add mlu label_smooth kernel (#43743)

paddle/fluid/operators/label_smooth_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 {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+class LabelSmoothMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in_t = ctx.Input<LoDTensor>("X");
+    auto* dist_t = ctx.Input<Tensor>("PriorDist");
+    auto* out_t = ctx.Output<LoDTensor>("Out");
+    auto epsilon = ctx.Attr<float>("epsilon");
+    auto epsilon_gt = 1.0f - epsilon;
+
+    if (in_t->numel() == 0) return;
+    out_t->mutable_data<T>(ctx.GetPlace());
+    auto label_dim = in_t->dims()[in_t->dims().size() - 1];
+
+    MLUCnnlTensorDesc x_desc(*in_t);
+    MLUCnnlTensorDesc out_desc(*out_t);
+    auto data_type = ToCnnlDataType<T>();
+    MLUCnnlOpTensorDesc op_tensor_desc(
+        CNNL_OP_TENSOR_ADD, data_type, CNNL_NOT_PROPAGATE_NAN);
+    if (ctx.HasInput("PriorDist")) {
+      MLUCnnlTensorDesc dist_desc(*dist_t);
+      MLUCnnl::OpTensor(ctx,
+                        op_tensor_desc.get(),
+                        x_desc.get(),
+                        GetBasePtr(in_t),
+                        dist_desc.get(),
+                        GetBasePtr(dist_t),
+                        out_desc.get(),
+                        GetBasePtr(out_t),
+                        data_type,
+                        epsilon_gt,
+                        epsilon);
+    } else {
+      auto& dev_ctx = ctx.template device_context<MLUDeviceContext>();
+      framework::Tensor dist_tensor =
+          ctx.AllocateTmpTensor<T, MLUDeviceContext>({1, label_dim}, dev_ctx);
+      MLUCnnlTensorDesc dist_desc(dist_tensor);
+      auto value = static_cast<T>(1.0f / label_dim);
+      MLUCnnl::Fill(ctx,
+                    CNNL_POINTER_MODE_HOST,
+                    &value,
+                    dist_desc.get(),
+                    GetBasePtr(&dist_tensor));
+      MLUCnnl::OpTensor(ctx,
+                        op_tensor_desc.get(),
+                        x_desc.get(),
+                        GetBasePtr(in_t),
+                        dist_desc.get(),
+                        GetBasePtr(&dist_tensor),
+                        out_desc.get(),
+                        GetBasePtr(out_t),
+                        data_type,
+                        epsilon_gt,
+                        epsilon);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(label_smooth,
+                       ops::LabelSmoothMLUKernel<float>,
+                       ops::LabelSmoothMLUKernel<plat::float16>);

python/paddle/fluid/tests/unittests/mlu/test_label_smooth_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
+
+SEED = 2022
+paddle.enable_static()
+
+
+class TestLabelSmoothOp(OpTest):
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def config(self):
+        self.op_type = "label_smooth"
+        self.place = paddle.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.__class__.no_need_check_grad = True
+        self.epsilon = 0.1
+        batch_size, self.label_dim = 10, 12
+        np.random.seed(SEED)
+        self.label = np.zeros((batch_size, self.label_dim)).astype(self.dtype)
+        nonzero_index = np.random.randint(self.label_dim, size=(batch_size))
+        self.label[np.arange(batch_size), nonzero_index] = 1
+
+    def setUp(self):
+        self.init_dtype()
+        self.config()
+        smoothed_label = (
+            1 - self.epsilon) * self.label + self.epsilon / self.label_dim
+        smoothed_label = smoothed_label.astype(self.dtype)
+        self.inputs = {'X': self.label}
+        self.attrs = {'epsilon': self.epsilon}
+        self.outputs = {'Out': smoothed_label}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestLabelSmoothOpWithPriorDist(TestLabelSmoothOp):
+
+    def setUp(self):
+        self.init_dtype()
+        self.config()
+        dist = np.random.random((1, self.label_dim)).astype(self.dtype)
+        smoothed_label = (1 - self.epsilon) * self.label + self.epsilon * dist
+        smoothed_label = smoothed_label.astype(self.dtype)
+        self.inputs = {'X': self.label, 'PriorDist': dist}
+        self.attrs = {'epsilon': self.epsilon}
+        self.outputs = {'Out': smoothed_label}
+
+
+class TestLabelSmoothOp3D(TestLabelSmoothOp):
+
+    def setUp(self):
+        super(TestLabelSmoothOp3D, self).setUp()
+        self.inputs['X'] = self.inputs['X'].reshape(
+            [2, -1, self.inputs['X'].shape[-1]])
+        self.outputs['Out'] = self.outputs['Out'].reshape(
+            self.inputs['X'].shape)
+
+
+class TestLabelSmoothOpWithPriorDist3D(TestLabelSmoothOpWithPriorDist):
+
+    def setUp(self):
+        super(TestLabelSmoothOpWithPriorDist3D, self).setUp()
+        self.inputs['X'] = self.inputs['X'].reshape(
+            [2, -1, self.inputs['X'].shape[-1]])
+        self.outputs['Out'] = self.outputs['Out'].reshape(
+            self.inputs['X'].shape)
+
+
+class TestLabelSmoothOpFP16(TestLabelSmoothOp):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestLabelSmoothOpWithPriorDistFP16(TestLabelSmoothOpWithPriorDist):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestLabelSmoothOp3DFP16(TestLabelSmoothOp3D):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestLabelSmoothOpWithPriorDist3DFP16(TestLabelSmoothOpWithPriorDist3D):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+if __name__ == '__main__':
+    unittest.main()