[MLU]: add elementwise_max mlu kernel (#43365)

* [MLU]: add elementwise_max mlu kernel

* [MLU]: add int32 support for elementwise maxk MLU kernel

paddle/fluid/operators/elementwise/elementwise_max_op_mlu.cc

+/* 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. */
+
+#ifdef PADDLE_WITH_MLU
+
+#include "paddle/fluid/operators/elementwise/elementwise_mlu.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class ElementwiseMaxMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    MLUBinaryOp<MAXIMUM, T>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_MLU_KERNEL(elementwise_max, ops::ElementwiseMaxMLUKernel<int>,
+                       ops::ElementwiseMaxMLUKernel<float>,
+                       ops::ElementwiseMaxMLUKernel<paddle::platform::float16>);
+#endif

paddle/fluid/operators/elementwise/elementwise_mlu.h

@@ -108,6 +108,7 @@ void MLUOpTensorKernel(const framework::ExecutionContext& ctx,
 enum BINARY_FUNCTOR {
   DIV,
   DIVNONAN,
+  MAXIMUM,
 };
 
 template <BINARY_FUNCTOR func>
@@ -126,6 +127,16 @@ inline void MLUBinary<DIV>(const framework::ExecutionContext& ctx,
   MLUCnnl::Div(ctx, prefer, x_desc, x, y_desc, y, out_desc, out);
 }
 
+template <>
+inline void MLUBinary<MAXIMUM>(
+    const framework::ExecutionContext& ctx,
+    cnnlComputationPreference_t prefer,  // useless, only for compatible
+    const cnnlTensorDescriptor_t x_desc, const void* x,
+    const cnnlTensorDescriptor_t y_desc, const void* y,
+    const cnnlTensorDescriptor_t out_desc, void* out) {
+  MLUCnnl::Maximum(ctx, x_desc, x, y_desc, y, out_desc, out);
+}
+
 template <BINARY_FUNCTOR Functor, typename T>
 void MLUBinaryOp(const framework::ExecutionContext& ctx) {
   auto* x = ctx.Input<Tensor>("X");

python/paddle/fluid/tests/unittests/mlu/test_elementwise_max_op_mlu.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 numpy as np
+import unittest
+import sys
+
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.core import ops
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestElementwiseMax(OpTest):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        y = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        out = np.maximum(x, y)
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(y)
+        }
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestElementwiseMaxFp16(OpTest):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
+        y = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
+        out = np.maximum(x, y)
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(y)
+        }
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.__class__.no_need_check_grad = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestElementwiseMaxInt32(OpTest):
+
+    def init_dtype(self):
+        self.dtype = np.int32
+
+
+class TestTestElementwiseMax_Vector(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseMax_broadcast_0(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [100, 3, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out': np.maximum(self.inputs['X'],
+                              self.inputs['Y'].reshape(100, 1, 1))
+        }
+
+
+class TestTestElementwiseMax_broadcast_1(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 100, 4]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out': np.maximum(self.inputs['X'],
+                              self.inputs['Y'].reshape(1, 100, 1))
+        }
+
+
+class TestTestElementwiseMax_broadcast_2(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [100]).astype("float32")
+        }
+
+        self.outputs = {
+            'Out': np.maximum(self.inputs['X'],
+                              self.inputs['Y'].reshape(1, 1, 100))
+        }
+
+
+class TestTestElementwiseMax_broadcast_3(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 10, 12, 5]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [10, 12]).astype("float32")
+        }
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 10, 12, 1))
+        }
+
+
+class TestTestElementwiseMax_broadcast_4(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 50]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 1, 50]).astype("float32")
+        }
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseMax_broadcast_5(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 4, 20]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 3, 1, 20]).astype("float32")
+        }
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseMax_commonuse_1(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [1, 1, 100]).astype("float32"),
+        }
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseMax_commonuse_2(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [30, 3, 1, 5]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [30, 1, 4, 1]).astype("float32"),
+        }
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestTestElementwiseMax_xsize_lessthan_ysize(TestElementwiseMax):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_max"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [10, 12]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [2, 3, 10, 12]).astype("float32"),
+        }
+
+        self.attrs = {'axis': 2}
+
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+if __name__ == '__main__':
+    unittest.main()