def test_conv_api(self):
"""Tests the correctness of the conv module.
The correctness is defined against the functional implementation.
"""
N, iC, H, W = 10, 10, 10, 3
oC, g, kH, kW = 16, 1, 3, 3
scale, zero_point = 1.0 / 255, 128
X = torch.randn(N, iC, H, W, dtype=torch.float32)
X = X.permute([0, 2, 3, 1]).contiguous()
qX = torch.quantize_linear(X, scale=scale, zero_point=128, dtype=torch.quint8)
w = torch.randn(oC, iC // g, kH, kW, dtype=torch.float32)
w = w.permute([0, 2, 3, 1]).contiguous()
qw = torch.quantize_linear(w, scale=scale, zero_point=0, dtype=torch.qint8)
b = torch.randn(oC, dtype=torch.float32)
qb = torch.quantize_linear(b, scale=1.0 / 1024, zero_point=0, dtype=torch.qint32)
conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=True,
padding_mode='zeros')
conv_under_test.weight = qw
conv_under_test.bias = qb
conv_under_test.scale = scale
conv_under_test.zero_point = zero_point
# Test members
self.assertTrue(hasattr(conv_under_test, '_packed_weight'))
self.assertTrue(hasattr(conv_under_test, '_scale'))
self.assertTrue(hasattr(conv_under_test, '_zero_point'))
# Test properties
# self.assertEqual(qw, conv_under_test.weight)
self.assertEqual(qb, conv_under_test.bias)
self.assertEqual(scale, conv_under_test.scale)
self.assertEqual(zero_point, conv_under_test.zero_point)
# Test forward
result_under_test = conv_under_test(qX)
result_reference = qF.conv2d(qX, qw, bias=qb,
scale=scale, zero_point=zero_point,
stride=1, padding=0,
dilation=1, groups=g,
prepacked=False, dtype=torch.quint8)
self.assertEqual(result_reference, result_under_test,
message="Tensors are not equal.")
def forward(self, input):
return qF.conv2d(input=input,
weight=self._packed_weight,
bias=self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=self.groups,
padding_mode=self.padding_mode,
scale=self.scale,
zero_point=self.zero_point,
dtype=torch.quint8,
prepacked=True)
def test_conv_api(self, use_bias, use_fused):
"""Tests the correctness of the conv module.
The correctness is defined against the functional implementation.
"""
N, iC, H, W = 10, 10, 10, 3
oC, g, kH, kW = 16, 1, 3, 3
scale, zero_point = 1.0 / 255, 128
X = torch.randn(N, iC, H, W, dtype=torch.float32)
X = X.permute([0, 2, 3, 1]).contiguous()
qX = torch.quantize_linear(X,
scale=scale,
zero_point=128,
dtype=torch.quint8)
w = torch.randn(oC, iC // g, kH, kW, dtype=torch.float32)
qw = torch.quantize_linear(w,
scale=scale,
zero_point=0,
dtype=torch.qint8)
b = torch.randn(oC, dtype=torch.float32) if use_bias else None
qb = torch.quantize_linear(
b, scale=1.0 /
1024, zero_point=0, dtype=torch.qint32) if use_bias else None
if use_fused:
conv_under_test = ConvReLU2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
else:
conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
# Run module with default-initialized parameters.
# This tests that the constructor is correct.
conv_under_test(qX)
conv_under_test.set_weight(qw)
conv_under_test.bias = qb
conv_under_test.scale = scale
conv_under_test.zero_point = zero_point
# Test members
self.assertTrue(hasattr(conv_under_test, '_packed_weight'))
self.assertTrue(hasattr(conv_under_test, 'scale'))
self.assertTrue(hasattr(conv_under_test, 'zero_point'))
# Test properties
self.assertEqual(qw, conv_under_test.weight())
self.assertEqual(qb, conv_under_test.bias)
self.assertEqual(scale, conv_under_test.scale)
self.assertEqual(zero_point, conv_under_test.zero_point)
# Test forward
result_under_test = conv_under_test(qX)
result_reference = qF.conv2d(qX,
qw,
bias=qb,
scale=scale,
zero_point=zero_point,
stride=1,
padding=0,
dilation=1,
groups=g,
dtype=torch.quint8)
if use_fused:
# result_reference < zero_point doesn't work for qtensor yet
# result_reference[result_reference < zero_point] = zero_point
MB, OC, OH, OW = result_reference.size()
for i in range(MB):
for j in range(OC):
for h in range(OH):
for w in range(OW):
if result_reference[i][j][h][w].int_repr(
) < zero_point:
# assign 0. that gets converted to zero_point
result_reference[i][j][h][w] = 0.
self.assertEqual(result_reference,
result_under_test,
message="Tensors are not equal.")
# Test serialization of quantized Conv Module using state_dict
model_dict = conv_under_test.state_dict()
self.assertEqual(model_dict['weight'], qw)
if use_bias:
self.assertEqual(model_dict['bias'], qb)
with tempfile.NamedTemporaryFile() as f:
torch.save(model_dict, f)
f.seek(0)
loaded_dict = torch.load(f)
for key in model_dict:
self.assertEqual(loaded_dict[key], model_dict[key])
if use_fused:
loaded_conv_under_test = ConvReLU2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
else:
loaded_conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
loaded_conv_under_test.load_state_dict(loaded_dict)
self.assertEqual(loaded_conv_under_test.weight(),
conv_under_test.weight())
if use_bias:
self.assertEqual(loaded_conv_under_test.bias, conv_under_test.bias)
self.assertEqual(loaded_conv_under_test.scale, conv_under_test.scale)
self.assertEqual(loaded_conv_under_test.zero_point,
conv_under_test.zero_point)
self.assertTrue(dir(loaded_conv_under_test) == dir(conv_under_test))
self.assertTrue(hasattr(conv_under_test, '_packed_weight'))
self.assertTrue(hasattr(loaded_conv_under_test, '_packed_weight'))
self.assertTrue(hasattr(conv_under_test, 'weight'))
self.assertTrue(hasattr(loaded_conv_under_test, 'weight'))
self.assertEqual(loaded_conv_under_test.weight(),
conv_under_test.weight())
self.assertEqual(loaded_conv_under_test.weight(), qw)
loaded_result = loaded_conv_under_test(qX)
self.assertEqual(loaded_result, result_reference)
with tempfile.NamedTemporaryFile() as f:
torch.save(conv_under_test, f)
f.seek(0)
loaded_conv = torch.load(f)
self.assertEqual(conv_under_test.bias, loaded_conv.bias)
self.assertEqual(conv_under_test.scale, loaded_conv.scale)
self.assertEqual(conv_under_test.zero_point, loaded_conv.zero_point)
# JIT testing
self.checkScriptable(conv_under_test,
list(zip([qX], [result_reference])),
check_save_load=True)
# Test from_float
float_conv = torch.nn.Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros').float()
float_conv.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(float_conv)
float_conv(X.float())
quantized_float_conv = torch.nn.Sequential(float_conv)
torch.quantization.convert(quantized_float_conv)
# Smoke test to make sure the module actually runs
quantized_float_conv(qX)
# Check that bias is quantized based on output scale
if use_bias:
qbias = torch.quantize_linear(
float_conv.bias, quantized_float_conv[0].scale / 2**16, 0,
torch.qint32)
self.assertEqual(quantized_float_conv[0].bias.dequantize(),
qbias.dequantize())
# Smoke test extra_repr
str(quantized_float_conv)
def test_conv_api(self, use_bias, use_fused):
"""Tests the correctness of the conv module.
The correctness is defined against the functional implementation.
"""
N, iC, H, W = 10, 10, 10, 3
oC, g, kH, kW = 16, 1, 3, 3
scale, zero_point = 1.0 / 255, 128
X = torch.randn(N, iC, H, W, dtype=torch.float32)
qX = torch.quantize_per_tensor(X,
scale=scale,
zero_point=128,
dtype=torch.quint8)
w = torch.randn(oC, iC // g, kH, kW, dtype=torch.float32)
qw = torch.quantize_per_tensor(w,
scale=scale,
zero_point=0,
dtype=torch.qint8)
b = torch.randn(oC, dtype=torch.float32) if use_bias else None
if use_fused:
conv_under_test = ConvReLU2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
else:
conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
# Run module with default-initialized parameters.
# This tests that the constructor is correct.
conv_under_test.set_weight_bias(qw, b)
conv_under_test(qX)
conv_under_test.scale = scale
conv_under_test.zero_point = zero_point
# Test members
self.assertTrue(hasattr(conv_under_test, '_packed_params'))
self.assertTrue(hasattr(conv_under_test, 'scale'))
self.assertTrue(hasattr(conv_under_test, 'zero_point'))
# Test properties
self.assertEqual(qw, conv_under_test.weight())
self.assertEqual(b, conv_under_test.bias())
self.assertEqual(scale, conv_under_test.scale)
self.assertEqual(zero_point, conv_under_test.zero_point)
# Test forward
result_under_test = conv_under_test(qX)
result_reference = qF.conv2d(qX,
qw,
bias=b,
scale=scale,
zero_point=zero_point,
stride=1,
padding=0,
dilation=1,
groups=g,
dtype=torch.quint8)
if use_fused:
# result_reference < zero_point doesn't work for qtensor yet
# result_reference[result_reference < zero_point] = zero_point
MB, OC, OH, OW = result_reference.size()
for i in range(MB):
for j in range(OC):
for h in range(OH):
for w in range(OW):
if result_reference[i][j][h][w].int_repr(
) < zero_point:
# assign 0. that gets converted to zero_point
result_reference[i][j][h][w] = 0.
self.assertEqual(result_reference,
result_under_test,
message="Tensors are not equal.")
# Test serialization of quantized Conv Module using state_dict
model_dict = conv_under_test.state_dict()
self.assertEqual(model_dict['weight'], qw)
if use_bias:
self.assertEqual(model_dict['bias'], b)
b = io.BytesIO()
torch.save(model_dict, b)
b.seek(0)
loaded_dict = torch.load(b)
for key in model_dict:
self.assertEqual(loaded_dict[key], model_dict[key])
if use_fused:
loaded_conv_under_test = ConvReLU2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
else:
loaded_conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
loaded_conv_under_test.load_state_dict(loaded_dict)
self.assertEqual(loaded_conv_under_test._weight_bias(),
conv_under_test._weight_bias())
if use_bias:
self.assertEqual(loaded_conv_under_test.bias(),
conv_under_test.bias())
self.assertEqual(loaded_conv_under_test.scale, conv_under_test.scale)
self.assertEqual(loaded_conv_under_test.zero_point,
conv_under_test.zero_point)
self.assertTrue(dir(loaded_conv_under_test) == dir(conv_under_test))
self.assertTrue(hasattr(conv_under_test, '_packed_params'))
self.assertTrue(hasattr(loaded_conv_under_test, '_packed_params'))
self.assertTrue(hasattr(conv_under_test, '_weight_bias'))
self.assertTrue(hasattr(loaded_conv_under_test, '_weight_bias'))
self.assertEqual(loaded_conv_under_test._weight_bias(),
conv_under_test._weight_bias())
self.assertEqual(loaded_conv_under_test.weight(), qw)
loaded_result = loaded_conv_under_test(qX)
self.assertEqual(loaded_result, result_reference)
# The below check is meant to ensure that `torch.save` and `torch.load`
# serialization works, however it is currently broken by the following:
# https://github.com/pytorch/pytorch/issues/24045
#
# Instead, we currently check that the proper exception is thrown on save.
# <start code>
# b = io.BytesIO()
# torch.save(conv_under_test, b)
# b.seek(0)
# loaded_conv = torch.load(b)
#
# self.assertEqual(conv_under_test.bias(), loaded_conv.bias())
# self.assertEqual(conv_under_test.scale, loaded_conv.scale)
# self.assertEqual(conv_under_test.zero_point, loaded_conv.zero_point)
# <end code>
with self.assertRaisesRegex(
RuntimeError, r'torch.save\(\) is not currently supported'):
b = io.BytesIO()
torch.save(conv_under_test, b)
# JIT testing
self.checkScriptable(conv_under_test,
list(zip([qX], [result_reference])),
check_save_load=True)
# Test from_float
float_conv = torch.nn.Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros').float()
float_conv.qconfig = torch.quantization.default_qconfig
torch.quantization.prepare(float_conv, inplace=True)
float_conv(X.float())
quantized_float_conv = torch.nn.Sequential(float_conv)
torch.quantization.convert(quantized_float_conv, inplace=True)
# Smoke test to make sure the module actually runs
quantized_float_conv(qX)
if use_bias:
self.assertEqual(quantized_float_conv[0].bias(), float_conv.bias)
# Smoke test extra_repr
str(quantized_float_conv)
def test_conv_api(self, Q, padH, padW, sH, sW, dH, dW, prepacked):
"""Tests the correctness of the conv functional.
The correctness is defined by the behavior being similar to the
`quantized._ops` implementation.
"""
# Random iunputs
X, (scale, zero_point), (qmin, qmax), torch_type = Q
(inputs, filters, bias, groups) = X
iC, oC = inputs.shape[1], filters.shape[0]
iH, iW = inputs.shape[2:]
kH, kW = filters.shape[2:]
assume(kH // 2 >= padH)
assume(kW // 2 >= padW)
oH = _conv_output_shape(iH, kH, padH, sH, dH)
assume(oH > 0)
oW = _conv_output_shape(iW, kW, padW, sW, dW)
assume(oW > 0)
inputs = torch.from_numpy(inputs).to(torch.float)
filters = torch.from_numpy(filters).to(torch.float)
bias = torch.from_numpy(bias).to(torch.float)
kernel_size = (kH, kW)
stride = (sH, sW)
i_padding = (padH, padW)
dilation = (dH, dW)
# Quantized inputs
i_NHWC = inputs.permute([0, 2, 3, 1]).contiguous()
w_RSCK = filters.permute([0, 2, 3, 1]).contiguous()
q_inputs = torch.quantize_linear(i_NHWC, scale, zero_point,
torch.quint8)
q_filters = torch.quantize_linear(w_RSCK, scale, zero_point,
torch.qint8)
q_filters_ref = torch.ops.quantized.fbgemm_conv_prepack(
q_filters, groups)
q_bias = torch.quantize_linear(bias, scale, zero_point, torch.qint32)
# Reference op
ref_op = torch.ops.quantized.fbgemm_conv2d
# Results check
try:
ref_result = ref_op(q_inputs, q_filters_ref, q_bias, stride,
i_padding, dilation, groups, scale, zero_point)
except RuntimeError as e:
e_msg = str(e).split("\n")[0].split("(")[0].strip()
np.testing.assert_raises_regex(type(e),
e_msg,
qF.conv2d,
q_inputs,
q_filters_ref,
bias=q_bias,
scale=scale,
zero_point=zero_point,
stride=stride,
padding=i_padding,
dilation=dilation,
groups=groups,
prepacked=True,
dtype=torch_type)
else:
if prepacked:
q_filters = torch.ops.quantized.fbgemm_conv_prepack(
q_filters, groups)
q_result = qF.conv2d(q_inputs,
q_filters,
bias=q_bias,
scale=scale,
zero_point=zero_point,
stride=stride,
padding=i_padding,
dilation=dilation,
groups=groups,
prepacked=prepacked,
dtype=torch_type)
np.testing.assert_equal(ref_result.int_repr().numpy(),
q_result.int_repr().numpy())
def test_conv_api(self, use_bias, use_fused):
"""Tests the correctness of the conv module.
The correctness is defined against the functional implementation.
"""
N, iC, H, W = 10, 10, 10, 3
oC, g, kH, kW = 16, 1, 3, 3
scale, zero_point = 1.0 / 255, 128
X = torch.randn(N, iC, H, W, dtype=torch.float32)
X = X.permute([0, 2, 3, 1]).contiguous()
qX = torch.quantize_linear(X,
scale=scale,
zero_point=128,
dtype=torch.quint8)
w = torch.randn(oC, iC // g, kH, kW, dtype=torch.float32)
qw = torch.quantize_linear(w,
scale=scale,
zero_point=0,
dtype=torch.qint8)
b = torch.randn(oC, dtype=torch.float32) if use_bias else None
qb = torch.quantize_linear(
b, scale=1.0 /
1024, zero_point=0, dtype=torch.qint32) if use_bias else None
if use_fused:
conv_under_test = ConvReLU2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
else:
conv_under_test = Conv2d(in_channels=iC,
out_channels=oC,
kernel_size=(kH, kW),
stride=1,
padding=0,
dilation=1,
groups=g,
bias=use_bias,
padding_mode='zeros')
conv_under_test.weight = qw
conv_under_test.bias = qb
conv_under_test.scale = torch.tensor([scale], dtype=torch.double)
conv_under_test.zero_point = torch.tensor([zero_point],
dtype=torch.long)
# Test members
self.assertTrue(hasattr(conv_under_test, '_packed_weight'))
self.assertTrue(hasattr(conv_under_test, 'scale'))
self.assertTrue(hasattr(conv_under_test, 'zero_point'))
# Test properties
self.assertEqual(qw, conv_under_test.weight)
self.assertEqual(qb, conv_under_test.bias)
self.assertEqual(scale, conv_under_test.scale)
self.assertEqual(zero_point, conv_under_test.zero_point)
# Test forward
result_under_test = conv_under_test(qX)
result_reference = qF.conv2d(qX,
qw,
bias=qb,
scale=scale,
zero_point=zero_point,
stride=1,
padding=0,
dilation=1,
groups=g,
dtype=torch.quint8)
if use_fused:
# result_reference < zero_point doesn't work for qtensor yet
# result_reference[result_reference < zero_point] = zero_point
MB, OC, OH, OW = result_reference.size()
for i in range(MB):
for j in range(OC):
for h in range(OH):
for w in range(OW):
if result_reference[i][j][h][w].int_repr(
) < zero_point:
# assign 0. that gets converted to zero_point
result_reference[i][j][h][w] = 0.
self.assertEqual(result_reference,
result_under_test,
message="Tensors are not equal.")
def test_conv_api(self, X, padH, padW, sH, sW, dH, dW):
"""Tests the correctness of the conv functional.
The correctness is defined by the behavior being similar to the
`quantized._ops` implementation.
"""
# Random inputs
# X, (scale, zero_point, torch_type) = X
(inputs, filters, bias, groups) = X
inputs, (inputs_scale, inputs_zero_point, inputs_qtype) = inputs
filters, (filters_scale, filters_zero_point, filters_qtype) = filters
bias, (bias_scale, bias_zero_point, bias_qtype) = bias
scale, zero_point = inputs_scale, inputs_zero_point
torch_type = inputs_qtype
iC, oC = inputs.shape[1], filters.shape[0]
iH, iW = inputs.shape[2:]
kH, kW = filters.shape[2:]
assume(kH // 2 >= padH)
assume(kW // 2 >= padW)
oH = _conv_output_shape(iH, kH, padH, sH, dH)
assume(oH > 0)
oW = _conv_output_shape(iW, kW, padW, sW, dW)
assume(oW > 0)
inputs = torch.from_numpy(inputs).to(torch.float)
filters = torch.from_numpy(filters).to(torch.float)
bias = torch.from_numpy(bias).to(torch.float)
kernel_size = (kH, kW)
stride = (sH, sW)
i_padding = (padH, padW)
dilation = (dH, dW)
# Quantized inputs
q_inputs = torch.quantize_linear(inputs, inputs_scale,
inputs_zero_point, inputs_qtype)
q_filters = torch.quantize_linear(filters, filters_scale,
filters_zero_point, filters_qtype)
q_filters_ref = torch.ops.quantized.fbgemm_conv_prepack(q_filters.permute([0, 2, 3, 1]), groups)
q_bias = torch.quantize_linear(bias, bias_scale, bias_zero_point,
bias_qtype)
# Reference op
ref_op = torch.ops.quantized.fbgemm_conv2d
# Results check
try:
ref_result = ref_op(q_inputs.permute([0, 2, 3, 1]), q_filters_ref,
q_bias, stride,
i_padding, dilation,
groups, scale, zero_point).permute([0, 3, 1, 2])
except RuntimeError as e:
e_msg = str(e).split("\n")[0].split("(")[0].strip()
np.testing.assert_raises_regex(
type(e), e_msg, qF.conv2d,
q_inputs, q_filters, bias=q_bias,
scale=scale, zero_point=zero_point,
stride=stride, padding=i_padding, dilation=dilation,
groups=groups, dtype=torch_type)
else:
q_result = qF.conv2d(q_inputs,
q_filters,
bias=q_bias, scale=scale,
zero_point=zero_point,
stride=stride, padding=i_padding,
dilation=dilation, groups=groups,
dtype=torch_type)
self.assertEqual(ref_result, q_result)
