Fix integer overflows in attention & cache ops (#1514)
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@@ -6,13 +6,13 @@ import torch
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from vllm import cache_ops
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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NUM_TOKENS = [7, 83, 2048] # Arbitrary values for testing
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NUM_LAYERS = [5] # Arbitrary values for testing
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NUM_TOKENS = [83] # Arbitrary values for testing
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NUM_LAYERS = [1] # Arbitrary values for testing
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NUM_HEADS = [8] # Arbitrary values for testing
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HEAD_SIZES = [64, 80, 96, 112, 128, 256]
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BLOCK_SIZES = [8, 16, 32]
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NUM_BLOCKS = [1024] # Arbitrary values for testing
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NUM_MAPPINGS = [32, 256] # Arbitrary values for testing
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NUM_BLOCKS = [1024, 36000] # Arbitrary values for testing
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NUM_MAPPINGS = [256] # Arbitrary values for testing
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SEEDS = [0]
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@@ -69,9 +69,9 @@ def test_copy_blocks(
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for src, dsts in block_mapping.items():
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for dst in dsts:
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for cloned_key_cache in cloned_key_caches:
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cloned_key_cache[dst] = cloned_key_cache[src]
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cloned_key_cache[dst].copy_(cloned_key_cache[src])
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for cloned_value_cache in cloned_value_caches:
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cloned_value_cache[dst] = cloned_value_cache[src]
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cloned_value_cache[dst].copy_(cloned_value_cache[src])
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# Compare the results.
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for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
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@@ -106,7 +106,7 @@ def test_reshape_and_cache(
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# Create a random slot mapping.
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num_slots = block_size * num_blocks
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slot_mapping = random.sample(range(num_slots), num_tokens)
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slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device="cuda")
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slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device="cuda")
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qkv = torch.randn(num_tokens,
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3,
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