// Copyright (c) 2019 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. #include "gflags/gflags.h" #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/cudnn_workspace_helper.h" #endif /** * NOTE(paddle-dev): This file is designed to define all public FLAGS. */ /** * Paddle initialization related FLAG * Name: FLAGS_paddle_num_threads * Since Version: 0.15.0 * Value Range: int32, default=1 * Example: FLAGS_paddle_num_threads=2, set the maximum thread number per * instance to 2 * Note: */ DEFINE_int32(paddle_num_threads, 1, "Number of threads for each paddle instance."); /** * Operator related FLAG * Name: FLAGS_check_nan_inf * Since Version: 0.13.0 * Value Range: bool, default=false * Example: * Note: Used to debug. Checking whether operator produce NAN/INF or not. */ DEFINE_bool(check_nan_inf, false, "Checking whether operator produce NAN/INF or not. It will be " "extremely slow so please use this flag wisely."); // NOTE(zhiqiu): better to share the flags, otherwise we will have too many flags. #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_ASCEND_CL) /** * CUDA related related FLAG * Name: FLAGS_enable_cublas_tensor_op_math * Since Version: 1.2.0 * Value Range: bool, default=false * Example: * Note: whether to use Tensor Core, faster but it may loss precision. */ DEFINE_bool( enable_cublas_tensor_op_math, false, "The enable_cublas_tensor_op_math indicate whether to use Tensor Core, " "but it may loss precision. Currently, There are two CUDA libraries that" " use Tensor Cores, cuBLAS and cuDNN. cuBLAS uses Tensor Cores to speed up" " GEMM computations(the matrices must be either half precision or single " "precision); cuDNN uses Tensor Cores to speed up both convolutions(the " "input and output must be half precision) and recurrent neural networks " "(RNNs)."); /** * CUDA related FLAG * Name: FLAGS_selected_gpus * Since Version: 1.3.0 * Value Range: integer list separated by comma, default empty list * Example: FLAGS_selected_gpus=0,1,2,3,4,5,6,7 to train or predict with 0~7 gpu * cards * Note: A list of device ids separated by comma, like: 0,1,2,3 */ DEFINE_string(selected_gpus, "", "A list of device ids separated by comma, like: 0,1,2,3. " "This option is useful when doing multi process training and " "each process have only one device (GPU). If you want to use " "all visible devices, set this to empty string. NOTE: the " "reason of doing this is that we want to use P2P communication" "between GPU devices, use CUDA_VISIBLE_DEVICES can only use" "share-memory only."); #endif #ifdef PADDLE_WITH_CUDA /** * CUDNN related FLAG * Name: FLAGS_cudnn_deterministic * Since Version: 0.13.0 * Value Range: bool, default=false * Example: * Note: whether to use deterministic algorithm in cudnn. * If true, it will slow down some operators such as conv and pooling. */ DEFINE_bool(cudnn_deterministic, false, "Whether allow using an autotuning algorithm for convolution " "operator. The autotuning algorithm may be non-deterministic. If " "true, the algorithm is deterministic."); /** * CUDNN related FLAG * Name: FLAGS_conv_workspace_size_limit * Since Version: 0.13.0 * Value Range: uint64, default=512 (MB) * Example: * Note: The internal function of cuDNN obtains the fastest matching algorithm * within this memory limit. Usually, faster algorithms can be chosen in * larger workspaces, but memory space can also be significantly * increased. * Users need to balance memory and speed. */ DEFINE_uint64(conv_workspace_size_limit, paddle::platform::kDefaultConvWorkspaceSizeLimitMB, "cuDNN convolution workspace limit in MB unit."); /** * CUDNN related FLAG * Name: FLAGS_cudnn_exhaustive_search * Since Version: 1.2.0 * Value Range: bool, default=false * Example: * Note: Represents whether an exhaustive search method is used to * select a convolution algorithm. There are two search methods in cuDNN, * heuristic search and exhaustive search. Exhaustive search attempts * all cuDNN algorithms to select the fastest. This method is very * time-consuming, and the selected algorithm will be cached for a given * layer specification. Once you change the layer specifications * (such as batch size, feature map size), it will search again. */ DEFINE_bool(cudnn_exhaustive_search, false, "Whether enable exhaustive search for cuDNN convolution or " "not, default is False."); /** * CUDNN related FLAG * Name: FLAGS_cudnn_exhaustive_search_times * Since Version: * Value Range: * Example: * Note: only used to predict for advanced developer */ DEFINE_int64(cudnn_exhaustive_search_times, -1, "Exhaustive search times for cuDNN convolution, " "default is -1, not exhaustive search"); /** * CUDNN related FLAG * Name: FLAGS_cudnn_batchnorm_spatial_persistent * Since Version: 1.4.0 * Value Range: bool, default=false * Example: * Note: CUDNN_BATCHNORM_SPATIAL_PERSISTENT in batchnorm. This mode can be * faster in * some tasks because an optimized path may be selected for * CUDNN_DATA_FLOAT * and CUDNN_DATA_HALF data types, compute capability 6.0 or higher. The * reason we set it to false by default is that this mode may use scaled * atomic integer reduction that may cause a numerical overflow for * certain * input data range. */ DEFINE_bool(cudnn_batchnorm_spatial_persistent, false, "Whether enable CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode for cudnn " "batch_norm, default is False."); #endif #ifdef PADDLE_WITH_CUDA /** * NCCL related FLAG * Name: FLAGS_sync_nccl_allreduce * Since Version: 1.3 * Value Range: bool, default=true * Example: * Note: asynchronous nccl allreduce or synchronous issue: * https://github.com/PaddlePaddle/Paddle/issues/15049 * If you want to change this default value, why?(gongwb) */ DEFINE_bool( sync_nccl_allreduce, true, "If set true, will call `cudaStreamSynchronize(nccl_stream)`" "after allreduce, this mode can get better performance in some scenarios."); #endif #ifdef PADDLE_WITH_DISTRIBUTE /** * Distributed related FLAG * Name: FLAGS_communicator_max_merge_var_num * Since Version: 1.5.0 * Value Range: int32, default=20 * Example: * Note: The maximum number of gradients to be merged into a gradient and * sent through the communicator. The trainer puts all the gradients * into the queue, and then the communicator takes the gradients out * of the queue and sends them after merging. */ DEFINE_int32(communicator_max_merge_var_num, 20, "max var num to merge and send"); DEFINE_bool(communicator_is_sgd_optimizer, true, "gradient sent to the server is the sum of the gradients " "calculated by each thread if optimizer is sgd"); /** * Distributed related FLAG * Name: FLAGS_communicator_send_queue_size * Since Version: 1.5.0 * Value Range: int32, default=20 * Example: * Note: Size for each gradient queue. The trainer puts the gradient into * the queue, and then the communicator takes it out of the queue and * sends it out. When the communicator is slow, the queue may be full, * and the trainer will be continuously blocked before the queue has * space. It is used to avoid training much faster than communication, * so that too many gradients are not sent out in time. */ DEFINE_int32(communicator_send_queue_size, 20, "queue size to recv gradient before send"); #endif /** * Distributed related FLAG * Name: FLAGS_dist_threadpool_size * Since Version: 1.0.0 * Value Range: int32, default=0 * Example: * Note: Control the number of threads used for distributed modules. * If it is not set, it is set to a hard thread. */ DEFINE_int32(dist_threadpool_size, 0, "number of threads used for distributed executed."); /** * Garbage collector related FLAG * Name: FLAGS_eager_delete_tensor_gb * Since Version: 1.0.0 * Value Range: double, default=kDefaultEagerDeleteTensorGB * Example: FLAGS_eager_delete_tensor_gb=0.0, Release memory garbage once it is * no longer used. * FLAGS_eager_delete_tensor_gb=1.0, Release memory garbage when * garbage occupies 1.0GB of memory. * FLAGS_eager_delete_tensor_gb=-1.0, Disable garbage collection * policy. * Note: Represents whether a garbage collection strategy is used to optimize * network memory usage. * It is recommended that users set FLAGS_eager_delete_tensor_gb=0.0 to * enable garbage collection strategy when training large networks. */ // Disable gc by default when inference library is built #ifdef PADDLE_ON_INFERENCE static const double kDefaultEagerDeleteTensorGB = -1; #else static const double kDefaultEagerDeleteTensorGB = 0; #endif DEFINE_double( eager_delete_tensor_gb, kDefaultEagerDeleteTensorGB, "Memory size threshold (GB) when the garbage collector clear tensors." "Disabled when this value is less than 0"); /** * Memory related FLAG * Name: FLAGS_fast_eager_deletion_mode * Since Version: 1.3.0 * Value Range: bool, default=true * Example: * Note: Whether to use fast garbage collection strategy. * If not set, the GPU memory is released at the end of the CUDA kernel. * Otherwise, the GPU memory will be released before the CUDA kernel * has finished, which will make the garbage collection strategy faster. * Only works when garbage collection strategy is enabled. */ DEFINE_bool(fast_eager_deletion_mode, true, "Fast eager deletion mode. If enabled, memory would release " "immediately without waiting GPU kernel ends."); /** * Memory related FLAG * Name: FLAGS_memory_fraction_of_eager_deletion * Since Version: 1.4 * Value Range: double [0.0, 1.0], default=1.0 * Example: * Note: The percentage of memory size of garbage collection policy * to release variables. * If FLAGS_memory_fraction_of_eager_deletion = 1.0, * all temporary variables in the network will be released. * If FLAGS_memory_fraction_of_eager_deletion = 0.0, * no temporary variables in the network are released. * If 0.0 < FLAGS_memory_fraction_of_eager_deletion < 1.0, * all temporary variables will be sorted in descending order * according to their memory size, and only variables with the * largest FLAGS_memory_fraction_of_eager_deletion ratio will be released. * The flag is only valid when running parallel data compilers. */ DEFINE_double(memory_fraction_of_eager_deletion, 1.0, "Fraction of eager deletion. If less than 1.0, all variables in " "the program would be sorted according to its memory size, and " "only the FLAGS_memory_fraction_of_eager_deletion of the largest " "variables would be deleted."); /** * Allocator related FLAG * Name: FLAGS_allocator_strategy * Since Version: 1.2 * Value Range: string, {naive_best_fit, auto_growth, thread_local}, * default=auto_growth * Example: * Note: For selecting allocator policy of PaddlePaddle. */ #ifdef PADDLE_ON_INFERENCE static constexpr char kDefaultAllocatorStrategy[] = "naive_best_fit"; #else static constexpr char kDefaultAllocatorStrategy[] = "auto_growth"; #endif DEFINE_string( allocator_strategy, kDefaultAllocatorStrategy, "The allocation strategy, enum in [naive_best_fit, auto_growth]. " "naive_best_fit means the original pre-allocated allocator of Paddle. " "auto_growth means the auto-growth allocator. " "These two strategies differ in GPU memory allocation. " "naive_best_fit strategy would occupy almost all GPU memory by default, " "which prevents users from starting several Paddle jobs on the same GPU " "card but leads to less memory fragmentation (i.e., maximum batch " "size of models may be larger). auto_growth strategy would allocate " "GPU memory on demand, which allows users to start several Paddle jobs " "on the same GPU card but may lead to more memory fragmentation " "(i.e., maximum batch size of models may be smaller)."); /** * Memory related FLAG * Name: FLAGS_fraction_of_cpu_memory_to_use * Since Version: 0.12.0 * Value Range: double, [0.0, 1.0], default=1 * Example: * Note: Represents the proportion of allocated CPU memory blocks * to the total memory size of the CPU. Future CPU memory usage * will be allocated from this memory block. If the memory block does * not have enough CUDA pinned memory, new memory blocks of the same * size as the memory block will be allocated from the CUDA pinned * request util the CPU does not have enough memory. */ DEFINE_double(fraction_of_cpu_memory_to_use, 1, "Default use 100% of CPU memory for PaddlePaddle," "reserve the rest for page tables, etc"); /** * Memory related FLAG * Name: FLAGS_initial_cpu_memory_in_mb * Since Version: 0.14.0 * Value Range: uint64, default=500 (MB) * Example: * Note: The CPU memory block size of the initial allocator in MB. * The allocator takes the minimum values of * FLAGS_initial_cpu_memory_in_mb and * FLAGS_fraction_of_cpu_memory_to_use*(total physical memory) * as memory block sizes. */ DEFINE_uint64(initial_cpu_memory_in_mb, 500ul, "Initial CPU memory for PaddlePaddle, in MD unit."); /** * Memory related FLAG * Name: FLAGS_fraction_of_cuda_pinned_memory_to_use * Since Version: 0.12.0 * Value Range: double, [0.0, 1.0], default=0.5 * Example: * Note: Represents the proportion of allocated CUDA pinned memory blocks * to the total memory size of the CPU. Future CUDA pinned memory usage * will be allocated from this memory block. If the memory block does * not have enough CPU memory, new memory blocks of the same * size as the memory block will be allocated from the CPU * request util the CPU does not have enough memory. */ DEFINE_double( fraction_of_cuda_pinned_memory_to_use, 0.5, "Default use 50% of CPU memory as the pinned_memory for PaddlePaddle," "reserve the rest for page tables, etc"); // NOTE(zhiqiu): better to share the flags, otherwise we will have too many flags. #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_ASCEND_CL) /** * Memory related FLAG * Name: FLAGS_fraction_of_gpu_memory_to_use * Since Version: 1.2.0 * Value Range: double, default=0.5 if win32, 0.92 else * Example: * Note: Represents the proportion of allocated memory blocks to the total * memory size * of the GPU. Future memory usage will be allocated from this memory * block. * If the memory block does not have enough GPU memory, new memory blocks * of * the same size as the memory block will be allocated from the GPU * request * until the GPU does not have enough memory. */ #ifndef _WIN32 constexpr static float fraction_of_gpu_memory_to_use = 0.92f; #else // fraction_of_gpu_memory_to_use cannot be too high on windows, // since the win32 graphic sub-system can occupy some GPU memory // which may lead to insufficient memory left for paddle constexpr static float fraction_of_gpu_memory_to_use = 0.5f; #endif DEFINE_double(fraction_of_gpu_memory_to_use, fraction_of_gpu_memory_to_use, "Allocate a trunk of gpu memory that is this fraction of the " "total gpu memory size. Future memory usage will be allocated " "from the trunk. If the trunk doesn't have enough gpu memory, " "additional trunks of the same size will be requested from gpu " "until the gpu has no memory left for another trunk."); /** * Memory related FLAG * Name: FLAGS_initial_gpu_memory_in_mb * Since Version: 1.4.0 * Value Range: uint64, default=0 (MB) * Example: * Note: Allocate a specified size of GPU memory block. Later memory usage * will be allocated from that memory block. If the memory block does not * have enough GPU memory, the memory block with the size * FLAGS_reallocate_gpu_memory_in_mb will be requested from the GPU until * the GPU has no remaining memory. */ DEFINE_uint64( initial_gpu_memory_in_mb, 0ul, "Allocate a trunk of gpu memory whose byte size is specified by " "the flag. Future memory usage will be allocated from the " "trunk. If the trunk doesn't have enough gpu memory, additional " "trunks of the gpu memory will be requested from gpu with size " "specified by FLAGS_reallocate_gpu_memory_in_mb until the gpu has " "no memory left for the additional trunk. Note: if you set this " "flag, the memory size set by " "FLAGS_fraction_of_gpu_memory_to_use will be overrided by this " "flag. If you don't set this flag, PaddlePaddle will use " "FLAGS_fraction_of_gpu_memory_to_use to allocate gpu memory"); /** * Memory related FLAG * Name: FLAGS_reallocate_gpu_memory_in_mb * Since Version: 1.4.0 * Value Range: uint64, default=0 (MB) * Example: * Note: If the allocated GPU memory blocks are exhausted, * additional GPU memory blocks are reallocated */ DEFINE_uint64(reallocate_gpu_memory_in_mb, 0ul, "If this flag is set, Paddle will reallocate the gpu memory with " "size specified by this flag. Else Paddle will reallocate by " "FLAGS_fraction_of_gpu_memory_to_use"); DEFINE_uint64(gpu_memory_limit_mb, 0UL, "The maximum gpu memory limit that the process can allocate. " "If it is equal to 0, there would be no limit and all gpu memory " "would be available to the process. If it is larger than 0, " "the process would raise out of memory error if the allocated " "memory exceeds the limit even though there is available " "memory on the gpu card. The unit is MB and default value is 0."); #endif /** * Scope related FLAG * Name: local_exe_sub_scope_limit * Since Version: 1.6.0 * Value Range: double, default=256 (MB) * Example: * Note: */ DEFINE_double(local_exe_sub_scope_limit, 256.0, // MBytes "The memory up limit of sub-scopes of local execution scope for " "each CUDAPlace. If you don't need to limit the memory, " "you should set FLAGS_local_exe_sub_scope_limit=-1. " "The default value is 256 MBytes."); /** * MKLDNN related FLAG * Name: use_mkldnn * Since Version: * Value Range: bool, default=false * Example: * Note: */ DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run"); /** * Debug related FLAG * Name: FLAGS_call_stack_level * Since Version: 2.0.0 * Value Range: int, default=2 * Example: * Note: Used to debug. Determine the call stack to print when error or * exeception happens. * If FLAGS_call_stack_level == 0, only the error message summary will be shown. * If FLAGS_call_stack_level == 1, the python stack and error message summary * will be shown. * If FLAGS_call_stack_level == 2, the python stack, c++ stack, and error * message summary will be shown. */ #ifdef PADDLE_ON_INFERENCE static const int32_t kDefaultCallStackLevel = 2; #else static const int32_t kDefaultCallStackLevel = 1; #endif DEFINE_int32( call_stack_level, kDefaultCallStackLevel, "Determine the call stack to print when error or exeception happens." // TODO(zhiqiu): implement logic of FLAGS_call_stack_level==0 // "If FLAGS_call_stack_level == 0, only the error message summary will be " // "shown. " "If FLAGS_call_stack_level == 1, the python stack and error message " "summary will be shown." "If FLAGS_call_stack_level == 2, the python stack, c++ stack, and " "error message summary will be shown."); /** * Debug related FLAG * Name: sort_sum_gradient * Since Version: 2.0.0 * Value Range: bool, default=false * Example: * Note: If True, gradients are summed by the reverse order of * the forward execution sequence. */ DEFINE_bool(sort_sum_gradient, false, "Sum gradients by the reverse order of " "the forward execution sequence."); /** * Performance related FLAG * Name: max_inplace_grad_add * Since Version: 2.0.0 * Value Range: int32, default=0 * Example: * Note: The maximum number of inplace grad_add. */ DEFINE_int32( max_inplace_grad_add, 0, "The maximum number of inplace grad_add. When doing " "gradient accumulation, if the number of gradients need to that " "less FLAGS_max_inplace_grad_add, than it will be use several grad_add" "instead of sum. Default is 0."); /** * Debug related FLAG * Name: tracer_mkldnn_ops_on * Since Version: 2.0.0 * Value Range: string, default=empty * Example: * Note: Holds list of operation types with OneDNN kernels to be enabled. */ DEFINE_string(tracer_mkldnn_ops_on, "", "List of OneDNN operation types to be turned on"); /** * Debug related FLAG * Name: tracer_mkldnn_ops_off * Since Version: 2.0.0 * Value Range: string, default=empty * Example: * Note: Holds list of operation types with OneDNN kernels to be disabled. */ DEFINE_string(tracer_mkldnn_ops_off, "", "List of OneDNN operation types to be turned off");