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[FAW] refactor reorder() for CachedParamMgr (#1514)

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Jiarui Fang 2022-08-29 14:22:07 +08:00 committed by GitHub
parent 9feee6d06b
commit af5438caa2
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2 changed files with 63 additions and 51 deletions
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39
colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py
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@ -172,44 +172,53 @@ class CachedParamMgr(torch.nn.Module):
ids_freq_mapping (List[int]): a list, whose offset is id number, value is freq. if None then not reorder the cpu weight. ids_freq_mapping (List[int]): a list, whose offset is id number, value is freq. if None then not reorder the cpu weight.
warmup_ratio (float): the amount of chunks preloaded in cuda cache warmup_ratio (float): the amount of chunks preloaded in cuda cache
""" """
if ids_freq_mapping is not None: # reorder phase: reorder the cpu weight according to their freq stats in the target dataset.
if not isinstance(ids_freq_mapping, torch.Tensor): # reorder only works for DATASET eviction strategy.
ids_freq_mapping = torch.tensor(ids_freq_mapping)
tmp_idx = torch.argsort(ids_freq_mapping, descending=True)
sorted_idx = torch.argsort(tmp_idx)
self.idx_map.data.copy_(sorted_idx)
if ids_freq_mapping is not None and not isinstance(ids_freq_mapping, torch.Tensor):
ids_freq_mapping = torch.tensor(ids_freq_mapping)
if self._evict_strategy == EvictionStrategy.DATASET:
if ids_freq_mapping is not None:
tmp_idx = torch.argsort(ids_freq_mapping, descending=True)
sorted_idx = torch.argsort(tmp_idx)
self.idx_map.data.copy_(sorted_idx)
# warmup phase: copy #preload_row_num rows from cpu to gpu.
preload_row_num = min(int(np.ceil(self.cuda_row_num * warmup_ratio)), self.num_embeddings) preload_row_num = min(int(np.ceil(self.cuda_row_num * warmup_ratio)), self.num_embeddings)
if preload_row_num > 0: if preload_row_num > 0:
with Timer() as timer: with Timer() as timer:
# extract rows from cpu weight # extract rows from cpu weight
preload_row_ids = torch.arange(preload_row_num) if self._evict_strategy == EvictionStrategy.LFU and ids_freq_mapping is not None:
preload_cuda_row_idxs = preload_row_ids.cuda() freq_value, preload_cpu_ids = torch.topk(ids_freq_mapping, preload_row_num, dim=0, largest=True)
preload_cuda_row_idxs = torch.arange(preload_row_num).cuda()
else:
preload_cpu_ids = torch.arange(preload_row_num)
preload_cuda_row_idxs = preload_cpu_ids.cuda()
if self.buffer_size > 0: if self.buffer_size > 0:
self.limit_buff_index_copyer.index_copy(0, self.limit_buff_index_copyer.index_copy(0,
src_index=preload_row_ids, src_index=preload_cpu_ids,
tgt_index=preload_cuda_row_idxs, tgt_index=preload_cuda_row_idxs,
src=self.weight.view(self.num_embeddings, -1), src=self.weight.view(self.num_embeddings, -1),
tgt=self.cuda_cached_weight.view(self.cuda_row_num, -1)) tgt=self.cuda_cached_weight.view(self.cuda_row_num, -1))
else: else:
preload_rows = self.weight.view(self.num_embeddings, -1).index_select(0, preload_row_ids).cuda() preload_rows = self.weight.view(self.num_embeddings, -1).index_select(0, preload_cpu_ids).cuda()
self.cuda_cached_weight.view(self.cuda_row_num, -1).index_copy_(0, preload_cuda_row_idxs, self.cuda_cached_weight.view(self.cuda_row_num, -1).index_copy_(0, preload_cuda_row_idxs,
preload_rows) preload_rows)
# update auxiliary info # update auxiliary info
slot_offsets = preload_cuda_row_idxs self.cached_idx_map[preload_cuda_row_idxs] = preload_cpu_ids.cuda()
self.cached_idx_map[preload_cuda_row_idxs] = preload_cuda_row_idxs self.inverted_cached_idx[preload_cpu_ids] = preload_cuda_row_idxs
self._cuda_available_row_num -= preload_row_num
if self._evict_strategy == EvictionStrategy.LFU: if self._evict_strategy == EvictionStrategy.LFU:
# if the ids_freq_mapping is not None, we initialize the embedding row's freq value in LFU as its freq in dataset. # if the ids_freq_mapping is not None, we initialize the embedding row's freq value in LFU as its freq in dataset.
if ids_freq_mapping is None: if ids_freq_mapping is None:
self.freq_cnter.index_fill_(0, preload_cuda_row_idxs, 0) self.freq_cnter.index_fill_(0, preload_cuda_row_idxs, 0)
else: else:
self.freq_cnter.index_fill_(0, preload_cuda_row_idxs, self.idx_map[preload_cuda_row_idxs]) self.freq_cnter[preload_cuda_row_idxs] = freq_value.cuda()
self.inverted_cached_idx[preload_cuda_row_idxs] = slot_offsets
self._cuda_available_row_num -= preload_row_num
print(f'Cache warmup finished cost {timer.elapsed} sec.') print(f'Cache warmup finished cost {timer.elapsed} sec.')
def flush(self): def flush(self):

75
tests/test_layers/test_cache_embedding.py
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@ -144,49 +144,52 @@ def test_freq_aware_embed(use_LFU: bool):
assert torch.allclose(model_weight, ref_weight), \ assert torch.allclose(model_weight, ref_weight), \
f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}" f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}"
def test_lfu_strategy():
# minimal test to check behavior
Bag = FreqAwareEmbeddingBag(
5,
5,
cuda_row_num=3,
buffer_size=0,
pin_weight=True,
warmup_ratio=0.0,
evict_strategy=EvictionStrategy.LFU
)
offsets = torch.tensor([0],device="cuda:0") @pytest.mark.parametrize('init_freq', [True, False])
def test_lfu_strategy(init_freq: bool):
# minimal test to check behavior
Bag = FreqAwareEmbeddingBag(5,
5,
cuda_row_num=3,
buffer_size=0,
pin_weight=True,
ids_freq_mapping=[4, 2, 1, 3, 1] if init_freq else None,
warmup_ratio=1.0,
evict_strategy=EvictionStrategy.LFU)
# print('cached_idx_map: ', Bag.cache_weight_mgr.cached_idx_map)
offsets = torch.tensor([0], device="cuda:0")
# prepare frequency learning info: # prepare frequency learning info:
Bag.forward(torch.tensor([2],device="cuda:0"),offsets) Bag.forward(torch.tensor([2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0],device="cuda:0"),offsets) Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0],device="cuda:0"),offsets) Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0],device="cuda:0"),offsets) Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0],device="cuda:0"),offsets) Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
# check strategy # check strategy
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([0,1,2],device="cuda:0"),offsets) Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
Bag.forward(torch.tensor([3],device="cuda:0"),offsets) # miss, evict 1 Bag.forward(torch.tensor([3], device="cuda:0"), offsets) # miss, evict 1
Bag.forward(torch.tensor([2],device="cuda:0"),offsets) # hit Bag.forward(torch.tensor([2], device="cuda:0"), offsets) # hit
Bag.forward(torch.tensor([4],device="cuda:0"),offsets) # miss, evict 3 Bag.forward(torch.tensor([4], device="cuda:0"), offsets) # miss, evict 3
Bag.forward(torch.tensor([2],device="cuda:0"),offsets) # hit Bag.forward(torch.tensor([2], device="cuda:0"), offsets) # hit
Bag.forward(torch.tensor([0],device="cuda:0"),offsets) # hit Bag.forward(torch.tensor([0], device="cuda:0"), offsets) # hit
assert torch.allclose(torch.Tensor(Bag.cache_weight_mgr.num_hits_history[-6:]), torch.Tensor([3, 0, 1, 0, 1, 1])), \ assert torch.allclose(torch.Tensor(Bag.cache_weight_mgr.num_hits_history[-6:]), torch.Tensor([3, 0, 1, 0, 1, 1])), \
"LFU strategy behavior failed" "LFU strategy behavior failed"
def gather_tensor(tensor, rank, world_size): def gather_tensor(tensor, rank, world_size):
gather_list = [] gather_list = []
if rank == 0: if rank == 0:
@ -279,4 +282,4 @@ def test_parallel_freq_aware_embed(world_size):
if __name__ == '__main__': if __name__ == '__main__':
# test_freq_aware_embed(True) # test_freq_aware_embed(True)
# test_parallel_freq_aware_embed(2) # test_parallel_freq_aware_embed(2)
test_lfu_strategy() test_lfu_strategy(False)