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# Owner(s): ["oncall: quantization"]
# Torch
import torch
import torch.ao.nn.quantized.functional as qF
import torch.nn.functional as F
# Standard library
import numpy as np
# Testing utils
from hypothesis import assume, given
from hypothesis import strategies as st
from torch.testing._internal.common_quantization import (
QuantizationTestCase,
_make_conv_test_input,
)
from torch.testing._internal.common_quantized import override_quantized_engine
from torch.testing._internal.common_utils import (
IS_PPC,
TEST_WITH_UBSAN,
)
class TestQuantizedFunctionalOps(QuantizationTestCase):
def test_relu_api(self):
X = torch.arange(-5, 5, dtype=torch.float)
scale = 2.0
zero_point = 1
qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8)
qY = torch.relu(qX)
qY_hat = F.relu(qX)
self.assertEqual(qY, qY_hat)
def _test_conv_api_impl(
self, qconv_fn, conv_fn, 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_channelwise,
):
for i in range(len(kernel_size)):
assume(input_feature_map_size[i] + 2 * padding[i]
>= dilation[i] * (kernel_size[i] - 1) + 1)
(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)
Y_exp = conv_fn(X, W, b, stride, padding, dilation, groups)
Y_exp = torch.quantize_per_tensor(
Y_exp, scale=Y_scale, zero_point=Y_zero_point, dtype=torch.quint8)
Y_act = qconv_fn(
X_q, W_q, b, stride, padding, dilation, groups,
padding_mode="zeros", scale=Y_scale, zero_point=Y_zero_point)
# 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)
@given(batch_size=st.integers(1, 3),
in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
L=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=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_channelwise=st.booleans(),
qengine=st.sampled_from(("qnnpack", "fbgemm")))
def test_conv1d_api(
self, batch_size, in_channels_per_group, L, out_channels_per_group,
groups, kernel, stride, pad, dilation,
X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point,
use_bias, use_channelwise, qengine,
):
# Tests the correctness of the conv1d function.
if qengine not in torch.backends.quantized.supported_engines:
return
if qengine == 'qnnpack':
if IS_PPC or TEST_WITH_UBSAN:
return
use_channelwise = False
input_feature_map_size = (L, )
kernel_size = (kernel, )
stride = (stride, )
padding = (pad, )
dilation = (dilation, )
with override_quantized_engine(qengine):
qconv_fn = qF.conv1d
conv_fn = F.conv1d
self._test_conv_api_impl(
qconv_fn, conv_fn, 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_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),
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_channelwise=st.booleans(),
qengine=st.sampled_from(("qnnpack", "fbgemm")))
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, dilation,
X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point,
use_bias, use_channelwise, qengine,
):
# Tests the correctness of the conv2d function.
if qengine not in torch.backends.quantized.supported_engines:
return
if qengine == 'qnnpack':
if IS_PPC or TEST_WITH_UBSAN:
return
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)
with override_quantized_engine(qengine):
qconv_fn = qF.conv2d
conv_fn = F.conv2d
self._test_conv_api_impl(
qconv_fn, conv_fn, 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_channelwise)
@given(batch_size=st.integers(1, 3),
in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
D=st.integers(4, 8),
H=st.integers(4, 8),
W=st.integers(4, 8),
out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]),
groups=st.integers(1, 4),
kernel_d=st.integers(1, 4),
kernel_h=st.integers(1, 4),
kernel_w=st.integers(1, 4),
stride_d=st.integers(1, 2),
stride_h=st.integers(1, 2),
stride_w=st.integers(1, 2),
pad_d=st.integers(0, 2),
pad_h=st.integers(0, 2),
pad_w=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_channelwise=st.booleans(),
qengine=st.sampled_from(("fbgemm",)))
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, dilation, X_scale,
X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias,
use_channelwise, qengine,
):
# Tests the correctness of the conv3d function.
# Currently conv3d only supports FbGemm engine
if qengine not in torch.backends.quantized.supported_engines:
return
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(qengine):
qconv_fn = qF.conv3d
conv_fn = F.conv3d
self._test_conv_api_impl(
qconv_fn, conv_fn, 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_channelwise)
@given(N=st.integers(1, 10),
C=st.integers(1, 10),
H=st.integers(4, 8),
H_out=st.integers(4, 8),
W=st.integers(4, 8),
W_out=st.integers(4, 8),
scale=st.floats(.1, 2),
zero_point=st.integers(0, 4))
def test_grid_sample(self, N, C, H, H_out, W, W_out, scale, zero_point):
X = torch.rand(N, C, H, W)
X_q = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, dtype=torch.quint8)
grid = torch.rand(N, H_out, W_out, 2)
out = F.grid_sample(X_q, grid)
out_exp = torch.quantize_per_tensor(F.grid_sample(X, grid), scale=scale, zero_point=zero_point, dtype=torch.quint8)
np.testing.assert_array_almost_equal(
out.int_repr().numpy(), out_exp.int_repr().numpy(), decimal=0)
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