From 6151ccd482cd20fe399eb2a7c46adb4d977e0289 Mon Sep 17 00:00:00 2001 From: veyron95 <87417304+veyron95@users.noreply.github.com> Date: Thu, 5 Aug 2021 18:01:05 +0800 Subject: [PATCH] [NPU] Support npu op: (1) cos (2) cos_grad (#34573) * [NPU] Support npu op: (1) cos (2) cos_grad * Update test_cos_op_npu.py * Update activation_op_npu.cc * rm redundant {1} --- paddle/fluid/operators/activation_op_npu.cc | 65 ++++++++ .../tests/unittests/npu/test_cos_op_npu.py | 146 ++++++++++++++++++ 2 files changed, 211 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/npu/test_cos_op_npu.py diff --git a/paddle/fluid/operators/activation_op_npu.cc b/paddle/fluid/operators/activation_op_npu.cc index ce629fa3a41..5988b4e2fee 100644 --- a/paddle/fluid/operators/activation_op_npu.cc +++ b/paddle/fluid/operators/activation_op_npu.cc @@ -472,6 +472,61 @@ class ReciprocalGradNPUKernel : public framework::OpKernel { } }; +template +class CosNPUKernel : 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(); + + const auto& runner = NpuOpRunner("Cos", {*x}, {*out}, {}); + runner.Run(stream); + } +}; + +template +class CosGradNPUKernel : 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); + + Tensor sin_out(x->type()); // Temporary Tensor + sin_out.Resize(x->dims()); + sin_out.mutable_data(place); + + auto stream = + ctx.template device_context() + .stream(); + const auto& runner = NpuOpRunner("Sin", {*x}, {sin_out}, {}); + runner.Run(stream); + + const auto& runner_dx = NpuOpRunner("Mul", {*dout, sin_out}, {*dx}, {}); + runner_dx.Run(stream); + + Tensor tmp(x->type()); // Temporary Tensor + tmp.Resize(framework::make_ddim({1, 1})); + tmp.mutable_data(place); + float factor = -1.; + FillNpuTensorWithConstant(&tmp, static_cast(factor)); + + const auto& runner_dx_ = NpuOpRunner("Xdivy", {*dx, tmp}, {*dx}, {}); + runner_dx_.Run(stream); + // dx = -dout * Sine(x); + } +}; + } // namespace operators } // namespace paddle @@ -583,3 +638,13 @@ REGISTER_OP_NPU_KERNEL( ops::ReciprocalGradNPUKernel, ops::ReciprocalGradNPUKernel); + +REGISTER_OP_NPU_KERNEL( + cos, ops::CosNPUKernel, + ops::CosNPUKernel); + +REGISTER_OP_NPU_KERNEL( + cos_grad, ops::CosGradNPUKernel, + ops::CosGradNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_cos_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_cos_op_npu.py new file mode 100644 index 00000000000..9b29fc812fa --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_cos_op_npu.py @@ -0,0 +1,146 @@ +# 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 + + +class TestCos(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "cos" + 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.cos(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, atol=1e-7) + + def test_check_grad(self): + if self.dtype == np.float16: + return + self.check_grad_with_place(self.place, ['X'], 'Out') + + +class TestCosFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "cos" + 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.cos(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) + + +class TestCosNet(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.cos(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() -- GitLab