mirror of https://github.com/hpcaitech/ColossalAI
[embedding] freq_aware_embedding: add small functions for caller application (#1537)
CsRic
GitHub
parent
70129603aa
commit
964123ae0f
|
|
@ -4,10 +4,11 @@ from .embedding import ColoEmbedding
|
|||
from .module_utils import register_colo_module, is_colo_module, get_colo_module, init_colo_module, check_colo_module
|
||||
|
||||
from .cache_embedding import FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, CachedParamMgr, LimitBuffIndexCopyer, EvictionStrategy, \
|
||||
ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
|
||||
ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig, ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
|
||||
|
||||
__all__ = [
|
||||
'ColoModule', 'register_colo_module', 'is_colo_module', 'get_colo_module', 'init_colo_module', 'check_colo_module',
|
||||
'ColoLinear', 'ColoEmbedding', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag', 'CachedParamMgr',
|
||||
'LimitBuffIndexCopyer', 'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig'
|
||||
'LimitBuffIndexCopyer', 'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig',
|
||||
'ParallelFreqAwareEmbeddingBagTablewiseSpiltCache'
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,8 +2,8 @@ from .cache_mgr import CachedParamMgr, EvictionStrategy
|
|||
from .copyer import LimitBuffIndexCopyer
|
||||
from .freq_aware_embedding import FreqAwareEmbeddingBag
|
||||
from .parallel_freq_aware_embedding import ParallelFreqAwareEmbeddingBag
|
||||
from .parallel_freq_aware_embedding_tablewise import ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
|
||||
from .parallel_freq_aware_embedding_tablewise import ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig, ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
|
||||
__all__ = [
|
||||
'CachedParamMgr', 'LimitBuffIndexCopyer', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag',
|
||||
'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig'
|
||||
'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig', 'ParallelFreqAwareEmbeddingBagTablewiseSpiltCache'
|
||||
]
|
||||
|
|
|
|||
|
|
@ -121,3 +121,9 @@ class ParallelFreqAwareEmbeddingBag(FreqAwareEmbeddingBag):
|
|||
buffer_size=buffer_size)
|
||||
embedding_bag.cache_weight_mgr.cuda_cached_weight.requires_grad_ = not freeze
|
||||
return embedding_bag
|
||||
|
||||
def print_comm_stats_(self):
|
||||
self.cache_weight_mgr.print_comm_stats()
|
||||
|
||||
def element_size(self):
|
||||
return self.weight.element_size()
|
||||
|
|
@ -1,9 +1,10 @@
|
|||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
from torch.profiler import record_function
|
||||
from typing import List
|
||||
import abc
|
||||
|
||||
import torch.nn.functional as F
|
||||
from .freq_aware_embedding import FreqAwareEmbeddingBag
|
||||
|
||||
from colossalai.tensor import ProcessGroup
|
||||
|
|
@ -38,7 +39,137 @@ class TablewiseEmbeddingBagConfig:
|
|||
self.name = name
|
||||
|
||||
|
||||
class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
|
||||
class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
|
||||
"""
|
||||
all tables assigned to this class instance are managed by a single FreqAwareEmbeddingBag.
|
||||
Those parameters in TablewiseEmbeddingBagConfig are ignored: cuda_row_num, buffer_size, initial_weight.
|
||||
"""
|
||||
def __init__(self,
|
||||
embedding_bag_config_list: List[TablewiseEmbeddingBagConfig],
|
||||
embedding_dim: int,
|
||||
padding_idx=None,
|
||||
max_norm=None,
|
||||
norm_type=2.,
|
||||
scale_grad_by_freq=False,
|
||||
sparse=False,
|
||||
_weight=None,
|
||||
mode='mean',
|
||||
include_last_offset=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
cuda_row_num=0,
|
||||
warmup_ratio=0.7,
|
||||
buffer_size=50_000,
|
||||
pin_weight=False,
|
||||
evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
|
||||
self.rank = dist.get_rank()
|
||||
self.world_size = dist.get_world_size()
|
||||
self.rank_of_tables = [config.assigned_rank for config in embedding_bag_config_list]
|
||||
self.global_table_num_embeddings_list = [config.num_embeddings for config in embedding_bag_config_list]
|
||||
self.global_tables_num = len(embedding_bag_config_list)
|
||||
self.global_tables_offsets = torch.cumsum(torch.tensor([0] + self.global_table_num_embeddings_list), 0).cuda()
|
||||
self.assigned_table_list: List[int] = []
|
||||
self.pg = ProcessGroup(tp_degree=self.world_size)
|
||||
self.num_embeddings = 0
|
||||
for i, rank in enumerate(self.rank_of_tables):
|
||||
if rank == self.rank:
|
||||
self.assigned_table_list.append(i)
|
||||
self.num_embeddings += self.global_table_num_embeddings_list[i]
|
||||
self.include_last_offset = include_last_offset
|
||||
|
||||
ids_freq_mapping = []
|
||||
for config in embedding_bag_config_list:
|
||||
if config.assigned_rank == self.rank:
|
||||
if config.ids_freq_mapping != None:
|
||||
ids_freq_mapping.extend(config.ids_freq_mapping)
|
||||
else:
|
||||
ids_freq_mapping = None
|
||||
break
|
||||
|
||||
# table-associate cache
|
||||
super(ParallelFreqAwareEmbeddingBagTablewise,
|
||||
self).__init__(self.num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq,
|
||||
sparse, _weight, mode, include_last_offset, dtype, device, cuda_row_num, ids_freq_mapping,
|
||||
warmup_ratio, buffer_size, pin_weight, evict_strategy)
|
||||
|
||||
# for assigned tables reconnection:
|
||||
self.idx_offset_list = []
|
||||
offset_cumsum = 0
|
||||
for table_i, table_num_embeddings in enumerate(self.global_table_num_embeddings_list):
|
||||
if self.rank_of_tables[table_i] == self.rank:
|
||||
self.idx_offset_list.append(offset_cumsum)
|
||||
else :
|
||||
offset_cumsum += table_num_embeddings
|
||||
|
||||
# prepare list shape for all_to_all output
|
||||
self.embedding_dim_per_rank = [0 for i in range(self.world_size)]
|
||||
for rank in self.rank_of_tables:
|
||||
self.embedding_dim_per_rank[rank] += embedding_dim
|
||||
|
||||
def forward(self, indices: torch.Tensor, offsets: torch.Tensor = None, per_sample_weights=None, shape_hook=None):
|
||||
batch_size = (offsets.shape[0]) // self.global_tables_num
|
||||
local_indices_list: List(torch.Tensor) = []
|
||||
local_offsets_list: List(torch.Tensor) = []
|
||||
if per_sample_weights != None:
|
||||
local_per_sample_weights_list: List(torch.Tensor) = []
|
||||
|
||||
offset_pre_end = 0 # local_offsets trick
|
||||
for i, handle_table in enumerate(self.assigned_table_list):
|
||||
indices_start_position = offsets[batch_size * handle_table]
|
||||
if (not self.include_last_offset) and (batch_size * (handle_table + 1) >= indices.shape[0]):
|
||||
# till-the-end special case
|
||||
indices_end_position = indices.shape[0]
|
||||
else:
|
||||
indices_end_position = offsets[batch_size * (handle_table + 1)]
|
||||
# 1. local_indices_list:
|
||||
local_indices_list.append(indices.narrow(0, indices_start_position, indices_end_position
|
||||
- indices_start_position).sub(self.idx_offset_list[i]))
|
||||
# 2. local_offsets_list:
|
||||
if i + 1 == len(self.assigned_table_list):
|
||||
# till-the-end special case
|
||||
if not self.include_last_offset:
|
||||
local_offsets = offsets.narrow(0, batch_size * handle_table,
|
||||
batch_size).add(offset_pre_end - offsets[batch_size * (handle_table)])
|
||||
else :
|
||||
local_offsets = offsets.narrow(0, batch_size * handle_table,
|
||||
batch_size + 1).add(offset_pre_end - offsets[batch_size * (handle_table)])
|
||||
local_offsets_list.append(local_offsets)
|
||||
else:
|
||||
local_offsets = offsets.narrow(0, batch_size * handle_table,
|
||||
batch_size + 1).add(offset_pre_end - offsets[batch_size * (handle_table)])
|
||||
offset_pre_end = local_offsets[-1]
|
||||
local_offsets_list.append(local_offsets[:-1])
|
||||
# 3. local_per_sample_weights_list:
|
||||
if per_sample_weights != None:
|
||||
local_per_sample_weights_list.append(per_sample_weights[indices_start_position:indices_end_position])
|
||||
|
||||
local_indices = torch.cat(local_indices_list, 0)
|
||||
local_offsets = torch.cat(local_offsets_list, 0)
|
||||
local_per_sample_weights = None
|
||||
if per_sample_weights != None:
|
||||
local_per_sample_weights = torch.cat(local_per_sample_weights_list, 0)
|
||||
with torch.no_grad():
|
||||
reorder_ids = self.cache_weight_mgr.prepare_ids(local_indices)
|
||||
|
||||
local_output = F.embedding_bag(reorder_ids.cuda(), self.cache_weight_mgr.cuda_cached_weight, local_offsets,
|
||||
self.max_norm, self.norm_type, self.scale_grad_by_freq, self.mode, self.sparse,
|
||||
local_per_sample_weights, self.include_last_offset, self.padding_idx)
|
||||
local_output = torch.cat(local_output.split(batch_size),1)
|
||||
|
||||
remains = batch_size % self.world_size
|
||||
scatter_strides = [batch_size // self.world_size + int(i < remains) for i in range(self.world_size)]
|
||||
output_full = dual_all_to_all_tablewise(local_output, self.pg, scatter_strides, self.embedding_dim_per_rank)
|
||||
if shape_hook is not None:
|
||||
output_full = shape_hook(output_full)
|
||||
return output_full
|
||||
|
||||
def print_comm_stats_(self):
|
||||
self.cache_weight_mgr.print_comm_stats()
|
||||
|
||||
def element_size(self):
|
||||
return self.weight.element_size()
|
||||
|
||||
class ParallelFreqAwareEmbeddingBagTablewiseSpiltCache(abc.ABC, nn.Module):
|
||||
"""
|
||||
every table assigned to this class instance is managed by a FreqAwareEmbeddingBag.
|
||||
"""
|
||||
|
|
@ -58,7 +189,7 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
|
|||
warmup_ratio=0.7,
|
||||
pin_weight=False,
|
||||
evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
|
||||
super(ParallelFreqAwareEmbeddingBagTablewise, self).__init__()
|
||||
super(ParallelFreqAwareEmbeddingBagTablewiseSpiltCache, self).__init__()
|
||||
self.rank = dist.get_rank()
|
||||
self.world_size = dist.get_world_size()
|
||||
self.rank_of_tables = [config.assigned_rank for config in embedding_bag_config_list]
|
||||
|
|
@ -109,26 +240,32 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
|
|||
batch_size = (offsets.shape[0]) // self.global_tables_num
|
||||
local_output_list = []
|
||||
for i, handle_table in enumerate(self.assigned_table_list):
|
||||
indices_start_position = offsets[batch_size * handle_table]
|
||||
if (not self.include_last_offset) and (batch_size * (handle_table + 1) >= indices.shape[0]):
|
||||
# till the end special case
|
||||
indices_end_position = indices.shape[0]
|
||||
else:
|
||||
indices_end_position = offsets[batch_size * (handle_table + 1)]
|
||||
|
||||
local_indices = indices[indices_start_position:indices_end_position] - \
|
||||
self.global_tables_offsets[handle_table]
|
||||
if self.include_last_offset:
|
||||
local_offsets = offsets[batch_size * handle_table:batch_size *
|
||||
(handle_table + 1) + 1] - offsets[batch_size * (handle_table)]
|
||||
else:
|
||||
local_offsets = offsets[batch_size * handle_table:batch_size *
|
||||
(handle_table + 1)] - offsets[batch_size * (handle_table)]
|
||||
local_per_sample_weights = None
|
||||
if per_sample_weights != None:
|
||||
local_per_sample_weights = per_sample_weights[indices_start_position:indices_end_position]
|
||||
local_output_list.append(self.freq_aware_embedding_bag_list[i](local_indices, local_offsets,
|
||||
local_per_sample_weights))
|
||||
with record_function("(tablewise) prepare indices and offsets"):
|
||||
with record_function("part 1"):
|
||||
indices_start_position = offsets[batch_size * handle_table]
|
||||
if (not self.include_last_offset) and (batch_size * (handle_table + 1) >= indices.shape[0]):
|
||||
# till the end special case
|
||||
indices_end_position = indices.shape[0]
|
||||
else:
|
||||
indices_end_position = offsets[batch_size * (handle_table + 1)]
|
||||
with record_function("part 2"):
|
||||
# local_indices = indices[indices_start_position:indices_end_position] - self.global_tables_offsets[handle_table]
|
||||
local_indices = indices.narrow(0, indices_start_position, indices_end_position
|
||||
- indices_start_position).sub(self.global_tables_offsets[handle_table])
|
||||
if self.include_last_offset:
|
||||
# local_offsets = offsets[batch_size * handle_table:batch_size * (handle_table + 1) + 1] - offsets[batch_size * (handle_table)]
|
||||
local_offsets = offsets.narrow(0, batch_size * handle_table,
|
||||
batch_size + 1).sub(offsets[batch_size * (handle_table)])
|
||||
else:
|
||||
# local_offsets = offsets[batch_size * handle_table:batch_size * (handle_table + 1)] - offsets[batch_size * (handle_table)]
|
||||
local_offsets = offsets.narrow(0, batch_size * handle_table,
|
||||
batch_size).sub(offsets[batch_size * (handle_table)])
|
||||
local_per_sample_weights = None
|
||||
if per_sample_weights != None:
|
||||
local_per_sample_weights = per_sample_weights[indices_start_position:indices_end_position]
|
||||
with record_function("(tablewise) tablewise forward"):
|
||||
local_output_list.append(self.freq_aware_embedding_bag_list[i](local_indices, local_offsets,
|
||||
local_per_sample_weights))
|
||||
|
||||
# get result of shape = (batch_size, (len(assigned_table_list)*embedding_dim))
|
||||
local_output = torch.cat(local_output_list, 1)
|
||||
|
|
@ -140,3 +277,21 @@ class ParallelFreqAwareEmbeddingBagTablewise(abc.ABC, nn.Module):
|
|||
if shape_hook is not None:
|
||||
output_full = shape_hook(output_full)
|
||||
return output_full
|
||||
|
||||
def element_size(self):
|
||||
if len(self.assigned_table_list) == 0:
|
||||
return 0
|
||||
return self.freq_aware_embedding_bag_list[0].cache_weight_mgr.weight.element_size()
|
||||
|
||||
def print_comm_stats_(self):
|
||||
cuda_to_cpu_elem_num = 0
|
||||
cpu_to_cuda_elem_num = 0
|
||||
for freq_aware_embedding_bag in self.freq_aware_embedding_bag_list:
|
||||
cuda_to_cpu_elem_num += freq_aware_embedding_bag.cache_weight_mgr._cuda_to_cpu_numel
|
||||
cpu_to_cuda_elem_num += freq_aware_embedding_bag.cache_weight_mgr._cpu_to_cuda_numel
|
||||
print(
|
||||
f"CUDA->CPU num: {cuda_to_cpu_elem_num / 1e6} M elem"
|
||||
)
|
||||
print(
|
||||
f"CPU->CUDA num: {cpu_to_cuda_elem_num / 1e6} M elem"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from colossalai.testing import rerun_if_address_is_in_use
|
|||
from colossalai.tensor import ColoParameter, ProcessGroup, ShardSpec, ComputePattern, ComputeSpec, \
|
||||
ColoTensor, ColoTensorSpec
|
||||
from colossalai.nn.parallel.layers import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, EvictionStrategy, \
|
||||
ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
|
||||
ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig, ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
|
||||
from typing import List
|
||||
|
||||
NUM_EMBED, EMBED_DIM = 10, 8
|
||||
|
|
@ -209,9 +209,10 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
|
|||
|
||||
# initialize weight
|
||||
# 3 feature tables. idx: 0~5, 6~10, 11~17
|
||||
weight_table1 = torch.rand(6, 5)
|
||||
weight_table2 = torch.rand(5, 5)
|
||||
weight_table3 = torch.rand(7, 5)
|
||||
weight_tables = torch.rand(18,5)
|
||||
weight_table1 = weight_tables[0:6]
|
||||
weight_table2 = weight_tables[6:11]
|
||||
weight_table3 = weight_tables[11:18]
|
||||
embedding_bag_config_list: List[TablewiseEmbeddingBagConfig] = []
|
||||
embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
|
||||
num_embeddings=6, cuda_row_num=4, assigned_rank=0, initial_weight=weight_table1.clone().detach().cpu()))
|
||||
|
|
@ -219,14 +220,20 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
|
|||
num_embeddings=5, cuda_row_num=4, assigned_rank=0, initial_weight=weight_table2.clone().detach().cpu()))
|
||||
embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
|
||||
num_embeddings=7, cuda_row_num=4, assigned_rank=1, initial_weight=weight_table3.clone().detach().cpu()))
|
||||
|
||||
if rank == 0:
|
||||
_weight = torch.cat([weight_table1, weight_table2],0)
|
||||
else:
|
||||
_weight = weight_table3
|
||||
model = ParallelFreqAwareEmbeddingBagTablewise(
|
||||
embedding_bag_config_list,
|
||||
embedding_dim=5,
|
||||
_weight=_weight,
|
||||
include_last_offset=True,
|
||||
cuda_row_num=8,
|
||||
buffer_size=0,
|
||||
evict_strategy=EvictionStrategy.LFU,
|
||||
include_last_offset=True
|
||||
)
|
||||
# demo explain:
|
||||
# explain
|
||||
'''
|
||||
batch feature 1 feature 2 feature 3
|
||||
input0 [1,2,3] [6,7] []
|
||||
|
|
@ -244,28 +251,27 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
|
|||
fake_grad = rand_grad[0:2]
|
||||
else :
|
||||
fake_grad = rand_grad[2:]
|
||||
|
||||
res.backward(fake_grad)
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# check correctness on weight_table2
|
||||
# check correctness
|
||||
if rank == 0:
|
||||
ref_model = torch.nn.EmbeddingBag.from_pretrained(weight_table2.detach().clone(),
|
||||
ref_model = torch.nn.EmbeddingBag.from_pretrained(weight_tables.detach().clone(),
|
||||
include_last_offset=True,
|
||||
freeze=False).to(device)
|
||||
ref_optimizer = torch.optim.SGD(ref_model.parameters(), lr=1e-2)
|
||||
ref_grad = rand_grad[:, 5:10]
|
||||
ref_res = ref_model(torch.tensor([0, 1, 3, 0, 2], device=device), torch.tensor([0, 2, 3, 5], device=device))
|
||||
ref_res.backward(ref_grad)
|
||||
ref_fake_grad = torch.cat(rand_grad.split(5,1),0)
|
||||
ref_res = ref_model(torch.tensor([1, 2, 3, 1, 5, 6, 7, 9, 6, 8, 13, 15, 11], device=device),
|
||||
torch.tensor([0, 3, 3, 5, 7, 8, 10, 10, 12, 13], device=device))
|
||||
ref_res.backward(ref_fake_grad)
|
||||
ref_optimizer.step()
|
||||
ref_optimizer.zero_grad()
|
||||
|
||||
model.freq_aware_embedding_bag_list[1].cache_weight_mgr.flush() # update cpu weight
|
||||
recover_weight = model.freq_aware_embedding_bag_list[1].cache_weight_mgr.weight
|
||||
assert torch.allclose(recover_weight, ref_model.weight.detach().cpu()
|
||||
), f"{recover_weight - ref_model.weight.detach().cpu()}"
|
||||
|
||||
|
||||
model.cache_weight_mgr.flush()
|
||||
recover_weight = model.cache_weight_mgr.weight.to(device)
|
||||
ref_weight = ref_model.weight.detach()[:11]
|
||||
assert torch.allclose(recover_weight, ref_weight), f"{recover_weight - ref_weight}"
|
||||
|
||||
def run_parallel_freq_aware_embed_columnwise(rank, world_size):
|
||||
device = torch.device('cuda', torch.cuda.current_device())
|
||||
|
|
|
|||
Loading…
Reference in New Issue