add log_softmax_op_npu

paddle/fluid/operators/log_softmax_op_npu.cc

@@ -0,0 +1,51 @@
+// 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/log_softmax_op.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+namespace paddle {
+namespace operators {
+template <typename DeviceContext, typename T>
+class LogSoftmaxNPUKernel : 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");
+    const int rank = X->dims().size();
+    const int axis = CanonicalAxis(ctx.Attr<int>("axis"), rank);
+    std::vector<int> axes;
+    axes.push_back(axis);
+    framework::NPUAttributeMap attr_input = {{"axes", axes}};
+    Out->mutable_data<T>(ctx.GetPlace());
+    const auto& runner = NpuOpRunner("LogSoftmaxV2", {*X}, {*Out}, attr_input);
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(
+    log_softmax,
+    ops::LogSoftmaxNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::LogSoftmaxNPUKernel<paddle::platform::NPUDeviceContext, double>,
+    // ops::LogSoftmaxNPUKernel<paddle::platform::NPUDeviceContext, int>, //
+    // used to debug
+    ops::LogSoftmaxNPUKernel<paddle::platform::NPUDeviceContext,
+                             paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_log_softmax_op_npu.py

@@ -0,0 +1,111 @@
+# 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
+from paddle.fluid import core
+import paddle.nn.functional as F
+paddle.enable_static()
+np.random.seed(10)
+
+
+def ref_log_softmax(x):
+    shiftx = (x - np.max(x))
+    out = shiftx - np.log(np.exp(shiftx).sum())
+    return out
+
+
+def ref_log_softmax_grad(x, axis):
+    if axis < 0:
+        axis += len(x.shape)
+    out = np.apply_along_axis(ref_log_softmax, axis, x)
+    axis_dim = x.shape[axis]
+    dout = np.full_like(x, fill_value=1. / x.size)
+    dx = dout - np.exp(out) * dout.copy().sum(axis=axis, keepdims=True).repeat(
+        axis_dim, axis=axis)
+    return dx
+
+
+class TestLogSoftmaxNPUOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.place = paddle.NPUPlace(0)
+        self.op_type = "log_softmax"
+        self.dtype = np.float32
+        self.shape = [2, 3, 4, 5]
+        self.axis = -1
+        self.set_attrs()
+        self.set_dtype()
+        x = np.random.uniform(0.1, 1., self.shape).astype(self.dtype)
+        out = np.apply_along_axis(ref_log_softmax, self.axis, x)
+        self.x_grad = ref_log_softmax_grad(x, self.axis)
+        self.inputs = {'X': x}
+        self.outputs = {'Out': out}
+        self.attrs = {'axis': self.axis}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.__class__.no_need_check_grad = True
+
+    def set_attrs(self):
+        pass
+
+    def set_dtype(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        pass
+
+
+def test_class(op_type, typename):
+    class TestLogSoftmaxShape(TestLogSoftmaxNPUOp):
+        def set_attrs(self):
+            self.shape = [12, 10]
+
+        def set_dtype(self):
+            self.dtype = typename
+
+    cls_name = "{0}_{1}_1".format(op_type, typename)
+    TestLogSoftmaxShape.__name__ = cls_name
+    globals()[cls_name] = TestLogSoftmaxShape
+
+
+def test_class2(op_type, typename):
+    class TestLogSoftmaxAxis(TestLogSoftmaxNPUOp):
+        def set_attrs(self):
+            self.axis = 0
+
+        def set_dtype(self):
+            self.dtype = typename
+
+    cls_name = "{0}_{1}_2".format(op_type, typename)
+
+    TestLogSoftmaxAxis.__name__ = cls_name
+    globals()[cls_name] = TestLogSoftmaxAxis
+
+
+for _typename in {'float32'}:
+    test_class("logsoftmax", _typename)
+    test_class2("logsoftmax", _typename)
+if __name__ == '__main__':
+    unittest.main()