remove fp16 support of depthwise_conv2d and add unittest for depthwise_conv2d, test=kunlun (#43483)

paddle/fluid/operators/conv_op_xpu.cc

@@ -173,12 +173,8 @@ REGISTER_OP_XPU_KERNEL(
                                paddle::platform::float16>);
 REGISTER_OP_XPU_KERNEL(
     depthwise_conv2d,
-    ops::GemmConvXPUKernel<paddle::platform::XPUDeviceContext, float>,
-    ops::GemmConvXPUKernel<paddle::platform::XPUDeviceContext,
-                           paddle::platform::float16>);
+    ops::GemmConvXPUKernel<paddle::platform::XPUDeviceContext, float>);
 REGISTER_OP_XPU_KERNEL(
     depthwise_conv2d_grad,
-    ops::GemmConvGradXPUKernel<paddle::platform::XPUDeviceContext, float>,
-    ops::GemmConvGradXPUKernel<paddle::platform::XPUDeviceContext,
-                               paddle::platform::float16>);
+    ops::GemmConvGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
 #endif

paddle/fluid/platform/device/xpu/xpu2_op_list.h

@@ -79,11 +79,9 @@ XPUOpMap& get_kl2_ops() {
       {"conv2d_transpose",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"depthwise_conv2d_grad",
-       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
-                     pOpKernelType(vartype::FP16, XPUPlace())})},
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"depthwise_conv2d",
-       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
-                     pOpKernelType(vartype::FP16, XPUPlace())})},
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"dropout_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},

python/paddle/fluid/tests/unittests/xpu/test_depthwise_conv2d_op_xpu.py

+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import sys
+
+sys.path.append("..")
+import unittest
+import numpy as np
+
+import paddle
+
+paddle.enable_static()
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test_xpu import XPUOpTest
+from paddle.fluid import Program, program_guard
+from test_conv2d_op_xpu import XPUTestConv2DOp, XPUTestConv2DOp_v2
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
+
+
+class XPUTestDepthwiseConv2DOp(XPUOpTestWrapper):
+
+    def __init__(self):
+        self.op_name = 'depthwise_conv2d'
+        self.use_dynamic_create_class = False
+
+    class TestDepthwiseConv(XPUTestConv2DOp.TestConv2DOp):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [2, 2]
+            self.input_size = [2, 12, 5, 5]  # NCHW
+            self.groups = 12
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [12, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+    class TestDepthwiseConv2(XPUTestConv2DOp.TestConv2DOp):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [1, 1]
+            self.input_size = [2, 12, 5, 5]  # NCHW
+            self.groups = 12
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [12, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+    class TestDepthwiseConv3(XPUTestConv2DOp.TestConv2DOp):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [1, 1]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+    class TestDepthwiseConvWithDilation(XPUTestConv2DOp.TestConv2DOp):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [2, 2]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            self.dilations = [2, 2]
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+    class TestDepthwiseConvWithDilation2(XPUTestConv2DOp.TestConv2DOp):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [1, 1]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            self.dilations = [2, 2]
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+
+class XPUTestDepthwiseConv2DOp_v2(XPUOpTestWrapper):
+
+    def __init__(self):
+        self.op_name = 'depthwise_conv2d'
+        self.use_dynamic_create_class = False
+
+    class TestDepthwiseConv_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.stride = [2, 2]
+            self.input_size = [2, 12, 5, 5]  # NCHW
+            self.groups = 12
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [12, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+        def init_paddings(self):
+            self.pad = [1, 1, 0, 1]
+            self.padding_algorithm = "EXPLICIT"
+
+    class TestDepthwiseConv2_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.stride = [1, 1]
+            self.input_size = [2, 12, 5, 5]  # NCHW
+            self.groups = 12
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [12, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+        def init_paddings(self):
+            self.pad = [0, 1, 0, 2]
+            self.padding_algorithm = "EXPLICIT"
+
+    class TestDepthwiseConv3_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.stride = [1, 1]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+        def init_paddings(self):
+            self.pad = [1, 1, 0, 0]
+            self.padding_algorithm = "EXPLICIT"
+
+    class TestDepthwiseConvWithDilation_AsyPadding(
+            XPUTestConv2DOp_v2.TestConv2DOp_v2):
+
+        def init_test_case(self):
+            self.use_cuda = False
+            self.pad = [1, 1]
+            self.stride = [2, 2]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            self.dilations = [2, 2]
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+        def init_paddings(self):
+            self.pad = [1, 1, 2, 1]
+            self.padding_algorithm = "EXPLICIT"
+
+    class TestDepthwiseConvWithDilation2_AsyPadding(
+            XPUTestConv2DOp_v2.TestConv2DOp_v2):
+
+        def init_test_case(self):
+            self.use_cuda = True
+            self.pad = [1, 1]
+            self.stride = [1, 1]
+            self.input_size = [2, 24, 5, 5]  # NCHW
+            self.groups = 24
+            self.dilations = [2, 2]
+            assert np.mod(self.input_size[1], self.groups) == 0
+            f_c = self.input_size[1] // self.groups
+            self.filter_size = [24, f_c, 3, 3]
+            self.op_type = "depthwise_conv2d"
+
+        def init_paddings(self):
+            self.pad = [0, 1, 1, 0]
+            self.padding_algorithm = "EXPLICIT"
+
+
+support_types = get_xpu_op_support_types('depthwise_conv2d')
+for stype in support_types:
+    create_test_class(globals(), XPUTestDepthwiseConv2DOp, stype)
+    create_test_class(globals(), XPUTestDepthwiseConv2DOp_v2, stype)
+
+#### depthwise conv2d
+
+# create_test_padding_SAME_class(TestDepthwiseConv_AsyPadding)
+# create_test_padding_SAME_class(TestDepthwiseConvWithDilation_AsyPadding)
+# create_test_padding_SAME_class(TestDepthwiseConvandFuse_AsyPadding)
+# create_test_padding_SAME_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
+
+# create_test_padding_VALID_class(TestDepthwiseConv_AsyPadding)
+# create_test_padding_VALID_class(TestDepthwiseConvWithDilation_AsyPadding)
+# create_test_padding_VALID_class(TestDepthwiseConvandFuse_AsyPadding)
+# create_test_padding_VALID_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
+
+#### channel last
+
+# create_test_channel_last_class(TestDepthwiseConv_AsyPadding)
+# create_test_channel_last_class(TestDepthwiseConvWithDilation2_AsyPadding)
+# create_test_channel_last_class(TestDepthwiseConvandFuse_AsyPadding)
+# create_test_channel_last_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
+
+if __name__ == '__main__':
+    unittest.main()