lamb_op_xpu;test=kunlun (#31012)
* lamb_op_xpu;test=kunlun * modify lamb_op_xpu.cc;test=kunlun * delete atol lamb_op_xpu; test=kunlun * update xpu.cmake;test=kunlun * test_error 1e-5,lamb_op_xpu;test=kunlun * error1e-5,lamb_op_xpu,test=kunlun * delete atol lamb_xpu;test=kunlun * modify atol,lamb_op_xpy;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu, XPUOptest;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu,modify xpu_cmake; test=kunlun * lamb_op_xpu;test=kunlun * lamb_op_xpu,modify xpucmake;test=kunluntest_model_benchmark_ci
Gradie
4 years ago
committed by
GitHub
parent
d1075df2e8
commit
d79fdc3d62
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/* Copyright (c) 2016 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 "paddle/fluid/operators/optimizers/lamb_op.h"
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#include "gflags/gflags.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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#ifdef PADDLE_WITH_XPU
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template <typename DeviceContext, typename T>
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class LambOpXPUKernel : 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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using paddle::framework::LoDTensor;
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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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using paddle::framework::LoDTensor;
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// inputs
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T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
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T weight_decay = static_cast<T>(ctx.Attr<float>("weight_decay"));
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T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
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T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
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auto& param = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Param"), "Input",
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"Param", "Lamb");
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auto* grad_var = ctx.InputVar("Grad");
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auto& mom1 = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Moment1"), "Input",
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"Moment1", "Lamb");
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auto& mom2 = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Moment2"), "Input",
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"Moment2", "Lamb");
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auto& lr = GET_DATA_SAFELY(ctx.Input<LoDTensor>("LearningRate"), "Input",
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"LearningRate", "Lamb");
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auto& beta1_pow = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Beta1Pow"), "Input",
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"Beta1Pow", "Lamb");
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auto& beta2_pow = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Beta2Pow"), "Input",
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"Beta2Pow", "Lamb");
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auto& param_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("ParamOut"),
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"Output", "ParamOut", "Lamb");
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auto& mom1_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Moment1Out"),
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"Output", "Moment1Out", "Lamb");
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auto& mom2_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Moment2Out"),
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"Output", "Moment2Out", "Lamb");
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auto& beta1_pow_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Beta1PowOut"),
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"Output", "Beta1PowOut", "Lamb");
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auto& beta2_pow_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Beta2PowOut"),
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"Output", "Beta2PowOut", "Lamb");
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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if (grad_var->IsType<framework::LoDTensor>()) {
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auto& grad = *ctx.Input<LoDTensor>("Grad");
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int r = xpu::lamb(dev_ctx.x_context(), grad.template data<T>(),
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mom1.template data<T>(), mom2.template data<T>(),
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param.template data<T>(), beta1_pow.template data<T>(),
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beta2_pow.template data<T>(), beta1, beta2, epsilon,
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weight_decay, lr.template data<T>(),
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mom1_out.template mutable_data<T>(ctx.GetPlace()),
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mom2_out.template mutable_data<T>(ctx.GetPlace()),
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param_out.template mutable_data<T>(ctx.GetPlace()),
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beta1_pow_out.template mutable_data<T>(ctx.GetPlace()),
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beta2_pow_out.template mutable_data<T>(ctx.GetPlace()),
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param.numel());
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if (r == xpu::Error_t::INVALID_PARAM) {
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::InvalidArgument(
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"XPU kernel error of LambOp, error message: INVALID_PARAM, "
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"please check your input & output."));
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} else if (r == xpu::Error_t::RUNTIME_ERROR) {
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::Unavailable(
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"XPU kernel error of LambOp, error message: "
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"RUNTIME_ERROR, please check whether Baidu "
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"Kunlun Card is properly installed."));
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} else if (r == xpu::Error_t::NO_ENOUGH_WORKSPACE) {
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::ResourceExhausted(
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"XPU kernel error of LambOp, error "
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"message: NO_ENOUGH_WORKSPACE, XPU "
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"has no enough memory."));
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} else {
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::ResourceExhausted(
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"XPU kernel error of LambOp, error "
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"message: OTHER "
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"XPU API returns error code: %d.",
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r));
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}
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} else {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Variable type not supported by lamb_op. Expect LoDTensor, "
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"but got %s",
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framework::ToTypeName(param_var->Type())));
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}
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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_XPU_KERNEL(
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lamb, ops::LambOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif
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@ -0,0 +1,121 @@
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# Copyright (c) 2020 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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from __future__ import print_function
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import sys
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sys.path.append("..")
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import unittest
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import numpy as np
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from op_test_xpu import XPUOpTest
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from paddle.fluid import core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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import paddle
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class TestLambOp1(XPUOpTest):
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def set_attrs(self):
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self.attrs = {
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'epsilon': 1e-6,
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'beta1': 0.9,
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'beta2': 0.999,
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'weight_decay': 0.01
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}
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def setUp(self):
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'''Test Lamb Op with supplied attributes
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'''
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self.op_type = "lamb"
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param = np.random.uniform(-1, 1, 5000).astype("float32")
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grad = np.random.uniform(-1, 1, 5000).astype("float32")
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moment1 = np.random.uniform(-1, 1, 5000).astype("float32")
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moment2 = np.random.random(5000).astype("float32")
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self.set_attrs()
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learning_rate = 0.001
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beta1_pow = self.attrs['beta1']
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beta2_pow = self.attrs['beta2']
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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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param_out, moment1_out, moment2_out, \
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beta1_pow_out, beta2_pow_out = lamb_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': beta1_pow_out,
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'Beta2PowOut': beta2_pow_out
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}
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def test_check_output(self):
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self.check_output_with_place(paddle.XPUPlace(0))
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def lamb_step(inputs, attributes):
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'''
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Simulate one step of the lamb optimizer
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:param inputs: dict of inputs
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:param attributes: dict of attributes
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:return tuple: tuple of output param, moment1, moment2,
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beta1 power accumulator and beta2 power accumulator
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'''
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param = inputs['Param']
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grad = inputs['Grad']
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moment1 = inputs['Moment1']
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moment2 = inputs['Moment2']
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lr = inputs['LearningRate']
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beta1_pow = inputs['Beta1Pow']
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beta2_pow = inputs['Beta2Pow']
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beta1 = attributes['beta1']
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beta2 = attributes['beta2']
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epsilon = attributes['epsilon']
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weight_decay = attributes['weight_decay']
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moment1_out = beta1 * moment1 + (1 - beta1) * grad
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moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
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moment1_unbiased = moment1_out / (1 - beta1_pow)
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moment2_unbiased = moment2_out / (1 - beta2_pow)
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r_1 = np.linalg.norm(param)
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r_2 = np.linalg.norm(moment1_unbiased / (np.sqrt(moment2_unbiased) + epsilon
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) + weight_decay * param)
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if r_1 > 0.0 and r_2 > 0.0:
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lr_t = lr * r_1 / r_2
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else:
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lr_t = 1.0
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param_out = param - lr_t * (moment1_unbiased / (
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np.sqrt(moment2_unbiased) + epsilon) + weight_decay * param)
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beta1_pow_out = beta1_pow * beta1
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beta2_pow_out = beta2_pow * beta2
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return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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