[Experimental] Prefix Caching Support (#1669)
Co-authored-by: DouHappy <2278958187@qq.com> Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
This commit is contained in:
shiyi.c_98
168
tests/kernels/test_prefix_prefill.py
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168
tests/kernels/test_prefix_prefill.py
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import random
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import pytest
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import time
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import torch
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from vllm.model_executor.layers.triton_kernel.prefix_prefill import (
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context_attention_fwd)
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from xformers import ops as xops
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from xformers.ops.fmha.attn_bias import BlockDiagonalCausalFromBottomRightMask
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NUM_HEADS = [12]
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HEAD_SIZES = [128]
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DTYPES = [torch.float16]
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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("dtype", DTYPES)
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@torch.inference_mode()
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def test_contexted_kv_attention(
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num_heads: int,
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head_size: int,
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dtype: torch.dtype,
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) -> None:
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random.seed(0)
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torch.manual_seed(0)
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MAX_SEQ_LEN = 1024
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MAX_CTX_LEN = 1024
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BS = 10
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cache_size = 640
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block_size = 32
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max_block_per_request = 64
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subquery_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
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ctx_lens = [random.randint(16, MAX_CTX_LEN) for _ in range(BS)]
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seq_lens = [a + b for a, b in zip(subquery_lens, ctx_lens)]
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num_tokens = sum(subquery_lens)
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query = torch.empty(num_tokens,
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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query.uniform_(-1e-3, 1e-3)
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output = torch.empty(num_tokens,
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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kv = torch.empty(sum(seq_lens),
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2,
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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kv.uniform_(-1e-3, 1e-3)
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key, value = kv.unbind(dim=1)
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k_cache = torch.zeros(cache_size,
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block_size,
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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v_cache = torch.zeros(cache_size,
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block_size,
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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k = torch.zeros(sum(subquery_lens),
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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v = torch.zeros(sum(subquery_lens),
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num_heads,
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head_size,
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dtype=dtype,
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device='cuda')
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values = torch.arange(0, cache_size, dtype=torch.long, device='cuda')
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values = values[torch.randperm(cache_size)]
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block_table = values[:BS * max_block_per_request].view(
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BS, max_block_per_request)
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b_seq_len = torch.tensor(seq_lens, dtype=torch.long, device='cuda')
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b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long, device='cuda')
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b_start_loc = torch.cumsum(torch.tensor([0] + subquery_lens[:-1],
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dtype=torch.long,
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device='cuda'),
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dim=0)
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max_input_len = MAX_SEQ_LEN
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# copy kv to cache
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b_seq_start_loc = torch.cumsum(torch.tensor([0] + seq_lens[:-1],
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dtype=torch.long,
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device='cuda'),
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dim=0)
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for i in range(BS):
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for j in range(subquery_lens[i]):
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k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
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j])
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v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
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b_ctx_len[i] + j])
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cur_ctx = 0
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block_id = 0
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while cur_ctx < b_ctx_len[i]:
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start_loc = b_seq_start_loc[i] + cur_ctx
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if cur_ctx + block_size > b_ctx_len[i]:
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end_loc = b_seq_start_loc[i] + b_ctx_len[i]
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else:
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end_loc = start_loc + block_size
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start_slot = block_table[i, block_id] * block_size
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end_slot = start_slot + end_loc - start_loc
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k_cache.view(-1, num_heads, head_size)[start_slot:end_slot].copy_(
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key[start_loc:end_loc])
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v_cache.view(-1, num_heads, head_size)[start_slot:end_slot].copy_(
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value[start_loc:end_loc])
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cur_ctx += block_size
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block_id += 1
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# transpose K_cache[num_blocks, block_size, num_kv_heads, head_size]
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# to K_cache[num_blocks, num_kv_heads, head_size/8, block_size, 8]
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k_cache = k_cache.view(-1, block_size, num_heads, head_size // 8,
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8).permute(0, 2, 3, 1, 4).contiguous()
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# transpose V_cache[num_blocks, block_size, num_kv_heads, head_size]
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# to V_cache[num_blocks, num_kv_heads, head_size, block_size]
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v_cache = v_cache.view(-1, block_size, num_heads,
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head_size).permute(0, 2, 3, 1).contiguous()
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context_attention_fwd(query, k, v, output, k_cache, v_cache, block_table,
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b_start_loc, b_seq_len, b_ctx_len, max_input_len)
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torch.cuda.synchronize()
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start_time = time.time()
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context_attention_fwd(query, k, v, output, k_cache, v_cache, block_table,
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b_start_loc, b_seq_len, b_ctx_len, max_input_len)
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torch.cuda.synchronize()
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end_time = time.time()
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print(f"triton Time: {(end_time - start_time)*1000:.2f} ms")
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scale = float(1.0 / (head_size**0.5))
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attn_op = xops.fmha.cutlass.FwOp()
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attn_bias = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
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subquery_lens, seq_lens)
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output_ref = xops.memory_efficient_attention_forward(
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query.unsqueeze(0),
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key.unsqueeze(0),
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value.unsqueeze(0),
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attn_bias=attn_bias,
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p=0.0,
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scale=scale,
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op=attn_op,
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)
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torch.cuda.synchronize()
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start_time = time.time()
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output_ref = xops.memory_efficient_attention_forward(
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query.unsqueeze(0),
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key.unsqueeze(0),
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value.unsqueeze(0),
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attn_bias=attn_bias,
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p=0.0,
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scale=scale,
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op=attn_op,
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)
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torch.cuda.synchronize()
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end_time = time.time()
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print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms")
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output_ref = output_ref.squeeze(0)
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assert torch.allclose(output_ref, output, atol=1e-6, rtol=0)
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