[MLU]add mlu kernel for log_softmax op (#43040)

paddle/fluid/operators/softmax_op_mlu.cc

@@ -19,7 +19,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T>
+template <cnnlSoftmaxAlgorithm_t softmax_algo, typename T>
 class SoftmaxMLUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
@@ -45,7 +45,7 @@ class SoftmaxMLUKernel : public framework::OpKernel<T> {
       regard_in_shape = {d1, d2, d3};
     }
 
-    static const cnnlSoftmaxAlgorithm_t algo = CNNL_SOFTMAX_ACCURATE;
+    static const cnnlSoftmaxAlgorithm_t algo = softmax_algo;
     MLUCnnlTensorDesc in_desc(cnnl_softmax_dims, regard_in_shape.data(),
                               ToCnnlDataType<T>());
     MLUCnnl::SoftmaxForward(ctx, algo, mode, NULL, in_desc.get(),
@@ -54,7 +54,7 @@ class SoftmaxMLUKernel : public framework::OpKernel<T> {
   }
 };
 
-template <typename T>
+template <cnnlSoftmaxAlgorithm_t softmax_algo, typename T>
 class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
@@ -82,7 +82,7 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
       regard_out_shape = {d1, d2, d3};
     }
 
-    static const cnnlSoftmaxAlgorithm_t algo = CNNL_SOFTMAX_ACCURATE;
+    static const cnnlSoftmaxAlgorithm_t algo = softmax_algo;
     MLUCnnlTensorDesc out_desc(cnnl_softmax_dims, regard_out_shape.data(),
                                ToCnnlDataType<T>());
     MLUCnnl::SoftmaxBackward(ctx, algo, mode, out_desc.get(), GetBasePtr(out),
@@ -97,7 +97,16 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 
-REGISTER_OP_MLU_KERNEL(softmax, ops::SoftmaxMLUKernel<float>,
-                       ops::SoftmaxMLUKernel<plat::float16>);
-REGISTER_OP_MLU_KERNEL(softmax_grad, ops::SoftmaxGradMLUKernel<float>,
-                       ops::SoftmaxGradMLUKernel<paddle::platform::float16>);
+REGISTER_OP_MLU_KERNEL(
+    softmax, ops::SoftmaxMLUKernel<CNNL_SOFTMAX_ACCURATE, float>,
+    ops::SoftmaxMLUKernel<CNNL_SOFTMAX_ACCURATE, plat::float16>);
+REGISTER_OP_MLU_KERNEL(softmax_grad,
+                       ops::SoftmaxGradMLUKernel<CNNL_SOFTMAX_ACCURATE, float>,
+                       ops::SoftmaxGradMLUKernel<CNNL_SOFTMAX_ACCURATE,
+                                                 paddle::platform::float16>);
+REGISTER_OP_MLU_KERNEL(
+    log_softmax, ops::SoftmaxMLUKernel<CNNL_SOFTMAX_LOG, float>,
+    ops::SoftmaxMLUKernel<CNNL_SOFTMAX_ACCURATE, plat::float16>);
+REGISTER_OP_MLU_KERNEL(
+    log_softmax_grad, ops::SoftmaxGradMLUKernel<CNNL_SOFTMAX_LOG, float>,
+    ops::SoftmaxGradMLUKernel<CNNL_SOFTMAX_LOG, paddle::platform::float16>);

python/paddle/fluid/tests/unittests/mlu/test_log_softmax_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.
+
+import unittest
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
+import paddle
+import paddle.fluid.core as core
+import paddle.nn.functional as F
+
+np.random.seed(10)
+paddle.enable_static()
+
+
+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 TestLogSoftmaxOp(OpTest):
+    def setUp(self):
+        self.op_type = 'log_softmax'
+        self.set_mlu()
+        self.python_api = F.log_softmax
+        self.dtype = 'float32'
+        self.shape = [2, 3, 4, 5]
+        self.axis = -1
+        self.set_attrs()
+
+        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_attrs(self):
+        pass
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(
+            self.place, ['X'], ['Out'], user_defined_grads=[self.x_grad])
+
+
+class TestLogSoftmaxShape(TestLogSoftmaxOp):
+    def set_attrs(self):
+        self.shape = [12, 10]
+
+
+class TestLogSoftmaxAxis(TestLogSoftmaxOp):
+    def set_attrs(self):
+        self.axis = 1
+
+
+class TestNNLogSoftmaxAPI(unittest.TestCase):
+    def setUp(self):
+        self.set_mlu()
+        self.x_shape = [2, 3, 4, 5]
+        self.x = np.random.uniform(-1., 1., self.x_shape).astype(np.float32)
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def check_api(self, axis=-1):
+        ref_out = np.apply_along_axis(ref_log_softmax, axis, self.x)
+
+        logsoftmax = paddle.nn.LogSoftmax(axis)
+        # test static api
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data(name='x', shape=self.x_shape)
+            y = logsoftmax(x)
+            exe = paddle.static.Executor(self.place)
+            out = exe.run(feed={'x': self.x}, fetch_list=[y])
+        self.assertTrue(np.allclose(out[0], ref_out))
+
+        # test dygrapg api
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        y = logsoftmax(x)
+        self.assertTrue(np.allclose(y.numpy(), ref_out))
+        paddle.enable_static()
+
+    def test_check_api(self):
+        for axis in [-1, 1]:
+            self.check_api(axis)
+
+
+class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
+    def setUp(self):
+        self.set_mlu()
+        self.x_shape = [2, 3, 4, 5]
+        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def check_api(self, axis=-1, dtype=None):
+        x = self.x.copy()
+        if dtype is not None:
+            x = x.astype(dtype)
+        ref_out = np.apply_along_axis(ref_log_softmax, axis, x)
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data(name='x', shape=self.x_shape)
+            y = F.log_softmax(x, axis, dtype)
+            exe = paddle.static.Executor(self.place)
+            out = exe.run(feed={'x': self.x}, fetch_list=[y])
+        self.assertTrue(np.allclose(out[0], ref_out))
+
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        y = F.log_softmax(x, axis, dtype)
+        self.assertTrue(np.allclose(y.numpy(), ref_out), True)
+        paddle.enable_static()
+
+    def test_check_api(self):
+        for axis in [-1, 1]:
+            self.check_api(axis)
+        self.check_api(-1, 'float32')
+
+    def test_errors(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data(name='X1', shape=[100], dtype='int32')
+            self.assertRaises(TypeError, F.log_softmax, x)
+
+            x = paddle.fluid.data(name='X2', shape=[100], dtype='float32')
+            self.assertRaises(TypeError, F.log_softmax, x, dtype='int32')
+
+
+if __name__ == "__main__":
+    unittest.main()