769 lines
28 KiB
Python
769 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import random
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import pytest
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import torch
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from tests.kernels.utils import DEFAULT_OPCHECK_TEST_UTILS, opcheck
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from vllm import _custom_ops as ops
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from vllm.platforms import current_platform
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from vllm.utils import align_to_256bytes
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COPYING_DIRECTION = [('cuda', 'cpu'), ('cuda', 'cuda'), ('cpu', 'cuda')]
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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NUM_TOKENS = [42] # 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, 120, 256]
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BLOCK_SIZES = [8, 16, 32]
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# Parameters for MLA tests.
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KV_LORA_RANKS = [512]
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QK_ROPE_HEAD_DIMS = [64]
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NUM_TOKENS_MLA = [42]
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BLOCK_SIZES_MLA = [16]
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NUM_BLOCKS_MLA = [8]
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# Arbitrary values for testing
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# don't make it too large. e.g. [1024, 36000] will OOM
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NUM_BLOCKS = [1024, 10000]
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NUM_MAPPINGS = [256] # Arbitrary values for testing
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SEEDS = [0]
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CUDA_DEVICES = [
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f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
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]
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# We assume fp8 is always enabled for testing.
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KV_CACHE_DTYPE = ["auto", "fp8"]
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@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
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@pytest.mark.parametrize("num_layers", NUM_LAYERS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
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@torch.inference_mode()
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def test_copy_blocks(
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kv_cache_factory,
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num_mappings: int,
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num_layers: int,
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num_heads: int,
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head_size: int,
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block_size: int,
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num_blocks: int,
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dtype: torch.dtype,
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seed: int,
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kv_cache_dtype: str,
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device: str,
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) -> None:
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if kv_cache_dtype == "fp8" and head_size % 16:
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pytest.skip()
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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# Generate random block mappings where each source block is mapped to two
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# destination blocks.
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assert 2 * num_mappings <= num_blocks
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src_blocks = random.sample(range(num_blocks), num_mappings)
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remainig_blocks = list(set(range(num_blocks)) - set(src_blocks))
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dst_blocks = random.sample(remainig_blocks, 2 * num_mappings)
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block_mapping: list[tuple[int, int]] = []
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for i in range(num_mappings):
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src = src_blocks[i]
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dst1 = dst_blocks[2 * i]
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dst2 = dst_blocks[2 * i + 1]
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block_mapping.append((src, dst1))
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block_mapping.append((src, dst2))
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# Create the KV caches.
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key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
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num_layers, num_heads,
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head_size, kv_cache_dtype,
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dtype, seed, device)
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# Clone the KV caches.
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cloned_key_caches = [key_cache.clone() for key_cache in key_caches]
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cloned_value_caches = [value_cache.clone() for value_cache in value_caches]
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# Call the copy blocks kernel.
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block_mapping_tensor = torch.tensor(block_mapping,
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dtype=torch.int64,
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device=device).view(-1, 2)
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opcheck(torch.ops._C_cache_ops.copy_blocks,
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(key_caches, value_caches, block_mapping_tensor),
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test_utils=DEFAULT_OPCHECK_TEST_UTILS,
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cond=(head_size == HEAD_SIZES[0]))
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ops.copy_blocks(key_caches, value_caches, block_mapping_tensor)
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# Run the reference implementation.
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for src, dst in block_mapping:
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for cloned_key_cache in cloned_key_caches:
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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].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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torch.testing.assert_close(key_cache, cloned_key_cache)
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for value_cache, cloned_value_cache in zip(value_caches,
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cloned_value_caches):
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torch.testing.assert_close(value_cache, cloned_value_cache)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
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@torch.inference_mode()
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def test_reshape_and_cache(
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kv_cache_factory,
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num_tokens: int,
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num_heads: int,
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head_size: int,
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block_size: int,
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num_blocks: int,
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dtype: torch.dtype,
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seed: int,
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device: str,
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kv_cache_dtype: str,
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) -> None:
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if kv_cache_dtype == "fp8" and head_size % 16:
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pytest.skip()
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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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_lst = random.sample(range(num_slots), num_tokens)
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slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long)
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qkv = torch.randn(num_tokens, 3, num_heads, head_size, dtype=dtype)
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_, key, value = qkv.unbind(dim=1)
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# Create the KV caches.
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key_caches, value_caches = kv_cache_factory(num_blocks, block_size, 1,
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num_heads, head_size,
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kv_cache_dtype, dtype, seed,
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device)
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key_cache, value_cache = key_caches[0], value_caches[0]
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# Using default kv_scale
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k_scale = (key.amax() / 64.0).to(torch.float32)
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v_scale = (value.amax() / 64.0).to(torch.float32)
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# Clone the KV caches.
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if kv_cache_dtype == "fp8":
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cloned_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
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ops.convert_fp8(cloned_key_cache, key_cache, k_scale.item())
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cloned_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
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ops.convert_fp8(cloned_value_cache, value_cache, v_scale.item())
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else:
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cloned_key_cache = key_cache.clone()
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cloned_value_cache = value_cache.clone()
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# Call the reshape_and_cache kernel.
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opcheck(torch.ops._C_cache_ops.reshape_and_cache,
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(key, value, key_cache, value_cache, slot_mapping, kv_cache_dtype,
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k_scale, v_scale),
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cond=(head_size == HEAD_SIZES[0]))
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ops.reshape_and_cache(key, value, key_cache, value_cache, slot_mapping,
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kv_cache_dtype, k_scale, v_scale)
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if kv_cache_dtype == "fp8":
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result_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
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ops.convert_fp8(result_key_cache, key_cache, k_scale.item())
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result_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
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ops.convert_fp8(result_value_cache, value_cache, v_scale.item())
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# Run the reference implementation.
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reshaped_key = key.reshape(num_tokens, *key_cache[0, :, :, 0, :].shape)
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block_indicies = torch.div(slot_mapping, block_size, rounding_mode="floor")
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block_indicies_lst = block_indicies.cpu().tolist()
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block_offsets = slot_mapping % block_size
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block_offsets_lst = block_offsets.cpu().tolist()
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for i in range(num_tokens):
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block_idx = block_indicies_lst[i]
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block_offset = block_offsets_lst[i]
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cloned_key_cache[block_idx, :, :, block_offset, :] = reshaped_key[i]
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cloned_value_cache[block_idx, :, :, block_offset] = value[i]
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if kv_cache_dtype == "fp8":
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torch.testing.assert_close(result_key_cache,
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cloned_key_cache,
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atol=0.001,
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rtol=0.1)
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torch.testing.assert_close(result_value_cache,
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cloned_value_cache,
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atol=0.001,
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rtol=0.1)
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else:
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torch.testing.assert_close(key_cache, cloned_key_cache)
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torch.testing.assert_close(value_cache, cloned_value_cache)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
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@torch.inference_mode()
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def test_reshape_and_cache_flash(
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kv_cache_factory_flashinfer,
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num_tokens: int,
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num_heads: int,
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head_size: int,
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block_size: int,
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num_blocks: int,
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dtype: torch.dtype,
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seed: int,
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device: str,
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kv_cache_dtype: str,
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) -> None:
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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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_lst = random.sample(range(num_slots), num_tokens)
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slot_mapping = torch.tensor(slot_mapping_lst,
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dtype=torch.long,
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device=device)
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qkv = torch.randn(num_tokens,
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3,
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num_heads,
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head_size,
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dtype=dtype,
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device=device)
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_, key, value = qkv.unbind(dim=1)
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# Create the KV caches.
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key_caches, value_caches = kv_cache_factory_flashinfer(
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num_blocks,
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block_size,
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1,
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num_heads,
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head_size,
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kv_cache_dtype,
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dtype,
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device=device,
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)
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key_cache, value_cache = key_caches[0].contiguous(
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), value_caches[0].contiguous()
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del key_caches
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del value_caches
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k_scale = (key.amax() / 64.0).to(torch.float32)
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v_scale = (value.amax() / 64.0).to(torch.float32)
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# Clone the KV caches.
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if kv_cache_dtype == "fp8":
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cloned_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
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ops.convert_fp8(cloned_key_cache, key_cache, k_scale.item(),
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kv_cache_dtype)
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cloned_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
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ops.convert_fp8(cloned_value_cache, value_cache, v_scale.item(),
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kv_cache_dtype)
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else:
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cloned_key_cache = key_cache.clone()
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cloned_value_cache = value_cache.clone()
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# Call the reshape_and_cache kernel.
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opcheck(torch.ops._C_cache_ops.reshape_and_cache_flash,
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(key, value, key_cache, value_cache, slot_mapping, kv_cache_dtype,
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k_scale, v_scale),
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cond=(head_size == HEAD_SIZES[0]))
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ops.reshape_and_cache_flash(key, value, key_cache, value_cache,
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slot_mapping, kv_cache_dtype, k_scale, v_scale)
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if kv_cache_dtype == "fp8":
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result_key_cache = torch.empty_like(key_cache, dtype=torch.float16)
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ops.convert_fp8(result_key_cache,
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key_cache,
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k_scale.item(),
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kv_dtype=kv_cache_dtype)
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result_value_cache = torch.empty_like(value_cache, dtype=torch.float16)
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ops.convert_fp8(result_value_cache,
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value_cache,
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v_scale.item(),
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kv_dtype=kv_cache_dtype)
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# Run the reference implementation.
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block_indicies = torch.div(slot_mapping, block_size, rounding_mode="floor")
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block_indicies_lst = block_indicies.cpu().tolist()
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block_offsets = slot_mapping % block_size
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block_offsets_lst = block_offsets.cpu().tolist()
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for i in range(num_tokens):
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block_idx = block_indicies_lst[i]
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block_offset = block_offsets_lst[i]
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cloned_key_cache[block_idx, block_offset, :, :] = key[i]
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cloned_value_cache[block_idx, block_offset, :, :] = value[i]
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if kv_cache_dtype == "fp8":
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torch.testing.assert_close(result_key_cache,
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cloned_key_cache,
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atol=0.001,
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rtol=0.1)
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torch.testing.assert_close(result_value_cache,
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cloned_value_cache,
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atol=0.001,
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rtol=0.1)
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else:
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torch.testing.assert_close(key_cache, cloned_key_cache)
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torch.testing.assert_close(value_cache, cloned_value_cache)
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@pytest.mark.parametrize("direction", COPYING_DIRECTION)
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@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
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@torch.inference_mode()
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def test_swap_blocks(
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kv_cache_factory,
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direction: tuple[str, str],
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num_mappings: int,
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num_heads: int,
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head_size: int,
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block_size: int,
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num_blocks: int,
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dtype: torch.dtype,
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seed: int,
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device: str,
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kv_cache_dtype: str,
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) -> None:
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if kv_cache_dtype == "fp8" and "cpu" in direction:
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pytest.skip()
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if kv_cache_dtype == "fp8" and head_size % 16:
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pytest.skip()
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current_platform.seed_everything(seed)
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src_device = device if direction[0] == "cuda" else 'cpu'
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dst_device = device if direction[1] == "cuda" else 'cpu'
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src_blocks = random.sample(range(num_blocks), num_mappings)
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# For the same device, mapping must not overlap
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if src_device == dst_device:
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remaining_blocks = list(set(range(num_blocks)) - set(src_blocks))
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dst_blocks = random.sample(remaining_blocks, num_mappings)
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else:
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dst_blocks = random.sample(range(num_blocks), num_mappings)
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block_mapping = list(zip(src_blocks, dst_blocks))
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block_mapping_tensor = torch.tensor(block_mapping,
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dtype=torch.int64,
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device="cpu").view(-1, 2)
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# Create the KV caches on the first device.
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src_key_caches, src_value_caches = kv_cache_factory(
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num_blocks, block_size, 1, num_heads, head_size, kv_cache_dtype, dtype,
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seed, src_device)
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# Create the KV caches on the second device.
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dist_key_caches, dist_value_caches = kv_cache_factory(
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num_blocks, block_size, 1, num_heads, head_size, kv_cache_dtype, dtype,
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seed, dst_device)
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src_key_caches_clone = src_key_caches[0].clone()
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src_value_caches_clone = src_value_caches[0].clone()
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# Call the swap_blocks kernel.
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do_opcheck = (head_size == HEAD_SIZES[0])
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opcheck(torch.ops._C_cache_ops.swap_blocks,
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(src_key_caches[0], dist_key_caches[0], block_mapping_tensor),
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cond=do_opcheck)
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opcheck(torch.ops._C_cache_ops.swap_blocks,
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(src_value_caches[0], dist_value_caches[0], block_mapping_tensor),
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cond=do_opcheck)
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ops.swap_blocks(src_key_caches[0], dist_key_caches[0],
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block_mapping_tensor)
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ops.swap_blocks(src_value_caches[0], dist_value_caches[0],
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block_mapping_tensor)
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for src, dst in block_mapping:
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torch.testing.assert_close(src_key_caches_clone[src].cpu(),
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dist_key_caches[0][dst].cpu())
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torch.testing.assert_close(src_value_caches_clone[src].cpu(),
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dist_value_caches[0][dst].cpu())
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@torch.inference_mode()
|
|
def test_fp8_e4m3_conversion(
|
|
num_heads: int,
|
|
head_size: int,
|
|
block_size: int,
|
|
num_blocks: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
device: str,
|
|
) -> None:
|
|
current_platform.seed_everything(seed)
|
|
|
|
low = -224.0
|
|
high = 224.0
|
|
shape = (num_blocks, num_heads, head_size, block_size)
|
|
cache = torch.empty(shape, dtype=dtype, device=device)
|
|
cache.uniform_(low, high)
|
|
|
|
cache_fp8 = torch.empty_like(cache, dtype=torch.uint8)
|
|
ops.convert_fp8(cache_fp8, cache)
|
|
|
|
converted_cache = torch.empty_like(cache)
|
|
ops.convert_fp8(converted_cache, cache_fp8)
|
|
|
|
torch.testing.assert_close(cache, converted_cache, atol=0.001, rtol=0.1)
|
|
|
|
|
|
def _create_mla_cache(
|
|
num_blocks: int,
|
|
block_size: int,
|
|
entry_size: int,
|
|
dtype: torch.dtype,
|
|
kv_cache_dtype: str,
|
|
device: str,
|
|
align_cache: bool,
|
|
) -> torch.Tensor:
|
|
cache_dtype = torch.uint8 if kv_cache_dtype == "fp8" else dtype
|
|
|
|
if align_cache:
|
|
alloc_entry_size = align_to_256bytes(entry_size, cache_dtype)
|
|
alloc_shape = (num_blocks, block_size, alloc_entry_size)
|
|
cache_full = torch.zeros(alloc_shape, dtype=cache_dtype, device=device)
|
|
cache = cache_full[..., :entry_size]
|
|
else:
|
|
cache = torch.zeros(num_blocks,
|
|
block_size,
|
|
entry_size,
|
|
dtype=cache_dtype,
|
|
device=device)
|
|
return cache
|
|
|
|
|
|
def _fill_mla_cache(cache: torch.Tensor, kv_cache_dtype: str):
|
|
rand_dtype = torch.float16 if kv_cache_dtype == "fp8" else cache.dtype
|
|
|
|
vals = torch.randn(*cache.shape, device=cache.device, dtype=rand_dtype)
|
|
if kv_cache_dtype == "fp8":
|
|
temp = torch.zeros_like(cache)
|
|
ops.convert_fp8(temp, vals, 1.0, kv_dtype=kv_cache_dtype)
|
|
vals = temp
|
|
cache.copy_(vals)
|
|
|
|
|
|
@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
|
|
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
|
|
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA)
|
|
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
|
|
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
|
|
@pytest.mark.parametrize("align_cache", [False])
|
|
@torch.inference_mode()
|
|
def test_concat_and_cache_mla(
|
|
kv_lora_rank: int,
|
|
qk_rope_head_dim: int,
|
|
num_tokens: int,
|
|
block_size: int,
|
|
num_blocks: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
device: str,
|
|
kv_cache_dtype: str,
|
|
align_cache: bool,
|
|
) -> None:
|
|
current_platform.seed_everything(seed)
|
|
torch.set_default_device(device)
|
|
|
|
total_slots = num_blocks * block_size
|
|
slot_mapping_lst = random.sample(range(total_slots), num_tokens)
|
|
slot_mapping = torch.tensor(slot_mapping_lst,
|
|
dtype=torch.long,
|
|
device=device)
|
|
|
|
kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device)
|
|
k_pe = torch.randn(num_tokens,
|
|
qk_rope_head_dim,
|
|
dtype=dtype,
|
|
device=device)
|
|
entry_size = kv_lora_rank + qk_rope_head_dim
|
|
|
|
scale = torch.tensor(0.1, dtype=torch.float32, device=device)
|
|
kv_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
|
|
kv_cache_dtype, device, align_cache)
|
|
ref_temp = torch.zeros(*kv_cache.shape, dtype=dtype, device=device)
|
|
|
|
for i in range(num_tokens):
|
|
slot = slot_mapping[i].item()
|
|
block_idx = slot // block_size
|
|
block_offset = slot % block_size
|
|
ref_temp[block_idx, block_offset, :kv_lora_rank] = kv_c[i]
|
|
ref_temp[block_idx, block_offset, kv_lora_rank:] = k_pe[i]
|
|
|
|
if kv_cache_dtype == "fp8":
|
|
ref_kv_cache = torch.empty_like(ref_temp, dtype=kv_cache.dtype)
|
|
ops.convert_fp8(ref_kv_cache,
|
|
ref_temp,
|
|
scale.item(),
|
|
kv_dtype=kv_cache_dtype)
|
|
else:
|
|
ref_kv_cache = ref_temp
|
|
|
|
opcheck(
|
|
torch.ops._C_cache_ops.concat_and_cache_mla,
|
|
(kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale),
|
|
test_utils=DEFAULT_OPCHECK_TEST_UTILS,
|
|
)
|
|
|
|
ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping,
|
|
kv_cache_dtype, scale)
|
|
|
|
if kv_cache_dtype == "fp8":
|
|
result_temp = torch.empty_like(kv_cache, dtype=torch.float16)
|
|
ops.convert_fp8(result_temp,
|
|
kv_cache.contiguous(),
|
|
scale.item(),
|
|
kv_dtype=kv_cache_dtype)
|
|
expected_temp = torch.empty_like(ref_kv_cache, dtype=torch.float16)
|
|
ops.convert_fp8(expected_temp,
|
|
ref_kv_cache,
|
|
scale.item(),
|
|
kv_dtype=kv_cache_dtype)
|
|
torch.testing.assert_close(result_temp,
|
|
expected_temp,
|
|
atol=0.001,
|
|
rtol=0.1)
|
|
else:
|
|
torch.testing.assert_close(kv_cache, ref_kv_cache)
|
|
|
|
|
|
@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
|
|
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
|
|
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
|
|
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
|
|
@pytest.mark.parametrize("num_layers", NUM_LAYERS)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
|
|
@pytest.mark.parametrize("align_cache", [False, True])
|
|
@torch.inference_mode()
|
|
def test_copy_blocks_mla(
|
|
kv_lora_rank: int,
|
|
qk_rope_head_dim: int,
|
|
block_size: int,
|
|
num_blocks: int,
|
|
num_layers: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
device: str,
|
|
kv_cache_dtype: str,
|
|
align_cache: bool,
|
|
) -> None:
|
|
current_platform.seed_everything(seed)
|
|
torch.set_default_device(device)
|
|
|
|
entry_size = kv_lora_rank + qk_rope_head_dim
|
|
|
|
kv_caches = []
|
|
for _ in range(num_layers):
|
|
kv_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
|
|
kv_cache_dtype, device, align_cache)
|
|
_fill_mla_cache(kv_cache, kv_cache_dtype=kv_cache_dtype)
|
|
kv_caches.append(kv_cache)
|
|
|
|
ref_caches = [kv_cache.clone() for kv_cache in kv_caches]
|
|
|
|
num_mappings = min(2, num_blocks // 2)
|
|
src_blocks = random.sample(range(num_blocks), num_mappings)
|
|
remaining = list(set(range(num_blocks)) - set(src_blocks))
|
|
dst_blocks = random.sample(remaining, 2 * num_mappings)
|
|
block_mapping = []
|
|
for i in range(num_mappings):
|
|
src = src_blocks[i]
|
|
dst1 = dst_blocks[2 * i]
|
|
dst2 = dst_blocks[2 * i + 1]
|
|
block_mapping.append((src, dst1))
|
|
block_mapping.append((src, dst2))
|
|
block_mapping_tensor = torch.tensor(block_mapping,
|
|
dtype=torch.int64,
|
|
device=device).view(-1, 2)
|
|
|
|
for src, dst in block_mapping:
|
|
for ref_cache in ref_caches:
|
|
ref_cache[dst].copy_(ref_cache[src])
|
|
|
|
opcheck(
|
|
torch.ops._C_cache_ops.copy_blocks_mla,
|
|
(kv_caches, block_mapping_tensor),
|
|
test_utils=DEFAULT_OPCHECK_TEST_UTILS,
|
|
)
|
|
ops.copy_blocks_mla(kv_caches, block_mapping_tensor)
|
|
|
|
for kv_cache, ref_cache in zip(kv_caches, ref_caches):
|
|
torch.testing.assert_close(kv_cache, ref_cache)
|
|
|
|
|
|
@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
|
|
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
|
|
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
|
|
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
|
|
@pytest.mark.parametrize("align_cache", [False, True])
|
|
@torch.inference_mode()
|
|
def test_swap_blocks_mla(
|
|
kv_lora_rank: int,
|
|
qk_rope_head_dim: int,
|
|
block_size: int,
|
|
num_blocks: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
device: str,
|
|
kv_cache_dtype: str,
|
|
align_cache: bool,
|
|
) -> None:
|
|
current_platform.seed_everything(seed)
|
|
torch.set_default_device(device)
|
|
|
|
entry_size = kv_lora_rank + qk_rope_head_dim
|
|
|
|
src_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
|
|
kv_cache_dtype, device, align_cache)
|
|
dst_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
|
|
kv_cache_dtype, device, align_cache)
|
|
|
|
_fill_mla_cache(src_cache, kv_cache_dtype)
|
|
_fill_mla_cache(dst_cache, kv_cache_dtype)
|
|
|
|
src_cache_clone = src_cache.clone()
|
|
|
|
num_mappings = min(2, num_blocks // 2)
|
|
src_blocks = random.sample(range(num_blocks), num_mappings)
|
|
remaining_blocks = list(set(range(num_blocks)) - set(src_blocks))
|
|
dst_blocks = random.sample(remaining_blocks, num_mappings)
|
|
block_mapping = list(zip(src_blocks, dst_blocks))
|
|
block_mapping_tensor = torch.tensor(block_mapping,
|
|
dtype=torch.int64,
|
|
device="cpu").view(-1, 2)
|
|
|
|
opcheck(
|
|
torch.ops._C_cache_ops.swap_blocks,
|
|
(src_cache, dst_cache, block_mapping_tensor),
|
|
test_utils=DEFAULT_OPCHECK_TEST_UTILS,
|
|
)
|
|
|
|
ops.swap_blocks(src_cache, dst_cache, block_mapping_tensor)
|
|
|
|
for src, dst in block_mapping:
|
|
torch.testing.assert_close(
|
|
src_cache_clone[src].cpu(),
|
|
dst_cache[dst].cpu(),
|
|
msg=f"Block {src} from src should have been swapped to block "
|
|
f"{dst} in dst_cache.")
|
|
|
|
|
|
@pytest.mark.parametrize("kv_lora_rank", [512])
|
|
@pytest.mark.parametrize("qk_rope_head_dim", [64])
|
|
@pytest.mark.parametrize("block_size", [16])
|
|
@pytest.mark.parametrize("num_blocks", [1024])
|
|
@pytest.mark.parametrize("max_seq_len", [512])
|
|
@pytest.mark.parametrize("batch_size", [8])
|
|
@pytest.mark.parametrize("dtype", [torch.float32])
|
|
@pytest.mark.parametrize("kv_cache_dtype",
|
|
["auto"]) # You can also test "fp8" if needed.
|
|
@pytest.mark.parametrize("align_cache", [True, False])
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@torch.inference_mode()
|
|
def test_gather_cache_mla(kv_lora_rank, qk_rope_head_dim, block_size,
|
|
num_blocks, max_seq_len, batch_size, dtype,
|
|
kv_cache_dtype, align_cache, device):
|
|
entry_size = kv_lora_rank + qk_rope_head_dim
|
|
src_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
|
|
kv_cache_dtype, device, align_cache)
|
|
_fill_mla_cache(src_cache, kv_cache_dtype=kv_cache_dtype)
|
|
|
|
seq_len_tensor = torch.randint(0,
|
|
max_seq_len + 1, (batch_size, ),
|
|
device=device)
|
|
|
|
total_tokens = seq_len_tensor.sum()
|
|
cu_seq_lens = torch.empty((batch_size + 1),
|
|
dtype=torch.int32,
|
|
device=device)
|
|
cu_seq_lens[0] = 0
|
|
cu_seq_lens[1:] = seq_len_tensor.cumsum(dim=0).to(dtype=torch.int32)
|
|
print("seq_len_tensor", seq_len_tensor)
|
|
|
|
tot_blocks_tensor = (seq_len_tensor + block_size - 1) // block_size
|
|
block_table = torch.empty((batch_size, num_blocks),
|
|
dtype=torch.int32,
|
|
device=device)
|
|
|
|
for b in range(batch_size):
|
|
perm = torch.randperm(num_blocks, device=device)
|
|
block_table[b, :] = perm
|
|
|
|
dst = torch.zeros((total_tokens, entry_size),
|
|
dtype=src_cache.dtype,
|
|
device=device)
|
|
|
|
expected_batches = []
|
|
for b in range(batch_size):
|
|
s = seq_len_tensor[b]
|
|
if s == 0:
|
|
continue
|
|
tot = tot_blocks_tensor[b]
|
|
blocks = block_table[b, :tot].tolist()
|
|
|
|
gathered_rows = []
|
|
for i in range(tot - 1):
|
|
gathered_rows.append(src_cache[blocks[i]])
|
|
remaining = s - (tot - 1) * block_size
|
|
gathered_rows.append(src_cache[blocks[-1], :remaining, :])
|
|
|
|
batch_expected = torch.cat(gathered_rows, dim=0)
|
|
expected_batches.append(batch_expected)
|
|
expected = torch.cat(expected_batches, dim=0)
|
|
|
|
opcheck(
|
|
torch.ops._C_cache_ops.gather_cache,
|
|
(src_cache, dst, block_table, cu_seq_lens, batch_size, None),
|
|
test_utils=DEFAULT_OPCHECK_TEST_UTILS,
|
|
)
|
|
|
|
ops.gather_cache(src_cache, dst, block_table, cu_seq_lens, batch_size)
|
|
torch.testing.assert_close(dst, expected)
|