# Copyright (c) 2020 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. from __future__ import print_function from paddle.fluid.wrapped_decorator import signature_safe_contextmanager, wrap_decorator from paddle.fluid import core import contextlib from paddle.fluid.framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter, _dygraph_tracer, dygraph_only, set_flags, get_flags import warnings import copy import functools import paddle import operator import types AMP_LEVEL = core.AmpLevel __all__ = ['amp_guard', 'amp_decorate'] # The set of ops that support fp16 calculation and are considered numerically- # safe and performance-critical. These ops are always converted to fp16. WHITE_LIST = { 'conv2d', 'matmul', 'matmul_v2', 'mul', 'fake_quantize_dequantize_abs_max', 'fake_quantize_dequantize_moving_average_abs_max', } # The set of ops that support fp16 calculation and are considered numerically- # dangerous and whose effects may also be observed in downstream ops. BLACK_LIST = { 'exp', 'square', 'log', 'mean', 'sum', 'cos_sim', 'softmax', 'softmax_with_cross_entropy', 'sigmoid_cross_entropy_with_logits', 'c_softmax_with_cross_entropy', 'cross_entropy', 'cross_entropy2', # default fp32 can avoid return inf when the sum value large than 65504 'reduce_sum', } AMP_RELATED_FLAGS = [ 'FLAGS_cudnn_exhaustive_search', 'FLAGS_conv_workspace_size_limit', 'FLAGS_cudnn_batchnorm_spatial_persistent', ] AMP_RELATED_FLAGS_SETTING = { 'FLAGS_cudnn_exhaustive_search': 1, 'FLAGS_conv_workspace_size_limit': 1000, 'FLAGS_cudnn_batchnorm_spatial_persistent': 1, } PURE_FP16_WHITE_LIST = {' '} PURE_FP16_BLACK_LIST = { 'lookup_table', 'lookup_table_v2', 'scatter', 'scatter_grad' } BF16_WHITE_LIST = {'conv2d'} BF16_BLACK_LIST = {' '} #NOTE(zhiqiu): similar as paddle.fluid.contrib.mixed_precision.fp16_lists.AutoMixedPrecisionLists._update_list # The reason why not use AutoMixedPrecisionLists is that custom_black_varnames is not suitable for imperative mode. def _update_list(custom_white_list, custom_black_list, level='O1', dtype='float16'): """ Update black and white list according to users' custom list. """ if dtype == 'float16': if level == 'O1': _white_list = copy.copy(WHITE_LIST) _black_list = copy.copy(BLACK_LIST) else: _white_list = copy.copy(PURE_FP16_WHITE_LIST) _black_list = copy.copy(PURE_FP16_BLACK_LIST) else: _white_list = copy.copy(BF16_WHITE_LIST) _black_list = copy.copy(BF16_BLACK_LIST) if custom_white_list and custom_black_list: for op_name in custom_white_list: if op_name in custom_black_list: raise ValueError("Custom white list overlap " "custom black list") if custom_white_list: for op_name in custom_white_list: if op_name in _black_list: _black_list.remove(op_name) _white_list.add(op_name) if custom_black_list: for op_name in custom_black_list: if op_name in _white_list: _white_list.remove(op_name) _black_list.add(op_name) return _white_list, _black_list def _in_amp_guard(): """ Judge whether current code block is in `amp_guard` context. """ tracer = _dygraph_tracer() if tracer: if tracer._amp_level == core.AmpLevel.O1: return True else: return False else: return False def _in_pure_fp16_guard(): tracer = _dygraph_tracer() return tracer and tracer._amp_level == core.AmpLevel.O2 def _is_gpu_float16_supported(): """ Judge whether current gpu support float16 amp. """ prop = paddle.device.cuda.get_device_capability() return prop[0] >= 7 def _is_gpu_bfloat16_supported(): """ Judge whether current gpu support bfloat16 amp. """ prop = paddle.device.cuda.get_device_capability() cuda_version = paddle.version.cuda() if cuda_version is not None and cuda_version != 'False': cuda_version_check = int(cuda_version.split('.')[0]) >= 11 else: cuda_version_check = False return prop[0] >= 8 and cuda_version_check @dygraph_only def pure_fp16_initialize(models): for idx in range(len(models)): for layer in models[idx].sublayers(include_self=True): layer._casted_by_pure_fp16 = True if (layer._dtype == 'float16') or isinstance( layer, (paddle.nn.BatchNorm, paddle.nn.BatchNorm1D, paddle.nn.BatchNorm2D, paddle.nn.BatchNorm3D, paddle.nn.LayerNorm)): continue layer._to_impl(dtype='float16', include_sublayers=False) return models def check_models(models): for model in models: if not isinstance(model, paddle.nn.Layer): raise RuntimeError( "Current train mode is pure fp16, models should be paddle.nn.Layer, but receive {}.". format(type(model))) if isinstance(model, paddle.DataParallel): raise RuntimeError( "For distributed AMP training, you should first use paddle.amp.decorate() to decotate origin model, and then call paddle.DataParallel get distributed model." ) def check_optimizers(optimizers): for optimizer in optimizers: if not isinstance(optimizer, (paddle.optimizer.Optimizer, paddle.fluid.optimizer.Optimizer)): raise RuntimeError( "Current train mode is pure fp16, optimizers should be paddle.optimizer.Optimizer or paddle.fluid.optimizer.Optimizer, but receive {}.". format(type(optimizer))) @signature_safe_contextmanager @dygraph_only def amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O1', dtype='float16'): """ :api_attr: imperative Create a context which enables auto-mixed-precision(AMP) of operators executed in dynamic graph mode. If enabled, the input data type (float32 or float16) of each operator is decided by autocast algorithm for better performance. Commonly, it is used together with `GradScaler` to achieve Auto-Mixed-Precision in imperative mode. It is used together with `decorator` to achieve Pure fp16 in imperative mode. Args: enable(bool, optional): Enable auto-mixed-precision or not. Default is True. custom_white_list(set|list|tuple, optional): The custom white_list. It's the set of ops that support fp16 calculation and are considered numerically-safe and performance-critical. These ops will be converted to fp16. custom_black_list(set|list|tuple, optional): The custom black_list. The set of ops that support fp16 calculation and are considered numerically-dangerous and whose effects may also be observed in downstream ops. These ops will not be converted to fp16. level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the input data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators parameters and input data will be casted to fp16, except operators in black_list, don't support fp16 kernel and batchnorm. Default is O1(amp) dtype(str, optional): Whether to use 'float16' or 'bfloat16'. Default is 'float16'. Examples: .. code-block:: python import numpy as np import paddle data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32') with paddle.fluid.dygraph.guard(): conv2d = paddle.fluid.dygraph.Conv2D(3, 2, 3) data = paddle.fluid.dygraph.to_variable(data) with paddle.fluid.dygraph.amp_guard(): conv = conv2d(data) print(conv.dtype) # FP16 with paddle.fluid.dygraph.amp_guard(enable=False): conv = conv2d(data) print(conv.dtype) # FP32 """ # check amp_level: O0-O2 level = level.upper() if not (level in ['O0', 'O1', 'O2']): raise ValueError( "level should be O0, O1 or O2. O0 represents fp32 train mode, O1 represents AMP train mode, O2 represents pure fp16/bf16 train mode." ) # check amp_dtype: float16 or bfloat16 dtype = dtype.lower() if not (dtype in ['float16', 'bfloat16']): raise ValueError("dtype should be 'float16' or 'bfloat16'.") # check tracer tracer = _dygraph_tracer() if not tracer: raise ValueError( "current_tracer is None, maybe it is not in imperative mode.") # check device_type: # NOTE: Now, amp only support gpu for float16 and bfloat16, xpu for float16. # Maybe we will support cpu for bfloat16. if enable and not (tracer._expected_place.is_gpu_place() or tracer._expected_place.is_xpu_place()): warnings.warn( 'amp_guard can only be enabled on CUDAPlace and XPUPlace, current place is %s, so it makes no effect.' % tracer._expected_place) enable = False # For xpu: if tracer._expected_place.is_xpu_place() and (dtype == 'bfloat16'): warnings.warn('XPUPlace only support float16 amp.') enable = False # For gpu float16: Compute Capability should >= 7. # For gpu bfloat16: Compute Capability should >= 8 & CUDA Version should >= 11. if tracer._expected_place.is_gpu_place(): if (dtype == 'float16') and not _is_gpu_float16_supported(): prop = paddle.device.cuda.get_device_capability() warnings.warn( "For float16, amp only support NVIDIA GPU with Compute Capability 7.0 or higher, current GPU is: %s, with Compute Capability: %d.%d." % (paddle.device.cuda.get_device_name(), prop[0], prop[1])) elif (dtype == 'bfloat16') and not _is_gpu_bfloat16_supported(): prop = paddle.device.cuda.get_device_capability() cuda_version = paddle.version.cuda() warnings.warn( "For bfloat16, amp only support NVIDIA GPU with Compute Capability 8.0 or higher and CUDA Version 11.0 or higher, current GPU is: %s, with Compute Capability: %d.%d, current CUDA Version is: %s." % (paddle.device.cuda.get_device_name(), prop[0], prop[1], cuda_version)) amp_dtype = dtype if level == 'O1': amp_level = AMP_LEVEL.O1 if dtype == 'float16': _white_list = WHITE_LIST _black_list = BLACK_LIST elif dtype == 'bfloat16': _white_list = BF16_WHITE_LIST _black_list = BF16_BLACK_LIST elif level == 'O2': amp_level = AMP_LEVEL.O2 if dtype == 'float16': _white_list = PURE_FP16_WHITE_LIST _black_list = PURE_FP16_BLACK_LIST elif dtype == 'bfloat16': _white_list = BF16_WHITE_LIST _black_list = BF16_BLACK_LIST elif level == 'O0': amp_level = AMP_LEVEL.O0 if dtype == 'float16': _white_list = WHITE_LIST _black_list = BLACK_LIST elif dtype == 'bfloat16': _white_list = BF16_WHITE_LIST _black_list = BF16_BLACK_LIST if custom_white_list or custom_black_list: _white_list, _black_list = _update_list(custom_white_list, custom_black_list, level, dtype) if not enable: amp_level = AMP_LEVEL.O0 amp_dtype = "float32" if tracer: # enable auto_cast original_amp_level = tracer._amp_level tracer._amp_level = amp_level # set amp op list original_white_list, original_black_list = tracer._get_amp_op_list() tracer._set_amp_op_list(_white_list, _black_list) # TODO(zhiqiu) set amp related flags automatically in this guard # Currently, if FLAGS_cudnn_batchnorm_spatial_persistent is set True in amp_guard, # batch_norm can run in fast mode, but batch_norm_grad can not if backward if not executed insise amp_guard. # So, users need to set related flags manually. # original_flags = get_flags(AMP_RELATED_FLAGS) # set_flags(AMP_RELATED_FLAGS_SETTING) # set amp dtype original_amp_dtype = tracer._amp_dtype tracer._amp_dtype = amp_dtype # restore status try: yield finally: if tracer: tracer._amp_level = original_amp_level tracer._set_amp_op_list(original_white_list, original_black_list) # set_flags(original_flags) tracer._amp_dtype = original_amp_dtype class StateDictHook(object): def __init__(self, save_dtype): self._save_dtype = save_dtype def __call__(self, state_dict): for key in state_dict: param = state_dict[key] with paddle.fluid.dygraph.guard(): param_applied = paddle.cast(param, self._save_dtype) param_applied.name = param.name state_dict[key] = param_applied @dygraph_only def amp_decorate(models, optimizers=None, level='O1', master_weight=None, save_dtype=None): """ Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing. When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm. Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode. Args: models(Layer|list of Layer, optional): The defined models by user, models must be either a single model or a list of models. Default is None. optimizers(Optimizer|list of Optimizer, optional): The defined optimizers by user, optimizers must be either a single optimizer or a list of optimizers. Default is None. level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the decorator will do nothing; O2 represent Pure fp16, the decorator will cast all parameters of models to FP16, except BatchNorm and LayerNorm. Default is O1(amp) master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None. save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, float32, float64 or None. The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None. Examples: .. code-block:: python # required: gpu # Demo1: single model and optimizer: import paddle model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False) optimzier = paddle.optimizer.SGD(parameters=model.parameters()) model, optimizer = paddle.fluid.dygraph.amp_decorate(models=model, optimizers=optimzier, level='O2') data = paddle.rand([10, 3, 32, 32]) with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'): output = model(data) print(output.dtype) # FP16 # required: gpu # Demo2: multi models and optimizers: model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False) optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters()) models, optimizers = paddle.fluid.dygraph.amp_decorate(models=[model, model2], optimizers=[optimzier, optimizer2], level='O2') data = paddle.rand([10, 3, 32, 32]) with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'): output = models[0](data) output2 = models[1](data) print(output.dtype) # FP16 print(output2.dtype) # FP16 # required: gpu # Demo3: optimizers is None: model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False) optimizer3 = paddle.optimizer.Adam(parameters=model2.parameters()) model = paddle.fluid.dygraph.amp_decorate(models=model3, level='O2') data = paddle.rand([10, 3, 32, 32]) with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'): output = model(data) print(output.dtype) # FP16 """ if not (level in ['O1', 'O2']): raise ValueError( "level should be O1 or O2, O1 represent AMP train mode, O2 represent Pure fp16 train mode." ) if level == 'O1': if optimizers is None: return models else: return models, optimizers models_is_list = False if isinstance(models, paddle.nn.Layer): models_is_list = False models = [models] check_models(models) elif isinstance(models, list): check_models(models) models_is_list = True else: raise TypeError( "models must be either a single model or a list of models.") models = pure_fp16_initialize(models=models) if optimizers is not None: # check optimizers optimizers_is_list = False if isinstance(optimizers, (paddle.optimizer.Optimizer, paddle.fluid.optimizer.Optimizer)): optimizers_is_list = False optimizers = [optimizers] check_optimizers(optimizers) elif isinstance(optimizers, list): check_optimizers(optimizers) optimizers_is_list = True else: raise TypeError( "optimizers must be either a single optimizer or a list of optimizers." ) # supprot master_weight for idx_opt in range(len(optimizers)): if hasattr(optimizers[idx_opt], '_multi_precision'): if master_weight is False: optimizers[idx_opt]._multi_precision = False else: optimizers[idx_opt]._multi_precision = True if save_dtype is not None: if not (save_dtype in ['float16', 'float32', 'float64']): raise ValueError( "save_dtype can only be float16 float32 or float64, but your input save_dtype is %s." % save_dtype) for idx in range(len(models)): for layer in models[idx].sublayers(include_self=True): layer.register_state_dict_hook(StateDictHook(save_dtype)) if models_is_list: if optimizers is not None: if optimizers_is_list: return models, optimizers else: return models, optimizers[0] else: return models else: if optimizers is not None: if optimizers_is_list: return models[0], optimizers else: return models[0], optimizers[0] else: return models[0]