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vllm/tests/kernels/test_prefix_prefill.py
Aleksandr Malyshev e73ff24e31
[ROCM][KERNEL] Paged attention for V1 (#15720) 
Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Signed-off-by: root <root@banff-cyxtera-s65-4.amd.com>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: root <root@banff-cyxtera-s65-4.amd.com>
2025-04-02 19:48:00 -07:00

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# SPDX-License-Identifier: Apache-2.0
import math
import random
import time
from collections.abc import Callable
import pytest
import torch
from xformers import ops as xops
from xformers.ops.fmha.attn_bias import BlockDiagonalCausalFromBottomRightMask
from vllm.attention.backends.xformers import _make_alibi_bias
from vllm.attention.ops.chunked_prefill_paged_decode import (
chunked_prefill_paged_decode)
from vllm.attention.ops.prefix_prefill import context_attention_fwd
from vllm.platforms import current_platform
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
NUM_HEADS = [64]
NUM_QUERIES_PER_KV = [1, 8, 64]
HEAD_SIZES = [128, 96, 24]
DTYPES = [torch.float16]
CUDA_DEVICES = [
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
]
SLIDING_WINDOW = [0, 16, 64, 128, 256, 512, 2048]
KV_CACHE_DTYPES = ["auto", "fp8", "fp8_e5m2"]
OPS = [chunked_prefill_paged_decode, context_attention_fwd]
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPES)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("sliding_window", SLIDING_WINDOW)
@pytest.mark.parametrize("op", OPS)
@torch.inference_mode()
def test_contexted_kv_attention(
num_heads: int,
num_queries_per_kv: int,
head_size: int,
sliding_window: int,
dtype: torch.dtype,
kv_cache_dtype: str,
device: str,
op: Callable,
) -> None:
if 'fp8' in kv_cache_dtype and not current_platform.has_device_capability(
89):
pytest.skip(
'Triton limitation: fp8e4nv data type is not supported on CUDA'
' arch < 89')
current_platform.seed_everything(0)
torch.set_default_device(device)
# Need this, otherwise when we capture the graph the process
# for GPU 1 would run on both GPU0 and GPU1 and things would hang
#
# see also similar issue: https://github.com/Dao-AILab/flash-attention/issues/523
torch.cuda.set_device(device)
MAX_SEQ_LEN = 1024
MAX_CTX_LEN = 1024
BS = 10
cache_size = 640
block_size = 32
max_block_per_request = 64
query_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
# ensure one sequence in batch is a decode
query_lens[-1] = 1
ctx_lens = [random.randint(16, MAX_CTX_LEN) for _ in range(BS)]
seq_lens = [a + b for a, b in zip(query_lens, ctx_lens)]
num_kv_heads = num_heads // num_queries_per_kv
num_tokens = sum(query_lens)
query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
query.uniform_(-1e-3, 1e-3)
output = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype)
kv.uniform_(-1e-3, 1e-3)
key, value = kv.unbind(dim=1)
if kv_cache_dtype == "auto":
cache_dtype = dtype
else:
cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[kv_cache_dtype]
k_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=cache_dtype)
v_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=cache_dtype)
k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
values = torch.arange(0, cache_size, dtype=torch.long)
values = values[torch.randperm(cache_size)]
block_table = values[:BS * max_block_per_request].view(
BS, max_block_per_request)
b_seq_len = torch.tensor(seq_lens, dtype=torch.long)
b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long)
b_start_loc = torch.cumsum(torch.tensor([0] + query_lens,
dtype=torch.long),
dim=0)
max_input_len = MAX_SEQ_LEN
# copy kv to cache
b_seq_start_loc = torch.cumsum(torch.tensor([0] + seq_lens[:-1],
dtype=torch.long),
dim=0)
for i in range(BS):
for j in range(query_lens[i]):
k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
j])
v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
b_ctx_len[i] + j])
cur_ctx = 0
block_id = 0
while cur_ctx < b_ctx_len[i]:
start_loc = b_seq_start_loc[i] + cur_ctx
if cur_ctx + block_size > b_ctx_len[i]:
end_loc = b_seq_start_loc[i] + b_ctx_len[i]
else:
end_loc = start_loc + block_size
start_slot = block_table[i, block_id] * block_size
end_slot = start_slot + end_loc - start_loc
k_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
key[start_loc:end_loc])
v_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
value[start_loc:end_loc])
cur_ctx += block_size
block_id += 1
# transpose K_cache[num_blocks, block_size, num_kv_heads, head_size]
# to K_cache[num_blocks, num_kv_heads, head_size/8, block_size, 8]
k_cache = k_cache.view(-1, block_size, num_kv_heads, head_size // 8,
8).permute(0, 2, 3, 1, 4).contiguous()
# transpose V_cache[num_blocks, block_size, num_kv_heads, head_size]
# to V_cache[num_blocks, num_kv_heads, head_size, block_size]
v_cache = v_cache.view(-1, block_size, num_kv_heads,
head_size).permute(0, 2, 3, 1).contiguous()
k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32, device=device)
# Warm up the Triton kernel by calling it once before actually measuring
# generation time
op(query,
k,
v,
output,
kv_cache_dtype,
k_cache,
v_cache,
block_table,
b_start_loc,
b_seq_len,
MAX_CTX_LEN,
max_input_len,
k_scale,
v_scale,
sliding_window=sliding_window)
torch.cuda.synchronize()
start_time = time.time()
op(query,
k,
v,
output,
kv_cache_dtype,
k_cache,
v_cache,
block_table,
b_start_loc,
b_seq_len,
MAX_CTX_LEN,
max_input_len,
k_scale,
v_scale,
sliding_window=sliding_window)
torch.cuda.synchronize()
end_time = time.time()
print(f"triton Time: {(end_time - start_time)*1000:.2f} ms")
scale = float(1.0 / (head_size**0.5))
attn_op = xops.fmha.cutlass.FwOp()
if num_kv_heads != num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
#
# see also: vllm/model_executor/layers/attention.py
query = query.view(query.shape[0], num_kv_heads, num_queries_per_kv,
query.shape[-1])
key = key[:, :, None, :].expand(key.shape[0], num_kv_heads,
num_queries_per_kv, key.shape[-1])
value = value[:, :,
None, :].expand(value.shape[0], num_kv_heads,
num_queries_per_kv, value.shape[-1])
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
attn_bias = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
query_lens, seq_lens)
if sliding_window > 0:
attn_bias = attn_bias.make_local_attention_from_bottomright(
sliding_window)
output_ref = xops.memory_efficient_attention_forward(
query,
key,
value,
attn_bias=attn_bias,
p=0.0,
scale=scale,
op=attn_op,
)
torch.cuda.synchronize()
start_time = time.time()
output_ref = xops.memory_efficient_attention_forward(
query,
key,
value,
attn_bias=attn_bias,
p=0.0,
scale=scale,
op=attn_op,
)
torch.cuda.synchronize()
end_time = time.time()
print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms")
output_ref = output_ref.reshape(output.shape)
atol = 1e-3 if "fp8" in kv_cache_dtype else 1e-4
torch.testing.assert_close(output, output_ref, atol=atol, rtol=0)
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPES)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("op", OPS)
@torch.inference_mode()
def test_contexted_kv_attention_alibi(
num_heads: int,
num_queries_per_kv: int,
head_size: int,
dtype: torch.dtype,
kv_cache_dtype: str,
device: str,
op: Callable,
) -> None:
if 'fp8' in kv_cache_dtype and not current_platform.has_device_capability(
89):
pytest.skip(
'Triton limitation: fp8e4nv data type is not supported on CUDA'
' arch < 89')
current_platform.seed_everything(0)
torch.set_default_device(device)
# Need this, otherwise when we capture the graph the process
# for GPU 1 would run on both GPU0 and GPU1 and things would hang
#
# see also similar issue: https://github.com/Dao-AILab/flash-attention/issues/523
torch.cuda.set_device(device)
def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
# Fork from: vllm/vllm/model_executor/models/bloom.py#L44
closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
base = torch.tensor(
2**(-(2**-(math.log2(closest_power_of_2) - 3))),
dtype=torch.float32,
)
powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
slopes = torch.pow(base, powers)
if closest_power_of_2 != total_num_heads:
extra_base = torch.tensor(
2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
dtype=torch.float32,
)
num_remaining_heads = min(closest_power_of_2,
total_num_heads - closest_power_of_2)
extra_powers = torch.arange(start=1,
end=1 + 2 * num_remaining_heads,
step=2,
dtype=torch.int32)
slopes = torch.cat(
[slopes, torch.pow(extra_base, extra_powers)], dim=0)
return slopes
alibi_slopes = _get_alibi_slopes(num_heads).to(device)
MAX_SEQ_LEN = 1024
MAX_CTX_LEN = 1024
BS = 10
cache_size = 640
block_size = 32
max_block_per_request = 64
query_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
ctx_lens = [random.randint(16, MAX_CTX_LEN) for _ in range(BS)]
seq_lens = [a + b for a, b in zip(query_lens, ctx_lens)]
num_kv_heads = num_heads // num_queries_per_kv
num_tokens = sum(query_lens)
query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
query.uniform_(-1e-3, 1e-3)
output = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype)
kv.uniform_(-1e-3, 1e-3)
key, value = kv.unbind(dim=1)
if kv_cache_dtype == "auto":
cache_dtype = dtype
else:
cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[kv_cache_dtype]
k_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=cache_dtype)
v_cache = torch.zeros(cache_size,
block_size,
num_kv_heads,
head_size,
dtype=cache_dtype)
k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
values = torch.arange(0, cache_size, dtype=torch.long)
values = values[torch.randperm(cache_size)]
block_table = values[:BS * max_block_per_request].view(
BS, max_block_per_request)
b_seq_len = torch.tensor(seq_lens, dtype=torch.long)
b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long)
b_start_loc = torch.cumsum(torch.tensor([0] + query_lens,
dtype=torch.long),
dim=0)
max_input_len = MAX_SEQ_LEN
# copy kv to cache
b_seq_start_loc = torch.cumsum(torch.tensor([0] + seq_lens[:-1],
dtype=torch.long),
dim=0)
for i in range(BS):
for j in range(query_lens[i]):
k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
j])
v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
b_ctx_len[i] + j])
cur_ctx = 0
block_id = 0
while cur_ctx < b_ctx_len[i]:
start_loc = b_seq_start_loc[i] + cur_ctx
if cur_ctx + block_size > b_ctx_len[i]:
end_loc = b_seq_start_loc[i] + b_ctx_len[i]
else:
end_loc = start_loc + block_size
start_slot = block_table[i, block_id] * block_size
end_slot = start_slot + end_loc - start_loc
k_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
key[start_loc:end_loc])
v_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
value[start_loc:end_loc])
cur_ctx += block_size
block_id += 1
# transpose K_cache[num_blocks, block_size, num_kv_heads, head_size]
# to K_cache[num_blocks, num_kv_heads, head_size/8, block_size, 8]
k_cache = k_cache.view(-1, block_size, num_kv_heads, head_size // 8,
8).permute(0, 2, 3, 1, 4).contiguous()
# transpose V_cache[num_blocks, block_size, num_kv_heads, head_size]
# to V_cache[num_blocks, num_kv_heads, head_size, block_size]
v_cache = v_cache.view(-1, block_size, num_kv_heads,
head_size).permute(0, 2, 3, 1).contiguous()
k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32, device=device)
# Warm up the Triton kernel by calling it once before actually measuring
# generation time
op(query,
k,
v,
output,
kv_cache_dtype,
k_cache,
v_cache,
block_table,
b_start_loc,
b_seq_len,
MAX_CTX_LEN,
max_input_len,
k_scale,
v_scale,
alibi_slopes=alibi_slopes)
torch.cuda.synchronize()
start_time = time.time()
op(query,
k,
v,
output,
kv_cache_dtype,
k_cache,
v_cache,
block_table,
b_start_loc,
b_seq_len,
MAX_CTX_LEN,
max_input_len,
k_scale,
v_scale,
alibi_slopes=alibi_slopes)
torch.cuda.synchronize()
end_time = time.time()
print(f"triton Time: {(end_time - start_time)*1000:.2f} ms")
scale = float(1.0 / (head_size**0.5))
# NOTE(DefTruth): In order to reuse _make_alibi_bias function,
# we have to pad query tensor before MQA/GQA expanding.
if query.shape[0] != key.shape[0]:
query_pad = torch.empty(sum(seq_lens),
num_heads,
head_size,
dtype=dtype)
query_pad.uniform_(-1e-3, 1e-3)
seq_start = 0
query_start = 0
for i, (query_len, seq_len) in enumerate(zip(query_lens, seq_lens)):
seq_end = seq_start + seq_len
query_end = query_start + query_len
query_pad[seq_start:seq_end, ...] = torch.cat([
torch.zeros(
seq_len - query_len, num_heads, head_size, dtype=dtype),
query[query_start:query_end, ...]
],
dim=0)
seq_start += seq_len
query_start += query_len
query = query_pad
if num_kv_heads != num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
#
# see also: vllm/model_executor/layers/attention.py
key = key[:, :, None, :].expand(key.shape[0], num_kv_heads,
num_queries_per_kv, key.shape[-1])
value = value[:, :,
None, :].expand(value.shape[0], num_kv_heads,
num_queries_per_kv, value.shape[-1])
# [seq, num_kv_heads, num_queries_per_kv, dk]=>
# [seq, num_kv_heads*num_queries_per_kv, dk] to comply with rest of the
# codebase. We save some time reshaping alibi matrix at runtime.
key = key.reshape(key.shape[0], -1, key.shape[-1])
value = value.reshape(value.shape[0], -1, value.shape[-1])
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
attn_bias = _make_alibi_bias(alibi_slopes, num_kv_heads, dtype, seq_lens)
output_ref = torch.empty_like(output)
seq_start = 0
query_start = 0
start_time = time.time()
# Attention with alibi slopes.
# FIXME(DefTruth): Because xformers does not support dynamic sequence
# lengths with custom attention bias, we process each prompt one by
# one. This is inefficient, especially when we have many short prompts.
# modified from: vllm/attention/backends/xformers.py#L343
for i, (query_len, seq_len) in enumerate(zip(query_lens, seq_lens)):
seq_end = seq_start + seq_len
query_end = query_start + query_len
out = xops.memory_efficient_attention_forward(query[:,
seq_start:seq_end],
key[:,
seq_start:seq_end],
value[:,
seq_start:seq_end],
attn_bias=attn_bias[i],
p=0.0,
scale=scale)
out = out.view_as(query[:, seq_start:seq_end]).view(
seq_len, num_heads, head_size)
output_ref[query_start:query_end, ...].copy_(out[seq_len - query_len:,
...])
seq_start += seq_len
query_start += query_len
torch.cuda.synchronize()
end_time = time.time()
print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms")
atol = 1e-3 if "fp8" in kv_cache_dtype else 1e-6
torch.testing.assert_close(output, output_ref, atol=atol, rtol=0)
# These tests are optional to only run when explicitly invoked
#
# pytest -v -s --optional \
# tests/kernels/test_prefix_prefill.py::test_contexted_kv_attention_f32
#
# These tests are useful to test model dtype float32 on Turing devices.
# We skip them to not increase the time when running tests on CI
@pytest.mark.optional
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("dtype", [torch.float32])
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPES)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("sliding_window", SLIDING_WINDOW)
@pytest.mark.parametrize("op", OPS)
@torch.inference_mode()
def test_contexted_kv_attention_f32(
num_heads: int,
num_queries_per_kv: int,
head_size: int,
sliding_window: int,
dtype: torch.dtype,
kv_cache_dtype: str,
device: str,
op: Callable,
) -> None:
test_contexted_kv_attention(num_heads, num_queries_per_kv, head_size,
sliding_window, dtype, kv_cache_dtype, device,
op)
@pytest.mark.optional
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("num_queries_per_kv", NUM_QUERIES_PER_KV)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("dtype", [torch.float32])
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPES)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("op", OPS)
@torch.inference_mode()
def test_contexted_kv_attention_alibi_f32(
num_heads: int,
num_queries_per_kv: int,
head_size: int,
dtype: torch.dtype,
kv_cache_dtype: str,
device: str,
op: Callable,
) -> None:
test_contexted_kv_attention_alibi(num_heads, num_queries_per_kv, head_size,
dtype, kv_cache_dtype, device, op)
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