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vllm/tests/kernels/test_flashinfer.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628) 
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**

commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:18:24 2025 -0500

    Add SPDX license headers to python source files
    
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
    also be easily used by tools to help manage license compliance.
    
The Linux Foundation runs license scans against the codebase to help
ensure
    we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
    
    More information can be found on the SPDX site:
    
    - https://spdx.dev/learn/handling-license-info/
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:36:32 2025 -0500

    Check for SPDX headers using pre-commit
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------

Signed-off-by: Russell Bryant <rbryant@redhat.com>
2025-02-02 11:58:18 -08:00

473 lines
18 KiB
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# SPDX-License-Identifier: Apache-2.0
from typing import List, Optional, Tuple
import flashinfer
import pytest
import torch
from vllm.platforms import current_platform
NUM_HEADS = [(16, 16), (32, 8), (64, 8), (6, 1)]
HEAD_SIZES = [128, 256]
BLOCK_SIZES = [16, 32]
DTYPES = [torch.float16, torch.bfloat16]
NUM_BLOCKS = 32768 # Large enough to test overflow in index calculation.
def ref_paged_attn(
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
query_lens: List[int],
kv_lens: List[int],
block_tables: torch.Tensor,
scale: float,
sliding_window: Optional[int] = None,
soft_cap: Optional[float] = None,
) -> torch.Tensor:
num_seqs = len(query_lens)
block_tables = block_tables.cpu().numpy()
_, block_size, num_kv_heads, head_size = key_cache.shape
outputs: List[torch.Tensor] = []
start_idx = 0
for i in range(num_seqs):
query_len = query_lens[i]
kv_len = kv_lens[i]
q = query[start_idx:start_idx + query_len]
q *= scale
num_kv_blocks = (kv_len + block_size - 1) // block_size
block_indices = block_tables[i, :num_kv_blocks]
k = key_cache[block_indices].view(-1, num_kv_heads, head_size)
k = k[:kv_len]
v = value_cache[block_indices].view(-1, num_kv_heads, head_size)
v = v[:kv_len]
if q.shape[1] != k.shape[1]:
k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1)
v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1)
attn = torch.einsum("qhd,khd->hqk", q, k).float()
empty_mask = torch.ones(query_len, kv_len)
mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool()
if sliding_window is not None:
sliding_window_mask = torch.triu(empty_mask,
diagonal=kv_len -
(query_len + sliding_window) +
1).bool().logical_not()
mask |= sliding_window_mask
if soft_cap is not None:
attn = soft_cap * torch.tanh(attn / soft_cap)
attn.masked_fill_(mask, float("-inf"))
attn = torch.softmax(attn, dim=-1).to(v.dtype)
out = torch.einsum("hqk,khd->qhd", attn, v)
outputs.append(out)
start_idx += query_len
return torch.cat(outputs, dim=0)
@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
@torch.inference_mode
def test_flashinfer_decode_with_paged_kv(
kv_lens: List[int],
num_heads: Tuple[int, int],
head_size: int,
dtype: torch.dtype,
block_size: int,
soft_cap: Optional[float],
) -> None:
torch.set_default_device("cuda")
current_platform.seed_everything(0)
num_seqs = len(kv_lens)
num_query_heads = num_heads[0]
num_kv_heads = num_heads[1]
assert num_query_heads % num_kv_heads == 0
max_kv_len = max(kv_lens)
scale = head_size**-0.5
query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
key_value_cache = torch.randn(NUM_BLOCKS,
2,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)
max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
block_tables = torch.randint(0,
NUM_BLOCKS,
(num_seqs, max_num_blocks_per_seq),
dtype=torch.int32)
kv_indptr = [0]
kv_indices = []
kv_last_page_lens = []
for i in range(num_seqs):
seq_len = kv_lens[i]
assert seq_len > 0
num_blocks = (seq_len + block_size - 1) // block_size
kv_indices.extend(block_tables[i, :num_blocks])
kv_indptr.append(kv_indptr[-1] + num_blocks)
kv_last_page_len = seq_len % block_size
if kv_last_page_len == 0:
kv_last_page_len = block_size
kv_last_page_lens.append(kv_last_page_len)
kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
wrapper = flashinfer.\
BatchDecodeWithPagedKVCacheWrapper(workspace_buffer, "NHD",
use_tensor_cores=(
(num_query_heads//num_kv_heads) > 4)
)
wrapper.plan(kv_indptr,
kv_indices,
kv_last_page_lens,
num_query_heads,
num_kv_heads,
head_size,
block_size,
"NONE",
q_data_type=dtype,
kv_data_type=dtype,
logits_soft_cap=soft_cap)
output = wrapper.run(query, key_value_cache)
ref_output = ref_paged_attn(query=query,
key_cache=key_cache,
value_cache=value_cache,
query_lens=[1] * num_seqs,
kv_lens=kv_lens,
block_tables=block_tables,
scale=scale,
soft_cap=soft_cap)
torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2), \
f"{torch.max(torch.abs(output - ref_output))}"
@pytest.mark.parametrize("seq_lens", [[(1, 1328), (5, 18), (129, 463)]])
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
@torch.inference_mode
def test_flashinfer_prefill_with_paged_kv(seq_lens: List[Tuple[int, int]],
num_heads: Tuple[int, int],
head_size: int, dtype: torch.dtype,
block_size: int,
soft_cap: Optional[float]) -> None:
torch.set_default_device("cuda")
current_platform.seed_everything(0)
num_seqs = len(seq_lens)
query_lens = [x[0] for x in seq_lens]
kv_lens = [x[1] for x in seq_lens]
num_query_heads = num_heads[0]
num_kv_heads = num_heads[1]
assert num_query_heads % num_kv_heads == 0
max_kv_len = max(kv_lens)
scale = head_size**-0.5
query = torch.randn(sum(query_lens),
num_query_heads,
head_size,
dtype=dtype)
key_value_cache = torch.randn(NUM_BLOCKS,
2,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)
# Normalize the scale of the key and value caches to mitigate
# numerical instability.
key_cache /= head_size**0.5
value_cache /= head_size**0.5
max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
block_tables = torch.randint(0,
NUM_BLOCKS,
(num_seqs, max_num_blocks_per_seq),
dtype=torch.int32)
qo_indptr = [0]
kv_indptr = [0]
kv_indices = []
kv_last_page_lens = []
for i in range(num_seqs):
seq_len = kv_lens[i]
assert seq_len > 0
num_blocks = (seq_len + block_size - 1) // block_size
kv_indices.extend(block_tables[i, :num_blocks])
kv_indptr.append(kv_indptr[-1] + num_blocks)
kv_last_page_len = seq_len % block_size
if kv_last_page_len == 0:
kv_last_page_len = block_size
kv_last_page_lens.append(kv_last_page_len)
qo_indptr.append(qo_indptr[-1] + query_lens[i])
qo_indptr = torch.tensor(qo_indptr, dtype=torch.int32)
kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, "NHD")
wrapper.plan(
qo_indptr,
kv_indptr,
kv_indices,
kv_last_page_lens,
num_query_heads,
num_kv_heads,
head_size,
block_size,
q_data_type=dtype,
kv_data_type=dtype,
logits_soft_cap=soft_cap,
)
output = wrapper.run(
query,
key_value_cache,
)
ref_output = ref_paged_attn(query=query,
key_cache=key_cache,
value_cache=value_cache,
query_lens=query_lens,
kv_lens=kv_lens,
block_tables=block_tables,
scale=scale,
soft_cap=soft_cap)
torch.testing.assert_close(output, ref_output, atol=5e-2, rtol=1e-2), \
f"{torch.max(torch.abs(output - ref_output))}"
@pytest.mark.parametrize("seq_lens", [[(1, 132), (5, 18)]])
@pytest.mark.parametrize("num_heads", [(32, 8), (6, 1)])
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
def test_flashinfer_prefill_with_paged_fp8_kv(
seq_lens: List[Tuple[int, int]], num_heads: Tuple[int, int],
head_size: int, dtype: torch.dtype, block_size: int,
soft_cap: Optional[float]) -> None:
torch.set_default_device("cuda")
current_platform.seed_everything(0)
num_seqs = len(seq_lens)
query_lens = [x[0] for x in seq_lens]
kv_lens = [x[1] for x in seq_lens]
num_query_heads = num_heads[0]
num_kv_heads = num_heads[1]
assert num_query_heads % num_kv_heads == 0
max_kv_len = max(kv_lens)
scale = head_size**-0.5
kv_cache_dtype = torch.float8_e4m3fn
query = torch.randn(sum(query_lens),
num_query_heads,
head_size,
dtype=dtype)
NUM_BLOCKS_FP8 = 2048
key_value_cache = torch.randn(NUM_BLOCKS_FP8,
2,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
key_cache, value_cache = torch.chunk(key_value_cache, 2, dim=1)
key_cache /= head_size**0.5
value_cache /= head_size**0.5
k_scale = key_cache.amax().item() / 448.0
v_scale = value_cache.amax().item() / 448.0
kv_cache_fp8 = torch.cat([key_cache / k_scale, value_cache / v_scale],
dim=1).to(kv_cache_dtype)
assert (kv_cache_fp8.shape == key_value_cache.shape)
max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
block_tables = torch.randint(0,
NUM_BLOCKS_FP8,
(num_seqs, max_num_blocks_per_seq),
dtype=torch.int32)
qo_indptr = [0]
kv_indptr = [0]
kv_indices = []
kv_last_page_lens = []
for i in range(num_seqs):
seq_len = kv_lens[i]
assert seq_len > 0
num_blocks = (seq_len + block_size - 1) // block_size
kv_indices.extend(block_tables[i, :num_blocks])
kv_indptr.append(kv_indptr[-1] + num_blocks)
kv_last_page_len = seq_len % block_size
if kv_last_page_len == 0:
kv_last_page_len = block_size
kv_last_page_lens.append(kv_last_page_len)
qo_indptr.append(qo_indptr[-1] + query_lens[i])
qo_indptr = torch.tensor(qo_indptr, dtype=torch.int32)
kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, "NHD")
wrapper.plan(
qo_indptr,
kv_indptr,
kv_indices,
kv_last_page_lens,
num_query_heads,
num_kv_heads,
head_size,
block_size,
q_data_type=dtype,
kv_data_type=kv_cache_dtype,
logits_soft_cap=soft_cap,
)
output = wrapper.run(query, kv_cache_fp8, k_scale=k_scale, v_scale=v_scale)
ref_output = ref_paged_attn(query=query,
key_cache=key_cache.squeeze(1),
value_cache=value_cache.squeeze(1),
query_lens=query_lens,
kv_lens=kv_lens,
block_tables=block_tables,
scale=scale,
soft_cap=soft_cap)
del query
del block_tables
# verify prefill fp8
torch.testing.assert_close(output, ref_output, atol=5e-2, rtol=1e-2), \
f"{torch.max(torch.abs(output - ref_output))}"
@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
@pytest.mark.parametrize("num_heads", [(32, 8), (64, 8), (6, 1)])
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
@torch.inference_mode
def test_flashinfer_decode_with_paged_fp8_kv(
kv_lens: List[int],
num_heads: Tuple[int, int],
head_size: int,
dtype: torch.dtype,
block_size: int,
soft_cap: Optional[float],
) -> None:
# test doesn't work for num_heads = (16,16)
torch.set_default_device("cuda")
current_platform.seed_everything(0)
num_seqs = len(kv_lens)
num_query_heads = num_heads[0]
num_kv_heads = num_heads[1]
assert num_query_heads % num_kv_heads == 0
max_kv_len = max(kv_lens)
scale = head_size**-0.5
use_tensor_cores = (num_query_heads // num_kv_heads) > 4
kv_cache_dtype = torch.float8_e4m3fn
query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
NUM_BLOCKS_FP8 = 2048
key_value_cache = torch.randn(NUM_BLOCKS_FP8,
2,
block_size,
num_kv_heads,
head_size,
dtype=dtype)
key_cache, value_cache = torch.chunk(key_value_cache, 2, dim=1)
key_cache /= head_size**0.5
value_cache /= head_size**0.5
k_scale = key_cache.amax().item() / 448.0
v_scale = value_cache.amax().item() / 448.0
key_cache_fp8 = (key_cache / k_scale).to(kv_cache_dtype)
value_cache_fp8 = (value_cache / v_scale).to(kv_cache_dtype)
assert (key_cache_fp8.shape[1] == 1 and value_cache_fp8.shape[1] == 1)
kv_cache_fp8 = torch.cat([key_cache_fp8, value_cache_fp8], dim=1)
max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
block_tables = torch.randint(0,
NUM_BLOCKS_FP8,
(num_seqs, max_num_blocks_per_seq),
dtype=torch.int32)
kv_indptr = [0]
kv_indices = []
kv_last_page_lens = []
for i in range(num_seqs):
seq_len = kv_lens[i]
assert seq_len > 0
num_blocks = (seq_len + block_size - 1) // block_size
kv_indices.extend(block_tables[i, :num_blocks])
kv_indptr.append(kv_indptr[-1] + num_blocks)
kv_last_page_len = seq_len % block_size
if kv_last_page_len == 0:
kv_last_page_len = block_size
kv_last_page_lens.append(kv_last_page_len)
kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
wrapper = flashinfer.\
BatchDecodeWithPagedKVCacheWrapper(workspace_buffer, "NHD",
use_tensor_cores=use_tensor_cores)
wrapper.plan(kv_indptr,
kv_indices,
kv_last_page_lens,
num_query_heads,
num_kv_heads,
head_size,
block_size,
"NONE",
q_data_type=dtype,
kv_data_type=kv_cache_dtype,
logits_soft_cap=soft_cap)
output = wrapper.run(query, kv_cache_fp8, k_scale=k_scale, v_scale=v_scale)
key_cache = key_value_cache[:, 0, :, :, :].squeeze(1)
value_cache = key_value_cache[:, 1, :, :, :].squeeze(1)
ref_output = ref_paged_attn(query=query,
key_cache=key_cache,
value_cache=value_cache,
query_lens=[1] * num_seqs,
kv_lens=kv_lens,
block_tables=block_tables,
scale=scale,
soft_cap=soft_cap)
# Temporary fix: Increasing the tolerance. Seems like a flashinfer issue
torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \
f"{torch.max(torch.abs(output - ref_output))}"
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