mirror of https://github.com/hpcaitech/ColossalAI
[embedding] add tablewise sharding for FAW (#1526)
CsRic
GitHub
parent
f1e1836218
commit
5156d5b4f8
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@ -3,10 +3,11 @@ from .linear import ColoLinear
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from .embedding import ColoEmbedding
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from .module_utils import register_colo_module, is_colo_module, get_colo_module, init_colo_module, check_colo_module
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from .cache_embedding import FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, CachedParamMgr, LimitBuffIndexCopyer, EvictionStrategy
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from .cache_embedding import FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, CachedParamMgr, LimitBuffIndexCopyer, EvictionStrategy, \
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ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
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__all__ = [
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'ColoModule', 'register_colo_module', 'is_colo_module', 'get_colo_module', 'init_colo_module', 'check_colo_module',
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'ColoLinear', 'ColoEmbedding', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag', 'CachedParamMgr',
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'LimitBuffIndexCopyer', 'EvictionStrategy'
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'LimitBuffIndexCopyer', 'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig'
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]
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@ -2,8 +2,8 @@ from .cache_mgr import CachedParamMgr, EvictionStrategy
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from .copyer import LimitBuffIndexCopyer
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from .freq_aware_embedding import FreqAwareEmbeddingBag
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from .parallel_freq_aware_embedding import ParallelFreqAwareEmbeddingBag
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from .parallel_freq_aware_embedding_tablewise import ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
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__all__ = [
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'CachedParamMgr', 'LimitBuffIndexCopyer', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag',
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'EvictionStrategy'
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'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig'
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]
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@ -99,7 +99,7 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
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def forward(self, indices, offsets=None, per_sample_weights=None, shape_hook=None):
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with torch.no_grad():
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reorder_ids = self.cache_weight_mgr.prepare_ids(indices)
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embeddings = F.embedding_bag(reorder_ids.cuda(), self.cache_weight_mgr.cuda_cached_weight, offsets,
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self.max_norm, self.norm_type, self.scale_grad_by_freq, self.mode, self.sparse,
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per_sample_weights, self.include_last_offset, self.padding_idx)
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@ -79,10 +79,8 @@ class ParallelFreqAwareEmbeddingBag(FreqAwareEmbeddingBag):
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output_shard = F.embedding_bag(reorder_ids.cuda(), self.cache_weight_mgr.cuda_cached_weight, offsets,
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self.max_norm, self.norm_type, self.scale_grad_by_freq, self.mode, self.sparse,
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per_sample_weights, self.include_last_offset, self.padding_idx)
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if shape_hook is not None:
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output_shard = shape_hook(output_shard)
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output_full = dual_all_to_all(output_shard,
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self.weight.get_process_group(),
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scatter_dim=scatter_dim,
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@ -0,0 +1,192 @@
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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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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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class TablewiseEmbeddingBagConfig:
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'''
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example:
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def prepare_tablewise_config(args, cache_ratio, ...):
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embedding_bag_config_list: List[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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assigned_rank: int = 0,
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buffer_size=50_000,
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ids_freq_mapping=None,
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initial_weight: torch.tensor = None,
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name: str = ""):
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self.num_embeddings = num_embeddings
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self.cuda_row_num = cuda_row_num
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self.assigned_rank = assigned_rank
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self.buffer_size = buffer_size
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self.ids_freq_mapping = ids_freq_mapping
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self.initial_weight = initial_weight
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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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every table assigned to this class instance is managed by a FreqAwareEmbeddingBag.
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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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embedding_dim: int,
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padding_idx=None,
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max_norm=None,
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norm_type=2.,
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scale_grad_by_freq=False,
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sparse=False,
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mode='mean',
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include_last_offset=False,
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dtype=None,
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device=None,
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warmup_ratio=0.7,
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pin_weight=False,
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evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
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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.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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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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self.pg = ProcessGroup(tp_degree=self.world_size)
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# prepare FreqAwareEmbeddingBag list
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self.freq_aware_embedding_bag_list: nn.ModuleList = nn.ModuleList()
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for config in embedding_bag_config_list:
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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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# 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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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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local_output_list = []
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for i, handle_table in enumerate(self.assigned_table_list):
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indices_start_position = offsets[batch_size * handle_table]
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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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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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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_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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# 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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# then concatenate those local_output on the second demension.
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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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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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@ -12,7 +12,9 @@ from colossalai.utils import free_port
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.tensor import ColoParameter, ProcessGroup, ShardSpec, ComputePattern, ComputeSpec, \
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ColoTensor, ColoTensorSpec
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from colossalai.nn.parallel.layers import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, EvictionStrategy
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from colossalai.nn.parallel.layers import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, EvictionStrategy, \
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ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
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from typing import List
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NUM_EMBED, EMBED_DIM = 10, 8
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BATCH_SIZE = 8
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@ -200,7 +202,72 @@ def gather_tensor(tensor, rank, world_size):
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return gather_list
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def run_parallel_freq_aware_embed(rank, world_size):
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def run_parallel_freq_aware_embed_tablewise(rank, world_size):
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if world_size != 2:
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return
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device = torch.device('cuda', torch.cuda.current_device())
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# initialize weight
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# 3 feature tables. idx: 0~5, 6~10, 11~17
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weight_table1 = torch.rand(6, 5)
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weight_table2 = torch.rand(5, 5)
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weight_table3 = torch.rand(7, 5)
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embedding_bag_config_list: List[TablewiseEmbeddingBagConfig] = []
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embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
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num_embeddings=6, cuda_row_num=4, assigned_rank=0, initial_weight=weight_table1.clone().detach().cpu()))
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embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
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num_embeddings=5, cuda_row_num=4, assigned_rank=0, initial_weight=weight_table2.clone().detach().cpu()))
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embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
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num_embeddings=7, cuda_row_num=4, assigned_rank=1, initial_weight=weight_table3.clone().detach().cpu()))
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model = ParallelFreqAwareEmbeddingBagTablewise(
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embedding_bag_config_list,
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embedding_dim=5,
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evict_strategy=EvictionStrategy.LFU,
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include_last_offset=True
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)
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# demo explain:
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'''
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batch feature 1 feature 2 feature 3
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input0 [1,2,3] [6,7] []
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input1 [] [9] [13,15]
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input2 [1,5] [6,8] [11]
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↑ ↑ ↑
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rank 0 rank 0 rank 1
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in KJT format
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'''
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res = model(torch.tensor([1, 2, 3, 1, 5, 6, 7, 9, 6, 8, 13, 15, 11], device=device),
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torch.tensor([0, 3, 3, 5, 7, 8, 10, 10, 12, 13], device=device))
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
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rand_grad = torch.rand(3, 5 * 3, dtype=res.dtype, device=res.device)
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if rank == 0:
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fake_grad = rand_grad[0:2]
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else :
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fake_grad = rand_grad[2:]
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res.backward(fake_grad)
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optimizer.step()
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optimizer.zero_grad()
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# check correctness on weight_table2
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if rank == 0:
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ref_model = torch.nn.EmbeddingBag.from_pretrained(weight_table2.detach().clone(),
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include_last_offset=True,
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freeze=False).to(device)
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ref_optimizer = torch.optim.SGD(ref_model.parameters(), lr=1e-2)
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ref_grad = rand_grad[:, 5:10]
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ref_res = ref_model(torch.tensor([0, 1, 3, 0, 2], device=device), torch.tensor([0, 2, 3, 5], device=device))
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ref_res.backward(ref_grad)
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ref_optimizer.step()
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ref_optimizer.zero_grad()
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model.freq_aware_embedding_bag_list[1].cache_weight_mgr.flush() # update cpu weight
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recover_weight = model.freq_aware_embedding_bag_list[1].cache_weight_mgr.weight
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assert torch.allclose(recover_weight, ref_model.weight.detach().cpu()
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), f"{recover_weight - ref_model.weight.detach().cpu()}"
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def run_parallel_freq_aware_embed_columnwise(rank, world_size):
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device = torch.device('cuda', torch.cuda.current_device())
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num_embed = 100
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@ -219,7 +286,8 @@ def run_parallel_freq_aware_embed(rank, world_size):
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model = ParallelFreqAwareEmbeddingBag.from_pretrained(coloweight,
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include_last_offset=True,
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freeze=False,
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cuda_row_num=batch_size * 2)
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cuda_row_num=batch_size * 2,
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)
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assert model.cache_weight_mgr.weight.device.type == 'cpu'
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assert model.cache_weight_mgr.cuda_cached_weight.requires_grad
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@ -269,7 +337,8 @@ def run_parallel_freq_aware_embed(rank, world_size):
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_parallel_freq_aware_embed(rank, world_size)
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# run_parallel_freq_aware_embed_columnwise(rank, world_size)
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run_parallel_freq_aware_embed_tablewise(rank, world_size)
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@pytest.mark.dist
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@ -281,6 +350,6 @@ def test_parallel_freq_aware_embed(world_size):
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if __name__ == '__main__':
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test_freq_aware_embed(True)
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# test_parallel_freq_aware_embed(2)
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# test_freq_aware_embed(True)
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test_parallel_freq_aware_embed(2)
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# test_lfu_strategy(False)
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