// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // Copyright (c) 2022 NVIDIA 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 "paddle/fluid/platform/flags.h" #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) #include "paddle/fluid/platform/cudnn_workspace_helper.h" #endif namespace paddle { namespace platform { const ExportedFlagInfoMap &GetExportedFlagInfoMap() { return *GetMutableExportedFlagInfoMap(); } ExportedFlagInfoMap *GetMutableExportedFlagInfoMap() { static ExportedFlagInfoMap g_exported_flag_info_map; return &g_exported_flag_info_map; } } // namespace platform } // namespace paddle PADDLE_DEFINE_EXPORTED_int32(inner_op_parallelism, 0, "number of threads for inner op"); /** * 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: */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_bool( check_nan_inf, false, "Checking whether operator produce NAN/INF or not. It will be " "extremely slow so please use this flag wisely."); /** * 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. */ PADDLE_DEFINE_EXPORTED_bool( enable_opt_get_features, 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_HIP) || \ 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. */ PADDLE_DEFINE_EXPORTED_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 related FLAG * Name: FLAGS_gemm_use_half_precision_compute_type * Since Version: 2.4 * Value Range: bool, default=true * Example: * Note: whether to use fp16 compute type when the input and output is fp16, * faster but it may loss precision. */ PADDLE_DEFINE_EXPORTED_bool( gemm_use_half_precision_compute_type, true, "Whether to use fp16 compute type when the input and output is fp16, " "faster but it may loss precision in most case. If true, the compute " "type will be set to fp32. Default is true."); /** * 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 */ PADDLE_DEFINE_EXPORTED_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 #if defined(PADDLE_WITH_CUDA) /** * CUDA related FLAG * Name: FLAGS_cublaslt_exhaustive_search_times * Since Version: 2.3.0 * Value Range: int64_t, default=0 * Example: * Note: Represents times of exhaustive search to evaluate performance of * cuBlasLt matmul algorithm (with/without epilogue). Set this flag * with value > 0 to enable exhaustive search. Default is 0, means * getting algorithms via heuristic search. There are two search methods * in cuBlasLt, heuristic search and exhaustive search. Exhaustive search * attempts all cuBlasLt algorithms to select the fastest, which is very * time-consuming, and the selected algorithm will be cached for a given * layer specification Once you change the layer specifications * (such as M, N and K), it will re-search again. */ PADDLE_DEFINE_EXPORTED_int64( cublaslt_exhaustive_search_times, 0, "The times of exhaustive search for cuBlasLt matmul with/without " " epilogue algorithms, default is 0, means disabling exhaustive search."); #endif #if defined(PADDLE_WITH_ASCEND_CL) PADDLE_DEFINE_EXPORTED_string( selected_npus, "", "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 (NPU). If you want to use " "all visible devices, set this to empty string."); PADDLE_DEFINE_EXPORTED_bool( hccl_check_nan, true, "Check Nan in tensor before hccl_allreduce_sum otherwise it'll " "core when meets Nan value"); PADDLE_DEFINE_EXPORTED_string( npu_config_path, "", "The absolute path of configuration json file, like: /tmp/config.json. " "If proveided, it will be passed to aclInit()."); PADDLE_DEFINE_EXPORTED_int32(min_loss_scaling, 1, "set minmum loss scaling value!"); PADDLE_DEFINE_EXPORTED_string( npu_precision_mode, "", "NPU operator precision mode, options are 'force_fp32', 'force_fp16', " "'allow_fp32_to_fp16', 'must_keep_origin_dtype' and " "'allow_mix_precision'. If you want to use the default mode (" "allow_fp32_to_fp16), set this to empty string. For more details, " "please refer to the documents"); #endif /* * Kernel related FLAG * Name: FLAGS_enable_api_kernel_fallback * Since Version: 2.4 * Value Range: bool, default=true * Example: FLAGS_enable_api_kernel_fallback=true would allow kernel of current * backend fallback to CPU one when not found */ PADDLE_DEFINE_EXPORTED_bool( enable_api_kernel_fallback, true, "Whether enable api kernel fallback to CPU one when not found"); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) /** * 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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_int64(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. */ PADDLE_DEFINE_EXPORTED_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 */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_bool( cudnn_batchnorm_spatial_persistent, false, "Whether enable CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode for cudnn " "batch_norm, default is False."); #endif #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) /** * 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) */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_int32(communicator_max_merge_var_num, 20, "max var num to merge and send"); PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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 static const double kDefaultEagerDeleteTensorGB = 0; PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ static constexpr char kDefaultAllocatorStrategy[] = "auto_growth"; PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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_HIP) || \ defined(PADDLE_WITH_ASCEND_CL) || defined(PADDLE_WITH_MLU) || \ defined(PADDLE_WITH_CUSTOM_DEVICE) /** * 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 PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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 */ PADDLE_DEFINE_EXPORTED_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"); PADDLE_DEFINE_EXPORTED_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: */ PADDLE_DEFINE_EXPORTED_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."); PADDLE_DEFINE_EXPORTED_bool( reader_queue_speed_test_mode, false, "If set true, the queue.pop will only get data from queue but not " "remove the data from queue for speed testing"); /** * MKLDNN related FLAG * Name: use_mkldnn * Since Version: * Value Range: bool, default=false * Example: * Note: */ PADDLE_DEFINE_EXPORTED_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_NO_PYTHON static const int32_t kDefaultCallStackLevel = 2; #else static const int32_t kDefaultCallStackLevel = 1; #endif PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_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. */ PADDLE_DEFINE_EXPORTED_string( tracer_mkldnn_ops_off, "", "List of OneDNN operation types to be turned off"); /** * Debug related FLAG * Name: check_kernel_launch * Since Version: 2.1.0 * Value Range: bool, default=false * Example: * Note: Check kernel launch status after every kernel compute. */ #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PADDLE_DEFINE_EXPORTED_bool( check_kernel_launch, false, "Check kernel launch status after every kernel compute"); #endif /** * CUDNN related FLAG * Name: conv2d_disable_cudnn * Since Version: * Value Range: bool, default=false * Example: * Note: Disable cudnn in conv2d. */ #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PADDLE_DEFINE_EXPORTED_bool(conv2d_disable_cudnn, false, "Disable cudnn in conv2d"); PADDLE_DEFINE_EXPORTED_bool(use_fast_math, false, "Whether to use fast math GPU functions."); #endif /** * Distributed related FLAG * Name: FLAGS_get_host_by_name_time * Since Version: 2.2.0 * Value Range: int32, default=120 * Example: * Note: Get host by name time. */ #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_XPU) || \ defined(PADDLE_WITH_ASCEND_CL) || defined(PADDLE_WITH_HIP) || \ defined(PADDLE_WITH_MLU) PADDLE_DEFINE_EXPORTED_int32(get_host_by_name_time, 120, "The maximum time for get host by name time"); #endif /** * Distributed related FLAG * Name: FLAGS_apply_pass_to_program * Since Version: 2.2.0 * Value Range: bool, default=false * Example: FLAGS_apply_pass_to_program=true would apply IR Pass to * program when using Fleet APIs. * Note: Apply IR pass to program. Be only useful when using Fleet APIs. */ PADDLE_DEFINE_EXPORTED_bool( apply_pass_to_program, false, "It controls whether to apply IR pass to program when using Fleet APIs"); /** * Distributed related FLAG * Name: FLAGS_graph_load_in_parallel * Since Version: 2.2.0 * Value Range: bool, default=false * Example: * Note: Control whether load graph node and edge with multi threads parallely * If it is not set, load graph data with one thread */ PADDLE_DEFINE_EXPORTED_bool(graph_load_in_parallel, false, "It controls whether load graph node and edge with " "mutli threads parallely."); /** * Distributed related FLAG * Name: FLAGS_graph_get_neighbor_id * Since Version: 2.2.0 * Value Range: bool, default=false * Example: * Note: Control get all neighbor id when running sub part graph * If it is not set, do not need get neighbor id when run all part graph */ PADDLE_DEFINE_EXPORTED_bool( graph_get_neighbor_id, false, "It controls get all neighbor id when running sub part graph."); /** * KP kernel related FLAG * Name: FLAGS_run_kp_kernel * Since Version: 2.3.0 * Value Range: bool, default=false * Example: FLAGS_run_kp_kernel=true would use the kp kernel to compute in the * Op. * Note: */ PADDLE_DEFINE_EXPORTED_bool(run_kp_kernel, false, "It controls whether to run PaddlePaddle using KP"); /** * Distributed related FLAG * Name: FLAGS_allreduce_record_one_event * Since Version: 2.2.0 * Value Range: bool, default=false * Example: FLAGS_allreduce_record_one_event=true makes the allreduce * operations would only wait one event instead of multiple events. * Note: Make the allreduce operations would only wait one event instead of * multiple events. Currently, only fuse allreduce supports this. * Otherwise, the precision may be wrong. */ PADDLE_DEFINE_EXPORTED_bool(allreduce_record_one_event, false, "It controls whether the allreduce operations " "would only wait one event instead of multiple " "events. Currently, only fuse allreduce supports " "this. Otherwise, the precision may be wrong."); #ifdef PADDLE_WITH_CINN /* * CINN related FLAG * Name: FLAGS_use_cinn * Since Version: 2.3 * Value Range: bool, default=false * Example: FLAGS_use_cinn=true would run PaddlePaddle using CINN */ PADDLE_DEFINE_EXPORTED_bool( use_cinn, false, "It controls whether to run PaddlePaddle using CINN"); /* * CINN related FLAG * Name: FLAGS_allow_cinn_ops * Since Version: 2.3 * Value Range: string, default="" * Example: FLAGS_allow_cinn_ops="mul;relu" would only cover `mul` and `relu` * when using CINN */ PADDLE_DEFINE_EXPORTED_string(allow_cinn_ops, "", "It controls the cinn op subset to be used, " "which has the highest priority."); /* * CINN related FLAG * Name: FLAGS_deny_cinn_ops * Since Version: 2.3 * Value Range: string, default="" * Example: FLAGS_deny_cinn_ops="mul;relu" would block `mul` and `relu` two ops * when using CINN */ PADDLE_DEFINE_EXPORTED_string(deny_cinn_ops, "", "It controls the cinn op subset to be not used."); /* * CINN related FLAG * Name: FLAGS_enable_pe_launch_cinn * Since Version: 2.3 * Value Range: bool, default=true * Example: FLAGS_enable_pe_launch_cinn=true would execute the CINN compiled * instructions of a paddle graph with ParallelExecutor, otherwise with the * CINN compiled runtime program in sequential order. */ PADDLE_DEFINE_EXPORTED_bool(enable_pe_launch_cinn, true, "It controls whether to execute cinn compiled " "program with ParallelExecutor"); /* * CINN related FLAG * Name: FLAGS_enable_cinn_auto_tune * Since Version: 2.3 * Value Range: bool, default=false * Example: FLAGS_enable_cinn_auto_tune=true would use CINN with its * auto-tune feature enabled */ PADDLE_DEFINE_EXPORTED_bool(enable_cinn_auto_tune, false, "It controls whether to use cinn with " "its auto-tune feature enabled"); #endif DEFINE_int32(record_pool_max_size, 2000000, "SlotRecordDataset slot record pool max size"); DEFINE_int32(slotpool_thread_num, 1, "SlotRecordDataset slot pool thread num"); DEFINE_bool(enable_slotpool_wait_release, false, "enable slotrecord obejct wait release, default false"); DEFINE_bool(enable_slotrecord_reset_shrink, false, "enable slotrecord obejct reset shrink memory, default false"); DEFINE_bool(enable_ins_parser_file, false, "enable parser ins file, default false"); PADDLE_DEFINE_EXPORTED_bool( gpugraph_enable_hbm_table_collision_stat, false, "enable hash collisions stat for hbm table, default false"); PADDLE_DEFINE_EXPORTED_double(gpugraph_hbm_table_load_factor, 0.75, "the load factor of hbm table, default 0.75"); PADDLE_DEFINE_EXPORTED_bool( gpugraph_enable_gpu_direct_access, false, "enable direct access bwtween multi gpu cards, default false"); PADDLE_DEFINE_EXPORTED_bool( gpugraph_enable_segment_merge_grads, false, "enable segment merge gradients while push sparse, default false"); PADDLE_DEFINE_EXPORTED_uint64( gpugraph_merge_grads_segment_size, 128, "segment size with segment gradient merge, default 128"); PADDLE_DEFINE_EXPORTED_int32( gpugraph_dedup_pull_push_mode, 0, "enable dedup keys while pull push sparse, default 0"); PADDLE_DEFINE_EXPORTED_bool(gpugraph_load_node_list_into_hbm, true, "enable load_node_list_into_hbm, default true"); /** * ProcessGroupNCCL related FLAG * Name: nccl_blocking_wait * Since Version: * Value Range: bool, default=false * Example: * Note: nccl blocking wait. */ #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PADDLE_DEFINE_EXPORTED_bool(nccl_blocking_wait, false, "nccl blocking wait"); #endif /** * Autotune related FLAG * Name: FLAGS_use_autotune * Since Version: 2.3.0 * Value Range: bool, default=false * Example: */ PADDLE_DEFINE_EXPORTED_bool(use_autotune, false, "Whether enable autotune."); /** * Conv Search cache max number related FLAG * Name: FLAGS_search_cache_max_number * Since Version: 2.3.0 * Value Range: int32, default=1000000 * Example: */ PADDLE_DEFINE_EXPORTED_int32(search_cache_max_number, 1000000, "search_cache_max_number."); /** * Preformance related FLAG * Name: einsum_opt * Since Version: 2.3.0 * Value Range: bool, default=false * Example: * Note: If True, EinsumOp will be optimimzed by innercache reuse, which * uses more gpu memory. */ PADDLE_DEFINE_EXPORTED_bool( einsum_opt, false, "EinsumOp backward will be speedup at the expense of more gpu memory."); /** * JitLayer related FLAG * Name: FLAGS_jit_engine_type * Since Version: 2.3.0 * Value Range: string, {Executor, PE}, * default=Predictor * Example: * Note: * FLAGS_jit_engine_type == Executor, using ExecutorEngine by default * FLAGS_jit_engine_type == PE, using PEEngine by default * FLAGS_jit_engine_type == New, using InterpreterEngine by default * FLAGS_jit_engine_type == Predictor, using inference Predictor by default */ PADDLE_DEFINE_EXPORTED_string(jit_engine_type, "Predictor", "Choose default funciton type in JitLayer.");