mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 20:15:35 +08:00
e489ad7a21
- **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>
473 lines
18 KiB
Python
473 lines
18 KiB
Python
# 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))}"
|