diff --git a/paddle/fluid/operators/activation_op_npu.cc b/paddle/fluid/operators/activation_op_npu.cc index a7dc0ff530d47bd3348fa0c0704a682fb74e1a9d..2f899825c5cc75cc8c02cb824cf4bced3fbfb995 100644 --- a/paddle/fluid/operators/activation_op_npu.cc +++ b/paddle/fluid/operators/activation_op_npu.cc @@ -143,23 +143,186 @@ class ReluGradNPUKernel : public framework::OpKernel { runner.Run(stream); } }; + +template +class SqrtNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + + auto* out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + + out->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + auto runner = NpuOpRunner("Sqrt", {*x}, {*out}, {}); + runner.Run(stream); + } +}; + +template +class SqrtGradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* out = ctx.Input("Out"); + auto* dout = ctx.Input(framework::GradVarName("Out")); + + auto* dx = ctx.Output(framework::GradVarName("X")); + + auto place = ctx.GetPlace(); + + dx->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + auto dx_runner = NpuOpRunner("SqrtGrad", {*out, *dout}, {*dx}, {}); + dx_runner.Run(stream); + } +}; + +template +class LogNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + + auto* out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + + out->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + Tensor one(x->type()); + one.mutable_data(x->dims(), place); + auto one_runner = NpuOpRunner("OnesLike", {*x}, {one}, {}); + one_runner.Run(stream); + + Tensor sub(x->type()); + sub.mutable_data(x->dims(), place); + auto sub_runner = NpuOpRunner("Sub", {*x, one}, {sub}, {}); + sub_runner.Run(stream); + + auto out_runner = NpuOpRunner("Log1p", {sub}, {*out}, {}); + out_runner.Run(stream); + } +}; + +template +class LogGradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* dout = ctx.Input(framework::GradVarName("Out")); + auto* x = ctx.Input("X"); + + auto* dx = ctx.Output(framework::GradVarName("X")); + + auto place = ctx.GetPlace(); + + dx->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + auto runner = NpuOpRunner("DivNoNan", {*dout, *x}, {*dx}, {}); + runner.Run(stream); + } +}; + +template +class TanhNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + + auto* out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + + out->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + auto runner = NpuOpRunner("Tanh", {*x}, {*out}, {}); + runner.Run(stream); + } +}; + +template +class TanhGradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* dout = ctx.Input(framework::GradVarName("Out")); + auto* out = ctx.Input("Out"); + + auto* dx = ctx.Output(framework::GradVarName("X")); + + auto place = ctx.GetPlace(); + + dx->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + auto dx_runner = NpuOpRunner("TanhGrad", {*out, *dout}, {*dx}, {}); + dx_runner.Run(stream); + } +}; + +template +class SquareNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + + auto* out = ctx.Output("Out"); + + auto place = ctx.GetPlace(); + + out->mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + + auto runner = NpuOpRunner("Square", {*x}, {*out}, {}); + runner.Run(stream); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_NPU_KERNEL( - pow, ops::PowNPUKernel, + pow, + ops::PowNPUKernel, ops::PowNPUKernel); REGISTER_OP_NPU_KERNEL( - pow_grad, ops::PowGradNPUKernel, + pow_grad, + ops::PowGradNPUKernel, ops::PowGradNPUKernel); REGISTER_OP_NPU_KERNEL( - relu, ops::ReluNPUKernel, + relu, + ops::ReluNPUKernel, ops::ReluNPUKernel); @@ -168,3 +331,46 @@ REGISTER_OP_NPU_KERNEL( ops::ReluGradNPUKernel, ops::ReluGradNPUKernel); + +REGISTER_OP_NPU_KERNEL( + sqrt, + ops::SqrtNPUKernel, + ops::SqrtNPUKernel); + +REGISTER_OP_NPU_KERNEL( + sqrt_grad, + ops::SqrtGradNPUKernel, + ops::SqrtGradNPUKernel); + +REGISTER_OP_NPU_KERNEL( + log, + ops::LogNPUKernel, + ops::LogNPUKernel); + +REGISTER_OP_NPU_KERNEL( + log_grad, + ops::LogGradNPUKernel, + ops::LogGradNPUKernel); + + +REGISTER_OP_NPU_KERNEL( + tanh, + ops::TanhNPUKernel, + ops::TanhNPUKernel); + +REGISTER_OP_NPU_KERNEL( + tanh_grad, + ops::TanhGradNPUKernel, + ops::TanhGradNPUKernel); + +REGISTER_OP_NPU_KERNEL( + square, + ops::SquareNPUKernel, + ops::SquareNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..3cdd2448628a0b0f1900cc8b15d884d578a445ca --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py @@ -0,0 +1,154 @@ +# 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 TestLog(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "log" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) + out = np.log(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + 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, check_dygraph=False) + + # TODO(ascendrc): Add grad test + # def test_check_grad(self): + # if self.dtype == np.float16: + # return + # self.check_grad(['X'], 'Out') + # + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestLogFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "log" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) + out = np.log(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + self.outputs = {'Out': out} + + def set_npu(self): + self.__class__.use_npu = True + self.__class__.no_need_check_grad = True + + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestLogNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(32, 32)).astype('float32') + b_np = np.random.random(size=(32, 32)).astype('float32') + label_np = np.random.randint(2, size=(32, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') + b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') + label = paddle.static.data( + name="label", shape=[32, 1], dtype='int64') + + c = paddle.multiply(a, b) + d = paddle.log(c) + + fc_1 = fluid.layers.fc(input=d, size=128) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run( + main_prog, + feed={"a": a_np, + "b": b_np, + "label": label_np}, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred, atol=1e-4)) + self.assertTrue(np.allclose(npu_loss, cpu_loss, atol=1e-4)) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/npu/test_sqrt_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_sqrt_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..556fa76424b8b60f2efff371c833f57bdc341e40 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_sqrt_op_npu.py @@ -0,0 +1,154 @@ +# 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 TestSqrt(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "sqrt" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) + out = np.sqrt(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + 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, check_dygraph=False) + + # TODO(ascendrc): Add grad test + # def test_check_grad(self): + # if self.dtype == np.float16: + # return + # self.check_grad(['X'], 'Out') + # + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSqrtFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "sqrt" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) + out = np.sqrt(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + self.outputs = {'Out': out} + + def set_npu(self): + self.__class__.use_npu = True + self.__class__.no_need_check_grad = True + + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSqrtNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(32, 32)).astype('float32') + b_np = np.random.random(size=(32, 32)).astype('float32') + label_np = np.random.randint(2, size=(32, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') + b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') + label = paddle.static.data( + name="label", shape=[32, 1], dtype='int64') + + c = paddle.multiply(a, b) + d = paddle.sqrt(c) + + fc_1 = fluid.layers.fc(input=d, size=128) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run( + main_prog, + feed={"a": a_np, + "b": b_np, + "label": label_np}, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred)) + self.assertTrue(np.allclose(npu_loss, cpu_loss)) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/npu/test_square_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_square_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..8c1a8d0070484a3b536256a6e8aafeb20fcf0ae0 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_square_op_npu.py @@ -0,0 +1,154 @@ +# 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 TestSquare(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "square" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) + out = np.square(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + 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, check_dygraph=False) + + # TODO(ascendrc): Add grad test + # def test_check_grad(self): + # if self.dtype == np.float16: + # return + # self.check_grad(['X'], 'Out') + # + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSquareFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "square" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) + out = np.square(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + self.outputs = {'Out': out} + + def set_npu(self): + self.__class__.use_npu = True + self.__class__.no_need_check_grad = True + + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSquareNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(32, 32)).astype('float32') + b_np = np.random.random(size=(32, 32)).astype('float32') + label_np = np.random.randint(2, size=(32, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') + b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') + label = paddle.static.data( + name="label", shape=[32, 1], dtype='int64') + + c = paddle.multiply(a, b) + d = paddle.square(c) + + fc_1 = fluid.layers.fc(input=d, size=128) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run( + main_prog, + feed={"a": a_np, + "b": b_np, + "label": label_np}, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred)) + self.assertTrue(np.allclose(npu_loss, cpu_loss)) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/npu/test_tanh_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_tanh_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..235fa2783fb3c8c507ebfa73c5631c551fce4f1a --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_tanh_op_npu.py @@ -0,0 +1,154 @@ +# 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 TestTanh(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "tanh" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) + out = np.tanh(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + 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, check_dygraph=False) + + # TODO(ascendrc): Add grad test + # def test_check_grad(self): + # if self.dtype == np.float16: + # return + # self.check_grad(['X'], 'Out') + # + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestTanhFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "tanh" + self.place = paddle.NPUPlace(0) + + self.init_dtype() + np.random.seed(SEED) + x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) + out = np.tanh(x) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {} + self.outputs = {'Out': out} + + def set_npu(self): + self.__class__.use_npu = True + self.__class__.no_need_check_grad = True + + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False, atol=1e-3) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestTanhNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(32, 32)).astype('float32') + b_np = np.random.random(size=(32, 32)).astype('float32') + label_np = np.random.randint(2, size=(32, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') + b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') + label = paddle.static.data( + name="label", shape=[32, 1], dtype='int64') + + c = paddle.multiply(a, b) + d = paddle.tanh(c) + + fc_1 = fluid.layers.fc(input=d, size=128) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run( + main_prog, + feed={"a": a_np, + "b": b_np, + "label": label_np}, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred)) + self.assertTrue(np.allclose(npu_loss, cpu_loss)) + + +if __name__ == '__main__': + unittest.main()