128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
import itertools
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import pytest
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import torch
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from sglang.jit_kernel.kvcache import can_use_store_cache, store_cache
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from sglang.jit_kernel.utils import get_ci_test_range
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BS_LIST = [2**n for n in range(0, 15)]
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BS_LIST += [x + 1 + i for i, x in enumerate(BS_LIST)]
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BS_LIST = get_ci_test_range(BS_LIST, [1, 9, 256, 16399])
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HIDDEN_DIMS = get_ci_test_range(
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[64, 128, 256, 512, 1024, 96, 98, 100], [64, 512, 1024, 98]
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)
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CACHE_SIZE = 1024 * 1024
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DTYPE = torch.bfloat16
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DEVICE = "cuda"
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@pytest.mark.parametrize(
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"batch_size,element_dim",
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list(itertools.product(BS_LIST, HIDDEN_DIMS)),
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)
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def test_store_cache(batch_size: int, element_dim: int) -> None:
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k = torch.randn((batch_size, element_dim), dtype=DTYPE, device=DEVICE)
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v = torch.randn((batch_size, element_dim), dtype=DTYPE, device=DEVICE)
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k_cache = torch.randn((CACHE_SIZE, element_dim), dtype=DTYPE, device=DEVICE)
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v_cache = torch.randn((CACHE_SIZE, element_dim), dtype=DTYPE, device=DEVICE)
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indices = torch.randperm(CACHE_SIZE, device=DEVICE)[:batch_size]
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# AOT store cache
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store_cache(k, v, k_cache, v_cache, indices)
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assert torch.all(k_cache[indices] == k)
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assert torch.all(v_cache[indices] == v)
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# Smaller subset for targeted tests below
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REPR_BS = get_ci_test_range([1, 7, 128], [1, 128])
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REPR_DIMS = get_ci_test_range([64, 128, 512, 1024, 96], [64, 1024, 96])
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SMALL_CACHE = 4096
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32])
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@pytest.mark.parametrize(
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"batch_size,element_dim",
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list(itertools.product(REPR_BS, REPR_DIMS)),
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)
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def test_store_cache_dtypes(
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batch_size: int, element_dim: int, dtype: torch.dtype
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) -> None:
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k = torch.randn((batch_size, element_dim), dtype=dtype, device=DEVICE)
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v = torch.randn((batch_size, element_dim), dtype=dtype, device=DEVICE)
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k_cache = torch.randn((SMALL_CACHE, element_dim), dtype=dtype, device=DEVICE)
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v_cache = torch.randn((SMALL_CACHE, element_dim), dtype=dtype, device=DEVICE)
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indices = torch.randperm(SMALL_CACHE, device=DEVICE)[:batch_size]
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store_cache(k, v, k_cache, v_cache, indices)
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assert torch.all(k_cache[indices] == k)
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assert torch.all(v_cache[indices] == v)
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@pytest.mark.parametrize(
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"batch_size,element_dim",
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list(itertools.product(REPR_BS, REPR_DIMS)),
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)
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def test_store_cache_int32_indices(batch_size: int, element_dim: int) -> None:
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k = torch.randn((batch_size, element_dim), dtype=DTYPE, device=DEVICE)
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v = torch.randn((batch_size, element_dim), dtype=DTYPE, device=DEVICE)
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k_cache = torch.randn((SMALL_CACHE, element_dim), dtype=DTYPE, device=DEVICE)
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v_cache = torch.randn((SMALL_CACHE, element_dim), dtype=DTYPE, device=DEVICE)
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# int32 indices exercise a different CUDA template instantiation than default int64
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indices = torch.randperm(SMALL_CACHE, device=DEVICE)[:batch_size].to(torch.int32)
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store_cache(k, v, k_cache, v_cache, indices)
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assert torch.all(k_cache[indices.long()] == k)
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assert torch.all(v_cache[indices.long()] == v)
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def _valid_num_splits(element_dim: int, dtype: torch.dtype) -> list:
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"""Return the list of valid num_split values for a given element_dim/dtype."""
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row_bytes = element_dim * dtype.itemsize
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splits = [1]
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if row_bytes % (2 * 128) == 0:
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splits.append(2)
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if row_bytes % (4 * 128) == 0:
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splits.append(4)
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return splits
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_NUM_SPLIT_CASES = [
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(_dim, _ns, _dtype)
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for _dtype in [torch.float16, torch.bfloat16, torch.float32]
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for _dim in REPR_DIMS
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for _ns in _valid_num_splits(_dim, _dtype)
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]
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@pytest.mark.parametrize("element_dim,num_split,dtype", _NUM_SPLIT_CASES)
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def test_store_cache_num_split(
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element_dim: int, num_split: int, dtype: torch.dtype
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) -> None:
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batch_size = 128
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k = torch.randn((batch_size, element_dim), dtype=dtype, device=DEVICE)
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v = torch.randn((batch_size, element_dim), dtype=dtype, device=DEVICE)
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k_cache = torch.randn((SMALL_CACHE, element_dim), dtype=dtype, device=DEVICE)
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v_cache = torch.randn((SMALL_CACHE, element_dim), dtype=dtype, device=DEVICE)
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indices = torch.randperm(SMALL_CACHE, device=DEVICE)[:batch_size]
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# Verify each num_split kernel path (1, 2, 4) produces correct results
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store_cache(k, v, k_cache, v_cache, indices, num_split=num_split)
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assert torch.all(k_cache[indices] == k)
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assert torch.all(v_cache[indices] == v)
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def test_can_use_store_cache() -> None:
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assert can_use_store_cache(128)
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assert can_use_store_cache(256)
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assert can_use_store_cache(1024)
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assert can_use_store_cache(2048)
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if __name__ == "__main__":
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pytest.main([__file__, "-v", "-s"])
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