mirror of https://github.com/hpcaitech/ColossalAI
[zero] ZeRO supports pipeline parallel (#477)
ver217
GitHub
parent
7f5e4592eb
commit
8d3250d74b
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@ -1,12 +1,14 @@
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#!/usr/bin/env python
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import torch.distributed as dist
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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from collections import defaultdict
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import torch
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import torch.distributed as dist
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from colossalai.core import global_context as gpc
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from colossalai.registry import GRADIENT_HANDLER
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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from ._base_gradient_handler import BaseGradientHandler
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from collections import defaultdict
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@GRADIENT_HANDLER.register_module
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@ -35,7 +37,7 @@ class PipelineSharedModuleGradientHandler(BaseGradientHandler):
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for group, group_buckets in buckets.items():
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for tp, bucket in group_buckets.items():
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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coalesced = _flatten_dense_tensors(grads).to(torch.cuda.current_device())
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dist.all_reduce(coalesced, op=dist.ReduceOp.SUM, group=group)
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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@ -12,7 +12,8 @@ from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from colossalai.utils import switch_virtual_pipeline_parallel_rank
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero import ShardedOptimizer, ShardedModel
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from colossalai.zero import ShardedModel, ShardedOptimizer
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from colossalai.zero.sharded_model import ShardedModelV2
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from ._base_schedule import BaseSchedule
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@ -79,8 +80,8 @@ class PipelineSchedule(BaseSchedule):
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def _get_data_slice(self, data, offset):
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if isinstance(data, torch.Tensor):
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return data[offset: offset + self.microbatch_size]
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else:
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return data[offset:offset + self.microbatch_size]
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elif isinstance(data, dict):
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return {k: v[offset:offset + self.microbatch_size] for k, v in data.items()}
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def load_micro_batch(self):
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@ -92,11 +93,9 @@ class PipelineSchedule(BaseSchedule):
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def pre_processing(self, engine):
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# TODO: remove this after testing new zero with pipeline parallelism
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if isinstance(engine.optimizer, ShardedOptimizer) or isinstance(engine.model, ShardedModel):
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raise TypeError(
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"Pipeline schedule is currently not compatible with ZeRO"
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)
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raise TypeError("Pipeline schedule is currently not compatible with ZeRO")
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model = engine.model
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if isinstance(model, NaiveAMPModel):
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if isinstance(model, (NaiveAMPModel, ShardedModelV2)):
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self.dtype = torch.half
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model = model.model
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sig = inspect.signature(model.forward)
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@ -107,6 +106,8 @@ class PipelineSchedule(BaseSchedule):
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def _call_engine(model, input_tensor, batch_data):
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if isinstance(model, NaiveAMPModel):
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sig = inspect.signature(model.model.forward)
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elif isinstance(model, ShardedModelV2):
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sig = inspect.signature(model.module.forward)
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else:
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sig = inspect.signature(model.forward)
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if isinstance(batch_data, torch.Tensor):
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@ -162,9 +163,11 @@ class PipelineSchedule(BaseSchedule):
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return output_tensor
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else:
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assert isinstance(
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output_tensor, torch.Tensor), 'Output of model using pipeline parallelism must be a tensor (except the last stage).'
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output_tensor,
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torch.Tensor), 'Output of model using pipeline parallelism must be a tensor (except the last stage).'
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self._logger.debug(
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f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_tensor.shape}, dtype {output_tensor.dtype}')
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f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_tensor.shape}, dtype {output_tensor.dtype}'
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)
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return output_tensor
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def backward_step(self, engine, input_tensor, output_tensor, output_tensor_grad):
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@ -203,12 +206,7 @@ class PipelineSchedule(BaseSchedule):
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return input_tensor_grad
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def forward_backward_step(self,
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engine,
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data_iter,
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forward_only=False,
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return_loss=True,
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return_output_label=True):
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def forward_backward_step(self, engine, data_iter, forward_only=False, return_loss=True, return_output_label=True):
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"""Runs non-interleaved 1F1B schedule, with communication between pipeline stages.
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Returns a tuple with losses if the last stage, an empty tuple otherwise.
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@ -231,10 +229,9 @@ class PipelineSchedule(BaseSchedule):
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'The argument \'return_loss\' has to be True when \'forward_only\' is False, but got False.'
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self.load_batch(data_iter)
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num_warmup_microbatches = \
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(gpc.get_world_size(ParallelMode.PIPELINE) -
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gpc.get_local_rank(ParallelMode.PIPELINE) - 1)
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num_warmup_microbatches = min(num_warmup_microbatches,
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self.num_microbatches)
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(gpc.get_world_size(ParallelMode.PIPELINE)
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- gpc.get_local_rank(ParallelMode.PIPELINE) - 1)
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num_warmup_microbatches = min(num_warmup_microbatches, self.num_microbatches)
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num_microbatches_remaining = self.num_microbatches - num_warmup_microbatches
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# Input, output tensors only need to be saved when doing backward passes
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@ -257,13 +254,14 @@ class PipelineSchedule(BaseSchedule):
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for i in range(num_warmup_microbatches):
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if not gpc.is_first_rank(ParallelMode.PIPELINE):
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ft_shape = comm.recv_tensor_meta(ft_shape)
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input_tensor = comm.recv_forward(ft_shape, dtype=self.dtype,
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input_tensor = comm.recv_forward(ft_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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output_tensor = self.forward_step(
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engine, input_tensor, return_tensors,
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return_output_label=return_output_label,
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accum_loss=accum_loss
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)
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output_tensor = self.forward_step(engine,
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input_tensor,
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return_tensors,
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return_output_label=return_output_label,
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accum_loss=accum_loss)
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if not gpc.is_last_rank(ParallelMode.PIPELINE):
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bt_shape = output_tensor.shape
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fs_checker = comm.send_tensor_meta(output_tensor, fs_checker)
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@ -279,28 +277,32 @@ class PipelineSchedule(BaseSchedule):
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if num_microbatches_remaining > 0:
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if not gpc.is_first_rank(ParallelMode.PIPELINE):
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ft_shape = comm.recv_tensor_meta(ft_shape)
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input_tensor = comm.recv_forward(ft_shape, dtype=self.dtype,
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input_tensor = comm.recv_forward(ft_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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# Run 1F1B in steady state.
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for i in range(num_microbatches_remaining):
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last_iteration = (i == (num_microbatches_remaining - 1))
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output_tensor = self.forward_step(
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engine, input_tensor, return_tensors,
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return_output_label=return_output_label,
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accum_loss=accum_loss
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)
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output_tensor = self.forward_step(engine,
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input_tensor,
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return_tensors,
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return_output_label=return_output_label,
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accum_loss=accum_loss)
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if forward_only:
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comm.send_forward(output_tensor, scatter_gather_tensors=self.scatter_gather_tensors)
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if not last_iteration:
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input_tensor = comm.recv_forward(ft_shape, dtype=self.dtype,
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input_tensor = comm.recv_forward(ft_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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else:
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output_tensor_grad = comm.send_forward_recv_backward(
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output_tensor, bt_shape, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors)
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output_tensor_grad = comm.send_forward_recv_backward(output_tensor,
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bt_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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# Add input_tensor and output_tensor to end of list.
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input_tensors.append(input_tensor)
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@ -311,18 +313,16 @@ class PipelineSchedule(BaseSchedule):
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input_tensor = input_tensors.pop(0)
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output_tensor = output_tensors.pop(0)
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input_tensor_grad = self.backward_step(
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engine,
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input_tensor, output_tensor,
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output_tensor_grad
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)
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input_tensor_grad = self.backward_step(engine, input_tensor, output_tensor, output_tensor_grad)
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if last_iteration:
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input_tensor = None
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comm.send_backward(input_tensor_grad, scatter_gather_tensors=self.scatter_gather_tensors)
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else:
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input_tensor = comm.send_backward_recv_forward(
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input_tensor_grad, ft_shape, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors)
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input_tensor = comm.send_backward_recv_forward(input_tensor_grad,
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ft_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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# Run cooldown backward passes.
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if not forward_only:
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@ -330,14 +330,11 @@ class PipelineSchedule(BaseSchedule):
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input_tensor = input_tensors.pop(0)
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output_tensor = output_tensors.pop(0)
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output_tensor_grad = comm.recv_backward(bt_shape, dtype=self.dtype,
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output_tensor_grad = comm.recv_backward(bt_shape,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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input_tensor_grad = self.backward_step(
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engine,
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input_tensor, output_tensor,
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output_tensor_grad
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)
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input_tensor_grad = self.backward_step(engine, input_tensor, output_tensor, output_tensor_grad)
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comm.send_backward(input_tensor_grad, scatter_gather_tensors=self.scatter_gather_tensors)
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@ -349,6 +346,7 @@ class PipelineSchedule(BaseSchedule):
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class InterleavedPipelineSchedule(PipelineSchedule):
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def __init__(self,
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num_microbatches,
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num_model_chunks,
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@ -372,21 +370,19 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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"""
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assert num_microbatches % gpc.get_world_size(ParallelMode.PIPELINE) == 0, \
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'num_microbatches must be an integer multiple of pipeline parallel world size'
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super().__init__(num_microbatches, batch_data_process_func=batch_data_process_func,
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tensor_shape=tensor_shape, scatter_gather_tensors=scatter_gather_tensors)
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super().__init__(num_microbatches,
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batch_data_process_func=batch_data_process_func,
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tensor_shape=tensor_shape,
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scatter_gather_tensors=scatter_gather_tensors)
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gpc.set_virtual_pipeline_parallel_size(num_model_chunks)
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gpc.set_virtual_pipeline_parallel_rank(0)
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self.num_model_chunks = num_model_chunks
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def pre_processing(self, engine):
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if isinstance(engine.optimizer, (ZeroRedundancyOptimizer_Level_2, ZeroRedundancyOptimizer_Level_3)):
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raise TypeError(
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"Pipeline schedule is currently not compatible with ZeRO Level 2 and Level 3"
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)
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if isinstance(engine.model[0], NaiveAMPModel):
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if isinstance(engine.model, ShardedModelV2):
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self.dtype = torch.half
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elif isinstance(engine.model[0], NaiveAMPModel):
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self.dtype = torch.half
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for model in engine.model:
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if isinstance(model, NaiveAMPModel):
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model = model.model
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@ -405,7 +401,13 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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self.microbatch_offset[model_chunk_id] += self.microbatch_size
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return self._move_to_device(data), self._move_to_device(label)
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def forward_step(self, engine, model_chunk_id, input_tensor, return_tensors, return_output_label=True, accum_loss=None):
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def forward_step(self,
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engine,
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model_chunk_id,
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input_tensor,
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return_tensors,
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return_output_label=True,
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accum_loss=None):
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"""Forward step for passed-in model. If it is the first stage, the input tensor
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is obtained from data_iterator, otherwise the passed-in input_tensor is used.
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Returns output tensor. This is a helper function and can be ignored by users.
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@ -425,9 +427,11 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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return output_tensor
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else:
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assert isinstance(
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output_tensor, torch.Tensor), 'Output of model using pipeline parallelism must be a tensor (except the last stage).'
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output_tensor,
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torch.Tensor), 'Output of model using pipeline parallelism must be a tensor (except the last stage).'
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self._logger.debug(
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f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_tensor.shape}, dtype {output_tensor.dtype}')
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f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_tensor.shape}, dtype {output_tensor.dtype}'
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)
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return output_tensor
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def forward_backward_step(self, engine, data_iter, forward_only=False, return_loss=True, return_output_label=True):
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@ -488,10 +492,8 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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else:
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num_warmup_microbatches = \
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(pipeline_parallel_size - pipeline_parallel_rank - 1) * 2
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num_warmup_microbatches += (
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num_model_chunks - 1) * pipeline_parallel_size
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num_warmup_microbatches = min(num_warmup_microbatches,
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num_microbatches)
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num_warmup_microbatches += (num_model_chunks - 1) * pipeline_parallel_size
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num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches)
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num_microbatches_remaining = \
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num_microbatches - num_warmup_microbatches
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@ -516,8 +518,12 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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len(output_tensors[model_chunk_id]):
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input_tensors[model_chunk_id].append(None)
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input_tensor = input_tensors[model_chunk_id][-1]
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output_tensor = self.forward_step(engine, model_chunk_id, input_tensor,
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return_tensors, return_output_label=return_output_label, accum_loss=accum_loss)
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output_tensor = self.forward_step(engine,
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model_chunk_id,
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input_tensor,
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return_tensors,
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return_output_label=return_output_label,
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accum_loss=accum_loss)
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output_tensors[model_chunk_id].append(output_tensor)
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# if forward-only, no need to save tensors for a backward pass
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@ -548,18 +554,20 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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gpc.set_virtual_pipeline_parallel_rank(0)
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if not gpc.is_pipeline_first_stage():
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input_tensor_shapes[0] = comm.recv_tensor_meta(input_tensor_shapes[0])
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input_tensors[0].append(comm.recv_forward(input_tensor_shapes[0], dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors))
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input_tensors[0].append(
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comm.recv_forward(input_tensor_shapes[0],
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors))
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for k in range(num_warmup_microbatches):
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model_chunk_id = get_model_chunk_id(k, forward=True)
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output_tensor = forward_step_helper(k)
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if not gpc.is_pipeline_last_stage():
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output_tensor_shapes[model_chunk_id] = output_tensor.shape
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send_tensor_shape_flags[model_chunk_id] = comm.send_tensor_meta(
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output_tensor, send_tensor_shape_flags[model_chunk_id])
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send_tensor_shape_flags[model_chunk_id] = comm.send_tensor_meta(output_tensor,
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send_tensor_shape_flags[model_chunk_id])
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# Determine if tensor should be received from previous stage.
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next_forward_model_chunk_id = get_model_chunk_id(k+1, forward=True)
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next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True)
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recv_prev = True
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if gpc.is_pipeline_first_stage(ignore_virtual=True):
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if next_forward_model_chunk_id == 0:
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@ -584,7 +592,7 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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recv_next = True
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if gpc.is_pipeline_last_stage(ignore_virtual=True):
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recv_next = False
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output_shape = output_tensor_shapes[num_model_chunks-1] if recv_next else None
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output_shape = output_tensor_shapes[num_model_chunks - 1] if recv_next else None
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input_tensor, output_tensor_grad = \
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comm.send_forward_backward_recv_forward_backward(
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output_tensor, input_tensor_grad,
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@ -593,7 +601,7 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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recv_prev=recv_prev, recv_next=recv_next,
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dtype=self.dtype,
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scatter_gather_tensors=self.scatter_gather_tensors)
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output_tensor_grads[num_model_chunks-1].append(output_tensor_grad)
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output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
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else:
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input_tensor = \
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comm.send_forward_recv_forward(
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@ -634,26 +642,23 @@ class InterleavedPipelineSchedule(PipelineSchedule):
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recv_prev = True
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if gpc.is_pipeline_first_stage(ignore_virtual=True):
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# First stage is ahead of last stage by (pipeline_parallel_size - 1).
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next_forward_model_chunk_id = get_model_chunk_id(
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forward_k - (pipeline_parallel_size - 1), forward=True)
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next_forward_model_chunk_id = get_model_chunk_id(forward_k - (pipeline_parallel_size - 1), forward=True)
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if next_forward_model_chunk_id == (num_model_chunks - 1):
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recv_prev = False
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next_forward_model_chunk_id += 1
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else:
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next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1,
|
||||
forward=True)
|
||||
next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True)
|
||||
|
||||
recv_next = True
|
||||
if gpc.is_pipeline_last_stage(ignore_virtual=True):
|
||||
# Last stage is ahead of first stage by (pipeline_parallel_size - 1).
|
||||
next_backward_model_chunk_id = get_model_chunk_id(
|
||||
backward_k - (pipeline_parallel_size - 1), forward=False)
|
||||
next_backward_model_chunk_id = get_model_chunk_id(backward_k - (pipeline_parallel_size - 1),
|
||||
forward=False)
|
||||
if next_backward_model_chunk_id == 0:
|
||||
recv_next = False
|
||||
next_backward_model_chunk_id -= 1
|
||||
else:
|
||||
next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1,
|
||||
forward=False)
|
||||
next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False)
|
||||
|
||||
# If last iteration, don't receive; we already received one extra
|
||||
# before the start of the for loop.
|
||||
|
|
@ -677,17 +682,17 @@ class InterleavedPipelineSchedule(PipelineSchedule):
|
|||
if recv_prev:
|
||||
input_tensors[next_forward_model_chunk_id].append(input_tensor)
|
||||
if recv_next:
|
||||
output_tensor_grads[next_backward_model_chunk_id].append(
|
||||
output_tensor_grad)
|
||||
output_tensor_grads[next_backward_model_chunk_id].append(output_tensor_grad)
|
||||
|
||||
# Run cooldown backward passes (flush out pipeline).
|
||||
if not forward_only:
|
||||
if all_warmup_microbatches:
|
||||
output_tensor_grads[num_model_chunks-1].append(
|
||||
comm.recv_backward(output_tensor_shapes[num_model_chunks-1], scatter_gather_tensors=self.scatter_gather_tensors))
|
||||
output_tensor_grads[num_model_chunks - 1].append(
|
||||
comm.recv_backward(output_tensor_shapes[num_model_chunks - 1],
|
||||
scatter_gather_tensors=self.scatter_gather_tensors))
|
||||
for k in range(num_microbatches_remaining, num_microbatches):
|
||||
input_tensor_grad = backward_step_helper(k)
|
||||
next_backward_model_chunk_id = get_model_chunk_id(k+1, forward=False)
|
||||
next_backward_model_chunk_id = get_model_chunk_id(k + 1, forward=False)
|
||||
recv_next = True
|
||||
if gpc.is_pipeline_last_stage(ignore_virtual=True):
|
||||
if next_backward_model_chunk_id == (num_model_chunks - 1):
|
||||
|
|
@ -696,12 +701,11 @@ class InterleavedPipelineSchedule(PipelineSchedule):
|
|||
recv_next = False
|
||||
output_shape = output_tensor_shapes[next_backward_model_chunk_id] if recv_next else None
|
||||
output_tensor_grads[next_backward_model_chunk_id].append(
|
||||
comm.send_backward_recv_backward(
|
||||
input_tensor_grad,
|
||||
output_shape,
|
||||
recv_next=recv_next,
|
||||
dtype=self.dtype,
|
||||
scatter_gather_tensors=self.scatter_gather_tensors))
|
||||
comm.send_backward_recv_backward(input_tensor_grad,
|
||||
output_shape,
|
||||
recv_next=recv_next,
|
||||
dtype=self.dtype,
|
||||
scatter_gather_tensors=self.scatter_gather_tensors))
|
||||
|
||||
if len(return_tensors) > 0:
|
||||
output, label = pack_return_tensors(return_tensors)
|
||||
|
|
|
|||
|
|
@ -262,3 +262,15 @@ class ShardedModelV2(nn.Module):
|
|||
|
||||
def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True):
|
||||
raise NotImplementedError
|
||||
|
||||
def __getitem__(self, idx: int):
|
||||
assert isinstance(self.module, nn.ModuleList)
|
||||
return self.module[idx]
|
||||
|
||||
def __len__(self):
|
||||
assert isinstance(self.module, nn.ModuleList)
|
||||
return len(self.module)
|
||||
|
||||
def __iter__(self):
|
||||
assert isinstance(self.module, nn.ModuleList)
|
||||
return iter(self.module)
|
||||
|
|
|
|||
Loading…
Reference in New Issue