未验证 提交 586f9429 编写于 作者: C cambriconhsq 提交者: GitHub

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

上级 2d6dd55f
...@@ -19,7 +19,7 @@ limitations under the License. */ ...@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle { namespace paddle {
namespace operators { namespace operators {
template <typename T> template <cnnlSoftmaxAlgorithm_t softmax_algo, typename T>
class SoftmaxMLUKernel : public framework::OpKernel<T> { class SoftmaxMLUKernel : public framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext& ctx) const override { void Compute(const framework::ExecutionContext& ctx) const override {
...@@ -45,7 +45,7 @@ class SoftmaxMLUKernel : public framework::OpKernel<T> { ...@@ -45,7 +45,7 @@ class SoftmaxMLUKernel : public framework::OpKernel<T> {
regard_in_shape = {d1, d2, d3}; 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(), MLUCnnlTensorDesc in_desc(cnnl_softmax_dims, regard_in_shape.data(),
ToCnnlDataType<T>()); ToCnnlDataType<T>());
MLUCnnl::SoftmaxForward(ctx, algo, mode, NULL, in_desc.get(), MLUCnnl::SoftmaxForward(ctx, algo, mode, NULL, in_desc.get(),
...@@ -54,7 +54,7 @@ class SoftmaxMLUKernel : public framework::OpKernel<T> { ...@@ -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> { class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext& ctx) const override { void Compute(const framework::ExecutionContext& ctx) const override {
...@@ -82,7 +82,7 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> { ...@@ -82,7 +82,7 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
regard_out_shape = {d1, d2, d3}; 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(), MLUCnnlTensorDesc out_desc(cnnl_softmax_dims, regard_out_shape.data(),
ToCnnlDataType<T>()); ToCnnlDataType<T>());
MLUCnnl::SoftmaxBackward(ctx, algo, mode, out_desc.get(), GetBasePtr(out), MLUCnnl::SoftmaxBackward(ctx, algo, mode, out_desc.get(), GetBasePtr(out),
...@@ -97,7 +97,16 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> { ...@@ -97,7 +97,16 @@ class SoftmaxGradMLUKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators; namespace ops = paddle::operators;
namespace plat = paddle::platform; namespace plat = paddle::platform;
REGISTER_OP_MLU_KERNEL(softmax, ops::SoftmaxMLUKernel<float>, REGISTER_OP_MLU_KERNEL(
ops::SoftmaxMLUKernel<plat::float16>); softmax, ops::SoftmaxMLUKernel<CNNL_SOFTMAX_ACCURATE, float>,
REGISTER_OP_MLU_KERNEL(softmax_grad, ops::SoftmaxGradMLUKernel<float>, ops::SoftmaxMLUKernel<CNNL_SOFTMAX_ACCURATE, plat::float16>);
ops::SoftmaxGradMLUKernel<paddle::platform::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>);
# 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()
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