# Copyright (c) 2021 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 annotations import contextlib import copy import inspect import random import weakref from typing import TYPE_CHECKING, Any, TypedDict import numpy as np import paddle from paddle import framework from paddle.autograd import PyLayer from paddle.base.framework import EagerParamBase from paddle.distributed.fleet.meta_parallel.parallel_layers.random import ( get_rng_state_tracker, ) from paddle.framework import core, in_dynamic_mode from ..utils.log_util import logger if TYPE_CHECKING: from collections.abc import Callable, Sequence from typing_extensions import NotRequired from paddle.nn import Sequential class _Ctx(TypedDict): segments: int = 1 preserve_rng_state: NotRequired[bool] __all__ = [] def _varbase_help(param): state = copy.deepcopy(param.__dict__) new_param = EagerParamBase( shape=param.shape, dtype=param.dtype, trainable=param.trainable, name=param.name, **state, ) param._share_buffer_to(new_param) return new_param def detach_variable(inputs): out = [] for inp in inputs: if not isinstance(inp, core.eager.Tensor) and ( type(inp) is not tuple or not isinstance(inp[0], core.eager.Tensor) ): # the inp is not a tensor or not a tuple of tensors out.append(inp) continue if isinstance(inp, EagerParamBase): out.append(_varbase_help(inp)) continue if type(inp) is tuple: detach_inp = [] for i in inp: # detach all tensors in the tuple assert isinstance(i, core.eager.Tensor) if isinstance(i, EagerParamBase): detach_inp.append(_varbase_help(i)) else: tmp_i = i.detach() tmp_i.stop_gradient = i.stop_gradient detach_inp.append(tmp_i) out.append(tuple(detach_inp)) continue x = inp.detach() x.stop_gradient = inp.stop_gradient out.append(x) return tuple(out) def check_recompute_necessary(inputs): necessary_for_each_input = [] for input_ in inputs: if isinstance(input_, paddle.Tensor): necessary_for_each_input.append(input_.stop_gradient) elif type(input_) is tuple: for i in input_: # traverse all tensors in the tuple if isinstance(i, paddle.Tensor): necessary_for_each_input.append(i.stop_gradient) if all(necessary_for_each_input): logger.warning( "[Recompute]: None of the inputs to current recompute block need grad, " "therefore there is NO need to recompute this block in backward !" ) class CustomStatesManager: """CustomStatesManager""" def __init__(self): """__init__""" self.custom_get_state_func = None self.custom_set_state_func = None def set_custom_get_state_func(self, custom_get_state_func): assert_msg = ( "The custom_state_manager does not support duplicate settings." ) assert self.custom_get_state_func is None, assert_msg self.custom_get_state_func = custom_get_state_func def set_custom_set_state_func(self, custom_set_state_func): assert_msg = ( "The custom_state_manager does not support duplicate settings." ) assert self.custom_set_state_func is None, assert_msg self.custom_set_state_func = custom_set_state_func custom_state_manager = CustomStatesManager() @contextlib.contextmanager def switch_rng_state_tracker( rng_state, tracker, numpy_state, random_state, custom_state=None, custom_get_state_func=None, custom_set_state_func=None, ): orig_rng_state = paddle.get_rng_state() orig_rng_tracker = get_rng_state_tracker().get_states_tracker() paddle.set_rng_state(rng_state) get_rng_state_tracker().set_states_tracker(tracker) orig_numpy_state = np.random.get_state() orig_random_state = random.getstate() np.random.set_state(numpy_state) random.setstate(random_state) if custom_state is not None: assert custom_get_state_func is not None assert custom_set_state_func is not None orig_custom_state = custom_get_state_func() custom_set_state_func(custom_state) try: yield finally: paddle.set_rng_state(orig_rng_state) get_rng_state_tracker().set_states_tracker(orig_rng_tracker) np.random.set_state(orig_numpy_state) random.setstate(orig_random_state) if custom_state is not None: custom_set_state_func(orig_custom_state) class RecomputeFunction(PyLayer): @staticmethod def forward( ctx, run_function, preserve_rng_state, offload_indices, custom_get_state_func, custom_set_state_func, *args, **kwargs, ): # store for recomputing ctx.run_function = run_function ctx.preserve_rng_state = preserve_rng_state ctx.offload_indices = offload_indices ctx.kwargs = kwargs # NOTE the number of outputs of backward() should be equal to the number of tensors in forward()'s input # the order of tensors in backward()'s output should be the same as tensors in forward()'s input # None tensor inputs will be filtered in backward inputs. # NOTE recompute with restore RNG only support one scenario where one process for one cuda gpu. # one process with multiple gpu and mix-gpu-cpu scenarios are not support if ctx.preserve_rng_state: ctx.fw_rng_state = paddle.get_rng_state() ctx.fwd_rng_state_tracker = ( get_rng_state_tracker().get_states_tracker() ) ctx.fwd_numpy_state = np.random.get_state() ctx.fwd_random_state = random.getstate() ctx.fwd_custom_state = custom_get_state_func() ctx.custom_get_state_func = custom_get_state_func ctx.custom_set_state_func = custom_set_state_func # TODO support AMP tracer = framework._dygraph_tracer() ctx.is_fw_autocast = ( False if tracer._amp_level == core.AmpLevel.O0 else True ) if tracer._amp_level == core.AmpLevel.O2: ctx.amp_level = 'O2' elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0): ctx.amp_level = 'O1' else: raise ValueError(f"unsupported amp level: {tracer._amp_level}") if tracer._amp_dtype == 'float16': ctx.amp_dtype = 'float16' elif tracer._amp_dtype in ('bfloat16', 'float32'): ctx.amp_dtype = 'bfloat16' else: raise ValueError(f"unsupported amp dtype: {tracer._amp_dtype}") ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list() with paddle.no_grad(): outputs = run_function(*args, **kwargs) # save input for backward ctx.inputs = [] ctx.tensor_indices = [] ctx.duplicate_tensor = [False for _ in range(len(args))] tensor_inputs = [] for i, arg in enumerate(args): if paddle.is_tensor(arg): if i in ctx.offload_indices: cpu_arg = ( arg.pin_memory() if core.is_compiled_with_cuda() else arg.cpu() ) cpu_arg._share_buffer_to(arg) tensor_inputs.append(arg) ctx.tensor_indices.append(i) ctx.inputs.append(None) elif type(arg) is tuple: assert i not in ctx.offload_indices, ( f"offload_indices should not contain tensor tuple in position{i}" ) is_tensors = [paddle.is_tensor(a) for a in arg] if all(is_tensors): # the tuple is a tuple of tensors tensors_stop_gradient = [a.stop_gradient for a in arg] if not all(tensors_stop_gradient) and any( tensors_stop_gradient ): # tensors in the tuple have different stop_gradient value, which pylayer doesn't support raise ValueError( "Recompute receive a tuple containing tensor holds different stop gradient." ) tensor_inputs.append(arg) ctx.tensor_indices.append(i) # Mark the tuple is a tuple of tensors ctx.duplicate_tensor[i] = True ctx.inputs.append(None) elif any(is_tensors): # the tuple contains tensors and non-tensor values raise ValueError( "Recompute receive a tuple containing tensor and non-tensor at same time." ) else: ctx.inputs.append(arg) else: ctx.inputs.append(arg) ctx.save_for_backward(*tensor_inputs) return outputs @staticmethod def backward(ctx, *args): with paddle.base.dygraph.guard(): # TODO need to check the recompute calling is valid or not # Restore inputs inputs = list(ctx.inputs) tensor_indices = ctx.tensor_indices duplicate_tensor = ctx.duplicate_tensor tensors = ctx.saved_tensor() for i, idx in enumerate(tensor_indices): inputs[idx] = ( tensors[i].to( paddle.base.framework._current_expected_place() ) if i in ctx.offload_indices else tensors[i] ) if i in ctx.offload_indices: # NOTE(zhiqiu): tensor.to(device) will set stop_gradient=True, which may break the gragh inputs[idx].stop_gradient = tensors[i].stop_gradient # paddle.enable_grad() tracer = framework._dygraph_tracer() tracer._has_grad = True # NOTE support AMP # need restore auto_cast state as well as w/b list if ctx.preserve_rng_state: with ( switch_rng_state_tracker( ctx.fw_rng_state, ctx.fwd_rng_state_tracker, ctx.fwd_numpy_state, ctx.fwd_random_state, ctx.fwd_custom_state, ctx.custom_get_state_func, ctx.custom_set_state_func, ), paddle.amp.auto_cast( enable=ctx.is_fw_autocast, custom_white_list=ctx.amp_white_list, custom_black_list=ctx.amp_black_list, level=ctx.amp_level, dtype=ctx.amp_dtype, ), ): detached_inputs = detach_variable(tuple(inputs)) outputs = ctx.run_function(*detached_inputs, **ctx.kwargs) else: with paddle.amp.auto_cast( enable=ctx.is_fw_autocast, custom_white_list=ctx.amp_white_list, custom_black_list=ctx.amp_black_list, level=ctx.amp_level, dtype=ctx.amp_dtype, ): detached_inputs = detach_variable(tuple(inputs)) outputs = ctx.run_function(*detached_inputs, **ctx.kwargs) if isinstance(outputs, core.eager.Tensor): outputs = (outputs,) assert len(outputs) == len(args) # run backward() with only tensor that requires grad forward_outputs_with_grad = [] # NOTE In Transformer-like network, if user put the attention mask into the recompute segment output, # pylayer will force the stop_gradient of attention mask to be False, which will make the number of # tensor that need grad does not match. # the following backward_inputs_with_grad is used to avoid this case. backward_inputs_with_grad = [] for i in range(len(outputs)): if ( isinstance(outputs[i], core.eager.Tensor) and not outputs[i].stop_gradient ): forward_outputs_with_grad.append(outputs[i]) backward_inputs_with_grad.append(args[i]) if len(forward_outputs_with_grad) == 0: raise RuntimeError( "none of output has requires_grad=True, this recompute() is not necessary" ) # actually backward with paddle.amp.auto_cast(enable=False): paddle.autograd.backward( forward_outputs_with_grad, backward_inputs_with_grad ) grads = [] for idx, inp in enumerate(detached_inputs): if isinstance(inp, core.eager.Tensor): grads.append(inp._grad_ivar()) elif type(inp) is tuple and duplicate_tensor[idx]: # input is a tuple and is a tuple of tensors if all(i.stop_gradient for i in inp): # all tensors in the tuple doesn't need grad, only return a None for the whole tuple grads.append(None) else: # all tensors in the tuple need grad, should return a tuple of grads grads.append(tuple(i._grad_ivar() for i in inp)) if in_dynamic_mode(): grads = tuple(grads) else: grads = list(grads) return grads def _recompute_without_reentrant( function, custom_get_state_func, custom_set_state_func, preserve_rng_state=True, *args, **kwargs, ): """ recompute without reentrant, that means use hook to implement the recompute function rather than re-entrant autograd. """ if preserve_rng_state: cur_device = paddle.get_device() if 'gpu:' in cur_device: fw_cuda_rng_state = paddle.get_cuda_rng_state() elif 'cpu' in cur_device: fw_cuda_rng_state = paddle.get_rng_state() elif 'xpu:' in cur_device: fw_cuda_rng_state = paddle.get_rng_state() elif ( cur_device.split(':')[0] in paddle.device.get_all_custom_device_type() ): fw_cuda_rng_state = paddle.get_rng_state(cur_device) else: raise RuntimeError( f"Recompute with RNG preserve is not support current device: {cur_device}." ) fwd_cuda_rng_state_tracker = ( get_rng_state_tracker().get_states_tracker() ) fwd_numpy_state = np.random.get_state() fwd_random_state = random.getstate() fwd_custom_state = custom_get_state_func() tracer = framework._dygraph_tracer() is_fw_autocast = False if tracer._amp_level == core.AmpLevel.O0 else True if tracer._amp_level == core.AmpLevel.O2: amp_level = 'O2' elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0): amp_level = 'O1' if tracer._amp_dtype == 'float16': amp_dtype = 'float16' elif tracer._amp_dtype in ('bfloat16', 'float32'): amp_dtype = 'bfloat16' amp_white_list, amp_black_list = tracer._get_amp_op_list() class Intermediate_Holder: pass storage = weakref.WeakKeyDictionary() holder_list = [] def pack(x): res = Intermediate_Holder() holder_list.append(weakref.ref(res)) return res def unpack(x): unpack_counter = 0 if len(storage) == 0: def inner_pack(inner_x): nonlocal unpack_counter unpack_counter += 1 if holder_list[unpack_counter - 1]() is None: return if inner_x is None: storage[holder_list[unpack_counter - 1]()] = None return if hasattr(inner_x, "main_grad") or inner_x.grad is not None: storage[holder_list[unpack_counter - 1]()] = inner_x else: if inner_x.is_dist(): tmp_tensor = core.eager.Tensor(inner_x) else: tmp_tensor = core.eager.Tensor( inner_x.dtype, inner_x.shape, inner_x.name + "cpy", core.VarDesc.VarType.DENSE_TENSOR, inner_x.persistable, ) inner_x._unsafe_share_buffer_to(tmp_tensor) storage[holder_list[unpack_counter - 1]()] = tmp_tensor return def inner_unpack(inner_x): raise Exception("An unexpected backward called on a tensor!") if preserve_rng_state: with ( switch_rng_state_tracker( fw_cuda_rng_state, fwd_cuda_rng_state_tracker, fwd_numpy_state, fwd_random_state, fwd_custom_state, custom_get_state_func, custom_set_state_func, ), paddle.set_grad_enabled(True), paddle.amp.auto_cast( enable=is_fw_autocast, custom_white_list=amp_white_list, custom_black_list=amp_black_list, level=amp_level, dtype=amp_dtype, ), paddle.autograd.saved_tensors_hooks( inner_pack, inner_unpack ), ): function(*args, **kwargs) else: with ( paddle.set_grad_enabled(True), paddle.amp.auto_cast( enable=is_fw_autocast, custom_white_list=amp_white_list, custom_black_list=amp_black_list, level=amp_level, dtype=amp_dtype, ), paddle.autograd.saved_tensors_hooks( inner_pack, inner_unpack ), ): function(*args, **kwargs) if x not in storage: raise Exception( "Not supported to retrieve a tensor saved by autograd multiple times that is no need to recompute." ) return storage.pop(x) with paddle.autograd.saved_tensors_hooks(pack, unpack): outputs = function(*args, **kwargs) return outputs def recompute(function, *args, **kwargs): """ recompute intermediate activations to save then memory. Parameters: function(paddle.nn.Layer): layer of sequence of layers that describes part of forward pass of the model whose intermediate activations will be released to save memory in forward stage and will be recomputed in backward stage for gradient calculation. *args(Tensor): inputs to the function. **kwargs(Dict): Kwargs should only contain two kinds of key-value params, the one is part of function's key-value params, and the other contains 'preserve_rng_state' and 'use_reentrant'. the key-value pair of preserve_rng_state, which is used to indicate whether to save the forward rng. If it is True, then the last forward rng value will be restored when the forward recalculation of backpropagation is performed, its default value is True. the key-value pair of use_reentrant is used to indicate which implementation of recompute you will be used. 'use_reentrant=True' means to use the PyLayer implementation of recompute, 'use_reentrant=False' means to use the Hook implementation of recompute, its default value is True. Returns: Output of function on args. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED, env:GPU) >>> import paddle >>> from paddle.distributed.fleet.utils import recompute >>> import random >>> paddle.seed(2023) >>> def get_fc_block(block_idx, input_size, is_last=False): ... block_name = "block_" + str(block_idx) ... block = paddle.nn.Sequential( ... (block_name + "_fc_0", paddle.nn.Linear(input_size, input_size, bias_attr=False)), ... (block_name + "_dropout", paddle.nn.Dropout(p=0.5)), ... (block_name + "_relu_1", paddle.nn.ReLU()), ... (block_name + "_fc_1", paddle.nn.Linear(input_size, input_size, bias_attr=False)), ... (block_name + "_relu_2", paddle.nn.ReLU()), ... ) ... if is_last: ... block.add_sublayer( ... block_name + "_fc_2", ... paddle.nn.Linear( ... input_size, 1, bias_attr=False ... ) ... ) ... else: ... block.add_sublayer( ... block_name + "_fc_2", ... paddle.nn.Linear(input_size, input_size, bias_attr=False) ... ) ... return block >>> class Naive_fc_net(paddle.nn.Layer): ... def __init__(self, input_size=10, ... recompute_blocks=[1, 3], ... recompute_kwargs={}): ... super().__init__() ... self.recompute_blocks = recompute_blocks ... self.recompute_kwargs = recompute_kwargs ... self.runfunc0 = get_fc_block(0, input_size, is_last=False) ... self.runfunc1 = get_fc_block(1, input_size, is_last=False) ... self.runfunc2 = get_fc_block(2, input_size, is_last=False) ... self.runfunc3 = get_fc_block(3, input_size, is_last=False) ... self.runfunc4 = get_fc_block(4, input_size, is_last=True) ... self.total_func = [self.runfunc0, self.runfunc1, self.runfunc2, self.runfunc3, self.runfunc4] ... def forward(self, inputs): ... nums = len(self.total_func) ... for i in range(nums): ... if i in self.recompute_blocks: ... inputs = recompute(self.total_func[i], inputs, **{"preserve_rng_state": True}) ... else: ... inputs = self.total_func[i](inputs) ... return inputs >>> def run_model(cuda_state, recompute_block=[], recompute_kwargs={}): ... gen = paddle.seed(10) ... gen.manual_seed(10) ... random.seed(10) ... if cuda_state: ... paddle.set_cuda_rng_state(cuda_state) ... batch_size, input_size = 1, 10 ... model = Naive_fc_net( ... input_size, ... recompute_blocks=recompute_block, ... recompute_kwargs=recompute_kwargs) ... optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) ... loss_ = [] ... param_ = [] ... grad_ = [] ... for _ in range(5): ... x = paddle.rand(shape=[batch_size, input_size], dtype="float32") ... y_pred = model(x) ... loss = y_pred.mean() ... loss_.append(loss.item()) ... loss.backward() ... optimizer.step() ... param_.append(model.parameters()[9]) ... grad_.append(model.parameters()[3]._grad_ivar()) ... optimizer.clear_grad() ... return loss_, param_, grad_ >>> cuda_state = paddle.get_cuda_rng_state() >>> # without recompute >>> loss_ref, param_ref, grad_ref = run_model( ... cuda_state, recompute_block=[] ... ) >>> loss, param, grad = run_model(cuda_state, recompute_block=[1, 2]) >>> print("normal_loss: {}, recompute_loss: {}".format(loss_ref, loss)) >>> # The result of the recompute_loss should be the same as the normal_loss. normal_loss: [0.0018744759727269411, 0.0, 0.035971127450466156, 0.0, 0.0], recompute_loss: [0.0018744759727269411, 0.0, 0.035971127450466156, 0.0, 0.0] """ # Hack to mix *args with **kwargs in a python 2.7-compliant way preserve = kwargs.pop('preserve_rng_state', True) # whether to use reentrant method to implement recompute use_reentrant = kwargs.pop('use_reentrant', True) if custom_state_manager.custom_get_state_func is None: assert custom_state_manager.custom_set_state_func is None custom_get_state_func = lambda x=None: None custom_set_state_func = lambda x=None: None else: custom_get_state_func = custom_state_manager.custom_get_state_func custom_set_state_func = custom_state_manager.custom_set_state_func if not in_dynamic_mode(): from paddle.distributed.auto_parallel.interface import ( recompute as static_auto_recompute, ) return static_auto_recompute(function)(*args, **kwargs) if framework._dygraph_tracer()._has_grad: check_args = list(args) check_args.extend(list(kwargs.values())) check_recompute_necessary(check_args) if use_reentrant: offload_indices = kwargs.pop('offload_indices', []) input_args = [] # rearrange `position-args + keyword-args` into `position-args` if isinstance(function, paddle.nn.Layer): dyfunc_sig = inspect.signature(function.forward) else: dyfunc_sig = inspect.signature(function) bound_args = dyfunc_sig.bind(*args, **kwargs) bound_args.apply_defaults() for arg, param in zip( bound_args.arguments.values(), dyfunc_sig.parameters.values() ): if param.kind == param.VAR_POSITIONAL: input_args.extend(arg) elif param.kind in ( param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD, ): input_args.append(arg) elif param.kind == param.VAR_KEYWORD: input_args.extend(arg.values()) elif param.kind == param.KEYWORD_ONLY: raise ValueError( "Currently, keyword-only arguments are not supported when you want to send kwargs(dict parameter) to function with use_reentrant=True." ) else: raise ValueError("Unknown parameter kind.") return RecomputeFunction.apply( function, preserve, offload_indices, custom_get_state_func, custom_set_state_func, *input_args, ) else: return _recompute_without_reentrant( function, custom_get_state_func, custom_set_state_func, preserve, *args, **kwargs, ) def recompute_sequential( ctx: _Ctx, functions: Sequential | Sequence[Callable[..., Any]], *args: Any, **kwargs: Any, ) -> Any: """ recompute intermediate activations to save the memory for 'Sequential' models. use 'ctx' to transmit some context params, it is similar to 'recompute_hybrid' API. Parameters: ctx(dict): include 'segments' and 'preserve_rng_state' keys, the key 'segments' (int, default 1), represents the number of chunks to create in the model, the key 'preserve_rng_state' (bool, optional, default=True) indicate whether to save the forward rng. If it is True, then the last forward rng value will be restored when the forward recalculation of backpropagation is performed. functions(paddle.nn.Sequential): layer of sequence of layers that describes part of forward pass of the model whose intermediate activations will be released to save memory in forward stage and will be recomputed in backward stage for gradient calculation. *args(Tensor): inputs(tuple) to the function. **kwargs(Dict): inputs(dict) to the function. Returns: Output of function on args and kwargs. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> import paddle >>> from paddle.incubate.distributed.fleet import recompute_sequential >>> input = paddle.ones(shape=[8, 10]) >>> model = paddle.nn.Sequential(paddle.nn.Linear(10, 10), paddle.nn.Linear(10, 2)) >>> output = recompute_sequential({'segments' : 1}, model, input) """ segments = ctx.get('segments', 1) preserve_rng_state = ctx.get('preserve_rng_state', True) def _run_func(begin, end, funcs): def do_run(input): for i in range(begin, end + 1): input = funcs[i](input) return input return do_run if isinstance(functions, paddle.nn.Sequential): functions = list(functions.children()) segment_size = len(functions) // segments end = -1 for begin in range(0, segment_size * (segments - 1), segment_size): end = begin + segment_size - 1 args = recompute( _run_func(begin, end, functions), *args, preserve_rng_state=preserve_rng_state, **kwargs, ) return _run_func(end + 1, len(functions) - 1, functions)(*args)