# 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. import paddle from paddle.fluid import core from paddle.autograd import PyLayer from paddle.fluid import framework from ..meta_parallel.parallel_layers.random import get_rng_state_tracker from .recompute import check_recompute_necessary, detach_variable, swith_rng_state_tracker from ..meta_parallel.pp_utils import utils __all__ = [] def _split_activation(tensor, mp_group): mp_degree = mp_group.nranks mp_rank = mp_group.rank if mp_degree < 2: return tensor tensor_numel = paddle.numel(tensor) assert tensor_numel != 0, "can't recompute zero element" assert tensor_numel % mp_degree == 0, "The capacity of the activation ({}) cannot be divisible by mp_degree({})".format( tensor_numel, mp_degree) # use inplace operation to save memory data = tensor.flatten_() part_size = tensor_numel // mp_degree start = part_size * mp_rank end = start + part_size return data[start:end] def _merge_activation(tensor, mp_group): mp_degree = mp_group.nranks mp_rank = mp_group.rank if mp_degree < 2: return tensor # adapt to new dygraph tensor_shape = list(tensor.shape) tensor_shape[0] *= mp_group.nranks out = paddle.empty(tensor_shape, tensor.dtype) task = mp_group.process_group.all_gather(tensor.cuda(), out) task.wait() return out class _HPRecomputeFunction(PyLayer): """ Compared with paddle.distributed.fleet.utils.recompute, there are the following differences: 1. In order to support PipeLineParallel, the input of recompute is modified to ensure that the input can be tuple type. 2. Offload support for activation 3. Support MP segmentation of activation to further reduce cuda memory 4. Adapt to the random state of MP """ @staticmethod def forward(ctx, run_function, all_outputs, mp_group, offload, partition, *args, **kwargs): check_recompute_necessary(args) # store for recomputing ctx.run_function = run_function ctx.kwargs = kwargs # store the rng states ctx.fwd_cuda_rng_state = paddle.get_cuda_rng_state() ctx.fwd_cuda_rng_state_tracker = get_rng_state_tracker( ).get_states_tracker() # save config info ctx.mp_group = mp_group ctx.offload = offload ctx.partition = partition # save input for backward ctx.inputs = [] ctx.tensor_indices = [] ctx.tensor_shapes = [] tensor_inputs = [] cur_device = paddle.get_device() assert 'gpu:' in paddle.get_device( ), "Recompute with RNG is not support current device: {}.".format( cur_device) # 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("unsupported amp level: {}".format( tracer._amp_level)) ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list() with paddle.no_grad(): outputs = run_function(*args, **kwargs) for i, arg in enumerate(args): if paddle.is_tensor(arg): state = arg.stop_gradient if partition: ctx.tensor_shapes.append(arg.shape) partition = _split_activation(arg.detach(), mp_group).clone() # TODO(shenliang03) not use calculate stream to D2H to speed arg = partition.cpu() if offload else partition else: arg = arg.cpu() if offload else arg arg.stop_gradient = state tensor_inputs.append(arg) ctx.tensor_indices.append(i) ctx.inputs.append(None) else: ctx.inputs.append(arg) ctx.save_for_backward(*tensor_inputs) if paddle.is_tensor(outputs): all_outputs += [outputs] return outputs else: all_outputs += outputs return tuple(outputs) @staticmethod def backward(ctx, *args): with paddle.fluid.dygraph.guard(): # Restore inputs inputs = list(ctx.inputs) tensor_indices = ctx.tensor_indices tensor_shapes = ctx.tensor_shapes tensors = list(ctx.saved_tensor()) device_id = paddle.distributed.ParallelEnv().device_id for i, idx in enumerate(tensor_indices): if ctx.partition: state = tensors[i].stop_gradient tensors[i] = _merge_activation( tensors[i], ctx.mp_group).detach().reshape_(tensor_shapes[i]) tensors[i].stop_gradient = state inputs[idx] = tensors[i].cuda( device_id) if ctx.offload else tensors[i] tracer = framework._dygraph_tracer() tracer._has_grad = True # need restore auto_cast state as well as w/b list with swith_rng_state_tracker(ctx.fwd_cuda_rng_state, ctx.fwd_cuda_rng_state_tracker): 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): detached_inputs = detach_variable(tuple(inputs)) outputs = ctx.run_function(*detached_inputs, **ctx.kwargs) if isinstance(outputs, (core.VarBase, core.eager.Tensor)): outputs = (outputs, ) assert len(outputs) == len(args) forward_outputs_with_grad = [] backward_inputs = [] for i in range(len(outputs)): if isinstance( outputs[i], (core.VarBase, core.eager.Tensor)) and not outputs[i].stop_gradient: forward_outputs_with_grad.append(outputs[i]) backward_inputs.append(args[i]) if len(forward_outputs_with_grad) == 0: raise RuntimeError( "none of output has stop_gradient=False, this recompute() is not necessary" ) # actually backward paddle.autograd.backward(forward_outputs_with_grad, backward_inputs) grads = tuple(inp._grad_ivar() for inp in detached_inputs if isinstance(inp, (core.VarBase, core.eager.Tensor))) return grads def recompute_hybrid(ctx, function, *args, **kwargs): """ # NODTE(shenliang03)The current hybrid parallel recompute has limitations. # It cannot handle the following situations: # 1. The calculation output of recompute, there are tensors that do not require gradients. # 2. The forward output tensor has no gradient. This problem can be solved temporarily by detach(). # 3. Here, we only use float dtype to distinguish whether a gradient is needed in output tensor Parameters: ctx(dict): include 'mp_group', 'offload', and 'partition' keys. the key 'mp_group' (Group), represents the avtivations are splitted in which group. the key 'offload' (bool, optional, default=False), represents whether to offload to cpu. the key 'partition' (bool, optional, default=False), represents whether to split activations in the mp_group. and some keys such as 'segments' and 'preserve_rng_state' are invalid here, they are useful in 'recompute_sequential' API. 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(tuple) to the function. **kwargs(Dict): inputs(dict) to the function. Returns: Output of function on args and kwargs. """ mp_group = ctx.get('mp_group', None) assert mp_group is not None, "ctx must contains mp_group and mp_group can not be None." offload = ctx.get('offload', False) partition = ctx.get('partition', False) all_outputs = [] _HPRecomputeFunction.apply(function, all_outputs, mp_group, offload, partition, *args, **kwargs) if len(all_outputs) == 1: return all_outputs[0] else: for output in all_outputs: if paddle.is_tensor(output) and not utils.is_float_tensor(output): output.stop_gradient = True return tuple(all_outputs)