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| // Copyright (c) 2024 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. | ||
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| #include "paddle/phi/kernels/radam_kernel.h" | ||
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| #include "paddle/phi/backends/cpu/cpu_context.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/kernels/impl/radam_kernel_impl.h" | ||
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| PD_REGISTER_KERNEL(radam, CPU, ALL_LAYOUT, phi::RAdamKernel, float, double) {} |
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| // Copyright (c) 2024 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. | ||
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| #include "paddle/phi/kernels/radam_kernel.h" | ||
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| #include "paddle/phi/backends/gpu/gpu_context.h" | ||
| #include "paddle/phi/backends/gpu/gpu_launch_config.h" | ||
| #include "paddle/phi/common/amp_type_traits.h" | ||
| #include "paddle/phi/core/dense_tensor.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/core/tensor_utils.h" | ||
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| namespace phi { | ||
| template <typename T, typename MT> | ||
| __global__ void RAdamGPUKernel(const T* param, | ||
| const T* grad, | ||
| const MT* learning_rate, | ||
| const MT* moment1, | ||
| const MT* moment2, | ||
| const MT* master_param, | ||
| MT beta1, | ||
| MT beta2, | ||
| MT epsilon, | ||
| MT beta1_pow_scalar, | ||
| MT beta2_pow_scalar, | ||
| MT rho_scalar, | ||
| MT rho_inf, | ||
| int num, | ||
| T* param_out, | ||
| MT* moment1_out, | ||
| MT* moment2_out, | ||
| MT* master_param_out) { | ||
| MT lr_scalar = static_cast<MT>(learning_rate[0]); | ||
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| int idx = blockIdx.x * blockDim.x + threadIdx.x; | ||
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| for (int index = idx; index < num; index += gridDim.x * blockDim.x) { | ||
| // load and cast input to MT | ||
| MT d_param = | ||
| master_param ? master_param[index] : static_cast<MT>(param[index]); | ||
| MT d_grad = static_cast<MT>(grad[index]); | ||
| MT d_moment1 = static_cast<MT>(moment1[index]); | ||
| MT d_moment2 = static_cast<MT>(moment2[index]); | ||
| // compute | ||
| MT m1_out = beta1 * d_moment1 + (static_cast<MT>(1) - beta1) * d_grad; | ||
| MT m2_out = | ||
| beta2 * d_moment2 + (static_cast<MT>(1) - beta2) * d_grad * d_grad; | ||
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| MT m1_hat = m1_out / (static_cast<MT>(1) - beta1_pow_scalar); | ||
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| MT rho_t = rho_inf - static_cast<MT>(2) * rho_scalar; | ||
| MT p_out = static_cast<MT>(0); | ||
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| if (rho_t > static_cast<MT>(5)) { | ||
| MT l_t = std::sqrt((static_cast<MT>(1) - beta2_pow_scalar)) / | ||
| (std::sqrt(m2_out) + epsilon); | ||
| MT r_t = std::sqrt(((rho_t - static_cast<MT>(4)) * | ||
| (rho_t - static_cast<MT>(2)) * rho_inf) / | ||
| ((rho_inf - static_cast<MT>(4)) * | ||
| (rho_inf - static_cast<MT>(2)) * rho_t)); | ||
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| p_out = d_param - lr_scalar * m1_hat * r_t * l_t; | ||
| } else { | ||
| p_out = d_param - lr_scalar * m1_hat; | ||
| } | ||
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| // store | ||
| param_out[index] = static_cast<T>(p_out); | ||
| moment1_out[index] = static_cast<MT>(m1_out); | ||
| moment2_out[index] = static_cast<MT>(m2_out); | ||
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| if (master_param_out) { | ||
| master_param_out[index] = p_out; | ||
| } | ||
| } | ||
| } | ||
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| template <typename T, typename Context> | ||
| void RAdamKernel(const Context& dev_ctx, | ||
| const DenseTensor& param, | ||
| const DenseTensor& grad, | ||
| const DenseTensor& learning_rate, | ||
| const DenseTensor& beta1_pow, | ||
| const DenseTensor& beta2_pow, | ||
| const DenseTensor& rho, | ||
| const DenseTensor& moment1, | ||
| const DenseTensor& moment2, | ||
| const paddle::optional<DenseTensor>& master_param, | ||
| float beta1, | ||
| float beta2, | ||
| float epsilon, | ||
| bool multi_precision, | ||
| DenseTensor* param_out, | ||
| DenseTensor* beta1_pow_out, | ||
| DenseTensor* beta2_pow_out, | ||
| DenseTensor* rho_out, | ||
| DenseTensor* moment1_out, | ||
| DenseTensor* moment2_out, | ||
| DenseTensor* master_param_out) { | ||
| using MPDType = typename phi::dtype::template MPTypeTrait<T>::Type; | ||
| T* param_out_data = dev_ctx.template Alloc<T>(param_out); | ||
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| MPDType* beta1_pow_out_data = | ||
| dev_ctx.template HostAlloc<MPDType>(beta1_pow_out); | ||
| MPDType* beta2_pow_out_data = | ||
| dev_ctx.template HostAlloc<MPDType>(beta2_pow_out); | ||
| MPDType* rho_out_data = dev_ctx.template HostAlloc<MPDType>(rho_out); | ||
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| MPDType* moment1_out_data = dev_ctx.template Alloc<MPDType>(moment1_out); | ||
| MPDType* moment2_out_data = dev_ctx.template Alloc<MPDType>(moment2_out); | ||
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| const MPDType* master_in_data = | ||
| multi_precision ? master_param->data<MPDType>() : nullptr; | ||
| MPDType* master_out_data = | ||
| multi_precision ? dev_ctx.template Alloc<MPDType>(master_param_out) | ||
| : nullptr; | ||
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| MPDType beta1_ = static_cast<MPDType>(beta1); | ||
| MPDType beta2_ = static_cast<MPDType>(beta2); | ||
| MPDType epsilon_ = static_cast<MPDType>(epsilon); | ||
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| MPDType rho_inf = | ||
| static_cast<MPDType>(2) / (static_cast<MPDType>(1) - beta2_) - | ||
| static_cast<MPDType>(1); | ||
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| // make cpu accumulator to tensor | ||
| DenseTensor beta1_pow_data; | ||
| phi::Copy(dev_ctx, beta1_pow, phi::CPUPlace(), false, &beta1_pow_data); | ||
| MPDType beta1_pow_scalar = beta1_pow_data.data<MPDType>()[0] * beta1_; | ||
| beta1_pow_out_data[0] = beta1_pow_scalar; | ||
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| DenseTensor beta2_pow_data; | ||
| phi::Copy(dev_ctx, beta2_pow, phi::CPUPlace(), false, &beta2_pow_data); | ||
| MPDType beta2_pow_scalar = beta2_pow_data.data<MPDType>()[0] * beta2_; | ||
| beta2_pow_out_data[0] = beta2_pow_scalar; | ||
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| DenseTensor rho_data; | ||
| phi::Copy(dev_ctx, rho, phi::CPUPlace(), false, &rho_data); | ||
| MPDType rho_scalar = | ||
| (rho_data.data<MPDType>()[0] * (beta2_ - beta2_pow_scalar) + | ||
| beta2_pow_scalar) / | ||
| (static_cast<MPDType>(1) - beta2_pow_scalar); | ||
| rho_out_data[0] = rho_scalar; | ||
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| int numel = param.numel(); | ||
| int block = 512; | ||
| int grid = (param.numel() + block - 1) / block; | ||
| auto stream = dev_ctx.stream(); | ||
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| RAdamGPUKernel<T, MPDType> | ||
| <<<block, grid, 0, stream>>>(param.data<T>(), | ||
| grad.data<T>(), | ||
| learning_rate.data<MPDType>(), | ||
| moment1.data<MPDType>(), | ||
| moment2.data<MPDType>(), | ||
| master_in_data, | ||
| beta1_, | ||
| beta2_, | ||
| epsilon_, | ||
| beta1_pow_scalar, | ||
| beta2_pow_scalar, | ||
| rho_scalar, | ||
| rho_inf, | ||
| numel, | ||
| param_out_data, | ||
| moment1_out_data, | ||
| moment2_out_data, | ||
| master_out_data); | ||
| } | ||
| } // namespace phi | ||
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| PD_REGISTER_KERNEL(radam, | ||
| GPU, | ||
| ALL_LAYOUT, | ||
| phi::RAdamKernel, | ||
| float, | ||
| double, | ||
| phi::dtype::float16) {} |
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