test_cache_embedding.py

import pytest
from functools import partial
import torch
import torch.multiprocessing as mp
import numpy as np

from colossalai.utils import free_port
from colossalai.testing import rerun_if_address_is_in_use

from colossalai.nn._ops.cache_embedding import CachedParamMgr, FreqAwareEmbeddingBag

NUM_EMBED, EMBED_DIM = 10, 8
BATCH_SIZE = 8


def synthesize_1d_sparse_feature(
    batch_size,
    num_embed,
    device,
):
    indices_in_batch = batch_size * 2
    indices = torch.randint(low=0, high=num_embed, size=(indices_in_batch,), device=device, dtype=torch.long)
    offsets = torch.from_numpy(
        np.array([
            0, *np.sort(np.random.randint(low=0, high=indices_in_batch, size=(indices_in_batch - 1,))), indices_in_batch
        ])).to(device).long()
    return indices, offsets


def test_cachemgr():
    model = torch.nn.EmbeddingBag(10000, 128)
    # 10 chunks, 5 in cuda
    mgr = CachedParamMgr(model.weight, 5)
    assert mgr.cuda_row_num == 5

    mgr._admit(1)
    assert not mgr._chunk_in_cuda(2)
    assert mgr._chunk_in_cuda(1)

    # print(mgr.cached_chunk_table)
    mgr._admit(8)

    # now 3 chunk is available
    assert mgr.cuda_available_chunk_num == 3

    mgr._evict()
    assert mgr.cuda_available_chunk_num == 4

    mgr._prepare_rows_on_cuda(torch.tensor([9, 6, 5], dtype=torch.long, device=0))
    mgr._prepare_rows_on_cuda(torch.tensor([3, 4, 5], dtype=torch.long, device=0))
    # print(mgr.cached_chunk_table)
    # mgr.print_comm_stats()

    mgr.flush()
    assert mgr.cuda_available_chunk_num == 5


def test_reorder_with_freq():
    num_embed = 100
    chunk_size = 1
    num_chunk = 5

    idx_map = np.random.randint(10000, size=(num_embed,))
    sorted_idx = np.flipud(np.argsort(idx_map)).tolist()
    chunkid, offset_in_chunk = [], []
    for i in range(num_embed):
        idx = sorted_idx.index(i)
        chunkid.append(idx // chunk_size)
        offset_in_chunk.append(idx % chunk_size)

    chunkid = torch.tensor(chunkid, dtype=torch.long, device=torch.cuda.current_device())
    offset_in_chunk = torch.tensor(offset_in_chunk, dtype=torch.long, device=torch.cuda.current_device())

    weight = torch.rand(num_embed, 2)
    mgr = CachedParamMgr(weight, num_chunk)

    mgr.reorder(idx_map)

    indices = mgr.idx_map.index_select(0, torch.arange(num_embed, dtype=torch.long, device=torch.cuda.current_device()))
    mgr_chunk_id = torch.div(indices, chunk_size, rounding_mode='floor')
    mgr_offsets = torch.remainder(indices, chunk_size)
    assert torch.allclose(chunkid, mgr_chunk_id), f"chunk id: {chunkid}, mgr: {mgr_chunk_id}"
    assert torch.allclose(offset_in_chunk, mgr_offsets), \
        f"offset in chunk: {offset_in_chunk}, mgr: {mgr_offsets}"


def test_freq_aware_embed():
    device = torch.device('cuda', 0)
    model = FreqAwareEmbeddingBag(
        NUM_EMBED,
        EMBED_DIM,
        mode='mean',
        include_last_offset=True,
    ).to(device)
    model.preprocess(cuda_row_num=BATCH_SIZE * 2, ids_freq_mapping=None)

    assert model.weight.shape[0] == NUM_EMBED
    ref_model = torch.nn.EmbeddingBag.from_pretrained(model.weight.detach().to(device),
                                                      mode='mean',
                                                      include_last_offset=True,
                                                      freeze=False)

    assert torch.allclose(ref_model.weight.detach(), model.weight.detach().to(device))

    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
    ref_optimizer = torch.optim.SGD(ref_model.parameters(), lr=1e-3)

    for i in range(5):
        indices, offsets = synthesize_1d_sparse_feature(BATCH_SIZE, NUM_EMBED, device)
        res = model(indices, offsets)
        ref_res = ref_model(indices, offsets)
        assert torch.allclose(res, ref_res), f"model result: {res}, reference: {ref_res}"

        grad = torch.rand_like(res)
        # comparing gradient here is nontrivial
        res.backward(grad)
        ref_res.backward(grad)
        optimizer.step()
        optimizer.zero_grad()

        ref_optimizer.step()
        ref_optimizer.zero_grad()

    model.cache_weight_mgr.flush()
    model_weight = model.weight.detach().to(device)
    ref_weight = ref_model.weight.detach()
    assert torch.allclose(model_weight, ref_weight), \
        f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}"


if __name__ == '__main__':
    # test_freq_aware_embed()
    # test_chunkmgr_admit()
    pass