mirror of https://github.com/hpcaitech/ColossalAI
[embedding] tablewise sharding polish (#1535)
Jiarui Fang
GitHub
parent
56159049e8
commit
87134524fd
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@ -1,5 +1,5 @@
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import torch
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from typing import Union, Optional
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from typing import Union, Optional, List
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from colossalai.tensor import ColoTensor
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import torch
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import torch.distributed as dist
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@ -231,3 +231,54 @@ class _DualAllToAll(torch.autograd.Function):
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def dual_all_to_all(x, pg, scatter_dim: int, gather_dim: int):
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return _DualAllToAll.apply(x, pg, scatter_dim, gather_dim)
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### table wise embedding shard
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def _all_to_all_for_tablewise(x: torch.Tensor,
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pg: ProcessGroup,
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scatter_strides: List[int],
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gather_strides: List[int],
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forward=True) -> torch.Tensor:
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world_size = pg.tp_world_size()
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rank = pg.tp_local_rank()
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if world_size == 1:
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return x
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assert x.device.type == 'cuda', f"Currently, the collective function dual_all_to_all only supports nccl backend"
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if forward:
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scatter_list = list(x.split(scatter_strides, 0))
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gather_list = [
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torch.empty(scatter_strides[rank], gather_strides[i], dtype=x.dtype, device=x.device)
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for i in range(world_size)
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]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 1).contiguous()
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else:
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# split on dim 1, lose contiguity
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scatter_list = [each.contiguous() for each in x.split(scatter_strides, 1)]
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gather_list = [
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torch.empty(gather_strides[i], scatter_strides[rank], dtype=x.dtype, device=x.device)
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for i in range(world_size)
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]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 0).contiguous()
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class _DualAllToAllForTablewise(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, pg, scatter_strides, gather_strides):
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ctx.pg = pg
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ctx.scatter_strides = scatter_strides
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ctx.gather_strides = gather_strides
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return _all_to_all_for_tablewise(x, pg, scatter_strides, gather_strides, forward=True)
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@staticmethod
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def backward(ctx, grad):
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return _all_to_all_for_tablewise(grad, ctx.pg, ctx.gather_strides, ctx.scatter_strides,
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forward=False), None, None, None
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def dual_all_to_all_tablewise(x, pg, scatter_strides, gather_strides):
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return _DualAllToAllForTablewise.apply(x, pg, scatter_strides, gather_strides)
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@ -1,14 +1,15 @@
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import torch
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import torch.nn.functional as F
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import torch.distributed as dist
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import torch.nn as nn
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from typing import List, Optional, Iterator, Tuple
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from typing import List
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import abc
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from .freq_aware_embedding import FreqAwareEmbeddingBag
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from colossalai.tensor import ColoParameter, ShardSpec, ComputePattern, ProcessGroup, ColoTensorSpec, ColoTensor
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from .cache_mgr import CachedParamMgr, EvictionStrategy
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from colossalai.tensor import ProcessGroup
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from .cache_mgr import EvictionStrategy
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from colossalai.nn._ops._utils import dual_all_to_all_tablewise
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class TablewiseEmbeddingBagConfig:
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@ -19,6 +20,7 @@ class TablewiseEmbeddingBagConfig:
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...
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return embedding_bag_config_list
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'''
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def __init__(self,
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num_embeddings: int,
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cuda_row_num: int,
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@ -36,50 +38,10 @@ class TablewiseEmbeddingBagConfig:
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self.name = name
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def _all_to_all_for_tablewise(x: torch.Tensor, pg: ProcessGroup, scatter_strides: List[int], gather_strides: List[int], forward=True) -> torch.Tensor:
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world_size = pg.tp_world_size()
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rank = pg.tp_local_rank()
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if world_size == 1:
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return x
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assert x.device.type == 'cuda', f"Currently, the collective function dual_all_to_all only supports nccl backend"
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if forward:
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scatter_list = list(x.split(scatter_strides, 0))
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gather_list = [torch.empty(scatter_strides[rank], gather_strides[i], dtype=x.dtype,
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device=x.device) for i in range(world_size)]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 1).contiguous()
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else:
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# split on dim 1, lose contiguity
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scatter_list = [each.contiguous() for each in x.split(scatter_strides, 1)]
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gather_list = [torch.empty(gather_strides[i], scatter_strides[rank], dtype=x.dtype,
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device=x.device) for i in range(world_size)]
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torch.distributed.all_to_all(gather_list, scatter_list, group=pg.tp_process_group())
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return torch.cat(gather_list, 0).contiguous()
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class _DualAllToAllForTablewise(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, pg, scatter_strides, gather_strides):
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ctx.pg = pg
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ctx.scatter_strides = scatter_strides
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ctx.gather_strides = gather_strides
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return _all_to_all_for_tablewise(x, pg, scatter_strides, gather_strides, forward=True)
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@staticmethod
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def backward(ctx, grad):
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return _all_to_all_for_tablewise(grad, ctx.pg, ctx.gather_strides, ctx.scatter_strides, forward=False), None, None, None
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def _dual_all_to_all(x, pg, scatter_strides, gather_strides):
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return _DualAllToAllForTablewise.apply(x, pg, scatter_strides, gather_strides)
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class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
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'''
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"""
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every table assigned to this class instance is managed by a FreqAwareEmbeddingBag.
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'''
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"""
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def __init__(self,
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embedding_bag_config_list: List[TablewiseEmbeddingBagConfig],
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@ -99,13 +61,13 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
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super(ParallelFreqAwareEmbeddingBagTablewise, self).__init__()
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self.rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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self.global_table_assign_list = [config.assigned_rank for config in embedding_bag_config_list]
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self.rank_of_tables = [config.assigned_rank for config in embedding_bag_config_list]
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self.global_table_num_embeddings_list = [config.num_embeddings for config in embedding_bag_config_list]
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self.global_tables_num = len(embedding_bag_config_list)
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self.global_tables_offsets = torch.cumsum(torch.tensor([0] + self.global_table_num_embeddings_list), 0)
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self.assigned_table_list: List[int] = []
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for i, rank in enumerate(self.global_table_assign_list):
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for i, rank in enumerate(self.rank_of_tables):
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if rank == self.rank:
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self.assigned_table_list.append(i)
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self.include_last_offset = include_last_offset
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@ -118,37 +80,30 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
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if config.assigned_rank != self.rank:
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continue
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self.freq_aware_embedding_bag_list.append(
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FreqAwareEmbeddingBag(
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num_embeddings=config.num_embeddings,
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embedding_dim=embedding_dim,
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padding_idx=padding_idx,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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sparse=sparse,
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_weight=config.initial_weight,
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mode=mode,
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include_last_offset=include_last_offset,
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dtype=dtype,
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device=device,
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cuda_row_num=config.cuda_row_num ,
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ids_freq_mapping=config.ids_freq_mapping,
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warmup_ratio=warmup_ratio,
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buffer_size=config.buffer_size,
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pin_weight=pin_weight,
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evict_strategy=evict_strategy
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)
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)
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FreqAwareEmbeddingBag(num_embeddings=config.num_embeddings,
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embedding_dim=embedding_dim,
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padding_idx=padding_idx,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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sparse=sparse,
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_weight=config.initial_weight,
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mode=mode,
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include_last_offset=include_last_offset,
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dtype=dtype,
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device=device,
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cuda_row_num=config.cuda_row_num,
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ids_freq_mapping=config.ids_freq_mapping,
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warmup_ratio=warmup_ratio,
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buffer_size=config.buffer_size,
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pin_weight=pin_weight,
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evict_strategy=evict_strategy))
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# prepare list shape for all_to_all output
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self.embedding_dim_per_rank = [0 for i in range(self.world_size)]
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for rank in self.global_table_assign_list:
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for rank in self.rank_of_tables:
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self.embedding_dim_per_rank[rank] += embedding_dim
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#print("global_table_assign_list {}".format(self.global_table_assign_list))
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#print("global_table_num_embeddings_list {}".format(self.global_table_num_embeddings_list))
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#print("global_tables_offsets {}".format(self.global_tables_offsets))
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#
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def forward(self, indices: torch.Tensor, offsets: torch.Tensor = None, per_sample_weights=None, shape_hook=None):
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# determine indices to handle
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batch_size = (offsets.shape[0]) // self.global_tables_num
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@ -158,27 +113,22 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
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if (not self.include_last_offset) and (batch_size * (handle_table + 1) >= indices.shape[0]):
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# till the end special case
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indices_end_position = indices.shape[0]
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else :
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else:
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indices_end_position = offsets[batch_size * (handle_table + 1)]
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local_indices = indices[indices_start_position:indices_end_position] - \
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self.global_tables_offsets[handle_table]
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if self.include_last_offset:
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local_offsets = offsets[batch_size * handle_table:batch_size
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* (handle_table + 1) + 1] - offsets[batch_size * (handle_table)]
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local_offsets = offsets[batch_size * handle_table:batch_size *
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(handle_table + 1) + 1] - offsets[batch_size * (handle_table)]
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else:
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local_offsets = offsets[batch_size * handle_table:batch_size
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* (handle_table + 1)] - offsets[batch_size * (handle_table)]
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local_offsets = offsets[batch_size * handle_table:batch_size *
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(handle_table + 1)] - offsets[batch_size * (handle_table)]
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local_per_sample_weights = None
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if per_sample_weights != None:
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local_per_sample_weights = per_sample_weights[indices_start_position:indices_end_position]
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local_output_list.append(
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self.freq_aware_embedding_bag_list[i](
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local_indices,
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local_offsets,
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local_per_sample_weights
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)
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)
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local_output_list.append(self.freq_aware_embedding_bag_list[i](local_indices, local_offsets,
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local_per_sample_weights))
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# get result of shape = (batch_size, (len(assigned_table_list)*embedding_dim))
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local_output = torch.cat(local_output_list, 1)
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@ -186,7 +136,7 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
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# use all_to_all
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remains = batch_size % self.world_size
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scatter_strides = [batch_size // self.world_size + int(i < remains) for i in range(self.world_size)]
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output_full = _dual_all_to_all(local_output, self.pg, scatter_strides, self.embedding_dim_per_rank)
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output_full = dual_all_to_all_tablewise(local_output, self.pg, scatter_strides, self.embedding_dim_per_rank)
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if shape_hook is not None:
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output_full = shape_hook(output_full)
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return output_full
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