IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[PT FE] Enable quantized conv and linear tests (#18723)

Browse Source
This commit is contained in:
Maxim Vafin 2023-07-24 13:33:11 +02:00 committed by GitHub
parent 7e1d8283f2
commit bc734dfaaa
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 25 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
tests/layer_tests/pytorch_tests/test_quantized_convnd.py
View File

@ -38,7 +38,8 @@ class TestQuantizedConv2D(PytorchLayerTest):
self.conv.zero_point = int(zero_point) self.conv.zero_point = int(zero_point)
def forward(self, x): def forward(self, x):
x_quantized = torch.quantize_per_tensor(x, 1.0, 0, torch.quint8) x_quantized = torch.quantize_per_tensor(
x, 1.0, 0, torch.quint8)
conv = self.conv(x_quantized) conv = self.conv(x_quantized)
return torch.dequantize(conv).contiguous() return torch.dequantize(conv).contiguous()
@ -54,18 +55,26 @@ class TestQuantizedConv2D(PytorchLayerTest):
"params", "params",
[ [
pytest.param( pytest.param(
{"weights_shape": [1, 3, 3, 3], "strides": 1, "pads": 0, "dilations": 1, "groups": 1}, {"weights_shape": [1, 3, 3, 3], "strides": 1,
"pads": 0, "dilations": 1, "groups": 1},
marks=pytest.mark.xfail( marks=pytest.mark.xfail(
reason="Output channels equal to 1 creates output that fails to cast to contiguous." reason="Output channels equal to 1 creates output that fails to cast to contiguous."
), ),
), ),
{"weights_shape": [2, 3, 3, 3], "strides": 1, "pads": 0, "dilations": 1, "groups": 1}, {"weights_shape": [2, 3, 3, 3], "strides": 1,
{"weights_shape": [2, 3, 3, 3], "strides": 2, "pads": 0, "dilations": 1, "groups": 1}, "pads": 0, "dilations": 1, "groups": 1},
{"weights_shape": [2, 3, 3, 3], "strides": 1, "pads": 1, "dilations": 1, "groups": 1}, {"weights_shape": [2, 3, 3, 3], "strides": 2,
{"weights_shape": [2, 3, 3, 3], "strides": 1, "pads": 0, "dilations": 2, "groups": 1}, "pads": 0, "dilations": 1, "groups": 1},
{"weights_shape": [2, 3, 3, 3], "strides": 1, "pads": [0, 1], "dilations": 1, "groups": 1}, {"weights_shape": [2, 3, 3, 3], "strides": 1,
{"weights_shape": [2, 3, 3, 3], "strides": 1, "pads": [1, 0], "dilations": 1, "groups": 1}, "pads": 1, "dilations": 1, "groups": 1},
{"weights_shape": [3, 1, 3, 3], "strides": 1, "pads": 0, "dilations": 1, "groups": 3}, {"weights_shape": [2, 3, 3, 3], "strides": 1,
"pads": 0, "dilations": 2, "groups": 1},
{"weights_shape": [2, 3, 3, 3], "strides": 1,
"pads": [0, 1], "dilations": 1, "groups": 1},
{"weights_shape": [2, 3, 3, 3], "strides": 1,
"pads": [1, 0], "dilations": 1, "groups": 1},
{"weights_shape": [3, 1, 3, 3], "strides": 1,
"pads": 0, "dilations": 1, "groups": 3},
], ],
) )
@pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("bias", [True, False])
@ -73,13 +82,10 @@ class TestQuantizedConv2D(PytorchLayerTest):
@pytest.mark.parametrize("scale", [1, 0.3, 1.3]) @pytest.mark.parametrize("scale", [1, 0.3, 1.3])
@pytest.mark.parametrize("zero_point", [0, 1]) @pytest.mark.parametrize("zero_point", [0, 1])
@pytest.mark.nightly @pytest.mark.nightly
# @pytest.mark.precommit Test disabled due to sporadic issues @pytest.mark.precommit
def test_quantized_conv2d(self, params, bias, relu, scale, zero_point, ie_device, precision, ir_version): def test_quantized_conv2d(self, params, bias, relu, scale, zero_point, ie_device, precision, ir_version):
self._test( self._test(
*self.create_model(**params, bias=bias, relu=relu, scale=scale, zero_point=zero_point), *self.create_model(**params, bias=bias, relu=relu,
ie_device, scale=scale, zero_point=zero_point),
precision, ie_device, precision, ir_version, trace_model=True, freeze_model=False, quantized_ops=True, quant_size=scale
ir_version,
trace_model=True,
freeze_model=False
) )

6
tests/layer_tests/pytorch_tests/test_quantized_linear.py
View File

@ -72,13 +72,13 @@ class TestQuantizedLinear(PytorchLayerTest):
@pytest.mark.parametrize("zero_point", [0, 1]) @pytest.mark.parametrize("zero_point", [0, 1])
@pytest.mark.parametrize("trace", [True, False]) @pytest.mark.parametrize("trace", [True, False])
@pytest.mark.nightly @pytest.mark.nightly
# @pytest.mark.precommit Test disabled due to sporadic issues @pytest.mark.precommit
def test_quantized_linear(self, params, scale, zero_point, trace, ie_device, precision, ir_version): def test_quantized_linear(self, params, scale, zero_point, trace, ie_device, precision, ir_version):
input_shape = params.get("input_shape") input_shape = params.get("input_shape")
weight_shape = params.get("weight_shape") weight_shape = params.get("weight_shape")
bias = params.get("bias", False) bias = params.get("bias", False)
self._test(*self.create_model(weight_shape, bias, scale, zero_point), ie_device, precision, ir_version, self._test(*self.create_model(weight_shape, bias, scale, zero_point), ie_device, precision, ir_version,
kwargs_to_prepare_input={"input_shape": input_shape}, trace_model=trace, freeze_model=False) kwargs_to_prepare_input={"input_shape": input_shape}, trace_model=trace, freeze_model=False, quantized_ops=True, quant_size=scale)
@pytest.mark.parametrize("trace", [True, False]) @pytest.mark.parametrize("trace", [True, False])
@pytest.mark.parametrize("inplace", [True, False]) @pytest.mark.parametrize("inplace", [True, False])
@ -86,4 +86,4 @@ class TestQuantizedLinear(PytorchLayerTest):
@pytest.mark.precommit @pytest.mark.precommit
def test_quantized_hardtanh_linear(self, trace, inplace, ie_device, precision, ir_version): def test_quantized_hardtanh_linear(self, trace, inplace, ie_device, precision, ir_version):
self._test(*self.create_hardtanh_model([10, 9], True, 1, 0.3, inplace), ie_device, precision, ir_version, self._test(*self.create_hardtanh_model([10, 9], True, 1, 0.3, inplace), ie_device, precision, ir_version,
kwargs_to_prepare_input={"input_shape": [2, 3, 9]}, trace_model=trace, freeze_model=False) kwargs_to_prepare_input={"input_shape": [2, 3, 9]}, trace_model=trace, freeze_model=False, quantized_ops=True, quant_size=0.3)