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Unverified Commit 64169f3e authored by Jiarui Fang's avatar Jiarui Fang Committed by GitHub
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[embedding] polish parallel embedding tablewise (#1545)

parent 46c6cc79
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Showing with 232 additions and 204 deletions
colossalai/nn/parallel/layers/cache_embedding/__init__.py
View file @ 64169f3e
......@@ -2,8 +2,12 @@ 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, ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
from .embedding_config import TablewiseEmbeddingBagConfig
from .parallel_freq_aware_embedding_tablewise import ParallelFreqAwareEmbeddingBagTablewise
from .parallel_freq_aware_embedding_tablewise_split_cache import ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
__all__ = [
'CachedParamMgr', 'LimitBuffIndexCopyer', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag',
'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig', 'ParallelFreqAwareEmbeddingBagTablewiseSpiltCache'
'EvictionStrategy', 'ParallelFreqAwareEmbeddingBagTablewise', 'TablewiseEmbeddingBagConfig',
'ParallelFreqAwareEmbeddingBagTablewiseSpiltCache'
]
colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py
View file @ 64169f3e
......@@ -293,7 +293,7 @@ class CachedParamMgr(torch.nn.Module):
Returns:
torch.Tensor: indices on the cuda_cached_weight.
"""
with record_function("(zhg) get unique indices"):
with record_function("(pre-id) get unique indices"):
ids = ids.to(self._cache_dev)
cpu_row_idxs, repeat_times = torch.unique(self.idx_map.index_select(0, ids), return_counts=True)
......@@ -303,7 +303,7 @@ class CachedParamMgr(torch.nn.Module):
f"Please increase cuda_row_num or decrease the training batch size."
self.evict_backlist = cpu_row_idxs
with record_function("(zhg) get cpu row idxs"):
with record_function("(pre-id) get cpu row idxs"):
comm_cpu_row_idxs = cpu_row_idxs[torch.isin(cpu_row_idxs, self.cached_idx_map, invert=True)]
self.num_hits_history.append(len(cpu_row_idxs) - len(comm_cpu_row_idxs))
......@@ -311,16 +311,16 @@ class CachedParamMgr(torch.nn.Module):
self.num_write_back_history.append(0)
# move sure the cuda rows will not be evicted!
with record_function("(zhg) cache update"):
with record_function("(pre-id) cache update"):
self._prepare_rows_on_cuda(comm_cpu_row_idxs)
self.evict_backlist = torch.tensor([], device=cpu_row_idxs.device, dtype=cpu_row_idxs.dtype)
with record_function("(zhg) embed cpu rows idx -> cache gpu row idxs"):
with record_function("(pre-id) embed cpu rows idx -> cache gpu row idxs"):
gpu_row_idxs = self._id_to_cached_cuda_id(ids)
# update for LFU.
if self._evict_strategy == EvictionStrategy.LFU:
with record_function("(pre-id) lfu cnter updates"):
unique_gpu_row_idxs = self.inverted_cached_idx[cpu_row_idxs]
self.freq_cnter.scatter_add_(0, unique_gpu_row_idxs, repeat_times)
......
colossalai/nn/parallel/layers/cache_embedding/embedding_config.py 0 → 100644
View file @ 64169f3e
import torch
class TablewiseEmbeddingBagConfig:
'''
example:
def prepare_tablewise_config(args, cache_ratio, ...):
embedding_bag_config_list: List[TablewiseEmbeddingBagConfig] = []
...
return embedding_bag_config_list
'''
def __init__(self,
num_embeddings: int,
cuda_row_num: int,
assigned_rank: int = 0,
buffer_size=50_000,
ids_freq_mapping=None,
initial_weight: torch.tensor = None,
name: str = ""):
self.num_embeddings = num_embeddings
self.cuda_row_num = cuda_row_num
self.assigned_rank = assigned_rank
self.buffer_size = buffer_size
self.ids_freq_mapping = ids_freq_mapping
self.initial_weight = initial_weight
self.name = name
colossalai/nn/parallel/layers/cache_embedding/parallel_freq_aware_embedding_tablewise.py
View file @ 64169f3e
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
from .freq_aware_embedding import FreqAwareEmbeddingBag
from .cache_mgr import EvictionStrategy
from .embedding_config import TablewiseEmbeddingBagConfig
from colossalai.tensor import ProcessGroup
from colossalai.nn._ops._utils import dual_all_to_all_tablewise
class TablewiseEmbeddingBagConfig:
'''
example:
def prepare_tablewise_config(args, cache_ratio, ...):
embedding_bag_config_list: List[TablewiseEmbeddingBagConfig] = []
...
return embedding_bag_config_list
'''
def __init__(self,
num_embeddings: int,
cuda_row_num: int,
assigned_rank: int = 0,
buffer_size=50_000,
ids_freq_mapping=None,
initial_weight: torch.tensor = None,
name: str = ""):
self.num_embeddings = num_embeddings
self.cuda_row_num = cuda_row_num
self.assigned_rank = assigned_rank
self.buffer_size = buffer_size
self.ids_freq_mapping = ids_freq_mapping
self.initial_weight = initial_weight
self.name = name
from typing import List
class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
......@@ -44,6 +16,7 @@ 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,
......@@ -98,7 +71,7 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
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 :
else:
offset_cumsum += table_num_embeddings
# prepare list shape for all_to_all output
......@@ -122,21 +95,23 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
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]))
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)])
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)])
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:
......@@ -154,7 +129,7 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
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)
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)]
......@@ -168,130 +143,3 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
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.
"""
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,
mode='mean',
include_last_offset=False,
dtype=None,
device=None,
warmup_ratio=0.7,
pin_weight=False,
evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
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]
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] = []
for i, rank in enumerate(self.rank_of_tables):
if rank == self.rank:
self.assigned_table_list.append(i)
self.include_last_offset = include_last_offset
self.pg = ProcessGroup(tp_degree=self.world_size)
# prepare FreqAwareEmbeddingBag list
self.freq_aware_embedding_bag_list: nn.ModuleList = nn.ModuleList()
for config in embedding_bag_config_list:
if config.assigned_rank != self.rank:
continue
self.freq_aware_embedding_bag_list.append(
FreqAwareEmbeddingBag(num_embeddings=config.num_embeddings,
embedding_dim=embedding_dim,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse,
_weight=config.initial_weight,
mode=mode,
include_last_offset=include_last_offset,
dtype=dtype,
device=device,
cuda_row_num=config.cuda_row_num,
ids_freq_mapping=config.ids_freq_mapping,
warmup_ratio=warmup_ratio,
buffer_size=config.buffer_size,
pin_weight=pin_weight,
evict_strategy=evict_strategy))
# 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):
# determine indices to handle
batch_size = (offsets.shape[0]) // self.global_tables_num
local_output_list = []
for i, handle_table in enumerate(self.assigned_table_list):
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)
# then concatenate those local_output on the second demension.
# use all_to_all
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 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"
)
colossalai/nn/parallel/layers/cache_embedding/parallel_freq_aware_embedding_tablewise_split_cache.py 0 → 100644
View file @ 64169f3e
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.profiler import record_function
from .freq_aware_embedding import FreqAwareEmbeddingBag
from colossalai.tensor import ProcessGroup
from colossalai.nn._ops._utils import dual_all_to_all_tablewise
from .embedding_config import TablewiseEmbeddingBagConfig
from .cache_mgr import EvictionStrategy
from typing import List
import abc
class ParallelFreqAwareEmbeddingBagTablewiseSpiltCache(abc.ABC, nn.Module):
"""
every table assigned to this class instance is managed by a FreqAwareEmbeddingBag.
"""
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,
mode='mean',
include_last_offset=False,
dtype=None,
device=None,
warmup_ratio=0.7,
pin_weight=False,
evict_strategy: EvictionStrategy = EvictionStrategy.LFU):
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]
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] = []
for i, rank in enumerate(self.rank_of_tables):
if rank == self.rank:
self.assigned_table_list.append(i)
self.include_last_offset = include_last_offset
self.pg = ProcessGroup(tp_degree=self.world_size)
# prepare FreqAwareEmbeddingBag list
self.freq_aware_embedding_bag_list: nn.ModuleList = nn.ModuleList()
for config in embedding_bag_config_list:
if config.assigned_rank != self.rank:
continue
self.freq_aware_embedding_bag_list.append(
FreqAwareEmbeddingBag(num_embeddings=config.num_embeddings,
embedding_dim=embedding_dim,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse,
_weight=config.initial_weight,
mode=mode,
include_last_offset=include_last_offset,
dtype=dtype,
device=device,
cuda_row_num=config.cuda_row_num,
ids_freq_mapping=config.ids_freq_mapping,
warmup_ratio=warmup_ratio,
buffer_size=config.buffer_size,
pin_weight=pin_weight,
evict_strategy=evict_strategy))
# 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):
# determine indices to handle
batch_size = (offsets.shape[0]) // self.global_tables_num
local_output_list = []
for i, handle_table in enumerate(self.assigned_table_list):
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)
# then concatenate those local_output on the second demension.
# use all_to_all
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 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")
tests/test_layers/test_cache_embedding.py
View file @ 64169f3e
......@@ -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, ParallelFreqAwareEmbeddingBagTablewiseSpiltCache
ParallelFreqAwareEmbeddingBagTablewise, TablewiseEmbeddingBagConfig
from typing import List
NUM_EMBED, EMBED_DIM = 10, 8
......@@ -209,19 +209,28 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
# initialize weight
# 3 feature tables. idx: 0~5, 6~10, 11~17
weight_tables = torch.rand(18,5)
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()))
embedding_bag_config_list.append(TablewiseEmbeddingBagConfig(
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()))
embedding_bag_config_list.append(
TablewiseEmbeddingBagConfig(num_embeddings=6,
cuda_row_num=4,
assigned_rank=0,
initial_weight=weight_table1.clone().detach().cpu()))
embedding_bag_config_list.append(
TablewiseEmbeddingBagConfig(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)
_weight = torch.cat([weight_table1, weight_table2], 0)
else:
_weight = weight_table3
model = ParallelFreqAwareEmbeddingBagTablewise(
......@@ -249,7 +258,7 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
rand_grad = torch.rand(3, 5 * 3, dtype=res.dtype, device=res.device)
if rank == 0:
fake_grad = rand_grad[0:2]
else :
else:
fake_grad = rand_grad[2:]
res.backward(fake_grad)
optimizer.step()
......@@ -261,7 +270,7 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
include_last_offset=True,
freeze=False).to(device)
ref_optimizer = torch.optim.SGD(ref_model.parameters(), lr=1e-2)
ref_fake_grad = torch.cat(rand_grad.split(5,1),0)
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)
......@@ -273,6 +282,7 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size):
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())
......@@ -289,7 +299,8 @@ def run_parallel_freq_aware_embed_columnwise(rank, world_size):
coloweight.set_process_group(ProcessGroup(tp_degree=world_size))
coloweight.set_tensor_spec(ShardSpec(dims=[-1], num_partitions=[world_size]), ComputeSpec(ComputePattern.TP1D))
model = ParallelFreqAwareEmbeddingBag.from_pretrained(coloweight,
model = ParallelFreqAwareEmbeddingBag.from_pretrained(
coloweight,
include_last_offset=True,
freeze=False,
cuda_row_num=batch_size * 2,
......
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