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test_quantized_module.py
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test_quantized_module.py
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import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nnq_fused
import torch.nn.intrinsic.quantized._reference as nniqr
import torch.nn.quantized as nnq
import torch.nn.quantized._reference as nnqr
import torch.nn.quantized.dynamic as nnqd
import torch.nn.functional as F
import torch.quantization
from torch.quantization import (
default_float_qparams_observer,
PerChannelMinMaxObserver
)
from torch.testing._internal.common_quantization import (
QuantizationTestCase,
prepare_dynamic,
_make_conv_test_input,
skipIfNoFBGEMM,
lengths_to_offsets
)
from torch.testing._internal.common_quantized import (
_calculate_dynamic_qparams,
override_quantized_engine,
override_qengines,
)
from hypothesis import assume, given
from hypothesis import strategies as st
import torch.testing._internal.hypothesis_utils as hu
hu.assert_deadline_disabled()
import io
import numpy as np
import itertools
"""
Note that tests in this file are just API test, to make sure we wrapped the
quantized operator implementations correctly in the user facing APIs, these are
not correctness test for the underlying quantized operators. For correctness
test please see `test/quantization/test_quantized_op.py`.
"""
class TestStaticQuantizedModule(QuantizationTestCase):
def test_relu(self):
relu_module = nn.ReLU()
relu6_module = nnq.ReLU6()
x = torch.arange(-10, 10, dtype=torch.float)
y_ref = torch.relu(x)
y6_ref = torch.nn.modules.ReLU6()(x)
qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.qint32)
qy = relu_module(qx)
qy6 = relu6_module(qx)
self.assertEqual(y_ref, qy.dequantize(),
msg="ReLU module API failed")
self.assertEqual(y6_ref, qy6.dequantize(),
msg="ReLU6 module API failed")
@override_qengines
def test_linear_api(self):
"""test API functionality for nn.quantized.linear and nn.intrinsic.quantized.linear_relu"""
options = itertools.product(
[1, 5],
[16, 32],
[4, 8],
[True, False],
[True, False],
[True, False],
[True, False])
for (batch_size, in_features, out_features, use_bias,
use_fused, per_channel, reference) in options:
self._test_linear_api_impl(
batch_size, in_features, out_features, use_bias, use_fused,
per_channel, reference)
def _test_linear_api_impl(self, batch_size, in_features, out_features, use_bias, use_fused, per_channel, reference):
if torch.backends.quantized.engine == 'qnnpack':
per_channel = False
# (use_fused, reference) -> quantized class
class_map = {
(True, True) : nniqr.LinearReLU,
(True, False) : nnq_fused.LinearReLU,
(False, True) : nnqr.Linear,
(False, False) : nnq.Linear,
}
W = torch.rand(out_features, in_features).float()
if per_channel:
scale_tensor = torch.ones(out_features, dtype=torch.double)
zero_point_tensor = torch.zeros(out_features, dtype=torch.long)
for i in range(len(scale_tensor)):
scale_tensor[i] = (i + 1.0) / 255.0
W_q = torch.quantize_per_channel(W, scales=scale_tensor,
zero_points=zero_point_tensor,
axis=0, dtype=torch.qint8)
else:
W_q = torch.quantize_per_tensor(W, 0.1, 4, torch.qint8)
X = torch.rand(batch_size, in_features).float()
X_q = torch.quantize_per_tensor(X, 0.2, 10, torch.quint8)
B = torch.rand(out_features).float() if use_bias else None
scale = 0.5
zero_point = 3
qlinear = class_map[(use_fused, reference)](in_features, out_features)
qlinear_copy = qlinear # deepcopy does not work right now
# qlinear_copy = copy.deepcopy(qlinear)
self.checkScriptable(qlinear_copy, [[X_q]], check_save_load=True)
# Run module with default-initialized parameters.
# This tests that the constructor is correct.
qlinear(X_q)
qlinear.set_weight_bias(W_q, B)
# Simple round-trip test to ensure weight()/set_weight() API
self.assertEqual(qlinear.weight(), W_q, atol=1e-5, rtol=0)
# testing packed param implementation
qlinear.scale = float(scale)
qlinear.zero_point = int(zero_point)
Z_q = qlinear(X_q)
# Check if the module implementation matches calling the
# ops directly
if reference:
weight = qlinear._qweight
bias = qlinear._bias
weight_dequant = weight.dequantize()
X_q_dq = X_q.dequantize()
Z_ref = F.linear(X_q_dq, weight_dequant, bias)
if use_fused:
Z_ref = F.relu(Z_ref, inplace=True)
Z_ref = torch.quantize_per_tensor(Z_ref, scale, zero_point, torch.quint8)
else:
W_pack = qlinear._packed_params._packed_params
if use_fused:
Z_ref = torch.ops.quantized.linear_relu(X_q, W_pack, scale, zero_point)
else:
Z_ref = torch.ops.quantized.linear(X_q, W_pack, scale, zero_point)
self.assertEqual(Z_ref, Z_q)
self.assertTrue(
("QuantizedLinearReLU" if use_fused else "QuantizedLinear") in str(qlinear))
# Test serialization of quantized Linear Module using state_dict
model_dict = qlinear.state_dict()
b = io.BytesIO()
torch.save(model_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in model_dict:
if isinstance(model_dict[key], torch._C.ScriptObject):
assert isinstance(loaded_dict[key], torch._C.ScriptObject)
w_model, b_model = torch.ops.quantized.linear_unpack(model_dict[key])
w_loaded, b_loaded = torch.ops.quantized.linear_unpack(loaded_dict[key])
self.assertEqual(w_model, w_loaded)
self.assertEqual(b_model, b_loaded)
else:
self.assertEqual(model_dict[key], loaded_dict[key])
loaded_qlinear = class_map[(use_fused, reference)](
in_features, out_features)
loaded_qlinear.load_state_dict(loaded_dict)
if reference:
self.assertEqual(qlinear._qweight, loaded_qlinear._qweight)
self.assertEqual(qlinear._bias, loaded_qlinear._bias)
else:
linear_unpack = torch.ops.quantized.linear_unpack
self.assertEqual(linear_unpack(qlinear._packed_params._packed_params),
linear_unpack(loaded_qlinear._packed_params._packed_params))
self.assertEqual(qlinear.scale, loaded_qlinear.scale)
self.assertEqual(qlinear.zero_point, loaded_qlinear.zero_point)
# make sure loaded_qlinear has the same dir as qlinear since
# scripting the module will add __overloads__ to __dict__
self.checkScriptable(loaded_qlinear, [[X_q]], check_save_load=True)
self.assertTrue(dir(qlinear) == dir(loaded_qlinear))
self.assertEqual(qlinear._weight_bias(), loaded_qlinear._weight_bias())
if not reference:
self.assertEqual(qlinear._weight_bias(), torch.ops.quantized.linear_unpack(qlinear._packed_params._packed_params))
Z_q2 = loaded_qlinear(X_q)
self.assertEqual(Z_q, Z_q2)
b = io.BytesIO()
torch.save(qlinear, b)
b.seek(0)
loaded = torch.load(b)
self.assertEqual(qlinear.weight(), loaded.weight())
self.assertEqual(qlinear.scale, loaded.scale)
self.assertEqual(qlinear.zero_point, loaded.zero_point)
# Test JIT
self.checkScriptable(qlinear, [[X_q]], check_save_load=True)
# Test from_float.
float_linear = torch.nn.Linear(in_features, out_features).float()
float_linear.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(float_linear, inplace=True)
float_linear(X.float())
# Sequential allows swapping using "convert".
quantized_float_linear = torch.nn.Sequential(float_linear)
quantized_float_linear = torch.quantization.convert(quantized_float_linear, inplace=True)
# Smoke test to make sure the module actually runs
quantized_float_linear(X_q)
# Smoke test extra_repr
self.assertTrue('QuantizedLinear' in str(quantized_float_linear))
def test_quant_dequant_api(self):
r = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.float)
scale, zero_point, dtype = 1.0, 2, torch.qint8
# testing Quantize API
qr = torch.quantize_per_tensor(r, scale, zero_point, dtype)
quant_m = nnq.Quantize(scale, zero_point, dtype)
qr2 = quant_m(r)
self.assertEqual(qr, qr2)
# testing Dequantize API
rqr = qr.dequantize()
dequant_m = nnq.DeQuantize()
rqr2 = dequant_m(qr2)
self.assertEqual(rqr, rqr2)
def _test_conv_api_impl(
self, module_name, qconv_module, conv_module, batch_size,
in_channels_per_group, input_feature_map_size, out_channels_per_group,
groups, kernel_size, stride, padding, dilation, X_scale, X_zero_point,
W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_fused,
use_channelwise,
):
for i in range(len(kernel_size)):
assume(input_feature_map_size[i] + 2 * padding[i]
>= dilation[i] * (kernel_size[i] - 1) + 1)
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
(X, X_q, W, W_q, b) = _make_conv_test_input(
batch_size, in_channels_per_group, input_feature_map_size,
out_channels_per_group, groups, kernel_size, X_scale, X_zero_point,
W_scale, W_zero_point, use_bias, use_channelwise)
qconv_module.set_weight_bias(W_q, b)
qconv_module.scale = Y_scale
qconv_module.zero_point = Y_zero_point
if use_fused:
conv_module[0].weight.data = W
if use_bias:
conv_module[0].bias.data = b
else:
conv_module.weight.data = W
if use_bias:
conv_module.bias.data = b
# Test members
self.assertTrue(module_name in str(qconv_module))
self.assertTrue(hasattr(qconv_module, '_packed_params'))
self.assertTrue(hasattr(qconv_module, 'scale'))
self.assertTrue(hasattr(qconv_module, 'zero_point'))
# Test properties
self.assertEqual(W_q, qconv_module.weight())
if use_bias:
self.assertEqual(b, qconv_module.bias())
self.assertEqual(Y_scale, qconv_module.scale)
self.assertEqual(Y_zero_point, qconv_module.zero_point)
# Test forward
Y_exp = conv_module(X)
Y_exp = torch.quantize_per_tensor(
Y_exp, scale=Y_scale, zero_point=Y_zero_point, dtype=torch.quint8)
Y_act = qconv_module(X_q)
# Make sure the results match
# assert_array_almost_equal compares using the following formula:
# abs(desired-actual) < 1.5 * 10**(-decimal)
# (https://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.assert_almost_equal.html)
# We use decimal = 0 to ignore off-by-1 differences between reference
# and test. Off-by-1 differences arise due to the order of round and
# zero_point addition operation, i.e., if addition followed by round is
# used by reference and round followed by addition is used by test, the
# results may differ by 1.
# For example, the result of round(2.5) + 1 is 3 while round(2.5 + 1) is
# 4 assuming the rounding mode is round-to-nearest, ties-to-even.
np.testing.assert_array_almost_equal(
Y_exp.int_repr().numpy(), Y_act.int_repr().numpy(), decimal=0)
# Test serialization of quantized Conv Module using state_dict
model_dict = qconv_module.state_dict()
self.assertEqual(model_dict['weight'], W_q)
if use_bias:
self.assertEqual(model_dict['bias'], b)
bytes_io = io.BytesIO()
torch.save(model_dict, bytes_io)
bytes_io.seek(0)
loaded_dict = torch.load(bytes_io)
for key in loaded_dict:
self.assertEqual(model_dict[key], loaded_dict[key])
loaded_qconv_module = type(qconv_module)(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, use_bias, padding_mode="zeros")
loaded_qconv_module.load_state_dict(loaded_dict)
self.assertTrue(dir(loaded_qconv_module) == dir(qconv_module))
self.assertTrue(module_name in str(loaded_qconv_module))
self.assertTrue(hasattr(loaded_qconv_module, '_packed_params'))
self.assertTrue(hasattr(loaded_qconv_module, '_weight_bias'))
self.assertEqual(qconv_module.weight(), loaded_qconv_module.weight())
if use_bias:
self.assertEqual(qconv_module.bias(), loaded_qconv_module.bias())
self.assertEqual(qconv_module.scale, loaded_qconv_module.scale)
self.assertEqual(qconv_module.zero_point,
loaded_qconv_module.zero_point)
Y_loaded = loaded_qconv_module(X_q)
np.testing.assert_array_almost_equal(
Y_exp.int_repr().numpy(), Y_loaded.int_repr().numpy(), decimal=0)
b = io.BytesIO()
torch.save(qconv_module, b)
b.seek(0)
loaded_conv = torch.load(b)
self.assertEqual(loaded_conv.bias(), qconv_module.bias())
self.assertEqual(loaded_conv.scale, qconv_module.scale)
self.assertEqual(loaded_conv.zero_point,
qconv_module.zero_point)
# JIT testing
self.checkScriptable(
qconv_module, [[X_q]],
check_save_load=True)
# Test from_float
fused_conv_module = torch.nn.intrinsic._FusedModule(conv_module)
fused_conv_module.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(fused_conv_module, inplace=True)
fused_conv_module(X.float())
converted_qconv_module = fused_conv_module
torch.quantization.convert(converted_qconv_module, inplace=True)
# Smoke test to make sure the module actually runs
if use_bias:
if use_fused:
self.assertEqual(conv_module[0].bias,
converted_qconv_module[0].bias())
else:
self.assertEqual(conv_module.bias,
converted_qconv_module[0].bias())
# Smoke test extra_repr
self.assertTrue(module_name in str(converted_qconv_module))
@given(batch_size=st.integers(1, 3),
in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
length=st.integers(4, 16),
out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
groups=st.integers(1, 4),
kernel=st.integers(1, 7),
stride=st.integers(1, 2),
pad_mode=st.sampled_from(['zeros', 'reflect']),
pad=st.integers(0, 2),
dilation=st.integers(1, 2),
X_scale=st.floats(1.2, 1.6),
X_zero_point=st.integers(0, 4),
W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2),
W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2),
Y_scale=st.floats(4.2, 5.6),
Y_zero_point=st.integers(0, 4),
use_bias=st.booleans(),
use_fused=st.booleans(),
use_channelwise=st.booleans())
@override_qengines
def test_conv1d_api(
self, batch_size, in_channels_per_group, length, out_channels_per_group,
groups, kernel, stride, pad_mode, pad, dilation,
X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point,
use_bias, use_fused, use_channelwise
):
# Tests the correctness of the conv2d module.
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
input_feature_map_size = (length,)
kernel_size = (kernel, )
stride = (stride, )
pad = (pad, )
dilation = (dilation, )
if torch.backends.quantized.engine == 'qnnpack':
use_channelwise = False
if use_fused:
module_name = "QuantizedConvReLU1d"
qconv_module = nnq_fused.ConvReLU1d(
in_channels, out_channels, kernel, stride, pad,
dilation, groups, use_bias, padding_mode=pad_mode)
else:
module_name = "QuantizedConv1d"
qconv_module = nnq.Conv1d(
in_channels, out_channels, kernel, stride, pad,
dilation, groups, use_bias, padding_mode=pad_mode)
conv_module = nn.Conv1d(
in_channels, out_channels, kernel, stride, pad,
dilation, groups, use_bias, padding_mode=pad_mode)
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU1d(conv_module, relu_module)
conv_module = conv_module.float()
self._test_conv_api_impl(
module_name, qconv_module, conv_module, batch_size,
in_channels_per_group, input_feature_map_size,
out_channels_per_group, groups, kernel_size, stride, pad,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise)
@given(batch_size=st.integers(1, 3),
in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
H=st.integers(4, 16),
W=st.integers(4, 16),
out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
groups=st.integers(1, 4),
kernel_h=st.integers(1, 7),
kernel_w=st.integers(1, 7),
stride_h=st.integers(1, 2),
stride_w=st.integers(1, 2),
pad_h=st.integers(0, 2),
pad_w=st.integers(0, 2),
pad_mode=st.sampled_from(['zeros', 'reflect']),
dilation=st.integers(1, 2),
X_scale=st.floats(1.2, 1.6),
X_zero_point=st.integers(0, 4),
W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2),
W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2),
Y_scale=st.floats(4.2, 5.6),
Y_zero_point=st.integers(0, 4),
use_bias=st.booleans(),
use_fused=st.booleans(),
use_channelwise=st.booleans())
@override_qengines
def test_conv2d_api(
self, batch_size, in_channels_per_group, H, W, out_channels_per_group,
groups, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, pad_mode,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise
):
# Tests the correctness of the conv2d module.
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
input_feature_map_size = (H, W)
kernel_size = (kernel_h, kernel_w)
stride = (stride_h, stride_w)
padding = (pad_h, pad_w)
dilation = (dilation, dilation)
if use_fused:
module_name = "QuantizedConvReLU2d"
qconv_module = nnq_fused.ConvReLU2d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
else:
module_name = "QuantizedConv2d"
qconv_module = nnq.Conv2d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
conv_module = nn.Conv2d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU2d(conv_module, relu_module)
conv_module = conv_module.float()
self._test_conv_api_impl(
module_name, qconv_module, conv_module, batch_size,
in_channels_per_group, input_feature_map_size,
out_channels_per_group, groups, kernel_size, stride, padding,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise)
@skipIfNoFBGEMM
@given(batch_size=st.integers(1, 3),
in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]),
D=st.integers(3, 6),
H=st.integers(3, 6),
W=st.integers(3, 6),
out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]),
groups=st.integers(1, 4),
kernel_d=st.integers(1, 3),
kernel_h=st.integers(1, 3),
kernel_w=st.integers(1, 3),
stride_d=st.integers(1, 2),
stride_h=st.integers(1, 2),
stride_w=st.integers(1, 2),
pad_d=st.integers(0, 1),
pad_h=st.integers(0, 1),
pad_w=st.integers(0, 1),
pad_mode=st.sampled_from(['zeros', 'reflect']),
dilation=st.integers(1, 2),
X_scale=st.floats(1.2, 1.6),
X_zero_point=st.integers(0, 4),
W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2),
W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2),
Y_scale=st.floats(4.2, 5.6),
Y_zero_point=st.integers(0, 4),
use_bias=st.booleans(),
use_fused=st.booleans(),
use_channelwise=st.booleans())
def test_conv3d_api(
self, batch_size, in_channels_per_group, D, H, W,
out_channels_per_group, groups, kernel_d, kernel_h, kernel_w,
stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, pad_mode, dilation,
X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point,
use_bias, use_channelwise, use_fused,
):
# Tests the correctness of the conv3d module.
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
input_feature_map_size = (D, H, W)
kernel_size = (kernel_d, kernel_h, kernel_w)
stride = (stride_d, stride_h, stride_w)
padding = (pad_d, pad_h, pad_w)
dilation = (dilation, dilation, dilation)
with override_quantized_engine('fbgemm'):
if use_fused:
module_name = "QuantizedConvReLU3d"
qconv_module = nnq_fused.ConvReLU3d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
else:
module_name = "QuantizedConv3d"
qconv_module = nnq.Conv3d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
conv_module = nn.Conv3d(
in_channels, out_channels, kernel_size, stride, padding,
dilation, groups, use_bias, padding_mode=pad_mode)
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU3d(conv_module, relu_module)
conv_module = conv_module.float()
self._test_conv_api_impl(
module_name, qconv_module, conv_module, batch_size,
in_channels_per_group, input_feature_map_size,
out_channels_per_group, groups, kernel_size, stride, padding,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise)
def test_pool_api(self):
"""Tests the correctness of the pool module.
The correctness is defined against the functional implementation.
"""
N, C, H, W = 10, 10, 10, 3
kwargs = {
'kernel_size': 2,
'stride': None,
'padding': 0,
'dilation': 1
}
scale, zero_point = 1.0 / 255, 128
X = torch.randn(N, C, H, W, dtype=torch.float32)
qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point,
dtype=torch.quint8)
qX_expect = torch.nn.functional.max_pool2d(qX, **kwargs)
pool_under_test = torch.nn.quantized.MaxPool2d(**kwargs)
qX_hat = pool_under_test(qX)
self.assertEqual(qX_expect, qX_hat)
# JIT Testing
self.checkScriptable(pool_under_test, [[X]])
def test_batch_norm2d(self):
"""Tests the correctness of the batchnorm2d module.
The correctness is defined against the functional implementation.
"""
x = torch.randn((2, 4, 6, 8), dtype=torch.float)
float_mod = torch.nn.BatchNorm2d(4)
float_mod.training = False
y_ref = float_mod(x)
quant_ref = torch.quantize_per_tensor(y_ref, 1.0, 0, dtype=torch.quint8)
quant_mod = nnq.BatchNorm2d(4)
qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.quint8)
qy = quant_mod(qx)
self.assertEqual(quant_ref.int_repr().numpy(), qy.int_repr().numpy(),
msg="BatchNorm2d module API failed")
def test_batch_norm3d(self):
"""Tests the correctness of the batchnorm3d module.
The correctness is defined against the functional implementation.
"""
x = torch.randn((2, 4, 6, 8, 10), dtype=torch.float)
float_mod = torch.nn.BatchNorm3d(4)
float_mod.training = False
y_ref = float_mod(x)
quant_ref = torch.quantize_per_tensor(y_ref, 1.0, 0, dtype=torch.quint8)
quant_mod = nnq.BatchNorm3d(4)
qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.quint8)
qy = quant_mod(qx)
self.assertEqual(quant_ref.int_repr().numpy(), qy.int_repr().numpy(),
msg="BatchNorm3d module API failed")
def test_layer_norm(self):
"""Tests the correctness of the layernorm module.
The correctness is defined against the functional implementation.
"""
x_scale = 10.0 / 256
x_zero_point = 0
y_scale = 5.0 / 256
y_zero_point = 127
dims = (1, 4, 8)
X = (torch.randn(dims, dtype=torch.float) - 0.5) * 10
qX = torch.quantize_per_tensor(X, x_scale, x_zero_point, dtype=torch.quint8)
dqX = qX.dequantize()
float_mod = torch.nn.LayerNorm(dqX.size()[1:]).float()
float_mod.weight = torch.nn.Parameter(torch.rand(*dims[1:]))
float_mod.bias = torch.nn.Parameter(torch.rand(*dims[1:]))
dqY_ref = float_mod(dqX)
qY_ref = torch.quantize_per_tensor(
dqY_ref, y_scale, y_zero_point, dtype=torch.quint8)
quant_mod = nnq.LayerNorm(
qX.size()[1:], float_mod.weight, float_mod.bias, y_scale, y_zero_point)
qY = quant_mod(qX)
self.assertEqual(qY_ref.int_repr().numpy(), qY.int_repr().numpy(),
msg="LayerNorm module API failed, qY_ref\n{} vs qY\n{}"
.format(qY_ref, qY))
def test_group_norm(self):
"""Tests the correctness of the groupnorm module.
The correctness is defined against the functional implementation.
"""
x_scale = 10.0 / 256
x_zero_point = 0
y_scale = 5.0 / 256
y_zero_point = 127
dims = (1, 4, 8)
X = (torch.randn(dims, dtype=torch.float) - 0.5) * 10
qX = torch.quantize_per_tensor(X, x_scale, x_zero_point, dtype=torch.quint8)
dqX = qX.dequantize()
float_mod = torch.nn.GroupNorm(2, 4).float()
float_mod.weight = torch.nn.Parameter(torch.rand(dims[1]))
float_mod.bias = torch.nn.Parameter(torch.rand(dims[1]))
dqY_ref = float_mod(dqX)
qY_ref = torch.quantize_per_tensor(
dqY_ref, y_scale, y_zero_point, dtype=torch.quint8)
quant_mod = nnq.GroupNorm(
2, 2, float_mod.weight, float_mod.bias, y_scale, y_zero_point)
qY = quant_mod(qX)
self.assertEqual(qY_ref.int_repr().numpy(), qY.int_repr().numpy(),
msg="GroupNorm module API failed, qY_ref\n{} vs qY\n{}"
.format(qY_ref, qY))
def test_instance_norm(self):
"""Tests the correctness of the instancenorm{n}d modules.
The correctness is defined against the functional implementation.
"""
x_scale = 10.0 / 256
x_zero_point = 0
y_scale = 5.0 / 256
y_zero_point = 127
dims_to_modules = [
((1, 4, 8), torch.nn.InstanceNorm1d, nnq.InstanceNorm1d),
((1, 4, 8, 1), torch.nn.InstanceNorm2d, nnq.InstanceNorm2d),
((1, 4, 8, 1, 1), torch.nn.InstanceNorm3d, nnq.InstanceNorm3d),
]
for dim_to_modules in dims_to_modules:
dims, float_cls, q_cls = dim_to_modules
X = (torch.randn(dims, dtype=torch.float) - 0.5) * 10
qX = torch.quantize_per_tensor(
X, x_scale, x_zero_point, dtype=torch.quint8)
dqX = qX.dequantize()
float_mod = float_cls(dims[1]).float()
float_mod.weight = torch.nn.Parameter(torch.rand(dims[1]))
float_mod.bias = torch.nn.Parameter(torch.rand(dims[1]))
dqY_ref = float_mod(dqX)
qY_ref = torch.quantize_per_tensor(
dqY_ref, y_scale, y_zero_point, dtype=torch.quint8)
quant_mod = q_cls(
dims[1], float_mod.weight, float_mod.bias, y_scale,
y_zero_point)
qY = quant_mod(qX)
self.assertEqual(
qY_ref.int_repr().numpy(), qY.int_repr().numpy(),
msg="InstanceNorm module API failed, qY_ref\n{} vs qY\n{}"
.format(qY_ref, qY))
def _test_activation_module_impl(self, name, float_module_class, quantized_module_class, extra_kwargs):
"""Tests the correctness of the ELU module.
The correctness is defined against the functional implementation.
"""
x_scale = 10.0 / 256
x_zero_point = 0
y_scale = 5.0 / 256
y_zero_point = 127
alpha = 1.5
dims = (1, 4, 8)
X = (torch.randn(dims, dtype=torch.float) - 0.5) * 10
qX = torch.quantize_per_tensor(X, x_scale, x_zero_point, dtype=torch.quint8)
dqX = qX.dequantize()
float_mod = float_module_class(**extra_kwargs).float()
dqY_ref = float_mod(dqX)
qY_ref = torch.quantize_per_tensor(
dqY_ref, y_scale, y_zero_point, dtype=torch.quint8)
quant_mod = quantized_module_class(y_scale, y_zero_point, **extra_kwargs)
qY = quant_mod(qX)
self.assertEqual(qY_ref.int_repr().numpy(), qY.int_repr().numpy(),
msg="{} module API failed, qY_ref\n{} vs qY\n{}"
.format(name, qY_ref, qY))
def _test_leaky_relu_serialization(self):
scale_original = 10.0 / 256
zero_point_original = 1.0
quant_mod_original = nnq.LeakyReLU(scale_original, zero_point_original)
state_dict = quant_mod_original.state_dict()
scale_new = 5.0 / 256
zero_point_new = 2.0
quant_mod_new = nnq.LeakyReLU(scale_new, zero_point_new)
quant_mod_new.load_state_dict(state_dict)
self.assertEqual(quant_mod_original.scale, quant_mod_new.scale)
self.assertEqual(quant_mod_original.zero_point, quant_mod_new.zero_point)
def test_elu(self):
"""Tests the correctness of the ELU module.
The correctness is defined against the functional implementation.
"""
self._test_activation_module_impl("ELU", nn.ELU, nnq.ELU, {"alpha": 1.5})
def test_leaky_relu(self):
self._test_activation_module_impl("LeakyReLU", nn.LeakyReLU, nnq.LeakyReLU, {"negative_slope": 0.2})
self._test_leaky_relu_serialization()
def test_sigmoid(self):
self._test_activation_module_impl("Sigmoid", nn.Sigmoid, nnq.Sigmoid, {})
@given(
num_embeddings=st.integers(10, 50),
embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0),
set_qconfig=st.booleans(),
)
@skipIfNoFBGEMM
def test_embedding_api(self, num_embeddings, embedding_dim, set_qconfig):
num_lengths = np.random.randint(1, 6)
lengths = np.random.randint(0, 21, size=num_lengths).astype(np.int32)
num_indices = np.sum(lengths)
indices = torch.from_numpy(np.random.randint(low=0, high=num_embeddings, size=num_indices, dtype=np.int64))
weights = torch.from_numpy((np.random.random_sample((num_embeddings, embedding_dim)) + 1).astype(np.float32))
obs = default_float_qparams_observer()
obs(weights)
qparams = obs.calculate_qparams()
# Quantize the weights to 8bits
qweight = torch.quantize_per_channel(weights, qparams[0], qparams[1], axis=0, dtype=torch.quint8)
qemb = nnq.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)
qemb.set_weight(qweight)
qemb(indices)
# Ensure the module has the correct weights
self.assertEqual(qweight, qemb.weight())
w_packed = qemb._packed_params._packed_weight
module_out = qemb(indices)
# Call the qembedding operator directly
ref = torch.ops.quantized.embedding_byte(w_packed, indices, pruned_weights=False)
self.assertEqual(module_out, ref)
self.checkEmbeddingSerialization(qemb, num_embeddings, embedding_dim, indices, None, set_qconfig=False, is_emb_bag=False)
@given(
num_embeddings=st.integers(10, 50),
embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0),
num_offsets=st.integers(1, 20),
set_qconfig=st.booleans(),
)
@skipIfNoFBGEMM
def test_embedding_bag_api(self, num_embeddings, embedding_dim, num_offsets, set_qconfig):
r"""Test execution and serialization for dynamic quantized embedding_bag modules on int8
"""
num_lengths = np.random.randint(1, 6)
lengths = np.random.randint(0, 21, size=num_lengths).astype(np.int32)
num_indices = np.sum(lengths)
indices = torch.from_numpy(np.random.randint(low=0, high=num_embeddings, size=num_indices, dtype=np.int64))
offsets = lengths_to_offsets(lengths)
# include the last offset
offsets = torch.cat((offsets, torch.tensor([indices.size(0)], dtype=torch.long)), 0)
weights = torch.from_numpy((np.random.random_sample((num_embeddings, embedding_dim)) + 1).astype(np.float32))
for qdtype in [torch.quint8, torch.quint4x2]:
obs = PerChannelMinMaxObserver(dtype=qdtype, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0)
obs(weights)
# Get the scale and zero point for the weight tensor
qparams = obs.calculate_qparams()
# Quantize the weights to 8bits
qweight = torch.quantize_per_channel(weights, qparams[0], qparams[1], axis=0, dtype=qdtype)
qemb = nnq.EmbeddingBag(num_embeddings=num_embeddings, embedding_dim=embedding_dim,
include_last_offset=True, mode='sum', _weight=qweight, dtype=qdtype)
qemb(indices, offsets)
# Ensure the module has the correct weights
self.assertEqual(qweight, qemb.weight())
w_packed = qemb._packed_params._packed_weight
module_out = qemb(indices, offsets)
# Call the qembedding_bag operator directly
if qdtype == torch.quint8:
ref = torch.ops.quantized.embedding_bag_byte(w_packed, indices, offsets, mode=0,
per_sample_weights=None,
include_last_offset=True)
else:
ref = torch.ops.quantized.embedding_bag_4bit(w_packed, indices, offsets, mode=0,
per_sample_weights=None,
include_last_offset=True)
self.assertEqual(module_out, ref)
self.checkEmbeddingSerialization(qemb, num_embeddings, embedding_dim, indices,
offsets, set_qconfig, is_emb_bag=True, dtype=qdtype)
class TestDynamicQuantizedModule(QuantizationTestCase):
@given(
batch_size=st.integers(1, 5),
in_features=st.integers(16, 32),
out_features=st.integers(4, 8),
use_bias=st.booleans(),
use_default_observer=st.booleans(),
)
@override_qengines
def test_linear_api(self, batch_size, in_features, out_features, use_bias, use_default_observer):
"""test API functionality for nn.quantized.dynamic.Linear"""
W = torch.rand(out_features, in_features).float()
W_scale, W_zp = _calculate_dynamic_qparams(W, torch.qint8)
W_q = torch.quantize_per_tensor(W, W_scale, W_zp, torch.qint8)
X = torch.rand(batch_size, in_features).float()
B = torch.rand(out_features).float() if use_bias else None
qlinear = nnqd.Linear(in_features, out_features)
# Run module with default-initialized parameters.
# This tests that the constructor is correct.
qlinear.set_weight_bias(W_q, B)
qlinear(X)
# Simple round-trip test to ensure weight()/set_weight() API
self.assertEqual(qlinear.weight(), W_q)
W_pack = qlinear._packed_params._packed_params
Z_dq = qlinear(X)
# Check if the module implementation matches calling the
# ops directly
Z_ref = torch.ops.quantized.linear_dynamic(X, W_pack, reduce_range=True)
self.assertEqual(Z_ref, Z_dq)
# Test serialization of dynamic quantized Linear Module using state_dict
model_dict = qlinear.state_dict()
b = io.BytesIO()
torch.save(model_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in model_dict:
if isinstance(model_dict[key], torch._C.ScriptObject):
assert isinstance(loaded_dict[key], torch._C.ScriptObject)
w_model, b_model = torch.ops.quantized.linear_unpack(model_dict[key])
w_loaded, b_loaded = torch.ops.quantized.linear_unpack(loaded_dict[key])
self.assertEqual(w_model, w_loaded)
self.assertEqual(b_model, b_loaded)
else:
self.assertEqual(model_dict[key], loaded_dict[key])
loaded_qlinear = nnqd.Linear(in_features, out_features)
loaded_qlinear.load_state_dict(loaded_dict)
linear_unpack = torch.ops.quantized.linear_unpack
self.assertEqual(linear_unpack(qlinear._packed_params._packed_params),
linear_unpack(loaded_qlinear._packed_params._packed_params))
if use_bias:
self.assertEqual(qlinear.bias(), loaded_qlinear.bias())
self.assertTrue(dir(qlinear) == dir(loaded_qlinear))
self.assertTrue(hasattr(qlinear, '_packed_params'))
self.assertTrue(hasattr(loaded_qlinear, '_packed_params'))
self.assertTrue(hasattr(qlinear, '_weight_bias'))
self.assertTrue(hasattr(loaded_qlinear, '_weight_bias'))
self.assertEqual(qlinear._weight_bias(), loaded_qlinear._weight_bias())
self.assertEqual(qlinear._weight_bias(), torch.ops.quantized.linear_unpack(qlinear._packed_params._packed_params))
Z_dq2 = qlinear(X)
self.assertEqual(Z_dq, Z_dq2)
b = io.BytesIO()
torch.save(qlinear, b)
b.seek(0)
loaded = torch.load(b)
self.assertEqual(qlinear.weight(), loaded.weight())
self.assertEqual(qlinear.zero_point, loaded.zero_point)
# Test JIT
self.checkScriptable(qlinear, [[X]], check_save_load=True)
# Test from_float
float_linear = torch.nn.Linear(in_features, out_features).float()
if use_default_observer:
float_linear.qconfig = torch.quantization.default_dynamic_qconfig
prepare_dynamic(float_linear)
float_linear(X.float())
quantized_float_linear = nnqd.Linear.from_float(float_linear)
# Smoke test to make sure the module actually runs
quantized_float_linear(X)
# Smoke test extra_repr
self.assertTrue('QuantizedLinear' in str(quantized_float_linear))
@given(
dtype=st.sampled_from([torch.qint8, torch.float16]),
bidirectional=st.booleans(),
)
@override_qengines
def test_lstm_api(self, dtype, bidirectional):
r"""Test execution and serialization for dynamic quantized lstm modules on int8 and fp16
"""
# Check that module matches the numerics of the op and ensure that module can be
# instantiated for all engines and dtypes
seq_len = 4
batch = 2
input_size = 3
hidden_size = 7
num_layers = 2
bias = True
weight_keys = []
bias_keys = []
num_directions = 2 if bidirectional else 1
for layer in range(num_layers):
for direction in range(num_directions):
suffix = '_reverse' if direction == 1 else ''
key_name1 = 'weight_ih_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
key_name2 = 'weight_hh_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
weight_keys.append(key_name1)
weight_keys.append(key_name2)
key_name1 = 'bias_ih_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
key_name2 = 'bias_hh_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
bias_keys.append(key_name1)
bias_keys.append(key_name2)
if not (dtype == torch.float16 and torch.backends.quantized.engine == "qnnpack"):
# fp16 dynamic quant is not supported for qnnpack
x = torch.randn(seq_len, batch, input_size)
h = torch.randn(num_layers * (bidirectional + 1), batch, hidden_size)
c = torch.randn(num_layers * (bidirectional + 1), batch, hidden_size)
cell_dq = torch.nn.quantized.dynamic.LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
bias=bias,
batch_first=False,
dropout=0.0,
bidirectional=bidirectional,
dtype=dtype)
ref_dq = torch.nn.quantized.dynamic.LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
bias=bias,
batch_first=False,
dropout=0.0,
bidirectional=bidirectional,
dtype=dtype)