_choose_qparams_per_tensor") \
.run(model.graph)
@override_qengines
def test_dynamic_shared_weights(self):
class myMod(torch.nn.Module):
def __init__(self, weight):
super().__init__()
self.linear = nn.Linear(5, 5)
self.linear.weight = weight
def forward(self, x):
return self.linear(x)
class DynamicModel(torch.nn.Module):
def __init__(self):
super(DynamicModel, self).__init__()
self.weight = torch.nn.Parameter(torch.ones(5, 5))
self.mod1 = myMod(self.weight)
def forward(self, x):
y = self.mod1(x)
z = torch.nn.functional.linear(y, self.weight)
return z
model = torch.jit.script(DynamicModel()).eval()
data = torch.randn(5, 5, dtype=torch.float)
quant_ops = ['mod1', '']
counts = [1, 2]
for op, count in zip(quant_ops, counts):
qconfig_dict = {op: default_dynamic_qconfig}
m1 = quantize_dynamic_jit(model, qconfig_dict)
out_graph = m1(data)
FileCheck().check_count("quantized::linear_dynamic(", count, exactly=True) \
.check_not("aten::_choose_qparams_per_tensor") \
.run(m1.graph)
# Explicitly call forward on model before convert
m2 = prepare_dynamic_jit(model, qconfig_dict)
m2(data)
m2 = convert_dynamic_jit(m2, debug=False)
out_ref = m2(data)
self.assertEqual(out_graph, out_ref)
@override_qengines
def test_dynamic_with_if(self):
class Res(torch.nn.Module):
def __init__(self):
super(Res, self).__init__()
self.weight = torch.nn.Parameter(torch.ones(5, 5))
def forward(self, x, cond):
# type: (Tensor, bool) -> Tensor
if cond:
return torch.nn.functional.linear(x, self.weight)
else:
return torch.nn.functional.linear(x, self.weight)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.res1 = Res()
self.res2 = Res()
def forward(self, x):
x = self.res1(x, True)
x = self.res2(x, False)
return x
model = torch.jit.script(M()).eval()
data = torch.randn(5, 5, dtype=torch.float)
qconfig_dict = {'': default_dynamic_qconfig}
for tracing in [True, False]:
m1 = self.checkGraphModeOp(M(), data, "quantized::linear_dynamic", tracing=tracing, dynamic=True)
FileCheck().check_count("quantized::linear_dynamic(", 2, exactly=True) \
.check_not("aten::_choose_qparams_per_tensor") \
.run(m1.graph)
# Check to make sure weight observers run correctly
ref_qparams = []
qconfig = script_qconfig(default_dynamic_qconfig)
wt_module = wrap_cpp_module(qconfig.weight)
for wt in [model.res1.weight, model.res2.weight]:
wt_module(wt)
qparams = wt_module.calculate_qparams()
ref_qparams.append((qparams[0].item(), qparams[1].item()))
m2 = quantize_dynamic_jit(model, qconfig_dict, debug=True)
graph_params = []
for x, obs in m2._modules._c.items():
if x == 'res1':
graph_params.append((obs.getattr('6_scale_0'), obs.getattr('6_zero_point_0')))
elif x == 'res2':
graph_params.append((obs.getattr('10_scale_0'), obs.getattr('10_zero_point_0')))
self.assertEqual(ref_qparams, graph_params)
def test_dynamic_weight_observer(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5).float()
self.fc2 = torch.nn.Linear(5, 5).float()
def forward(self, x):
x = self.fc(x)
return self.fc2(x)
qconfig_dict = {'': default_dynamic_qconfig}
eager_model = M().eval()
x = torch.rand(5, 5)
for tracing in [True, False]:
model = get_script_module(eager_model, tracing, x)
qconfig = script_qconfig(default_dynamic_qconfig)
ref_qparams = []
wt_module = wrap_cpp_module(qconfig.weight)
for wt in [model.fc.weight, model.fc2.weight]:
wt_module(wt)
qparams = wt_module.calculate_qparams()
ref_qparams.append((qparams[0].item(), qparams[1].item()))
model = quantize_dynamic_jit(model, qconfig_dict, debug=True)
graph_params = []
for x, obs in model._modules._c.items():
if tracing:
graph_params.append((obs.getattr('4_scale_0'), obs.getattr('4_zero_point_0')))
else:
graph_params.append((obs.getattr('3_scale_0'), obs.getattr('3_zero_point_0')))
self.assertEqual(ref_qparams, graph_params)
class TestQuantizeDynamicJitOps(QuantizationTestCase):
""" Test graph mode post training dynamic quantization works
for individual ops end to end.
"""
@override_qengines
def test_quantized_linear_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5).float()
def forward(self, x):
return self.fc(x)
x = torch.rand(5, 5)
for tracing in [True, False]:
model = self.checkGraphModeOp(M(), x, "quantized::linear_dynamic", tracing=tracing, dynamic=True)
class TestQuantizeJitJit(JitTestCase):
def _test_lower_graph_impl(self, model, data):
model.qconfig = torch.quantization.default_qconfig
model = torch.quantization.prepare(model)
model = torch.quantization.convert(model)
outputs = model(data)
input_names = ["x"]
def export_to_onnx(model, input, input_names):
outputs = model(input)
traced = torch.jit.trace(model, input)
buf = io.BytesIO()
torch.jit.save(traced, buf)
buf.seek(0)
model = torch.jit.load(buf)
f = io.BytesIO()
torch.onnx.export(model, input, f, input_names=input_names, example_outputs=outputs,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK)
onnx_model = export_to_onnx(model, data, input_names)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
def test_lower_graph_linear(self):
model = torch.quantization.QuantWrapper(torch.nn.Linear(5, 10, bias=True)).to(dtype=torch.float)
data_numpy = np.random.rand(1, 2, 5).astype(np.float32)
data = torch.from_numpy(data_numpy).to(dtype=torch.float)
self._test_lower_graph_impl(model, data)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
def test_lower_graph_conv2d(self):
model = torch.quantization.QuantWrapper(torch.nn.Conv2d(3, 5, 2, bias=True)).to(dtype=torch.float)
data_numpy = np.random.rand(1, 3, 6, 6).astype(np.float32)
data = torch.from_numpy(data_numpy).to(dtype=torch.float)
self._test_lower_graph_impl(model, data)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
@unittest.skip("onnx opset9 does not support quantize_per_tensor and caffe2 \
does not support conv3d")
def test_lower_graph_conv3d(self):
model = torch.quantization.QuantWrapper(torch.nn.Conv3d(3, 5, 2, bias=True)).to(dtype=torch.float)
data_numpy = np.random.rand(1, 3, 6, 6, 6).astype(np.float32)
data = torch.from_numpy(data_numpy).to(dtype=torch.float)
self._test_lower_graph_impl(model, data)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
def test_rnn_cell_quantized(self):
d_in, d_hid = 2, 2
for cell in [
torch.nn.LSTMCell(d_in, d_hid).float(),
torch.nn.GRUCell(d_in, d_hid).float(),
torch.nn.RNNCell(d_in, d_hid).float(),
]:
if isinstance(cell, torch.nn.LSTMCell):
num_chunks = 4
elif isinstance(cell, torch.nn.GRUCell):
num_chunks = 3
elif isinstance(cell, torch.nn.RNNCell):
num_chunks = 1
# 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]]
vals = vals[:d_hid * num_chunks]
cell.weight_ih = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float),
requires_grad=False)
cell.weight_hh = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float),
requires_grad=False)
ref = copy.deepcopy(cell)
cell = torch.jit.quantized.quantize_rnn_cell_modules(cell)
x = torch.tensor([[100, -155],
[-155, 100],
[100, -155]], dtype=torch.float)
h0_vals = [[-155, 100],
[-155, 155],
[100, -155]]
hx = torch.tensor(h0_vals, dtype=torch.float)
if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
cx = torch.tensor(h0_vals, dtype=torch.float)
hiddens = (hx, cx)
else:
hiddens = hx
if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x, hiddens):
# type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]
return self.cell(x, hiddens)
else:
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x, hiddens):
# type: (torch.Tensor, torch.Tensor) -> torch.Tensor
return self.cell(x, hiddens)
cell = ScriptWrapper(cell)
outs = cell(x, hiddens)
cell = self.getExportImportCopyWithPacking(cell)
outs = cell(x, hiddens)
ref_outs = ref(x, hiddens)
self.assertEqual(len(outs), len(ref_outs))
for out, ref_out in zip(outs, ref_outs):
torch.testing.assert_allclose(out, ref_out)
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
'Quantized RNN requires FBGEMM. FBGEMM is only optimized for CPUs'
' with instruction set support avx2 or newer.')
def test_rnn_quantized(self):
d_in, d_hid = 2, 2
for cell in [
torch.nn.LSTM(d_in, d_hid).float(),
torch.nn.GRU(d_in, d_hid).float(),
]:
# 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
elif isinstance(cell, torch.nn.GRU):
num_chunks = 3
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)
cell_int8 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.int8)
cell_fp16 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.float16)
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)
elif isinstance(ref, torch.nn.GRU):
hiddens = hx
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)
for out, ref in zip(final_hiddens_int8, ref_hid):
torch.testing.assert_allclose(out, ref)
# Compare fp16 quantized to unquantized
output_fp16, final_hiddens_fp16 = cell_fp16(x, hiddens)
torch.testing.assert_allclose(output_fp16, ref_out)
for out, ref in zip(final_hiddens_fp16, ref_hid):
torch.testing.assert_allclose(out, ref)
def compare_quantized_unquantized(ScriptWrapper, cell):
wrapper = ScriptWrapper(cell)
# Compare quantize scripted module to unquantized
script_out, script_hid = wrapper(x, hiddens)
torch.testing.assert_allclose(script_out, ref_out)
for out, ref in zip(script_hid, ref_hid):
torch.testing.assert_allclose(out, ref)
# Compare export/import to unquantized
export_import_wrapper = self.getExportImportCopyWithPacking(wrapper)
ei_out, ei_hid = export_import_wrapper(x, hiddens)
torch.testing.assert_allclose(ei_out, ref_out)
for out, ref in zip(ei_hid, ref_hid):
torch.testing.assert_allclose(out, ref)
if isinstance(cell, torch.jit.quantized.QuantizedGRU):
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x, hiddens):
# type: (torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
return self.cell(x, hiddens)
compare_quantized_unquantized(ScriptWrapper, cell)
elif isinstance(cell, torch.jit.quantized.QuantizedLSTM):
for cell in [cell_int8, cell_fp16]:
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
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)
compare_quantized_unquantized(ScriptWrapper, cell)
if 'fbgemm' in torch.backends.quantized.supported_engines:
# Suppression: using deprecated quant api
@suppress_warnings
def test_quantization_modules(self):
K1, N1 = 2, 2
class FooBar(torch.nn.Module):
def __init__(self):
super(FooBar, self).__init__()
self.linear1 = torch.nn.Linear(K1, N1).float()
def forward(self, x):
x = self.linear1(x)
return x
fb = FooBar()
fb.linear1.weight = torch.nn.Parameter(
torch.tensor([[-150, 100], [100, -150]], dtype=torch.float), requires_grad=False)
fb.linear1.bias = torch.nn.Parameter(torch.zeros_like(fb.linear1.bias), requires_grad=False)
x = (torch.rand(1, K1).float() - 0.5) / 10.0
value = torch.tensor([[100, -150]], dtype=torch.float)
y_ref = fb(value)
fb_int8 = torch.jit.quantized.quantize_linear_modules(fb)
traced_int8 = torch.jit.trace(fb_int8, (x,))
fb_int8 = self.getExportImportCopyWithPacking(traced_int8)
y_int8 = fb_int8(value)
fb_fp16 = torch.jit.quantized.quantize_linear_modules(fb, torch.float16)
traced_fp16 = torch.jit.trace(fb_fp16, (x,))
fb_fp16 = self.getExportImportCopyWithPacking(traced_fp16)
y_fp16 = fb_fp16(value)
torch.testing.assert_allclose(y_int8, y_ref, rtol=0.0001, atol=1e-3)
torch.testing.assert_allclose(y_fp16, y_ref, rtol=0.0001, atol=1e-3)
def _test_pickle_checkpoint_qtensor(self, device):
with TemporaryFileName() as fname:
class M(torch.jit.ScriptModule):
__constants__ = ['fname']
def __init__(self):
super(M, self).__init__()
self.fname = fname
@torch.jit.script_method
def forward(self, x, y):
torch.save((x, y), self.fname)
return y
q = torch.quantize_per_tensor(
torch.rand(2, 3, dtype=torch.float), scale=0.1, zero_point=10, dtype=torch.quint8).to(device)
qc = torch.quantize_per_channel(
torch.rand(2, 3, dtype=torch.float),
scales=torch.tensor([0.1, 0.5, 0.01]),
zero_points=torch.tensor([10, 0, 20]),
axis=1, dtype=torch.quint8).to(device)
m = M()
m(q, qc)
with open(fname, "rb") as handle:
loaded_q, loaded_qc = torch.load(fname)
self.assertEqual(loaded_q, q)
self.assertEqual(loaded_qc, qc)
def test_pickle_checkpoint_qtensor(self):
self._test_pickle_checkpoint_qtensor('cpu')
def test_serialize_qtensor(self):
class SimpleQTensor(torch.jit.ScriptModule):
def __init__(self, per_channel):
super(SimpleQTensor, self).__init__()
x = torch.rand(5, 5).float()
if not per_channel:
x_q = torch.quantize_per_tensor(x, 0.2, 10, torch.quint8)
else:
s = torch.rand(5, dtype=torch.float64) + 0.1
zp = torch.randint(5, 15, (5,))
x_q = torch.quantize_per_channel(x, s, zp, 1, torch.quint8)
self.register_buffer('x', x_q)
@torch.jit.script_method
def forward(self):
return self.x
for per_channel in [False, True]:
model = SimpleQTensor(per_channel)
buffer = io.BytesIO()
torch.jit.save(model, buffer)
buffer.seek(0)
model_loaded = torch.jit.load(buffer)
self.assertEqual(model_loaded(), model())
@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines, "requires FBGEMM")
def test_erase_class_tensor_shapes(self):
class Linear(torch.nn.Module):
def __init__(self, in_features, out_features):
super(Linear, self).__init__()
qweight = torch._empty_affine_quantized(
[out_features, in_features], scale=1, zero_point=0,
dtype=torch.qint8)
self._packed_weight = torch.ops.quantized.linear_prepack(qweight)
@torch.jit.export
def __getstate__(self):
return (torch.ops.quantized.linear_unpack(self._packed_weight)[0], self.training)
def forward(self):
return self._packed_weight
@torch.jit.export
def __setstate__(self, state):
self._packed_weight = torch.ops.quantized.linear_prepack(state[0])
self.training = state[1]
@property
def weight(self):
return torch.ops.quantized.linear_unpack(self._packed_weight)[0]
@weight.setter
def weight(self, w):
self._packed_weight = torch.ops.quantized.linear_prepack(w)
with torch.jit._disable_emit_hooks():
x = torch.jit.script(Linear(10, 10))
torch._C._jit_pass_erase_shape_information(x.graph)
class TestQuantizeJit(QuantizationTestCase):
@override_qengines
def test_single_linear(self):
r"""Compare the result of quantizing single linear layer in
eager mode and graph mode
"""
# eager mode
annotated_linear_model = AnnotatedSingleLayerLinearModel(torch.backends.quantized.engine).eval()
linear_model = SingleLayerLinearModel().eval()
# 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 = {'': get_default_qconfig(torch.backends.quantized.engine)}
model_traced = torch.jit.trace(linear_model, self.calib_data[0][0])
model_script = torch.jit.script(linear_model)
result_eager = model_eager(self.calib_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_jit(
model_under_test,
qconfig_dict,
test_only_eval_fn,
[self.calib_data],
inplace=False)
self.assertEqual(model_quantized(self.calib_data[0][0]), result_eager)
@skipIfNoFBGEMM
def test_observer_with_ignored_function(self):
r"""Test observers with ignored function and make sure it works in
graph mode
"""
# eager mode
annotated_linear_model = AnnotatedSingleLayerLinearModel('fbgemm').eval()
for qconfig in [
QConfig(
activation=default_observer,
weight=default_weight_observer),
QConfig(
activation=default_histogram_observer,
weight=default_weight_observer),
QConfig(
activation=default_observer,
weight=default_per_channel_weight_observer),
]:
annotated_linear_model.qconfig = qconfig
linear_model = SingleLayerLinearModel().eval()
# 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}
model_traced = torch.jit.trace(linear_model, self.calib_data[0][0])
model_script = torch.jit.script(linear_model)
result_eager = model_eager(self.calib_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_jit(
model_under_test,
qconfig_dict,
test_only_eval_fn,
[self.calib_data],
inplace=False)
self.assertEqual(model_quantized(self.calib_data[0][0]), result_eager)
@override_qengines
def test_conv(self):
r"""Compare the result of quantizing conv layer in
eager mode and graph mode
"""
# eager mode
annotated_conv_model = AnnotatedConvModel(torch.backends.quantized.engine).eval()
conv_model = ConvModel().eval()
# copy the weight from eager mode so that we can
# compare the result of the two quantized models later
conv_model.conv.weight = torch.nn.Parameter(annotated_conv_model.conv.weight.detach())
model_eager = quantize(annotated_conv_model, default_eval_fn, self.img_data)
qconfig_dict = {'': get_default_qconfig(torch.backends.quantized.engine)}
model_traced = torch.jit.trace(conv_model, self.img_data[0][0])
model_script = torch.jit.script(conv_model)
result_eager = model_eager(self.img_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_jit(
model_under_test,
qconfig_dict,
default_eval_fn,
[self.img_data],
inplace=False)
self.assertEqual(model_quantized(self.img_data[0][0]), result_eager)
@override_qengines
def test_conv_bn(self):
r"""Compare the result of quantizing conv + bn layer in
eager mode and graph mode
"""
# eager mode
conv_model = AnnotatedConvBnModel().eval()
conv_model_to_script = ConvBnModel().eval()
# copy the weight from eager mode so that we can
# compare the result of the two quantized models later
conv_model_to_script.conv.weight = torch.nn.Parameter(conv_model.conv.weight.detach())
fuse_modules(conv_model, ['conv', 'bn'], inplace=True)
model_eager = quantize(conv_model, default_eval_fn,
self.img_data)
qconfig_dict = {
'': default_qconfig
}
model_script = quantize_jit(
torch.jit.script(conv_model_to_script),
qconfig_dict,
default_eval_fn,
[self.img_data],
inplace=False)
result_eager = model_eager(self.img_data[0][0])
result_script = model_script(self.img_data[0][0])
self.assertEqual(result_eager, result_script)
@override_qengines
def test_nested(self):
# Eager mode
eager_model = AnnotatedNestedModel(torch.backends.quantized.engine).eval()
# Graph mode
script_model = NestedModel().eval()
# Copy weights for eager_model
script_model.sub1.fc.weight = torch.nn.Parameter(eager_model.sub1.fc.weight.detach())
script_model.sub1.fc.bias = torch.nn.Parameter(eager_model.sub1.fc.bias.detach())
script_model.sub2.fc1.weight = torch.nn.Parameter(eager_model.sub2.fc1.module.weight.detach())
script_model.sub2.fc1.bias = torch.nn.Parameter(eager_model.sub2.fc1.module.bias.detach())
script_model.sub2.fc2.weight = torch.nn.Parameter(eager_model.sub2.fc2.weight.detach())
script_model.sub2.fc2.bias = torch.nn.Parameter(eager_model.sub2.fc2.bias.detach())
script_model.fc3.weight = torch.nn.Parameter(eager_model.fc3.module.weight.detach())
script_model.fc3.bias = torch.nn.Parameter(eager_model.fc3.module.bias.detach())
model_eager = quantize(eager_model, test_only_eval_fn, self.calib_data)
qconfig_dict = {
'sub2.fc1': default_per_channel_qconfig if qengine_is_fbgemm() else default_qconfig,
'fc3': default_qconfig
}
model_traced = torch.jit.trace(script_model, self.calib_data[0][0])
model_script = torch.jit.script(script_model)
result_eager = model_eager(self.calib_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_jit(
model_under_test,
qconfig_dict,
test_only_eval_fn,
[self.calib_data],
inplace=False)
self.assertEqual(model_quantized(self.calib_data[0][0]), result_eager)
@override_qengines
def test_skip_quant(self):
""" Test None qconfig
"""
# Eager mode
eager_model = AnnotatedSkipQuantModel(torch.backends.quantized.engine).eval()
# Graph mode
script_model = SkipQuantModel().eval()
# Copy weights for eager_model
script_model.sub.fc1.weight = torch.nn.Parameter(eager_model.sub.module.fc1.weight.detach())
script_model.sub.fc1.bias = torch.nn.Parameter(eager_model.sub.module.fc1.bias.detach())
script_model.sub.fc2.weight = torch.nn.Parameter(eager_model.sub.module.fc2.weight.detach())
script_model.sub.fc2.bias = torch.nn.Parameter(eager_model.sub.module.fc2.bias.detach())
script_model.fc.weight = torch.nn.Parameter(eager_model.fc.weight.detach())
script_model.fc.bias = torch.nn.Parameter(eager_model.fc.bias.detach())
eager_model.fuse_modules()
model_eager = quantize(eager_model, test_only_eval_fn, self.calib_data)
qconfig_dict = {
'': get_default_qconfig(torch.backends.quantized.engine),
'fc': None
}
model_traced = torch.jit.trace(script_model, self.calib_data[0][0])
model_script = torch.jit.script(script_model)
result_eager = model_eager(self.calib_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_jit(
model_under_test,
qconfig_dict,
test_only_eval_fn,
[self.calib_data],
inplace=False)
self.assertEqual(model_quantized(self.calib_data[0][0]), result_eager)
@override_qengines
def test_single_linear_dynamic(self):
r"""Compare the result of dynamic quantization of single linear layer in
eager mode and graph mode.
"""
if qengine_is_qnnpack():
# eager mode
annotated_linear_model = AnnotatedSingleLayerLinearModel('qnnpack').eval()
linear_model = SingleLayerLinearModel().eval()
# 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())
qconfig_dict = {'': default_dynamic_qconfig}
model_eager = quantize_dynamic(annotated_linear_model, qconfig_dict)
model_traced = torch.jit.trace(linear_model, self.calib_data[0][0])
model_script = torch.jit.script(linear_model)
result_eager = model_eager(self.calib_data[0][0])
for model_under_test in [model_traced, model_script]:
model_quantized = quantize_dynamic_jit(
model_under_test,
qconfig_dict)
self.assertEqual(model_quantized(self.calib_data[0][0]), result_eager)
# Check to make sure choose_qparams->quant->dequant->linear is numerically
# equivalent to the final quantized model.
model_fake_quantized = quantize_dynamic_jit(
model_under_test,
qconfig_dict,
debug=True)
self.assertEqual(model_fake_quantized(self.calib_data[0][0]), result_eager)
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