add mlu arg_max kernel (#43624)

paddle/fluid/operators/arg_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.
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class ArgMaxMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    auto axis = static_cast<int>(ctx.Attr<int64_t>("axis"));
+    auto dtype = ctx.Attr<int>("dtype");
+    const bool& flatten = ctx.Attr<bool>("flatten");
+
+    if (x->numel() == 0) return;
+    PADDLE_ENFORCE_EQ(
+        (dtype == 2 || dtype == 3), true,
+        platform::errors::InvalidArgument(
+            "The attribute of dtype in argmax op must be [%s] or [%s], "
+            "but "
+            "received [%s]",
+            paddle::framework::DataTypeToString(
+                framework::proto::VarType::INT64),
+            paddle::framework::DataTypeToString(
+                framework::proto::VarType::INT32),
+            paddle::framework::DataTypeToString(
+                static_cast<framework::proto::VarType::Type>(dtype))));
+
+    if (axis < 0) {
+      framework::DDim x_dims;
+      x_dims = x->dims();
+      axis += x_dims.size();
+    }
+
+    framework::Tensor flatten_x(x->type());
+    flatten_x.ShareDataWith(*x);
+    if (flatten) {
+      flatten_x.Resize(phi::make_ddim({x->numel()}));
+      // if flatten, the axis just as 0
+      axis = 0;
+    }
+    std::vector<int> reduce_dims;
+    reduce_dims.push_back(axis);
+
+    auto out_dims = out->dims();
+    int out_count = out_dims[0];
+    for (int i = 1; i < out_dims.size(); i++) {
+      out_count = out_count * out_dims[i];
+    }
+    size_t indices_size_inbytes = out_count * sizeof(int32_t);
+    auto& dev_ctx = ctx.template device_context<MLUDeviceContext>();
+    framework::Tensor value_out =
+        ctx.AllocateTmpTensor<T, MLUDeviceContext>(out->dims(), dev_ctx);
+    MLUCnnlTensorDesc value_out_desc(value_out);
+    MLUCnnlTensorDesc input_desc(flatten_x, CNNL_LAYOUT_ARRAY,
+                                 ToCnnlDataType(flatten_x.dtype()));
+    MLUCnnlReduceDesc reduction_desc(
+        reduce_dims, CNNL_REDUCE_MAX_LAST_INDEX, ToCnnlDataType<T>(),
+        CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_ONLY_INDICES, CNNL_32BIT_INDICES);
+
+    if (dtype == 2) {
+      out->template mutable_data<int32_t>(ctx.GetPlace());
+      MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                      nullptr, input_desc.get(), GetBasePtr(&flatten_x),
+                      indices_size_inbytes /*indices_size*/, GetBasePtr(out),
+                      nullptr, value_out_desc.get(), GetBasePtr(&value_out));
+    } else {
+      out->template mutable_data<int64_t>(ctx.GetPlace());
+      framework::Tensor out_int32 =
+          ctx.AllocateTmpTensor<int32_t, MLUDeviceContext>(out->dims(),
+                                                           dev_ctx);
+      MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                      nullptr, input_desc.get(), GetBasePtr(&flatten_x),
+                      indices_size_inbytes /*indices_size*/,
+                      GetBasePtr(&out_int32), nullptr, value_out_desc.get(),
+                      GetBasePtr(&value_out));
+
+      // cast indices type to int64
+      MLUCnnlTensorDesc out_int32_desc(out_int32);
+      MLUCnnlTensorDesc cast_output_desc(*out);
+      cnnlCastDataType_t cast_type = GetCastDataType(VT::INT32, VT::INT64);
+      MLUCnnl::Cast(ctx, cast_type, out_int32_desc.get(),
+                    GetBasePtr(&out_int32), cast_output_desc.get(),
+                    GetBasePtr(out));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_MLU_KERNEL(arg_max, ops::ArgMaxMLUKernel<int>,
+                       ops::ArgMaxMLUKernel<float>,
+                       ops::ArgMaxMLUKernel<paddle::platform::float16>);

python/paddle/fluid/tests/unittests/mlu/test_arg_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 unittest
+import numpy as np
+import sys
+
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import Program, program_guard
+
+paddle.enable_static()
+
+
+class BaseTestCase(OpTest):
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+    def setUp(self):
+        self.set_mlu()
+        self.initTestCase()
+        self.x = (1000 * np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+# test argmax, dtype: float16
+class TestArgMaxFloat16Case1(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMaxFloat16Case2(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case3(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMaxFloat16Case4(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 2
+
+
+class TestArgMaxFloat16Case5(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMaxFloat16Case6(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case7(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMaxFloat16Case8(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (1, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case9(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (2, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case10(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+# test argmax, dtype: float32
+class TestArgMaxFloat32Case1(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case2(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case3(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case4(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 2
+
+
+class TestArgMaxFloat32Case5(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case6(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case7(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case8(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (1, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case9(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (2, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case10(BaseTestCase):
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class BaseTestComplex1_1(OpTest):
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (4, 5, 6)
+        self.dtype = 'float32'
+        self.axis = 2
+
+    def setUp(self):
+        self.set_mlu()
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {
+            'axis': self.axis,
+            'dtype': int(core.VarDesc.VarType.INT32)
+        }
+        self.outputs = {
+            'Out': np.argmax(self.x, axis=self.axis).astype("int32")
+        }
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class BaseTestComplex1_2(OpTest):
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (4, 5, 6)
+        self.dtype = 'float16'
+        self.axis = 2
+
+    def setUp(self):
+        self.set_mlu()
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {
+            'axis': self.axis,
+            'dtype': int(core.VarDesc.VarType.INT32)
+        }
+        self.outputs = {
+            'Out': np.argmax(self.x, axis=self.axis).astype("int32")
+        }
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestArgMaxAPI(unittest.TestCase):
+
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_mlu = True
+        self.place = [paddle.MLUPlace(0)]
+
+    def test_dygraph_api(self):
+
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2022)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input, axis=self.axis)
+            paddle_output = paddle.argmax(tensor_input, axis=self.axis)
+            self.assertEqual(np.allclose(numpy_output, paddle_output.numpy()),
+                             True)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestArgMaxAPI_2(unittest.TestCase):
+
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+        self.keep_dims = True
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_mlu = True
+        self.place = [paddle.MLUPlace(0)]
+
+    def test_dygraph_api(self):
+
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2022)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input,
+                                     axis=self.axis).reshape(1, 4, 5)
+            paddle_output = paddle.argmax(tensor_input,
+                                          axis=self.axis,
+                                          keepdim=self.keep_dims)
+            self.assertEqual(np.allclose(numpy_output, paddle_output.numpy()),
+                             True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestArgMaxAPI_3(unittest.TestCase):
+
+    def initTestCase(self):
+        self.dims = (1, 9)
+        self.dtype = 'float32'
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_mlu = True
+        self.place = [paddle.MLUPlace(0)]
+
+    def test_dygraph_api(self):
+
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2022)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input).reshape([1])
+            paddle_output = paddle.argmax(tensor_input)
+            self.assertEqual(np.allclose(numpy_output, paddle_output.numpy()),
+                             True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+if __name__ == '__main__':
+    unittest.main()