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Skip test for PT2E quantized ops in fbcode (#138792)
Skip those tests as they are failing in fbcode. Submit this PR per request from @jerryzh168 Pull Request resolved: https://github.com/pytorch/pytorch/pull/138792 Approved by: https://github.com/jerryzh168
This commit is contained in:
Xia, Weiwen
PyTorch MergeBot
parent
b4e4f84a06
commit
edcab61f93
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@ -23,7 +23,7 @@ hu.assert_deadline_disabled()
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from torch.testing._internal.common_cuda import SM80OrLater
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from torch.testing._internal.common_utils import TestCase
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from torch.testing._internal.common_utils import IS_PPC, TEST_WITH_UBSAN, IS_MACOS, IS_SANDCASTLE
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from torch.testing._internal.common_utils import IS_PPC, TEST_WITH_UBSAN, IS_MACOS, IS_SANDCASTLE, IS_FBCODE
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from torch.testing._internal.common_quantization import skipIfNoFBGEMM, skipIfNoQNNPACK, skipIfNoONEDNN
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from torch.testing._internal.common_quantized import _quantize, _dequantize, _calculate_dynamic_qparams, \
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override_quantized_engine, supported_qengines, override_qengines, _snr
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@ -1479,6 +1479,7 @@ class TestQuantizedOps(TestCase):
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msg="ops.quantized.max_pool2d results are off")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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def test_max_pool2d_pt2e(self):
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kernel_list = [2, 3]
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stride_list = [1, 2]
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@ -4464,37 +4465,44 @@ class TestQuantizedLinear(TestCase):
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y_s: {y_scale}, y_zp: {y_zp}""",
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)
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise
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self._test_qlinear_pt2e_helper(qlinear, "none")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_relu_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise
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self._test_qlinear_pt2e_helper(qlinear, "relu")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_gelu_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise
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post_op_algorithms = ['none', 'tanh']
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self._test_qlinear_pt2e_helper(qlinear, "gelu", post_op_algorithms=post_op_algorithms)
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_sum_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise.binary
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self._test_qlinear_pt2e_helper(qlinear, "sum")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_sum_relu_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise.binary
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self._test_qlinear_pt2e_helper(qlinear, "sum_relu")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_add_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise.binary
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self._test_qlinear_pt2e_helper(qlinear, "add")
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qlinear_add_relu_pt2e(self):
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qlinear = torch.ops.onednn.qlinear_pointwise.binary
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@ -6857,6 +6865,7 @@ class TestQuantizedConv(TestCase):
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# Return the quantized data for later reuse
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return X_q, W_q, bias_float
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv1d_pt2e(self):
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groups_list = [1, 3]
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@ -6909,6 +6918,7 @@ class TestQuantizedConv(TestCase):
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qconv_output_dtype=output_dtype,
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)
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_pt2e(self):
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groups_list = [1, 3]
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@ -6969,6 +6979,7 @@ class TestQuantizedConv(TestCase):
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weight_in_channel_last_format=channel_last_weight_format,
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)
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv3d_pt2e(self):
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input_channels_per_group = 2
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@ -7030,6 +7041,7 @@ class TestQuantizedConv(TestCase):
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)
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# Test qconv with post op relu
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_relu_pt2e(self):
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input_channels_per_group = 2
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@ -7080,6 +7092,7 @@ class TestQuantizedConv(TestCase):
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)
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# Test qconv with post op hardtanh
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_hardtanh_pt2e(self):
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input_channels_per_group = 2
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@ -7130,6 +7143,7 @@ class TestQuantizedConv(TestCase):
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)
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# Test qconv with post op silu
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_silu_pt2e(self):
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input_channels_per_group = 2
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@ -7179,58 +7193,60 @@ class TestQuantizedConv(TestCase):
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qconv_output_dtype=output_dtype,
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)
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# Test qconv with post op hardswish
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@skipIfNoONEDNN
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def test_qconv2d_hardswish_pt2e(self):
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input_channels_per_group = 2
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output_channels_per_group = 2
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groups_list = [1, 10]
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input_feature_map_shape = (10, 10)
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kernels = (3, 3)
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strides = (2, 2)
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pads = (1, 1)
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dilations = (1, 1)
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W_scale = [1.5]
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W_zero_point = [0]
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use_bias_list = [False, True]
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use_channelwise_list = [False, True]
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output_dtype_list = [None, torch.float32, torch.bfloat16]
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options = itertools.product(groups_list, use_bias_list, use_channelwise_list, output_dtype_list)
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# Test qconv with post op hardswish
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_hardswish_pt2e(self):
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input_channels_per_group = 2
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output_channels_per_group = 2
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groups_list = [1, 10]
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input_feature_map_shape = (10, 10)
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kernels = (3, 3)
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strides = (2, 2)
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pads = (1, 1)
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dilations = (1, 1)
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W_scale = [1.5]
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W_zero_point = [0]
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use_bias_list = [False, True]
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use_channelwise_list = [False, True]
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output_dtype_list = [None, torch.float32, torch.bfloat16]
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options = itertools.product(groups_list, use_bias_list, use_channelwise_list, output_dtype_list)
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for groups, use_bias, use_channelwise, output_dtype in options:
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qconv = torch.ops.onednn.qconv2d_pointwise
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qconv_prepack = torch.ops.onednn.qconv_prepack
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conv_op = torch.nn.Conv2d(
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input_channels_per_group * groups,
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output_channels_per_group * groups,
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kernels,
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strides,
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pads,
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dilations,
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groups,
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)
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pointwise_post_op = PointwisePostOp(unary_attr="hardswish")
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self._test_qconv_impl_cpu_tensor(
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qconv,
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qconv_prepack,
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conv_op,
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input_channels_per_group=input_channels_per_group,
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input_feature_map_shape=input_feature_map_shape,
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output_channels_per_group=output_channels_per_group,
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groups=groups,
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kernels=kernels,
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strides=strides,
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pads=pads,
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dilations=dilations,
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W_scale=W_scale,
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W_zero_point=W_zero_point,
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use_bias=use_bias,
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post_op=pointwise_post_op,
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use_channelwise=use_channelwise,
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qconv_output_dtype=output_dtype,
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)
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for groups, use_bias, use_channelwise, output_dtype in options:
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qconv = torch.ops.onednn.qconv2d_pointwise
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qconv_prepack = torch.ops.onednn.qconv_prepack
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conv_op = torch.nn.Conv2d(
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input_channels_per_group * groups,
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output_channels_per_group * groups,
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kernels,
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strides,
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pads,
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dilations,
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groups,
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)
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pointwise_post_op = PointwisePostOp(unary_attr="hardswish")
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self._test_qconv_impl_cpu_tensor(
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qconv,
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qconv_prepack,
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conv_op,
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input_channels_per_group=input_channels_per_group,
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input_feature_map_shape=input_feature_map_shape,
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output_channels_per_group=output_channels_per_group,
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groups=groups,
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kernels=kernels,
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strides=strides,
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pads=pads,
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dilations=dilations,
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W_scale=W_scale,
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W_zero_point=W_zero_point,
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use_bias=use_bias,
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post_op=pointwise_post_op,
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use_channelwise=use_channelwise,
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qconv_output_dtype=output_dtype,
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)
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# Test qconv with post op sum
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_sum_pt2e(self):
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groups_list = [1, 3]
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@ -7286,6 +7302,7 @@ class TestQuantizedConv(TestCase):
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)
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# Test qconv with post op sum relu
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_sum_relu_pt2e(self):
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groups_list = [1, 3]
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@ -7338,6 +7355,7 @@ class TestQuantizedConv(TestCase):
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)
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# Test qconv with post op sum
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@unittest.skipIf(IS_FBCODE, "Skip pt2e ops in fbcode")
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@skipIfNoONEDNN
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def test_qconv2d_sum_relu_float_output_pt2e(self):
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groups = 1
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