[NPU] add npu kernel for adam (#31644)
* add npu kernel for adam * refine code * disable test * modify atolrevert-31562-mean
Leo Chen
4 years ago
committed by
GitHub
parent
795b0f92d3
commit
1e956001ec
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/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <memory>
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#include <string>
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#include "paddle/fluid/operators/npu_op_runner.h"
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#include "paddle/fluid/operators/optimizers/adam_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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template <typename DeviceContext, typename T>
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class AdamNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const auto* param_var = ctx.InputVar("Param");
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PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
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platform::errors::InvalidArgument(
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"The Var(%s)'s type should be LoDTensor, "
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"but the received is %s",
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ctx.InputNames("Param").front(),
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framework::ToTypeName(param_var->Type())));
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T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
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auto* param = ctx.Input<LoDTensor>("Param");
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auto* grad_var = ctx.InputVar("Grad");
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PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
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platform::errors::InvalidArgument(
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"The Grad(%s)'s type should be LoDTensor, "
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"but the received is %s",
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ctx.InputNames("Grad").front(),
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framework::ToTypeName(param_var->Type())));
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auto* grad = ctx.Input<LoDTensor>("Grad");
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auto* mom1 = ctx.Input<LoDTensor>("Moment1");
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auto* mom2 = ctx.Input<LoDTensor>("Moment2");
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auto* lr = ctx.Input<LoDTensor>("LearningRate");
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auto* beta1_pow = ctx.Input<LoDTensor>("Beta1Pow");
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auto* beta2_pow = ctx.Input<LoDTensor>("Beta2Pow");
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auto* param_out = ctx.Output<LoDTensor>("ParamOut");
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auto* mom1_out = ctx.Output<LoDTensor>("Moment1Out");
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auto* mom2_out = ctx.Output<LoDTensor>("Moment2Out");
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auto* beta1_pow_out = ctx.Output<LoDTensor>("Beta1PowOut");
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auto* beta2_pow_out = ctx.Output<LoDTensor>("Beta2PowOut");
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param_out->mutable_data<T>(ctx.GetPlace());
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mom1_out->mutable_data<T>(ctx.GetPlace());
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mom2_out->mutable_data<T>(ctx.GetPlace());
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beta1_pow_out->mutable_data<T>(ctx.GetPlace());
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beta2_pow_out->mutable_data<T>(ctx.GetPlace());
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T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
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if (ctx.HasInput("Beta1Tensor")) {
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auto* beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
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PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1,
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platform::errors::InvalidArgument(
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"Input(Beta1Tensor) size must be 1, but get %d",
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beta1_tensor->numel()));
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beta1 = static_cast<T>(GetAttrFromTensor(beta1_tensor));
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}
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T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
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if (ctx.HasInput("Beta2Tensor")) {
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auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
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PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1,
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platform::errors::InvalidArgument(
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"Input(Beta2Tensor) size must be 1, but get %d",
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beta2_tensor->numel()));
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beta2 = static_cast<T>(GetAttrFromTensor(beta2_tensor));
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}
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VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel()
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<< "beta2_pow.numel() : " << beta2_pow->numel();
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VLOG(3) << "param.numel(): " << param->numel();
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PADDLE_ENFORCE_EQ(beta1_pow_out->numel(), 1,
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platform::errors::InvalidArgument(
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"beta1 pow output size should be 1, but received "
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"value is:%d.",
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beta1_pow_out->numel()));
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PADDLE_ENFORCE_EQ(beta2_pow_out->numel(), 1,
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platform::errors::InvalidArgument(
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"beta2 pow output size should be 1, but received "
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"value is:%d.",
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beta2_pow_out->numel()));
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// reshape
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Tensor beta1_tensor(framework::proto::VarType::FP32);
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beta1_tensor.mutable_data<float>({1}, ctx.GetPlace());
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TensorFromVector(std::vector<T>{beta1}, ctx.device_context(),
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&beta1_tensor);
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Tensor beta2_tensor(framework::proto::VarType::FP32);
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beta2_tensor.mutable_data<float>({1}, ctx.GetPlace());
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TensorFromVector(std::vector<T>{beta2}, ctx.device_context(),
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&beta2_tensor);
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Tensor epsilon_tensor(framework::proto::VarType::FP32);
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epsilon_tensor.mutable_data<T>({1}, ctx.GetPlace());
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TensorFromVector(std::vector<T>{epsilon}, ctx.device_context(),
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&epsilon_tensor);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto runner =
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NpuOpRunner("ApplyAdamD",
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{
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*param, *mom1, *mom2, *beta1_pow, *beta2_pow, *lr,
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beta1_tensor, beta2_tensor, epsilon_tensor, *grad,
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},
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{
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*param_out, *mom1_out, *mom2_out,
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},
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{});
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runner.Run(stream);
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// NOTE(zhiqiu): ApplyAdamD updates params inplace, so
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// if param and param_out is not same, we need to do copy.
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if (param_out->data<T>() != param->data<T>()) {
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ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
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framework::TensorCopySync(*param, ctx.GetPlace(), param_out);
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}
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if (mom1_out->data<T>() != mom1->data<T>()) {
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ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
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framework::TensorCopySync(*mom1, ctx.GetPlace(), mom1_out);
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}
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if (mom2_out->data<T>() != mom2->data<T>()) {
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ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
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framework::TensorCopySync(*mom2, ctx.GetPlace(), mom2_out);
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}
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auto runner_m1 =
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NpuOpRunner("Mul", {*beta1_pow, beta1_tensor}, {*beta1_pow_out}, {});
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runner_m1.Run(stream);
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auto runner_m2 =
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NpuOpRunner("Mul", {*beta2_pow, beta2_tensor}, {*beta2_pow_out}, {});
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runner_m2.Run(stream);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_NPU_KERNEL(
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adam, ops::AdamNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::AdamNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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@ -0,0 +1,148 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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from test_adam_op import adam_step
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestSGD(OpTest):
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def setUp(self):
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self.set_npu()
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self.place = paddle.NPUPlace(0)
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self.op_type = "adam"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The second moment is positive
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moment2 = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.004
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beta1 = 0.78
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beta2 = 0.836
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epsilon = 1e-4
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beta1_pow = beta1**10
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beta2_pow = beta2**10
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment1': moment1,
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'Moment2': moment2,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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'Beta2Pow': np.array([beta2_pow]).astype("float32")
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}
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self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
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param_out, moment1_out, \
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moment2_out = adam_step(self.inputs, self.attrs)
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self.outputs = {
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'Moment1Out': moment1_out,
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'Moment2Out': moment2_out,
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'ParamOut': param_out,
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'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
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'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
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}
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def set_npu(self):
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self.__class__.use_npu = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False)
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'''
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# TODO(zhiqiu): The following test may let 0-3 card down.
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# we need to analyze it and open it.
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestNet(unittest.TestCase):
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def _test(self, run_npu=True):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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main_prog.random_seed = SEED
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startup_prog.random_seed = SEED
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np.random.seed(SEED)
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a_np = np.random.random(size=(32, 32)).astype('float32')
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b_np = np.random.random(size=(32, 32)).astype('float32')
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label_np = np.random.randint(2, size=(32, 1)).astype('int64')
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with paddle.static.program_guard(main_prog, startup_prog):
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a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
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b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
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label = paddle.static.data(
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name="label", shape=[32, 1], dtype='int64')
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sum = paddle.add(a, b)
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z = paddle.pow(sum, 2.0)
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fc_1 = fluid.layers.fc(input=z, size=128)
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prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.reduce_mean(cost)
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adam = fluid.optimizer.Adam(learning_rate=0.01)
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adam.minimize(loss)
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if run_npu:
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place = paddle.NPUPlace(0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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print("Start run on {}".format(place))
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for epoch in range(100):
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pred_res, loss_res = exe.run(
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main_prog,
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feed={"a": a_np,
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"b": b_np,
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"label": label_np},
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fetch_list=[prediction, loss])
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if epoch % 10 == 0:
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print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
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epoch, pred_res[0], loss_res))
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return pred_res, loss_res
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def test_npu(self):
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cpu_pred, cpu_loss = self._test(False)
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npu_pred, npu_loss = self._test(True)
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self.assertTrue(np.allclose(npu_pred, cpu_pred))
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self.assertTrue(np.allclose(npu_loss, cpu_loss))
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'''
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if __name__ == '__main__':
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unittest.main()
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