mirror of https://github.com/hpcaitech/ColossalAI
378 lines
14 KiB
Python
378 lines
14 KiB
Python
import random
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from typing import List
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import numpy as np
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import pytest
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import torch
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import colossalai
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from colossalai.nn.parallel.layers import (
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CachedEmbeddingBag,
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CachedParamMgr,
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EvictionStrategy,
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ParallelCachedEmbeddingBag,
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ParallelCachedEmbeddingBagTablewise,
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TablewiseEmbeddingBagConfig,
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)
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from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
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from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
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NUM_EMBED, EMBED_DIM = 10, 8
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BATCH_SIZE = 8
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def set_seed(seed):
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"""
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To achieve reproducible results, it's necessary to fix random seeds
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"""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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def synthesize_1d_sparse_feature(
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batch_size,
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num_embed,
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device,
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):
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indices_in_batch = batch_size * 2
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indices = torch.randint(low=0, high=num_embed, size=(indices_in_batch,), device=device, dtype=torch.long)
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offsets = torch.from_numpy(
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np.array([
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0, *np.sort(np.random.randint(low=0, high=indices_in_batch, size=(indices_in_batch - 1,))), indices_in_batch
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])).to(device).long()
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return indices, offsets
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@pytest.mark.skip
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@clear_cache_before_run()
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def test_cachemgr():
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model = torch.nn.EmbeddingBag(10000, 128)
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# 10 chunks, 5 in cuda
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mgr = CachedParamMgr(model.weight.detach(), 5)
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assert mgr.cuda_row_num == 5
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mgr._admit(1)
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assert not mgr._chunk_in_cuda(2)
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assert mgr._chunk_in_cuda(1)
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# print(mgr.cached_chunk_table)
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mgr._admit(8)
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# now 3 chunk is available
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assert mgr.cuda_available_chunk_num == 3
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mgr._evict()
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assert mgr.cuda_available_chunk_num == 4
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mgr._prepare_rows_on_cuda(torch.tensor([9, 6, 5], dtype=torch.long, device=0))
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mgr._prepare_rows_on_cuda(torch.tensor([3, 4, 5], dtype=torch.long, device=0))
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# print(mgr.cached_chunk_table)
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# mgr.print_comm_stats()
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mgr.flush()
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assert mgr.cuda_available_chunk_num == 5
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@clear_cache_before_run()
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def test_reorder_with_freq():
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num_embed = 100
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chunk_size = 1
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num_chunk = 5
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idx_map = torch.randint(10000, size=(num_embed,))
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sorted_idx = torch.argsort(idx_map, descending=True).tolist()
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chunkid, offset_in_chunk = [], []
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for i in range(num_embed):
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idx = sorted_idx.index(i)
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chunkid.append(idx // chunk_size)
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offset_in_chunk.append(idx % chunk_size)
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dev = torch.device('cuda')
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chunkid = torch.tensor(chunkid, dtype=torch.long, device=dev)
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offset_in_chunk = torch.tensor(offset_in_chunk, dtype=torch.long, device=dev)
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weight = torch.rand(num_embed, 2)
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mgr = CachedParamMgr(weight, num_chunk)
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mgr.reorder(idx_map)
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indices = mgr.idx_map.index_select(0, torch.arange(num_embed, dtype=torch.long, device=dev))
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mgr_chunk_id = torch.div(indices, chunk_size, rounding_mode='floor')
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mgr_offsets = torch.remainder(indices, chunk_size)
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assert torch.allclose(chunkid, mgr_chunk_id), f"chunk id: {chunkid}, mgr: {mgr_chunk_id}"
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assert torch.allclose(offset_in_chunk, mgr_offsets), \
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f"offset in chunk: {offset_in_chunk}, mgr: {mgr_offsets}"
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@clear_cache_before_run()
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@parameterize('use_LFU', [True, False])
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def test_freq_aware_embed(use_LFU: bool):
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device = torch.device('cuda', 0)
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evict_strategy = EvictionStrategy.LFU if use_LFU else EvictionStrategy.DATASET
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model = CachedEmbeddingBag(NUM_EMBED,
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EMBED_DIM,
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mode='mean',
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include_last_offset=True,
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cache_ratio=min(BATCH_SIZE * 2 / NUM_EMBED, 1.0),
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ids_freq_mapping=None,
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evict_strategy=evict_strategy).to(device)
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assert model.weight.shape[0] == NUM_EMBED
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ref_model = torch.nn.EmbeddingBag.from_pretrained(model.weight.detach().to(device),
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mode='mean',
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include_last_offset=True,
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freeze=False)
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assert torch.allclose(ref_model.weight.detach(), model.weight.detach().to(device))
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
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ref_optimizer = torch.optim.SGD(ref_model.parameters(), lr=1e-3)
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for i in range(5):
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indices, offsets = synthesize_1d_sparse_feature(BATCH_SIZE, NUM_EMBED, device)
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res = model(indices, offsets)
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ref_res = ref_model(indices, offsets)
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assert torch.allclose(res, ref_res), f"model result: {res}, reference: {ref_res}"
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grad = torch.rand_like(res)
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# comparing gradient here is nontrivial
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res.backward(grad)
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ref_res.backward(grad)
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optimizer.step()
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optimizer.zero_grad()
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ref_optimizer.step()
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ref_optimizer.zero_grad()
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model.cache_weight_mgr.flush()
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model_weight = model.weight.detach().to(device)
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ref_weight = ref_model.weight.detach()
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assert torch.allclose(model_weight, ref_weight), \
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f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}"
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@clear_cache_before_run()
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@parameterize('init_freq', [True, False])
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def test_lfu_strategy(init_freq: bool):
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# minimal test to check behavior
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Bag = CachedEmbeddingBag(5,
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5,
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cache_ratio=3 / 5,
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buffer_size=0,
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pin_weight=True,
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ids_freq_mapping=[4, 2, 1, 3, 1] if init_freq else None,
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warmup_ratio=1.0,
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evict_strategy=EvictionStrategy.LFU)
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# print('cached_idx_map: ', Bag.cache_weight_mgr.cached_idx_map)
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offsets = torch.tensor([0], device="cuda:0")
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# prepare frequency learning info:
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Bag.forward(torch.tensor([2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0], device="cuda:0"), offsets)
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# check strategy
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([0, 1, 2], device="cuda:0"), offsets)
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Bag.forward(torch.tensor([3], device="cuda:0"), offsets) # miss, evict 1
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Bag.forward(torch.tensor([2], device="cuda:0"), offsets) # hit
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Bag.forward(torch.tensor([4], device="cuda:0"), offsets) # miss, evict 3
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Bag.forward(torch.tensor([2], device="cuda:0"), offsets) # hit
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Bag.forward(torch.tensor([0], device="cuda:0"), offsets) # hit
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assert torch.allclose(torch.Tensor(Bag.cache_weight_mgr.num_hits_history[-6:]), torch.Tensor([3, 0, 1, 0, 1, 1])), \
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"LFU strategy behavior failed"
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def gather_tensor(tensor, rank, world_size):
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gather_list = []
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if rank == 0:
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gather_list = [torch.empty_like(tensor) for _ in range(world_size)]
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torch.distributed.gather(tensor, gather_list, dst=0)
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return gather_list
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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_tables = torch.rand(18, 5)
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weight_table1 = weight_tables[0:6]
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weight_table2 = weight_tables[6:11]
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weight_table3 = weight_tables[11:18]
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embedding_bag_config_list: List[TablewiseEmbeddingBagConfig] = []
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embedding_bag_config_list.append(
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TablewiseEmbeddingBagConfig(num_embeddings=6,
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cuda_row_num=4,
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assigned_rank=0,
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initial_weight=weight_table1.clone().detach().cpu()))
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embedding_bag_config_list.append(
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TablewiseEmbeddingBagConfig(num_embeddings=5,
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cuda_row_num=4,
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assigned_rank=0,
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initial_weight=weight_table2.clone().detach().cpu()))
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embedding_bag_config_list.append(
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TablewiseEmbeddingBagConfig(num_embeddings=7,
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cuda_row_num=4,
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assigned_rank=1,
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initial_weight=weight_table3.clone().detach().cpu()))
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if rank == 0:
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_weight = torch.cat([weight_table1, weight_table2], 0)
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else:
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_weight = weight_table3
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model = ParallelCachedEmbeddingBagTablewise(
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embedding_bag_config_list,
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embedding_dim=5,
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_weight=_weight,
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include_last_offset=True,
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cache_ratio=0.5,
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buffer_size=0,
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evict_strategy=EvictionStrategy.LFU,
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)
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# 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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already_split_along_rank=False)
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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
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if rank == 0:
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ref_model = torch.nn.EmbeddingBag.from_pretrained(weight_tables.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_fake_grad = torch.cat(rand_grad.split(5, 1), 0)
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ref_res = ref_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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ref_res.backward(ref_fake_grad)
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ref_optimizer.step()
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ref_optimizer.zero_grad()
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model.cache_weight_mgr.flush()
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recover_weight = model.cache_weight_mgr.weight.to(device)
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ref_weight = ref_model.weight.detach()[:11]
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assert torch.allclose(recover_weight, ref_weight), f"{recover_weight - ref_weight}"
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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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embed_dim = 16
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batch_size = 4
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set_seed(4321)
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weight = torch.rand(num_embed, embed_dim)
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coloweight = ColoTensor(weight.clone().detach().cpu(), spec=None)
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# initialize the tensor spec for the embedding weight parameter,
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# which is an ColoParameter.
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coloweight.set_process_group(ProcessGroup(tp_degree=world_size))
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coloweight.set_tensor_spec(ShardSpec(dims=[-1], num_partitions=[world_size]), ComputeSpec(ComputePattern.TP1D))
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model = ParallelCachedEmbeddingBag.from_pretrained(
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coloweight,
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include_last_offset=True,
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freeze=False,
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cache_ratio=batch_size * 2 / num_embed,
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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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weight_in_rank = torch.tensor_split(weight, world_size, -1)[rank]
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print(f"model weight: {model.cache_weight_mgr.weight.shape}, ref weight: {weight_in_rank.shape}")
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assert torch.allclose(weight_in_rank,
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model.cache_weight_mgr.weight.detach()), f"{weight_in_rank - model.cache_weight_mgr.weight}"
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
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if rank == 0:
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ref_model = torch.nn.EmbeddingBag.from_pretrained(weight.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-3)
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set_seed(4321)
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for i in range(5):
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indices, offsets = synthesize_1d_sparse_feature(batch_size, num_embed, device)
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res = model(indices, offsets)
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grad = torch.rand(batch_size * 2, embed_dim, dtype=res.dtype, device=res.device)
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grad_in_rank = torch.tensor_split(grad, world_size, 0)[rank]
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res.backward(grad_in_rank)
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optimizer.step()
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optimizer.zero_grad()
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res_list = gather_tensor(res.detach(), rank, world_size)
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if rank == 0:
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ref_res = ref_model(indices, offsets)
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recover_res = torch.cat(res_list, dim=0)
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assert torch.allclose(ref_res, recover_res)
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ref_res.backward(grad)
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ref_optimizer.step()
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ref_optimizer.zero_grad()
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model.cache_weight_mgr.flush()
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weight_list = gather_tensor(model.cache_weight_mgr.weight.detach().cuda(), rank, world_size)
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if rank == 0:
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recover_weight = torch.cat(weight_list, dim=1)
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assert torch.allclose(recover_weight, ref_model.weight.detach()), f"{recover_weight - ref_model.weight}"
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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_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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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_parallel_freq_aware_embed(world_size):
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spawn(run_dist, 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_lfu_strategy(False)
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