Support FP16 for more ops (#38123)

* support FP16 for more ops

* add amp list tests

* refine reduce_mean_grad

* fix OP benchmark ci

* fix fp16 reduce_mean

* updat ut, but still have some problems

* remove mean/reduce_mean fp16 kernel

paddle/fluid/inference/tests/api/ipu_resnet50_test.cc

@@ -112,4 +112,4 @@ TEST(Analyzer_Resnet50_ipu, compare_results_2_batch) {
 }
 
 }  // namespace inference
-}  // namespace paddle
\ No newline at end of file
+}  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_min_op.cu

@@ -41,12 +41,16 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_min,
+    ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext,
+                              paddle::platform::float16>,
     ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_min_grad,
+    ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext,
+                                  paddle::platform::float16>,
     ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, int>,

paddle/fluid/operators/elementwise/elementwise_min_op.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
 #include "paddle/fluid/platform/eigen_ext.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -67,6 +68,28 @@ struct MinGradDy {
   }
 };
 
+#ifdef PADDLE_CUDA_FP16
+template <>
+struct MinGradDx<platform::float16> {
+  HOSTDEVICE platform::float16 operator()(platform::float16 x,
+                                          platform::float16 y,
+                                          platform::float16 out,
+                                          platform::float16 dout) const {
+    return x < y ? dout : static_cast<platform::float16>(0);
+  }
+};
+
+template <>
+struct MinGradDy<platform::float16> {
+  HOSTDEVICE platform::float16 operator()(platform::float16 x,
+                                          platform::float16 y,
+                                          platform::float16 out,
+                                          platform::float16 dout) const {
+    return x >= y ? dout : static_cast<platform::float16>(0);
+  }
+};
+#endif
+
 template <typename DeviceContext, typename T>
 class ElementwiseMinGradKernel : public ElemwiseGradKernel<T> {
  public:

python/paddle/fluid/contrib/mixed_precision/fp16_lists.py

@@ -17,6 +17,9 @@ from ... import core
 
 __all__ = ["CustomOpLists", "AutoMixedPrecisionLists"]
 
+# lookup_table fp16 is slower than fp32, though fp16 is supported.
+_extra_unsupported_fp16_list = {'lookup_table', 'lookup_table_v2'}
+
 
 class AutoMixedPrecisionLists(object):
     """
@@ -60,6 +63,8 @@ class AutoMixedPrecisionLists(object):
                 elif op_name in self.gray_list:
                     self.gray_list.remove(op_name)
                 self.white_list.add(op_name)
+                if op_name in _extra_unsupported_fp16_list:
+                    self.unsupported_list.remove(op_name)
         if self._custom_black_list:
             for op_name in self._custom_black_list:
                 if op_name in self.white_list:
@@ -170,7 +175,6 @@ else:
     _, _, _sys_unsupported_fp16_list = core.op_supported_infos(
         'GPU', core.VarDesc.VarType.FP16)
 
-unsupported_fp16_list = {'lookup_table',
-                         'lookup_table_v2'} | _sys_unsupported_fp16_list
+unsupported_fp16_list = _extra_unsupported_fp16_list | _sys_unsupported_fp16_list
 
 CustomOpLists = AutoMixedPrecisionLists

python/paddle/fluid/contrib/tests/test_amp_list.py

+# Copyright (c) 2021 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.
+
+import paddle
+import unittest
+from paddle.fluid.contrib.mixed_precision.fp16_lists import AutoMixedPrecisionLists
+
+
+class TestAMPList(unittest.TestCase):
+    def test_main(self):
+        custom_white_list = [
+            'lookup_table',
+            'lookup_table_v2',
+        ]
+        amp_list = AutoMixedPrecisionLists(custom_white_list=custom_white_list)
+        for op in custom_white_list:
+            self.assertTrue(op in amp_list.white_list)
+            self.assertTrue(op not in amp_list.black_list)
+            self.assertTrue(op not in amp_list.unsupported_list)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_elementwise_min_op.py

@@ -17,6 +17,11 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+
+paddle.enable_static()
 
 
 class TestElementwiseOp(OpTest):
@@ -142,5 +147,54 @@ class TestElementwiseMinOp_broadcast_4(TestElementwiseOp):
         self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
 
 
+class TestElementwiseMinOpFP16(unittest.TestCase):
+    def get_out_and_grad(self, x_np, y_np, axis, place, use_fp32=False):
+        assert x_np.dtype == np.float16
+        assert y_np.dtype == np.float16
+        if use_fp32:
+            x_np = x_np.astype(np.float32)
+            y_np = y_np.astype(np.float32)
+        dtype = np.float16
+
+        with fluid.dygraph.guard(place):
+            x = paddle.to_tensor(x_np)
+            y = paddle.to_tensor(y_np)
+            x.stop_gradient = False
+            y.stop_gradient = False
+            z = fluid.layers.elementwise_min(x, y, axis)
+            x_g, y_g = paddle.grad([z], [x, y])
+            return z.numpy().astype(dtype), x_g.numpy().astype(
+                dtype), y_g.numpy().astype(dtype)
+
+    def check_main(self, x_shape, y_shape, axis=-1):
+        if not paddle.is_compiled_with_cuda():
+            return
+        place = paddle.CUDAPlace(0)
+        if not core.is_float16_supported(place):
+            return
+
+        x_np = np.random.random(size=x_shape).astype(np.float16)
+        y_np = np.random.random(size=y_shape).astype(np.float16)
+
+        z_1, x_g_1, y_g_1 = self.get_out_and_grad(x_np, y_np, axis, place,
+                                                  False)
+        z_2, x_g_2, y_g_2 = self.get_out_and_grad(x_np, y_np, axis, place, True)
+        self.assertTrue(np.array_equal(z_1, z_2), "{} vs {}".format(z_1, z_2))
+        self.assertTrue(
+            np.array_equal(x_g_1, x_g_2), "{} vs {}".format(x_g_1, x_g_2))
+        self.assertTrue(
+            np.array_equal(y_g_1, y_g_2), "{} vs {}".format(y_g_1, y_g_2))
+
+    def test_main(self):
+        self.check_main((13, 17), (13, 17))
+        self.check_main((10, 3, 4), (1, ))
+        self.check_main((100, ), (100, ))
+        self.check_main((100, 3, 2), (100, ), 0)
+        self.check_main((2, 100, 3), (100, ), 1)
+        self.check_main((2, 3, 100), (100, ))
+        self.check_main((2, 25, 4, 1), (25, 4), 1)
+        self.check_main((2, 10, 2, 5), (2, 10, 1, 5))
+
+
 if __name__ == '__main__':
     unittest.main()