From 187248f568876c9aa3fa2874c6f361153fbb37e9 Mon Sep 17 00:00:00 2001 From: liym27 <33742067+liym27@users.noreply.github.com> Date: Fri, 26 Feb 2021 15:07:49 +0800 Subject: [PATCH] [NPU] Support npu op pow and pow grad (#31247) * [NPU] Support npu op: (1) pow (2) pow_grad * Support fp16 --- paddle/fluid/memory/memcpy.cc | 16 ++ paddle/fluid/operators/activation_op_npu.cc | 127 +++++++++++++++ .../tests/unittests/npu/test_pow_op_npu.py | 152 ++++++++++++++++++ 3 files changed, 295 insertions(+) create mode 100644 paddle/fluid/operators/activation_op_npu.cc create mode 100644 python/paddle/fluid/tests/unittests/npu/test_pow_op_npu.py diff --git a/paddle/fluid/memory/memcpy.cc b/paddle/fluid/memory/memcpy.cc index 22dd7eb48a..d616051ea6 100644 --- a/paddle/fluid/memory/memcpy.cc +++ b/paddle/fluid/memory/memcpy.cc @@ -208,8 +208,16 @@ void Copy(platform::NPUPlace dst_place, if (UNLIKELY(num == 0)) return; platform::SetNPUDeviceId(dst_place.device); + + // NOTE(ascendrc): NPU memcpy async from host to device is a "real" async, + // which is different from CUDA. In Paddle, when async is called, "sync" + // is run actually, which means Paddle doesn't fully supported async. + // TODO(ascendrc): Support NPU memcpy async for better performance. + stream = nullptr; + VLOG(4) << "memory::Copy " << num << " Bytes from " << src_place << " to " << dst_place << " by thream(" << stream << ")"; + if (stream) { platform::RecordEvent record_event("NpuMemcpyAsync:CPU->NPU"); platform::NPUMemcpyAsync(dst, src, num, ACL_MEMCPY_HOST_TO_DEVICE, stream); @@ -228,8 +236,16 @@ void Copy(platform::CPUPlace dst_place, if (UNLIKELY(num == 0)) return; platform::SetNPUDeviceId(src_place.device); + + // NOTE(ascendrc): NPU memcpy async from device to host is a "real" async, + // which is different from CUDA. In Paddle, when async is called, "sync" + // is run actually, which means Paddle doesn't fully supported async. + // TODO(ascendrc): Support NPU memcpy async for better performance. + stream = nullptr; + VLOG(4) << "memory::Copy " << num << " Bytes from " << src_place << " to " << dst_place << " by thream(" << stream << ")"; + if (stream) { platform::RecordEvent record_event("NpuMemcpyAsync:NPU->CPU"); platform::NPUMemcpyAsync(dst, src, num, ACL_MEMCPY_DEVICE_TO_HOST, stream); diff --git a/paddle/fluid/operators/activation_op_npu.cc b/paddle/fluid/operators/activation_op_npu.cc new file mode 100644 index 0000000000..33d8214a75 --- /dev/null +++ b/paddle/fluid/operators/activation_op_npu.cc @@ -0,0 +1,127 @@ +/* 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 Licnse. */ + +#ifdef PADDLE_WITH_ASCEND_CL +#include +#include + +#include "paddle/fluid/framework/ddim.h" +#include "paddle/fluid/framework/tensor_util.h" +#include "paddle/fluid/operators/activation_op.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class PowNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* out = ctx.Output("Out"); + auto factor = ctx.Attr("factor"); + + out->mutable_data(ctx.GetPlace()); + + auto runner = NpuOpRunner("Power", {*x}, {*out}, + {{"power", factor}, + {"scale", static_cast(1.0)}, + {"shift", static_cast(0.0)}}); + + auto stream = + ctx.template device_context() + .stream(); + runner.Run(stream); + } +}; + +template +class PowGradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* dout = ctx.Input(framework::GradVarName("Out")); + auto* dx = ctx.Output(framework::GradVarName("X")); + auto factor = ctx.Attr("factor"); + + auto x_dims = x->dims(); + + auto place = ctx.GetPlace(); + auto stream = + ctx.template device_context() + .stream(); + + // NOTE(liym27): dx = dout * factor * x.pow(factor-1) + + // Step1: Compute x_pow = x.pow(factor-1) + Tensor x_pow(x->type()); + x_pow.mutable_data(x->dims(), place); + auto runner_pow = NpuOpRunner("Power", {*x}, {x_pow}, + {{"power", factor - static_cast(1)}}); + runner_pow.Run(stream); + + // Step 2: Construct a broadcast factor, which has the same shape with x. + // 2.1 Get the shape of x + Tensor x_shape(framework::proto::VarType::INT32); + x_shape.mutable_data({x_dims.size()}, place); + TensorFromVector(framework::vectorize(x_dims), + ctx.device_context(), &x_shape); + + // 2.2 Get a factor tensor with shape [1]. + Tensor factor_tensor(framework::proto::VarType::FP32); + factor_tensor.mutable_data({1}, place); + TensorFromVector(std::vector{factor}, ctx.device_context(), + &factor_tensor); + + // 2.3 Get the factor which has the shape with x and the same value with + // factor. + Tensor factor_bc_tensor(framework::proto::VarType::FP32); + factor_bc_tensor.mutable_data(x_dims, place); + auto runner_bc = NpuOpRunner("BroadcastTo", {factor_tensor, x_shape}, + {factor_bc_tensor}, {}); + runner_bc.Run(stream); + + // Step 3: Compute x_power_mul_factor = factor * x.pow(factor-1) + Tensor x_power_mul_factor(x->type()); + x_power_mul_factor.mutable_data(x->dims(), place); + auto runner_mul_1 = + NpuOpRunner("Mul", {factor_bc_tensor, *x}, {x_power_mul_factor}, {}); + runner_mul_1.Run(stream); + + // Step 4: Compute dx = dout * factor * x.pow(factor-1) + dx->mutable_data(place); + auto runner_mul_2 = + NpuOpRunner("Mul", {*dout, x_power_mul_factor}, {*dx}, {}); + runner_mul_2.Run(stream); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + pow, ops::PowNPUKernel, + ops::PowNPUKernel); + +REGISTER_OP_NPU_KERNEL( + pow_grad, ops::PowGradNPUKernel, + ops::PowGradNPUKernel); + +#endif diff --git a/python/paddle/fluid/tests/unittests/npu/test_pow_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_pow_op_npu.py new file mode 100644 index 0000000000..d14910f577 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_pow_op_npu.py @@ -0,0 +1,152 @@ +# 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 TestPow(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "pow" + 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.power(x, 3) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {'factor': 3.0} + 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 TestPowFp16(OpTest): + def setUp(self): + self.set_npu() + self.op_type = "pow" + 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.power(x, 3) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.attrs = {'factor': 3.0} + self.outputs = {'Out': out} + + def set_npu(self): + self.__class__.use_npu = True + + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSubtractNet(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') + + sum = paddle.add(a, b) + z = paddle.pow(sum, 2.0) + + fc_1 = fluid.layers.fc(input=z, 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) + + 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