eric-haibin-lin commented on a change in pull request #16893: Multi-tensor LAMB URL: https://github.com/apache/incubator-mxnet/pull/16893#discussion_r360227997
########## File path: src/operator/contrib/multi_lamb-inl.h ########## @@ -0,0 +1,357 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file multi_lamb-inl.h + * \brief multi-tensor LAMB optimizer + * \author Moises Hernandez + */ +#ifndef MXNET_OPERATOR_CONTRIB_MULTI_LAMB_INL_H_ +#define MXNET_OPERATOR_CONTRIB_MULTI_LAMB_INL_H_ + +#include <dmlc/parameter.h> +#include <mxnet/operator.h> +#include <mxnet/operator_util.h> +#include <mxnet/op_attr_types.h> +#include <mshadow/base.h> +#include <nnvm/op.h> +#include <nnvm/op_attr_types.h> +#include <vector> +#include "../operator_common.h" +#include "../mshadow_op.h" +#include "../mxnet_op.h" +#include "../tensor/init_op.h" +#include "../tensor/util/tensor_util-inl.h" +#include "multi_sum_sq-inl.h" + +namespace mxnet { +namespace op { + +namespace multilamb { +enum MultiLambUpdateResource {kTempSpace}; +} // namespace multilamb + +struct MultiLAMBParam : public dmlc::Parameter<MultiLAMBParam> { + mxnet::Tuple<float> learning_rates; + mxnet::Tuple<float> wds; + float beta1; + float beta2; + float epsilon; + float rescale_grad; + float lower_bound; + float upper_bound; + float clip_gradient; + bool bias_correction; + int num_tensors; + mxnet::Tuple<int> step_count; + + DMLC_DECLARE_PARAMETER(MultiLAMBParam) { + DMLC_DECLARE_FIELD(learning_rates) + .describe("List of learning rates"); + DMLC_DECLARE_FIELD(beta1) + .set_default(0.9f) + .describe("Exponential decay rate for the first moment estimates."); + DMLC_DECLARE_FIELD(beta2) + .set_default(0.999f) + .describe("Exponential decay rate for the second moment estimates."); + DMLC_DECLARE_FIELD(epsilon) + .set_default(1e-6f) + .describe("Small value to avoid division by 0."); + DMLC_DECLARE_FIELD(wds) + .describe("List of Weight decays." + "Weight decay augments the objective function with a " + "regularization term that penalizes large weights. " + "The penalty scales with the square of the magnitude of each weight."); + DMLC_DECLARE_FIELD(rescale_grad) + .set_default(1.0f) + .describe("Gradient rescaling factor"); + DMLC_DECLARE_FIELD(lower_bound) + .set_default(-1.0f) + .describe("Lower limit of norm of weight. If lower_bound <= 0, Lower limit is not set"); + DMLC_DECLARE_FIELD(upper_bound) + .set_default(-1.0f) + .describe("Upper limit of norm of weight. If upper_bound <= 0, Upper limit is not set"); + DMLC_DECLARE_FIELD(clip_gradient) + .set_default(-1.0f) + .describe("Clip gradient to the range of [-clip_gradient, clip_gradient] " + "If clip_gradient <= 0, gradient clipping is turned off. " + "grad = max(min(grad, clip_gradient), -clip_gradient)."); + DMLC_DECLARE_FIELD(bias_correction) + .set_default(true) + .describe("Whether to use bias correction."); + DMLC_DECLARE_FIELD(step_count) + .describe("Step count for each tensor"); + DMLC_DECLARE_FIELD(num_tensors) + .set_default(1) + .describe("Number of tensors"); + } +}; + +template<typename ParamType, int input_stride> +inline bool MultiLAMB_InferShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_attrs, + mxnet::ShapeVector *out_attrs) { + const ParamType& param = dmlc::get<ParamType>(attrs.parsed); + CHECK_EQ(in_attrs->size(), input_stride * param.num_tensors); + CHECK_EQ(out_attrs->size(), param.num_tensors); + + bool all_inferred = true; + auto& input_shapes = *in_attrs; + auto& output_shapes = *out_attrs; + + CHECK_EQ(param.learning_rates.ndim(), param.num_tensors) + << "Number of learning rates is inconsistent with num_tensors " + << "parameter passed. Expected number of learning rates: " + << param.num_tensors << ", and got " << param.learning_rates.ndim(); + CHECK_EQ(param.wds.ndim(), param.num_tensors) + << "Number of weight decays is inconsistent with num_tensors " + << "parameter passed. Expected number of weight decays: " + << param.num_tensors << ", and got " << param.wds.ndim(); + CHECK_EQ(param.step_count.ndim(), param.num_tensors) + << "Number of step counts is inconsistent with num_tensors." + << "Expected number of step counts: " + << param.num_tensors << ", and got " << param.step_count.ndim(); + + // Weights, gradients, mean and variance + for (int i = 0; i < param.num_tensors; ++i) { + mxnet::ShapeVector input_vec; + mxnet::ShapeVector output_vec({output_shapes[i]}); + for (int j = 0; j < input_stride; ++j) { + input_vec.push_back(input_shapes[i * input_stride + j]); + } + all_inferred = all_inferred && ElemwiseShape<input_stride, 1>(attrs, &input_vec, &output_vec); + } + return all_inferred; +} + +template <typename ParamType, int input_stride> +inline bool MP_MultiLAMB_InferType(const nnvm::NodeAttrs& attrs, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + const ParamType& param = dmlc::get<ParamType>(attrs.parsed); + CHECK_EQ(in_attrs->size(), input_stride * param.num_tensors); + CHECK_EQ(out_attrs->size(), param.num_tensors); + + bool all_inferred = true; + auto& input_types = *in_attrs; + auto& output_types = *out_attrs; + + // weights, gradients + for (int i = 0; i < param.num_tensors; ++i) { + std::vector<int> input_vec; + std::vector<int> output_vec({output_types[i]}); + for (int j = 0; j < 2; ++j) { + input_vec.push_back(input_types[i * input_stride + j]); + } + all_inferred = all_inferred && + ElemwiseType<2, 1>(attrs, &input_vec, &output_vec); + } + + // mean, var, weights32 (master copies of weights) + for (int i = 0; i < param.num_tensors; ++i) { + TYPE_ASSIGN_CHECK(input_types, input_stride * i + 2, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(input_types, input_stride * i + 3, mshadow::kFloat32); + TYPE_ASSIGN_CHECK(input_types, input_stride * i + input_stride - 1, mshadow::kFloat32); + } + return all_inferred; +} + +template<typename T> +class LAMB_type_identity { + public: + using type = T; +}; + +template<typename T> +class LAMB_single_precision { + public: + using type = float; +}; + +template<typename DType, typename MPDType> +struct MultiLAMBKernelParam { + static const int N = 45; + size_t ntensors; + size_t max_size; + size_t total_size; + size_t sizes[N]; + size_t tensor2temp_g[N]; + DType* weights[N]; + DType* grads[N]; + MPDType* mean[N]; + MPDType* var[N]; + MPDType* weights32[N]; + DType* out_data[N]; + int step_count[N]; + MPDType learning_rates[N]; + MPDType wds[N]; + + // gpu + int chunk_size = 65536; + int nchunks; +}; + +template<typename xpu, + typename DType, + typename MPDType, + typename ParamType = MultiLAMBParam, + int input_stride = 5> +void FillMultiLAMBKernelParam(const nnvm::NodeAttrs& attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<TBlob> &outputs, + MultiLAMBKernelParam<DType, MPDType> *multi_param) { + const ParamType& p = nnvm::get<ParamType>(attrs.parsed); + mxnet_op::Stream<xpu>* s = ctx.get_stream<xpu>(); + + multi_param->ntensors = p.num_tensors; + multi_param->total_size = 0; + multi_param->max_size = 0; + multi_param->nchunks = 0; + + constexpr bool is_same = std::is_same<DType, MPDType>::value; + for (size_t i = 0; i < multi_param->ntensors; ++i) { + const auto idx = i * input_stride; + multi_param->sizes[i] = inputs[idx].shape_.Size(); + multi_param->tensor2temp_g[i] = multi_param->total_size; + multi_param->total_size += multi_param->sizes[i]; + if (multi_param->max_size < multi_param->sizes[i]) + multi_param->max_size = multi_param->sizes[i]; + + multi_param->weights[i] = inputs[idx].FlatTo2D<xpu, DType>(s).dptr_; + multi_param->grads[i] = inputs[idx + 1].FlatTo2D<xpu, DType>(s).dptr_; + multi_param->mean[i] = inputs[idx + 2].FlatTo2D<xpu, MPDType>(s).dptr_; + multi_param->var[i] = inputs[idx + 3].FlatTo2D<xpu, MPDType>(s).dptr_; + + // if mixed precision, then the last input in a set + // is 32-bit master copy of the weights + if (!is_same) + multi_param->weights32[i] = inputs[idx + input_stride - 1].FlatTo2D<xpu, MPDType>(s).dptr_; + multi_param->out_data[i] = outputs[i].FlatTo2D<xpu, DType>(s).dptr_; + multi_param->nchunks += (multi_param->sizes[i] + multi_param->chunk_size - 1) + /multi_param->chunk_size; + multi_param->learning_rates[i] = static_cast<MPDType>(p.learning_rates[i]); + multi_param->wds[i] = static_cast<MPDType>(p.wds[i]); + } + memcpy(multi_param->step_count, p.step_count.begin(), multi_param->ntensors * sizeof(int)); +} + +using namespace mxnet_op; +template<typename MPDType, typename DType> +void call_kernel1(Stream<cpu>* s); +template<typename MPDType, typename DType> +void call_kernel1(Stream<gpu>* s); + +template<typename MPDType, typename DType> +void call_kernel2(Stream<cpu>* s); +template<typename MPDType, typename DType> +void call_kernel2(Stream<gpu>* s); + +template<typename xpu, template<typename> class MPTypeChooser, int input_stride> +inline void multiLAMB(const nnvm::NodeAttrs& attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<OpReqType> &req, + const std::vector<TBlob> &outputs) { + auto param = nnvm::get<MultiLAMBParam>(attrs.parsed); + Stream<xpu>* s = ctx.get_stream<xpu>(); + + MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, { + using MPDType = typename MPTypeChooser<DType>::type; + MultiLAMBKernelParam<DType, MPDType> kernel_params; + FillMultiLAMBKernelParam<xpu, DType, MPDType, MultiLAMBParam, input_stride> + (attrs, ctx, inputs, outputs, &kernel_params); + + // create vector of TBlob with all the weights contiguous + std::vector<TBlob> weights; + for (size_t index = 0; index < kernel_params.ntensors; ++index) { + weights.emplace_back(inputs[index*input_stride]); + } + + // Calculate amount of temporary storage (temp_g, r1, r2, block_to_tensor, block_to_chunk) + size_t workspace_size = kernel_params.total_size * sizeof(float) + + 2 * kernel_params.ntensors * sizeof(float) + + 2 * kernel_params.nchunks * sizeof(int); + // take into account the required storage required within MultiSumSqRun + size_t required_storage_multi_sum_sq = 0; + if (ctx.run_ctx.ctx.dev_mask()==kGPU) + required_storage_multi_sum_sq = GetRequiredStorageMultiSumSq(inputs); + workspace_size += required_storage_multi_sum_sq; + + // Request temporary storage + Tensor<xpu, 1, char> workspace = + ctx.requested[multilamb::kTempSpace].get_space_typed<xpu, 1, char>( + Shape1(workspace_size), s); + + // Create tensors + size_t pos_wspace = required_storage_multi_sum_sq; + Tensor<xpu, 1, float> temp_g(reinterpret_cast<float*>(&workspace[pos_wspace]), + Shape1(kernel_params.total_size), s); + // create vector of TBlob with all the temp_g contiguous + std::vector<TBlob> temp_g_tblobs; + for (size_t index = 0; index < kernel_params.ntensors; ++index) { + Tensor<xpu, 1, float> aux(reinterpret_cast<float*>(&workspace[pos_wspace]), + Shape1(kernel_params.sizes[index]), s); + TBlob newtblob(aux); + temp_g_tblobs.emplace_back(newtblob); + pos_wspace += kernel_params.sizes[index] * sizeof(float); + } + Tensor<xpu, 1, float> r1(reinterpret_cast<float*>(&workspace[pos_wspace]), + Shape1(kernel_params.ntensors), s); + pos_wspace += kernel_params.ntensors * sizeof(float); + Tensor<xpu, 1, float> r2(reinterpret_cast<float*>(&workspace[pos_wspace]), + Shape1(kernel_params.ntensors), s); + pos_wspace += kernel_params.ntensors * sizeof(float); + Tensor<xpu, 1, int> block_to_tensor(reinterpret_cast<int*>(&workspace[pos_wspace]), + Shape1(kernel_params.nchunks), s); + pos_wspace += kernel_params.nchunks * sizeof(int); + Tensor<xpu, 1, int> block_to_chunk(reinterpret_cast<int*>(&workspace[pos_wspace]), + Shape1(kernel_params.nchunks), s); + + MultiSumSqRun<xpu>(weights, kernel_params.ntensors, r1.dptr_, ctx); + call_kernel1<MPDType, DType>(s, kernel_params, param, temp_g.dptr_, + block_to_tensor.dptr_, + block_to_chunk.dptr_); + MultiSumSqRun<xpu>(temp_g_tblobs, kernel_params.ntensors, r2.dptr_, ctx); + call_kernel2<MPDType, DType>(s, kernel_params, param, r1.dptr_, r2.dptr_, + temp_g.dptr_, + block_to_tensor.dptr_, block_to_chunk.dptr_, + req[0]); + }); +} + +template<typename xpu, bool MP> +inline void multiLAMBUpdate(const nnvm::NodeAttrs& attrs, Review comment: MultiLAMBUpdate ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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