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|
# Owner(s): ["module: unknown"]
import io
import caffe2.python.onnx.backend as c2
import numpy as np
import onnx
import torch.ao.nn.quantized as nnq
import torch.nn as nn
import torch.onnx
from torch.testing._internal import common_utils
class TestQuantizedOps(common_utils.TestCase):
def generic_test(
self, model, sample_inputs, input_names=None, decimal=3, relaxed_check=False
):
torch.backends.quantized.engine = "qnnpack"
pt_inputs = tuple(torch.from_numpy(x) for x in sample_inputs)
model.qconfig = torch.ao.quantization.get_default_qconfig("qnnpack")
q_model = torch.ao.quantization.prepare(model, inplace=False)
q_model = torch.ao.quantization.convert(q_model, inplace=False)
traced_model = torch.jit.trace(q_model, pt_inputs)
buf = io.BytesIO()
torch.jit.save(traced_model, buf)
buf.seek(0)
q_model = torch.jit.load(buf)
q_model.eval()
output = q_model(*pt_inputs)
f = io.BytesIO()
torch.onnx.export(
q_model,
pt_inputs,
f,
input_names=input_names,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
# Caffe2 doesn't support newer opset versions
opset_version=9,
)
f.seek(0)
onnx_model = onnx.load(f)
caffe_res = c2.run_model(onnx_model, dict(zip(input_names, sample_inputs)))[0]
# Due to change in requantization logic for certain ops such conv, linear
# in pytorch's integration of qnnpack, numerics may have a mismatc with C2.
# This mismatch should not be off my more than 1.
# This flag helps us override default behavior under certain circumstances.
if relaxed_check:
output_diff = np.absolute(np.squeeze(output.detach().numpy()) - caffe_res)
max_diff = np.amax(output_diff)
# This check had to be changed to account for changes in
# qnnpack's requant logic.
np.testing.assert_(
max_diff <= 1, "Maximum absolute difference must be less than 1"
)
else:
np.testing.assert_almost_equal(
output.detach().numpy(), caffe_res, decimal=decimal
)
def generic_unary_test(self, op):
class QModule(torch.nn.Module):
def __init__(self, op):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.op = op
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
res = self.op(self.quant1(x))
return self.dequant(res)
x = np.random.random((1, 2)).astype("float32")
self.generic_test(QModule(op), (x,), input_names=["x"])
def test_quantized_add(self):
class QAddModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.quant2 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x, y):
res = torch.ops.quantized.add(self.quant1(x), self.quant2(y), 1.0, 0)
return self.dequant(res)
x = np.random.random(2).astype("float32")
y = np.random.random(2).astype("float32")
self.generic_test(QAddModule(), (x, y), input_names=["x", "y"])
def test_quantized_relu(self):
self.generic_unary_test(torch.nn.ReLU())
def export_to_onnx(self, model, input, input_names):
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,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
# Caffe2 doesn't support newer opset versions
opset_version=9,
)
f.seek(0)
onnx_model = onnx.load(f)
return onnx_model
def test_qlinear_model(self):
class LinearModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.qconfig = torch.ao.quantization.default_qconfig
self.fc1 = torch.ao.quantization.QuantWrapper(
torch.nn.Linear(5, 10).to(dtype=torch.float)
)
def forward(self, x):
x = self.fc1(x)
return x
torch.backends.quantized.engine = "qnnpack"
qconfig = torch.ao.quantization.default_qconfig
model = LinearModel()
model.qconfig = qconfig
model = torch.ao.quantization.prepare(model)
model = torch.ao.quantization.convert(model)
x_numpy = np.random.rand(1, 2, 5).astype(np.float32)
x = torch.from_numpy(x_numpy).to(dtype=torch.float)
outputs = model(x)
input_names = ["x"]
onnx_model = self.export_to_onnx(model, x, input_names)
caffe_res = c2.run_model(onnx_model, dict(zip(input_names, x_numpy)))[0]
output_diff = np.absolute(np.squeeze(outputs.numpy()) - caffe_res)
max_diff = np.amax(output_diff)
# Permute pytorch output to NHWC
# This check had to be changed to account for changes in
# qnnpack's requant logic.
np.testing.assert_(
max_diff <= 1, "Maximum absolute difference must be less than 1"
)
def test_qconv_model(self):
class ConvModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.qconfig = torch.ao.quantization.default_qconfig
self.fc1 = torch.ao.quantization.QuantWrapper(
torch.nn.Conv2d(3, 5, 2, bias=True).to(dtype=torch.float)
)
def forward(self, x):
x = self.fc1(x)
return x
torch.backends.quantized.engine = "qnnpack"
qconfig = torch.ao.quantization.default_qconfig
model = ConvModel()
model.qconfig = qconfig
model = torch.ao.quantization.prepare(model)
model = torch.ao.quantization.convert(model)
x_numpy = np.random.rand(1, 3, 6, 6).astype(np.float32)
x = torch.from_numpy(x_numpy).to(dtype=torch.float)
outputs = model(x)
input_names = ["x"]
onnx_model = self.export_to_onnx(model, x, input_names)
y = np.expand_dims(x_numpy, axis=0)
caffe_res = c2.run_model(onnx_model, dict(zip(input_names, y)))[0]
output_diff = np.absolute(np.squeeze(outputs.numpy()) - caffe_res)
max_diff = np.amax(output_diff)
# Permute pytorch output to NHWC
# This check had to be changed to account for changes in
# qnnpack's requant logic.
np.testing.assert_(
max_diff <= 1, "Maximum absolute difference must be less than 1"
)
def test_upsample(self):
class QUpsampleModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
res = torch.ao.nn.quantized.functional.interpolate(
self.quant1(x), size=[6, 8], mode="nearest"
)
return self.dequant(res)
x = np.random.rand(1, 2, 3, 4).astype("float32")
self.generic_test(QUpsampleModule(), (x,), input_names=["x"], decimal=5)
def test_avg_pool2d(self):
class QAvgPool2dModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
res = torch.nn.functional.avg_pool2d(
self.quant1(x), kernel_size=2, stride=1, padding=0
)
return self.dequant(res)
x = np.random.rand(1, 2, 8, 8).astype("float32")
self.generic_test(
QAvgPool2dModule(), (x,), input_names=["x"], relaxed_check=True
)
def test_reshape(self):
class QReshapeModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
res = self.quant1(x).reshape((1, 2, 1, 12))
return self.dequant(res)
x = np.random.rand(1, 2, 3, 4).astype("float32")
self.generic_test(QReshapeModule(), (x,), input_names=["x"], decimal=5)
def test_slice(self):
class QSliceModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
qx = self.quant1(x)
res = qx[:, 1:2]
return self.dequant(res)
x = np.random.rand(1, 2, 3, 4).astype("float32")
self.generic_test(QSliceModule(), (x,), input_names=["x"], decimal=5)
def test_cat(self):
class QConcatModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x, y):
res = torch.ops.quantized.cat(
[self.quant1(x), self.quant1(y)], dim=1, scale=1.0, zero_point=0
)
return self.dequant(res)
x = np.random.rand(1, 2, 3, 4).astype("float32")
y = np.random.rand(1, 4, 3, 4).astype("float32")
self.generic_test(
QConcatModule(),
(
x,
y,
),
input_names=["x", "y"],
)
def test_max_pool2d(self):
class QMaxPool2dModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant1 = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
res = torch.nn.functional.max_pool2d(
self.quant1(x), kernel_size=2, stride=1, padding=0
)
return self.dequant(res)
x = np.random.rand(1, 2, 8, 8).astype("float32")
self.generic_test(QMaxPool2dModule(), (x,), input_names=["x"], decimal=5)
def test_quantized_sigmoid(self):
self.generic_unary_test(torch.nn.Sigmoid())
def test_small_model(self):
class SimpleModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
self.func_add = nnq.FloatFunctional()
self.conv1 = nn.Conv2d(3, 2, 5, bias=None).to(dtype=torch.float)
self.act1 = nn.Sigmoid()
self.conv2 = nn.Conv2d(2, 2, 1, bias=None).to(dtype=torch.float)
self.fc = nn.Linear(72, 10).to(dtype=torch.float)
self.fc.qconfig = None
def forward(self, x):
x = self.quant(x)
x = self.func_add.add(x, x)
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.dequant(x)
x = x.reshape(-1, 72).contiguous()
x = self.fc(x)
return x
x = np.random.rand(2, 3, 10, 10).astype("float32")
self.generic_test(SimpleModel(), (x,), input_names=["x"], relaxed_check=True)
def test_sequential(self):
class ConvBNReLUModule(nn.Sequential):
def __init__(self):
super().__init__(
nn.Conv2d(3, 3, 1, 1, bias=False),
nn.BatchNorm2d(3),
nn.ReLU(inplace=False),
)
class ModelWithClassifierHead(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 3, 1)
self.relu1 = nn.ReLU(inplace=False)
layers = []
for i in range(3):
layers.append(ConvBNReLUModule())
self.features = nn.Sequential(*layers)
head = [nn.Linear(300, 10), nn.ReLU(inplace=False)]
self.classifier = nn.Sequential(*head)
self.seq = nn.Sequential()
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
x = self.quant(x)
x = self.conv1(x)
x = self.relu1(x)
x = self.features(x)
x = torch.reshape(x, (-1, 3 * 10 * 10))
x = self.classifier(x)
x = self.seq(x)
x = self.dequant(x)
return x
model = ModelWithClassifierHead().eval()
torch.ao.quantization.fuse_modules(
model,
[
["conv1", "relu1"],
["features.0.0", "features.0.1", "features.0.2"],
["features.1.0", "features.1.1", "features.1.2"],
["features.2.0", "features.2.1", "features.2.2"],
],
inplace=True,
)
x = np.random.rand(1, 3, 10, 10).astype("float32")
self.generic_test(model, (x,), input_names=["x"], relaxed_check=True)
if __name__ == "__main__":
common_utils.run_tests()
|
