[NPU] Add label_smooth_op (#34828)

* [NPU] Add label_smooth_op

* [NPU] Add label_smooth_op

paddle/fluid/operators/label_smooth_op_npu.cc

+// 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.
+
+#include "paddle/fluid/operators/label_smooth_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+void LabelSmoothMuls(const platform::Place& place, const aclrtStream& stream,
+                     const Tensor* in, float val, Tensor* out) {
+  out->mutable_data<T>(in->dims(), place);
+  const auto& runner = NpuOpRunner("Muls", {*in}, {*out}, {{"value", val}});
+  runner.Run(stream);
+}
+
+template <typename T>
+void LabelSmoothAdds(const platform::Place& place, const aclrtStream& stream,
+                     const Tensor* in, float val, Tensor* out) {
+  out->mutable_data<T>(in->dims(), place);
+  const auto& runner = NpuOpRunner("Adds", {*in}, {*out}, {{"value", val}});
+  runner.Run(stream);
+}
+
+template <typename T>
+void LabelSmoothAddBroadCast(const platform::Place& place,
+                             const aclrtStream& stream, const Tensor* in1,
+                             const Tensor* in2, Tensor* out) {
+  out->mutable_data<T>(place);
+  const auto& runner = NpuOpRunner("AddV2", {*in1, *in2}, {*out}, {});
+  runner.Run(stream);
+}
+
+template <typename T>
+class LabelSmoothNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* out_t = ctx.Output<LoDTensor>("Out");
+    auto* in_t = ctx.Input<LoDTensor>("X");
+    auto* dist_t = ctx.Input<Tensor>("PriorDist");
+    auto epsilon = ctx.Attr<float>("epsilon");
+
+    auto label_dim = in_t->dims()[in_t->dims().size() - 1];
+    auto place = ctx.GetPlace();
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    if (dist_t) {
+      Tensor tmp;
+      Tensor dist;
+      Tensor tmp2;
+      LabelSmoothMuls<T>(place, stream, in_t, (1 - epsilon), &tmp);
+      LabelSmoothMuls<T>(place, stream, dist_t, epsilon, &tmp2);
+      tmp2.Resize({1, label_dim});
+      LabelSmoothAddBroadCast<T>(place, stream, &tmp, &tmp2, out_t);
+    } else {
+      Tensor tmp;
+      LabelSmoothMuls<T>(place, stream, in_t, (1 - epsilon), &tmp);
+      LabelSmoothAdds<T>(place, stream, &tmp, (epsilon / label_dim), out_t);
+    }
+  }
+};
+
+template <typename T>
+class LabelSmoothGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* d_in_t = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto epsilon = ctx.Attr<float>("epsilon");
+
+    auto place = ctx.GetPlace();
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    LabelSmoothMuls<T>(place, stream, d_out_t, 1 - epsilon, d_in_t);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(label_smooth, ops::LabelSmoothNPUKernel<float>,
+                       ops::LabelSmoothNPUKernel<plat::float16>);
+REGISTER_OP_NPU_KERNEL(label_smooth_grad, ops::LabelSmoothGradNPUKernel<float>,
+                       ops::LabelSmoothGradNPUKernel<plat::float16>);

python/paddle/fluid/tests/unittests/npu/test_label_smooth_op_npu.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.
+
+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
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLabelSmoothOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "label_smooth"
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+        np.random.seed(SEED)
+
+        self.set_inputs()
+        self.set_attrs()
+        self.set_outputs()
+
+    def calc_out(self, label, epsilon, dist=None):
+        label_dim = label.shape[-1]
+        y = (1 - epsilon) * label
+        if dist is not None:
+            y += epsilon * dist
+        else:
+            y += epsilon / label_dim
+        return y.astype(self.dtype)
+
+    def set_inputs(self):
+        batch_size, label_dim = 10, 12
+        x = np.zeros((batch_size, label_dim)).astype(self.dtype)
+        nonzero_index = np.random.randint(label_dim, size=(batch_size))
+        x[np.arange(batch_size), nonzero_index] = 1
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+
+    def set_attrs(self):
+        epsilon = 0.1
+        self.attrs = {"epsilon": epsilon}
+
+    def set_outputs(self):
+        dist = None if 'PriorDist' not in self.inputs else self.inputs[
+            'PriorDist']
+        out = self.calc_out(self.inputs['X'], self.attrs['epsilon'], dist)
+        self.outputs = {'Out': out}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+
+
+class TestLabelSmoothOpWithPriorDist(TestLabelSmoothOp):
+    def set_inputs(self):
+        super(TestLabelSmoothOpWithPriorDist, self).set_inputs()
+        label_dim = self.inputs['X'].shape[-1]
+        dist = np.random.random((1, label_dim)).astype(self.dtype)
+        self.inputs['PriorDist'] = dist
+
+
+class TestLabelSmoothOp3D(TestLabelSmoothOp):
+    def set_inputs(self):
+        super(TestLabelSmoothOp3D, self).set_inputs()
+        self.inputs['X'].reshape([2, -1, self.inputs['X'].shape[-1]])
+
+
+class TestLabelSmoothOpWithPriorDist3D(TestLabelSmoothOpWithPriorDist):
+    def set_inputs(self):
+        super(TestLabelSmoothOpWithPriorDist3D, self).set_inputs()
+        self.inputs['X'].reshape([2, -1, self.inputs['X'].shape[-1]])
+
+
+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()