anirudh2290 commented on a change in pull request #14173: [WIP] MXNet AMP (automatic mixed precision) URL: https://github.com/apache/incubator-mxnet/pull/14173#discussion_r284581117
########## File path: python/mxnet/contrib/amp/amp.py ########## @@ -0,0 +1,342 @@ +# 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. + +# coding: utf-8 +"""Functions for enabling AMP (automatic mixed precision).""" +__all__ = ['init', 'init_trainer', 'scale_loss', 'unscale'] + +from types import MethodType +import logging +import contextlib +import numpy as np + +from ... import symbol +from ...symbol import Symbol +from ...symbol import contrib as symbol_contrib +from ... import ndarray +from ...ndarray import NDArray +from . import lists +from ...gluon import trainer +from ... import base +from ... import optimizer as opt +from .loss_scaler import LossScaler + +def _cast_symbol_NDArray(s, dtype): + float_types = (np.float16, np.float32) + if isinstance(s, Symbol): + return symbol.amp_cast(s, dtype=dtype) + elif isinstance(s, NDArray): + if (s.dtype != dtype and + s.dtype in float_types and + s.context.device_type != 'cpu'): + return ndarray.amp_cast(s, dtype=dtype) + else: + return s + else: + return s + +def _get_fun_to_wrap(name, module, submodule_dict): + module_internal = getattr(module, "_internal") + prefix = base._get_op_name_prefix(name) + if len(prefix) > 0: + if prefix != '_random_' or name.endswith('_like'): + func_name = name[len(prefix):] + cur_module = submodule_dict[prefix] + else: + func_name = name + cur_module = module_internal + elif name.startswith('_'): + func_name = name + cur_module = module_internal + else: + func_name = name + cur_module = module + return func_name, cur_module + +def _wrap_symbol_functions(module, target_dtype, target_precision_ops=None, + conditional_fp32_ops=None, fp32_ops=None): + def _ndarray_wrapper(f, target_dtype, cond_arg=None): + def _new_fun(*args, **kwargs): + if cond_arg is not None: + if (cond_arg[0] not in kwargs or + kwargs[cond_arg[0]] not in cond_arg[1]): + return f(*args, **kwargs) + new_args = list(map(lambda x: _cast_symbol_NDArray(x, target_dtype), args)) + args = tuple(new_args) + kwargs = {k: _cast_symbol_NDArray(v, target_dtype) for k, v in kwargs.items()} + return f(*args, **kwargs) + _new_fun.__name__ = f.__name__ + _new_fun.__module__ = f.__module__ + _new_fun.__doc__ = f.__doc__ + return _new_fun + + def _symbol_wrapper(f, target_dtype, cond_arg=None): + def _new_fun(*args, **kwargs): + if cond_arg is not None: + if (cond_arg[0] not in kwargs or + kwargs[cond_arg[0]] not in cond_arg[1]): + return f(*args, **kwargs) + sym = f(*args, **kwargs) + inputs = sym.get_children() + aux = sym.list_auxiliary_states() + inputs = list(map(lambda x: _cast_symbol_NDArray(x, target_dtype) + if x.name not in aux else x, inputs)) + atomic_sym = sym._gen_atomic_symbol() + wrapped_sym = atomic_sym(*inputs) + wrapped_sym._set_attr(name=sym.name) + return wrapped_sym + _new_fun.__name__ = f.__name__ + _new_fun.__module__ = f.__module__ + _new_fun.__doc__ = f.__doc__ + return _new_fun + + def _symbol_widest_wrapper(f): + def _new_fun(*args, **kwargs): + symbols = [] + is_symbol = False + args = list(args) + for i, arg in enumerate(args): + if isinstance(arg, (Symbol, NDArray)): + symbols.append((args, i, arg)) + is_symbol = is_symbol or isinstance(arg, Symbol) + for k, arg in kwargs.items(): + if isinstance(arg, (Symbol, NDArray)): + symbols.append((kwargs, k, arg)) + is_symbol = is_symbol or isinstance(arg, Symbol) + if not is_symbol: + # NDArray case + widest_type = target_dtype + for _, _, arg in symbols: + if isinstance(arg, NDArray): + if arg.dtype == np.float32: + widest_type = np.float32 + for arr, index, arg in symbols: + if arg.dtype != widest_type and arg.dtype == target_dtype: + arr[index] = ndarray.amp_cast(arg, dtype=widest_type) + else: + # Symbol case + sym_to_check = list(map(lambda x: x[2], symbols)) + casted_syms = symbol.amp_multicast(*sym_to_check, num_outputs=len(sym_to_check)) + symbols = list(map(lambda x_y: (x_y[0][0], x_y[0][1], x_y[1]), + zip(symbols, casted_syms))) + for arr, index, arg in symbols: + arr[index] = arg + + return f(*args, **kwargs) + _new_fun.__name__ = f.__name__ + _new_fun.__module__ = f.__module__ + _new_fun.__doc__ = f.__doc__ + return _new_fun + + _wrapper = _symbol_wrapper if module in (symbol, Symbol, symbol_contrib) else _ndarray_wrapper + + submodule_dict = {} + for op_name_prefix in base._OP_NAME_PREFIX_LIST: + submodule_dict[op_name_prefix] =\ + getattr(module, op_name_prefix[1:-1]) + + wrap_list = target_precision_ops if target_precision_ops is not None \ + else lists.symbol.TARGET_DTYPE_FUNCS + for fun_name in wrap_list: + try: + fun_name, cur_module = _get_fun_to_wrap(fun_name, module, submodule_dict) + f_to_wrap = getattr(cur_module, fun_name) + setattr(cur_module, fun_name, _wrapper(f_to_wrap, target_dtype)) + if cur_module == module: + setattr(module.op, fun_name, _wrapper(f_to_wrap, target_dtype)) + except AttributeError: + logging.warning("Failed to find " + fun_name + " in " + cur_module.__name__) + + wrap_list = fp32_ops if fp32_ops is not None else lists.symbol.FP32_FUNCS + for fun_name in wrap_list: + try: + fun_name, cur_module = _get_fun_to_wrap(fun_name, module, submodule_dict) + f_to_wrap = getattr(cur_module, fun_name) + setattr(cur_module, fun_name, _wrapper(f_to_wrap, np.float32)) + if cur_module == module: + setattr(module.op, fun_name, _wrapper(f_to_wrap, np.float32)) + except AttributeError: + logging.warning("Failed to find " + fun_name + " in " + module.__name__) + + wrap_list = conditional_fp32_ops if conditional_fp32_ops is not None \ + else lists.symbol.CONDITIONAL_FP32_FUNCS + for fun_name, arg, arg_values in wrap_list: + try: + fun_name, cur_module = _get_fun_to_wrap(fun_name, module, submodule_dict) + f_to_wrap = getattr(cur_module, fun_name) + setattr(cur_module, fun_name, _wrapper(f_to_wrap, np.float32, (arg, arg_values))) + if cur_module == module: + setattr(module.op, fun_name, _wrapper(f_to_wrap, np.float32, (arg, arg_values))) + except AttributeError: + logging.warning("Failed to find " + fun_name + " in " + module.__name__) + + for fun_name in lists.symbol.WIDEST_TYPE_CASTS: + try: + fun_name, cur_module = _get_fun_to_wrap(fun_name, module, submodule_dict) + f_to_wrap = getattr(cur_module, fun_name) + setattr(cur_module, fun_name, _symbol_widest_wrapper(f_to_wrap)) + if cur_module == module: + setattr(module.op, fun_name, _symbol_widest_wrapper(f_to_wrap)) + except AttributeError: + logging.warning("Failed to find " + fun_name + " in " + module.__name__) + +def _wrap_loss_output_functions(module, ls): + if module == ndarray: + def _wrapper(f): + def _scaling_wrapper(*args, **kwargs): + if 'grad_scale' in kwargs: + kwargs['grad_scale'] = kwargs['grad_scale'] * ls.loss_scale + else: + kwargs['grad_scale'] = ls.loss_scale + return f(*args, **kwargs) + _scaling_wrapper.__name__ = f.__name__ + _scaling_wrapper.__module__ = f.__module__ + _scaling_wrapper.__doc__ = f.__doc__ + return _scaling_wrapper + else: + def _wrapper(f): + def _warning_wrapper(*args, **kwargs): + logging.warning("%s does not support dynamic loss scaling " + "in symbolic and hybridized execution.", f.__name__) + return f(*args, **kwargs) + _warning_wrapper.__name__ = f.__name__ + _warning_wrapper.__module__ = f.__module__ + _warning_wrapper.__doc__ = f.__doc__ + return _warning_wrapper + + for fun_name in lists.symbol.LOSS_OUTPUT_FUNCTIONS: + try: + f_to_wrap = getattr(module, fun_name) + setattr(module, fun_name, _wrapper(f_to_wrap)) + except AttributeError: + pass + +_amp_initialized = False +_amp_loss_scale_initialized = False +_loss_scaler = None + [email protected] +def scale_loss(loss, optimizer_or_trainer): + assert optimizer_or_trainer._amp_loss_scaler is not None, \ + 'Loss scaler is not initialized, did you forget to call amp.init_trainer()?' + optimizer_or_trainer._scale = (optimizer_or_trainer._amp_original_scale / + optimizer_or_trainer._amp_loss_scaler.loss_scale) + if isinstance(loss, (list, tuple)): + yield [l * optimizer_or_trainer._amp_loss_scaler.loss_scale for l in loss] + else: + yield optimizer_or_trainer._amp_loss_scaler.loss_scale * loss + +def init(target_dtype='float16', target_precision_ops=None, + conditional_fp32_ops=None, fp32_ops=None): + """Initialize AMP (automatic mixed precision). + + This needs to be done before model creation. + + Parameters + ---------- + target_dtype : {'float16'} + Target low precision type for AMP. Currently only float16 is supported. + target_precision_ops : list of string + Override the list of functions casted to FP16. Entries in this list + are names of the functions casted to FP16. + conditional_fp32_ops : list of (string, string, list of string) + Override the list of functions conditionally casted to FP32. The format + of the list is (name of the function, name of the parameter, list of + values of the parameter that make the function be casted to FP32). + fp32_ops : list of string + Override the list of functions casted to FP32. Entries in this list + are names of the functions casted to FP32. + """ + global _amp_initialized + global _loss_scaler + if not _amp_initialized: + _amp_initialized = True + logging.info("Using AMP") + target_dtype = np.dtype(target_dtype) Review comment: assert that target_dtype is float16 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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