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Unverified Commit 14006840 authored by Woosuk Kwon's avatar Woosuk Kwon Committed by GitHub
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[V0 Deprecation] Remove V0 FlashInfer attention backend (#22776) 


Signed-off-by: default avatarWoosuk Kwon <woosuk.kwon@berkeley.edu>
parent 66032887
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Showing with 9 additions and 1133 deletions
tests/basic_correctness/test_basic_correctness.py
View file @ 14006840
......@@ -12,7 +12,6 @@ import pytest
import torch
from vllm import LLM, envs
from vllm.platforms import current_platform
from vllm.v1.engine.llm_engine import LLMEngine as LLMEngineV1
from ..conftest import HfRunner, VllmRunner
......@@ -78,11 +77,7 @@ def test_models(
"VLLM_USE_V1") and envs.VLLM_USE_V1:
pytest.skip("enable_prompt_embeds is not supported in v1.")
if backend == "FLASHINFER" and current_platform.is_rocm():
pytest.skip("Flashinfer does not support ROCm/HIP.")
if backend in ("XFORMERS",
"FLASHINFER") and model == "google/gemma-2-2b-it":
if backend == "XFORMERS" and model == "google/gemma-2-2b-it":
pytest.skip(
f"{backend} does not support gemma2 with full context length.")
......@@ -141,8 +136,6 @@ def test_models(
("meta-llama/Llama-3.2-1B-Instruct", "mp", "", "L4", {}),
("distilbert/distilgpt2", "ray", "", "A100", {}),
("distilbert/distilgpt2", "mp", "", "A100", {}),
("distilbert/distilgpt2", "mp", "FLASHINFER", "A100", {}),
("meta-llama/Meta-Llama-3-8B", "ray", "FLASHINFER", "A100", {}),
])
@pytest.mark.parametrize("enable_prompt_embeds", [True, False])
def test_models_distributed(
......
tests/compile/test_basic_correctness.py
View file @ 14006840
......@@ -34,7 +34,7 @@ class TestSetting:
model_args=["--max-model-len", "2048"],
pp_size=2,
tp_size=2,
attn_backend="FLASHINFER",
attn_backend="FLASH_ATTN",
method="generate",
fullgraph=True,
),
......
tests/core/block/e2e/test_correctness_sliding_window.py
View file @ 14006840
......@@ -32,7 +32,7 @@ BLOCK_SIZE = 16
@pytest.mark.parametrize("test_llm_kwargs", [{}])
@pytest.mark.parametrize("batch_size", [5])
@pytest.mark.parametrize("seed", [1])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
def test_sliding_window_retrieval(baseline_llm_generator, test_llm_generator,
batch_size, seed, backend, monkeypatch):
"""
......@@ -43,8 +43,6 @@ def test_sliding_window_retrieval(baseline_llm_generator, test_llm_generator,
Additionally, we compare the results of the v1 and v2 managers.
"""
if backend == "FLASHINFER" and current_platform.is_rocm():
pytest.skip("Flashinfer does not support ROCm/HIP.")
if backend == "XFORMERS" and current_platform.is_rocm():
pytest.skip("Xformers does not support ROCm/HIP.")
......@@ -96,7 +94,7 @@ def test_sliding_window_retrieval(baseline_llm_generator, test_llm_generator,
@pytest.mark.parametrize("test_llm_kwargs", [{"enable_chunked_prefill": True}])
@pytest.mark.parametrize("batch_size", [5])
@pytest.mark.parametrize("seed", [1])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed,
backend, monkeypatch):
"""
......@@ -107,8 +105,6 @@ def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed,
The results with and without chunked prefill are not the same due to
numerical instabilities.
"""
if backend == "FLASHINFER" and current_platform.is_rocm():
pytest.skip("Flashinfer does not support ROCm/HIP.")
if backend == "XFORMERS" and current_platform.is_rocm():
pytest.skip("Xformers does not support ROCm/HIP.")
override_backend_env_variable(monkeypatch, backend)
......
tests/distributed/test_pp_cudagraph.py
View file @ 14006840
......@@ -17,7 +17,6 @@ if TYPE_CHECKING:
])
@pytest.mark.parametrize("ATTN_BACKEND", [
"FLASH_ATTN",
"FLASHINFER",
])
@create_new_process_for_each_test()
def test_pp_cudagraph(
......
tests/kernels/attention/test_attention_selector.py
View file @ 14006840
......@@ -81,6 +81,9 @@ def test_env(
m.setenv(STR_BACKEND_ENV_VAR, name)
m.setenv("VLLM_MLA_DISABLE", "1" if use_mla else "0")
if name == "FLASHINFER" and not use_v1:
pytest.skip("FlashInfer backend is only available on V1 engine")
if device == "cpu":
if not use_v1:
pytest.skip("CPU backend only supports V1")
......
tests/models/quantization/test_fp8.py
View file @ 14006840
......@@ -32,7 +32,7 @@ from ..utils import check_logprobs_close
# Due to low-precision numerical divergence, we only test logprob of 4 tokens
@pytest.mark.parametrize("max_tokens", [4])
@pytest.mark.parametrize("enforce_eager", [True])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"])
@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
# NOTE: Increasing this in this suite will fail CI because we currently cannot
# reset distributed env properly. Use a value > 1 just when you test.
@pytest.mark.parametrize("tensor_parallel_size", [1])
......@@ -57,9 +57,6 @@ def test_models(
numerical sensitive kernels.
"""
if backend == "FLASHINFER" and current_platform.is_rocm():
pytest.skip("Flashinfer does not support ROCm/HIP.")
if kv_cache_dtype == "fp8_e5m2" and current_platform.is_rocm():
pytest.skip(
f"{kv_cache_dtype} is currently not supported on ROCm/HIP.")
......
vllm/attention/backends/flashinfer.py deleted 100644 → 0
View file @ 66032887
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import dataclasses
from collections import defaultdict
from contextlib import contextmanager
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Type
from vllm.multimodal import MultiModalPlaceholderMap
try:
from flashinfer import BatchDecodeWithPagedKVCacheWrapper
from flashinfer.decode import (CUDAGraphBatchDecodeWithPagedKVCacheWrapper,
trtllm_batch_decode_with_kv_cache)
from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
from vllm.vllm_flash_attn import flash_attn_varlen_func
FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
except ImportError:
# Avoid turning these types into variables during type checking
if not TYPE_CHECKING:
BatchDecodeWithPagedKVCacheWrapper = None
CUDAGraphBatchDecodeWithPagedKVCacheWrapper = None
BatchPrefillWithPagedKVCacheWrapper = None
trtllm_batch_decode_with_kv_cache = None
FLASHINFER_WORKSPACE_BUFFER_SIZE = 0
raise ImportError("FlashInfer is not installed. Please install it from "
"https://github.com/flashinfer-ai/flashinfer") from None
import torch
import vllm.envs as envs
from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer,
AttentionMetadata,
AttentionMetadataBuilder,
AttentionState, AttentionType)
from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
compute_slot_mapping_start_idx,
is_block_tables_empty)
from vllm.attention.layer import Attention
from vllm.attention.ops.paged_attn import PagedAttention
from vllm.config import VllmConfig, get_layers_from_vllm_config
from vllm.logger import init_logger
from vllm.utils import (async_tensor_h2d, get_kv_cache_torch_dtype,
make_tensor_with_pad)
from vllm.utils.flashinfer import use_trtllm_attention
logger = init_logger(__name__)
if TYPE_CHECKING:
from vllm.worker.model_runner import ModelInputForGPUBuilder
class FlashInferBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "FLASHINFER"
@staticmethod
def get_impl_cls() -> Type["FlashInferImpl"]:
return FlashInferImpl
@staticmethod
def get_metadata_cls() -> Type["AttentionMetadata"]:
return FlashInferMetadata
@staticmethod
def get_builder_cls() -> Type["FlashInferMetadataBuilder"]:
return FlashInferMetadataBuilder
@staticmethod
def get_state_cls() -> Type["FlashInferState"]:
return FlashInferState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return (num_blocks, 2, block_size, num_kv_heads, head_size)
@staticmethod
def get_kv_cache_stride_order() -> Tuple[int, ...]:
cache_layout = FlashInferState.get_kv_cache_layout()
assert (cache_layout in ("NHD", "HND"))
stride_order = (0, 1, 2, 3, 4) if cache_layout == "NHD" else (0, 1, 3,
2, 4)
return stride_order
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
PagedAttention.copy_blocks(kv_caches, src_to_dists)
@staticmethod
def get_supported_head_sizes() -> List[int]:
return [64, 128, 256]
@staticmethod
def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
if kv_cache_dtype in ("fp8", "fp8_e4m3"):
return torch.float8_e4m3fn
elif kv_cache_dtype == "fp8_e5m2":
return torch.float8_e5m2
else:
raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
@dataclass
class PerLayerParameters:
"""
Currently, FlashInfer backend only support models in which all layers share
the same values for the following hyperparameters.
"""
window_left: int
logits_soft_cap: Optional[float]
sm_scale: float
def get_per_layer_parameters(
vllm_config: VllmConfig) -> Dict[str, PerLayerParameters]:
"""
Scan all attention layers and determine some hyperparameters
to use during `plan`.
"""
layers = get_layers_from_vllm_config(vllm_config, Attention)
per_layer_params: Dict[str, PerLayerParameters] = {}
for key, layer in layers.items():
impl = layer.impl
assert isinstance(impl, FlashInferImpl)
# Infer hyperparameters from the attention layer
window_size = impl.sliding_window
window_left = window_size[0] if window_size is not None else -1
logits_soft_cap = impl.logits_soft_cap
sm_scale = impl.scale
per_layer_params[key] = PerLayerParameters(window_left,
logits_soft_cap, sm_scale)
return per_layer_params
def infer_global_hyperparameters(
per_layer_params: Dict[str, PerLayerParameters]) -> PerLayerParameters:
"""
Currently, FlashInfer backend only support models in which all layers share
the same values for the following hyperparameters:
- `window_left`
- `logits_soft_cap`
- `sm_scale`
So this function asserts that all layers share the same values for these
hyperparameters and returns the global values.
"""
assert len(per_layer_params) > 0, "No attention layers found in the model."
param_sets = list(per_layer_params.values())
global_params = param_sets[0]
for params in param_sets:
assert params == global_params, (
"FlashInfer backend currently only supports models in which all "
"layers share the same values for the following hyperparameters: "
"`window_left`, `logits_soft_cap`, `sm_scale`.")
return global_params
class FlashInferState(AttentionState):
def __init__(self, runner):
self.runner = runner
self._is_graph_capturing = False
self._workspace_buffer = None
self._decode_wrapper = None
self._prefill_wrapper = None
# Global hyperparameters shared by all attention layers
self.global_hyperparameters: Optional[PerLayerParameters] = None
self.vllm_config = self.runner.vllm_config
self._kv_cache_layout = None
def _get_workspace_buffer(self):
if self._workspace_buffer is None:
self._workspace_buffer = torch.zeros(
FLASHINFER_WORKSPACE_BUFFER_SIZE,
dtype=torch.uint8,
device=self.runner.device)
return self._workspace_buffer
@staticmethod
def get_kv_cache_layout():
from vllm.v1.attention.backends.utils import _KV_CACHE_LAYOUT_OVERRIDE
if _KV_CACHE_LAYOUT_OVERRIDE is not None:
logger.info_once("Using KV cache layout %s",
_KV_CACHE_LAYOUT_OVERRIDE)
return _KV_CACHE_LAYOUT_OVERRIDE
cache_layout = envs.VLLM_KV_CACHE_LAYOUT
if cache_layout is None:
logger.info_once("Using default KV cache layout NHD")
return "NHD"
logger.info_once("Using KV cache layout %s", cache_layout)
return cache_layout
def _get_prefill_wrapper(self):
if self._prefill_wrapper is None:
self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
self._get_workspace_buffer(), self.get_kv_cache_layout())
return self._prefill_wrapper
def _get_decode_wrapper(self):
if self._decode_wrapper is None:
num_qo_heads = (self.runner.model_config.get_num_attention_heads(
self.runner.parallel_config))
num_kv_heads = self.runner.model_config.get_num_kv_heads(
self.runner.parallel_config)
use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
num_qo_heads // num_kv_heads > 4)
self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
self._get_workspace_buffer(),
self.get_kv_cache_layout(),
use_tensor_cores=use_tensor_cores)
return self._decode_wrapper
@contextmanager
def graph_capture(self, max_batch_size: int):
self._is_graph_capturing = True
self._graph_decode_wrapper = None
self._graph_slot_mapping = torch.full((max_batch_size, ),
PAD_SLOT_ID,
dtype=torch.long,
device=self.runner.device)
self._graph_seq_lens = torch.ones(max_batch_size,
dtype=torch.int32,
device=self.runner.device)
self._graph_block_tables = torch.from_numpy(
self.runner.graph_block_tables).to(device=self.runner.device)
self._graph_decode_workspace_buffer = self._get_workspace_buffer()
self._graph_indices_buffer = torch.empty(
max_batch_size * self.runner.cache_config.num_gpu_blocks,
dtype=torch.int32,
device=self.runner.device)
self._graph_indptr_buffer = torch.empty(max_batch_size + 1,
dtype=torch.int32,
device=self.runner.device)
self._graph_last_page_len_buffer = torch.empty(
max_batch_size, dtype=torch.int32, device=self.runner.device)
yield
self._is_graph_capturing = False
del self._graph_slot_mapping
del self._graph_seq_lens
del self._graph_block_tables
del self._graph_decode_workspace_buffer
del self._graph_indices_buffer
del self._graph_indptr_buffer
del self._graph_last_page_len_buffer
del self._graph_decode_wrapper
def graph_clone(self, batch_size: int):
assert self._is_graph_capturing
state = self.__class__(self.runner)
state._workspace_buffer = self._graph_decode_workspace_buffer
state._decode_wrapper = self._graph_decode_wrapper
state._prefill_wrapper = self._get_prefill_wrapper()
return state
def graph_capture_get_metadata_for_batch(
self, batch_size: int, is_encoder_decoder_model: bool = False):
assert self._is_graph_capturing
_indptr_buffer = self._graph_indptr_buffer[:batch_size + 1]
_last_page_len_buffer = self._graph_last_page_len_buffer[:batch_size]
num_qo_heads = (self.runner.model_config.get_num_attention_heads(
self.runner.parallel_config))
num_kv_heads = self.runner.model_config.get_num_kv_heads(
self.runner.parallel_config)
use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
num_qo_heads // num_kv_heads > 4)
self._graph_decode_wrapper = \
CUDAGraphBatchDecodeWithPagedKVCacheWrapper(
self._graph_decode_workspace_buffer, _indptr_buffer,
self._graph_indices_buffer, _last_page_len_buffer,
self.get_kv_cache_layout(),
use_tensor_cores)
if self.runner.kv_cache_dtype.startswith("fp8"):
kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
self.runner.kv_cache_dtype)
else:
kv_cache_dtype = get_kv_cache_torch_dtype(
self.runner.kv_cache_dtype, self.runner.model_config.dtype)
paged_kv_indptr_tensor_host = torch.arange(0,
batch_size + 1,
dtype=torch.int32)
paged_kv_indices_tensor_host = torch.arange(0,
batch_size,
dtype=torch.int32)
paged_kv_last_page_len_tensor_host = torch.full((batch_size, ),
self.runner.block_size,
dtype=torch.int32)
query_start_loc_host = torch.arange(0,
batch_size + 1,
dtype=torch.int32)
global_params = infer_global_hyperparameters(
get_per_layer_parameters(self.vllm_config))
attn_metadata = self.runner.attn_backend.make_metadata(
num_prefills=0,
slot_mapping=self._graph_slot_mapping[:batch_size],
multi_modal_placeholder_index_maps=None,
enable_kv_scales_calculation=False,
num_prefill_tokens=0,
num_decode_tokens=batch_size,
max_prefill_seq_len=0,
max_decode_seq_len=0,
seq_lens_tensor=self._graph_seq_lens,
block_tables=self._graph_block_tables,
paged_kv_indptr=paged_kv_indptr_tensor_host,
paged_kv_indices=paged_kv_indices_tensor_host,
paged_kv_last_page_len=paged_kv_last_page_len_tensor_host,
num_qo_heads=num_qo_heads,
num_kv_heads=num_kv_heads,
head_dim=self.runner.model_config.get_head_size(),
page_size=self.runner.block_size,
seq_start_loc=None,
query_start_loc=query_start_loc_host,
device=self.runner.device,
data_type=kv_cache_dtype,
q_data_type=self.runner.model_config.dtype,
use_cuda_graph=True,
decode_wrapper=self._graph_decode_wrapper,
prefill_wrapper=None,
**dataclasses.asdict(global_params),
)
attn_metadata.begin_forward()
return attn_metadata
def get_graph_input_buffers(self,
attn_metadata,
is_encoder_decoder_model: bool = False):
return {
"block_tables": attn_metadata.block_tables,
"seq_lens_tensor": attn_metadata.seq_lens_tensor,
"slot_mapping": attn_metadata.slot_mapping,
}
def prepare_graph_input_buffers(self,
input_buffers,
attn_metadata,
is_encoder_decoder_model: bool = False):
# FlashInfer-specific logic: copy additional tensors
num_total_blocks = attn_metadata.decode_metadata.seq_lens_tensor.shape[
0]
input_buffers["seq_lens_tensor"][:num_total_blocks].copy_(
attn_metadata.seq_lens_tensor, non_blocking=True)
input_buffers["block_tables"][:num_total_blocks].copy_(
attn_metadata.block_tables, non_blocking=True)
def begin_forward(self, model_input):
assert not self._is_graph_capturing
state = self
use_cuda_graph = model_input.attn_metadata.use_cuda_graph
is_decode = model_input.attn_metadata.num_prefills == 0
# In case of multistep chunked-prefill, there might be prefill requests
# scheduled while CUDA graph mode is enabled. We don't run graph in that
# case.
if use_cuda_graph and is_decode:
if model_input.inputs_embeds is None:
batch_size = model_input.input_tokens.shape[0]
state = (
self.runner.graph_runners[model_input.virtual_engine][(
batch_size, False)].attn_state)
else:
batch_size = model_input.inputs_embeds.shape[0]
state = (
self.runner.graph_runners[model_input.virtual_engine][(
batch_size, True)].attn_state)
model_input.attn_metadata.prefill_wrapper = state._get_prefill_wrapper(
)
model_input.attn_metadata.decode_wrapper = state._get_decode_wrapper()
model_input.attn_metadata.begin_forward()
@dataclass
class FlashInferMetadata(AttentionMetadata):
# Maximum sequence length among prefill batch. 0 if there are decoding
# requests only.
max_prefill_seq_len: int
max_decode_seq_len: int
# Number of query tokens for each request in the batch.
# Currently, we require that all requests have the same number of query
# tokens during the decoding phase. When speculavie decoding is enabled,
# decode_query_len might be greater than 1. In all other cases, it is 1.
decode_query_len: Optional[int] = 1
use_cuda_graph: bool = True
prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
# Metadata for the prefill stage
seq_start_loc: Optional[torch.Tensor] = None
query_start_loc: Optional[torch.Tensor] = None
block_tables: Optional[torch.Tensor] = None
# used for GPU operations
seq_lens_tensor: Optional[torch.Tensor] = None
block_table_bound: Optional[torch.Tensor] = None
# An example for paged_kv_indices, paged_kv_indptr:
# request 1, page indices [0, 5, 8]
# request 2, page indices [1, 6, 7]
# request 3, page indices [3, 4]
# paged_kv_indices is a concatenation of page indices of all requests:
# [0, 5, 8, 1, 6, 7, 3, 4]
# paged_kv_indptr is used to index into paged_kv_indices:
# [0, 3, 6, 8]
# The indptr of the paged kv cache, shape: [batch_size + 1]
paged_kv_indptr: Optional[torch.Tensor] = None
# The page indices of the paged kv cache
paged_kv_indices: Optional[torch.Tensor] = None
# The number of entries in the last page of each request in
# the paged kv cache, shape: [batch_size]
paged_kv_last_page_len: Optional[torch.Tensor] = None
# The number of query/output heads
num_qo_heads: Optional[int] = None
# The number of key/value heads
num_kv_heads: Optional[int] = None
# The dimension of the attention heads
head_dim: Optional[int] = None
# Block size of vllm
page_size: Optional[int] = None
# The data type of the paged kv cache
data_type: torch.dtype = None
# The data type of the query
q_data_type: torch.dtype = None
# FlashInfer 0.2 encourages passing host tensors
device: torch.device = torch.device("cpu")
is_profile_run: bool = False
# The FlashInfer backend currently supports only models in which all layers
# share the same following hyperparameters:
# The left (inclusive) window size for the attention window, when
# set to `-1`, the window size will be set to the full length of
# the sequence. Defaults to `-1`.
window_left: int = -1
# The attention logits soft capping value (used in Gemini, Grok and
# Gemma-2, etc.), if not provided, will be set to `0`. If greater
# than 0, the logits will be capped according to formula:
# $$\texttt{logits\_soft\_cap} \times
# \mathrm{tanh}(x / \texttt{logits\_soft\_cap})$$,
# where $x$ is the input logits.
logits_soft_cap: Optional[float] = None
# The scale used in softmax, if not provided, will be set to
# `1.0 / sqrt(head_dim)`.
sm_scale: Optional[float] = None
def __post_init__(self):
# Refer to
# https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
supported_head_sizes = FlashInferBackend.get_supported_head_sizes()
if self.head_dim is not None and self.head_dim \
not in supported_head_sizes:
raise ValueError(
f"Only {supported_head_sizes} are supported for head_dim,",
f" received {self.head_dim}.")
def begin_forward(self):
if self.num_prefill_tokens > 0:
if self.paged_kv_indices is None:
return
assert self.prefill_wrapper is not None
assert self.query_start_loc is not None
assert self.paged_kv_indices is not None
assert self.paged_kv_indptr is not None
assert self.paged_kv_last_page_len is not None
assert self.block_table_bound is not None
assert self.seq_lens_tensor is not None
self.query_start_loc = self.query_start_loc[:self.num_prefills + 1]
batch_size = self.query_start_loc.shape[0] - 1
assert batch_size >= 0
# We will use flash attention for profiling to
# determine the number of blocks. Therefore,
# we don't need to prepare the input for flashinfer for profile run.
if not self.is_profile_run:
self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
self.device)
self.block_table_bound = self.block_table_bound.to(self.device)
self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
self.paged_kv_indices = self.paged_kv_indices.to(self.device)
self.prefill_wrapper.plan(
self.query_start_loc,
self.paged_kv_indptr[:self.num_prefills + 1],
self.paged_kv_indices,
self.paged_kv_last_page_len[:self.num_prefills],
self.num_qo_heads,
self.num_kv_heads,
self.head_dim,
self.page_size,
causal=True,
sm_scale=self.sm_scale,
window_left=self.window_left,
logits_soft_cap=self.logits_soft_cap,
q_data_type=self.q_data_type,
kv_data_type=self.data_type)
if self.num_decode_tokens > 0:
assert self.paged_kv_indices is not None
assert self.paged_kv_indptr is not None
assert self.paged_kv_last_page_len is not None
self.paged_kv_indices = self.paged_kv_indices.to(self.device)
self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
self.device)
# handle model warmup path
if self.block_table_bound is not None:
self.block_table_bound = self.block_table_bound.to(self.device)
if self.seq_lens_tensor is not None:
self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
assert self.decode_wrapper is not None
self.decode_wrapper.plan(
self.paged_kv_indptr[self.num_prefills:],
self.paged_kv_indices,
self.paged_kv_last_page_len[self.num_prefills:],
self.num_qo_heads,
self.num_kv_heads,
self.head_dim,
self.page_size,
# Disable flashinfer's pos encoding and use vllm's rope.
pos_encoding_mode="NONE",
window_left=self.window_left,
logits_soft_cap=self.logits_soft_cap,
sm_scale=self.sm_scale,
# kv-cache data type.
kv_data_type=self.data_type,
# query data type.
q_data_type=self.q_data_type)
def asdict_zerocopy(self,
skip_fields: Optional[Set[str]] = None
) -> Dict[str, Any]:
if skip_fields is None:
skip_fields = set()
# We need to skip the prefill/decode_wrapper field since it cannot be
# broadcasted with nccl when TP is enabled.
skip_fields.add('prefill_wrapper')
skip_fields.add('decode_wrapper')
return super().asdict_zerocopy(skip_fields)
@property
def prefill_metadata(self) -> Optional["FlashInferMetadata"]:
if self.num_prefills == 0:
return None
return self
@property
def decode_metadata(self) -> Optional["FlashInferMetadata"]:
if self.num_decode_tokens == 0:
return None
return self
class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
def __init__(self, input_builder: "ModelInputForGPUBuilder"):
self.input_builder = input_builder
self.runner = input_builder.runner
self.sliding_window = input_builder.sliding_window
self.block_size = input_builder.block_size
# Global hyperparameters shared by all attention layers
self.global_hyperparameters: Optional[PerLayerParameters] = None
self.vllm_config = self.runner.vllm_config
def prepare(self):
self.slot_mapping: List[int] = []
self.prefill_seq_lens: List[int] = []
self.context_lens: List[int] = []
self.block_tables: List[List[int]] = []
self.curr_seq_lens: List[int] = []
self.multimodal_placeholder_maps: Dict[
str,
MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
self.num_prefills = 0
self.num_prefill_tokens = 0
self.num_decode_tokens = 0
# Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
# for the precise definition of the following fields.
# An example:
# request 1, page indices [0, 5, 8]
# request 2, page indices [1, 6, 7]
# request 3, page indices [3, 4]
# paged_kv_indices is a concatenation of page indices of all requests:
# [0, 5, 8, 1, 6, 7, 3, 4]
# paged_kv_indptr is used to index into paged_kv_indices:
# [0, 3, 6, 8]
self.paged_kv_indices: List[int] = []
# 0 at the beginning of paged_kv_indptr indicates the start of the
# first request’s page indices in the paged_kv_indices list.
self.paged_kv_indptr: List[int] = [0]
# paged_kv_last_page_len is the length of the last page of each request
self.paged_kv_last_page_len: List[int] = []
self.total_blocks = 0
self.is_profile_run: bool = False
if self.global_hyperparameters is None:
# Infer global hyperparameters, since currently we only support
# models in which all layers share the same values for the
# following hyperparameters:
# - `window_left`
# - `logits_soft_cap`
# - `sm_scale`
inferred_params = infer_global_hyperparameters(
get_per_layer_parameters(self.vllm_config))
self.global_hyperparameters = inferred_params
self.window_left = inferred_params.window_left
self.logits_soft_cap = inferred_params.logits_soft_cap
self.sm_scale = inferred_params.sm_scale
def _add_seq_group(
self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
chunked_prefill_enabled: bool):
"""Add a sequence group to the metadata. Specifically update/append
1. context length.
2. block table.
3. slot mapping.
"""
is_prompt = inter_data.is_prompt
block_tables = inter_data.block_tables
computed_block_nums = inter_data.computed_block_nums
for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
curr_sliding_window_block) in zip(
inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
inter_data.orig_seq_lens, inter_data.seq_lens,
inter_data.query_lens, inter_data.context_lens,
inter_data.curr_sliding_window_blocks):
self.context_lens.append(context_len)
if is_prompt:
mm_maps = inter_data.multi_modal_placeholder_maps
if mm_maps:
for modality, placeholders in mm_maps.items():
self.multimodal_placeholder_maps[modality].extend(
placeholders)
self.num_prefills += 1
self.num_prefill_tokens += token_len
self.prefill_seq_lens.append(seq_len)
else:
assert query_len == 1, (
"seq_len: {}, context_len: {}, query_len: {}".format(
seq_len, context_len, query_len))
self.num_decode_tokens += query_len
self.curr_seq_lens.append(curr_seq_len)
# Compute block table.
# TODO(sang): Combine chunked prefill and prefix caching by
# only allowing multiple of block_size chunk size.
# NOTE: This only works for oooooooxxx style attention.
block_table = []
if inter_data.prefix_cache_hit:
block_table = computed_block_nums
elif ((chunked_prefill_enabled or not is_prompt)
and block_tables is not None):
block_table = block_tables[seq_id][-curr_sliding_window_block:]
self.block_tables.append(block_table)
is_profile_run = is_block_tables_empty(block_tables)
# Compute slot mapping.
start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
context_len,
self.sliding_window)
compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
seq_len, context_len, start_idx,
self.block_size, inter_data.block_tables)
# It is not necessary to add paged_kv_indices, paged_kv_indptr,
# and paged_kv_last_page_len for profile run because we will
# create dummy inputs.
if is_profile_run:
self.is_profile_run = is_profile_run
return
block_table = block_tables[seq_id]
self._update_paged_kv_tensors(block_table, seq_len)
def _update_paged_kv_tensors(self, block_table: List[int], seq_len: int):
# Get the number of valid blocks based on sequence length.
# If seq_len = 16, block_size = 16,
# block_table_bound is 1 with 1 valid block.
# If seq_len = 15, block_size = 16,
# block_table_bound is 0 + 1 with 1 valid block.
self.total_blocks += len(block_table)
block_table_bound = seq_len // self.block_size + 1 \
if seq_len % self.block_size != 0 \
else seq_len // self.block_size
self.paged_kv_indices.extend(block_table[:block_table_bound])
self.paged_kv_indptr.append(self.paged_kv_indptr[-1] +
block_table_bound)
last_page_len = seq_len % self.block_size
if last_page_len == 0:
last_page_len = self.block_size
self.paged_kv_last_page_len.append(last_page_len)
def build(self, seq_lens: List[int], query_lens: List[int],
cuda_graph_pad_size: int, batch_size: int):
"""Build attention metadata with on-device tensors.
Args:
seq_lens: The maybe padded sequence lengths of the input sequences.
query_lens: The query lengths of the input sequences.
cuda_graph_pad_size: The padding size for cuda graph.
-1 if cuda graph is not used.
batch_size: The maybe padded batch size.
"""
for inter_data in self.input_builder.inter_data_list:
self._add_seq_group(inter_data,
self.input_builder.chunked_prefill_enabled)
device = self.runner.device
use_captured_graph = cuda_graph_pad_size != -1
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
max_decode_seq_len = max(self.curr_seq_lens, default=0)
num_decode_tokens = self.num_decode_tokens
decode_query_len = max(query_lens[self.num_prefills:], default=1)
if use_captured_graph:
self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
self.block_tables.extend([] * cuda_graph_pad_size)
num_decode_tokens = batch_size - self.num_prefill_tokens
# The shape of graph_block_tables is
# [max batch size, max context len // block size].
input_block_tables = self.runner.graph_block_tables[:batch_size]
max_blocks = input_block_tables.shape[1]
for i, block_table in enumerate(self.block_tables):
if block_table:
num_blocks = len(block_table)
if num_blocks <= max_blocks:
input_block_tables[i, :num_blocks] = block_table
else:
# It may be possible to have more blocks allocated due
# to lookahead slots of multi-step, however, they are
# not used anyway, so can be safely ignored.
input_block_tables[
i, :max_blocks] = block_table[:max_blocks]
block_tables = torch.from_numpy(input_block_tables).to(
device, non_blocking=True)
last_paged_kv_indptr = self.paged_kv_indptr[-1]
self.paged_kv_indptr.extend([last_paged_kv_indptr] *
cuda_graph_pad_size)
self.paged_kv_last_page_len.extend([0] * cuda_graph_pad_size)
else:
block_tables = make_tensor_with_pad(
self.block_tables,
pad=0,
dtype=torch.int,
device=device,
)
assert device is not None
seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
self.runner.pin_memory)
query_lens_tensor = async_tensor_h2d(query_lens, torch.long, device,
self.runner.pin_memory)
slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.long,
device, self.runner.pin_memory)
query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
dtype=torch.int32,
device=device)
seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
dtype=torch.int32,
device=device)
placeholder_index_maps = {
modality: placeholder_map.index_map()
for modality, placeholder_map in
self.multimodal_placeholder_maps.items()
}
torch.cumsum(seq_lens_tensor,
dim=0,
dtype=seq_start_loc.dtype,
out=seq_start_loc[1:])
torch.cumsum(query_lens_tensor,
dim=0,
dtype=query_start_loc.dtype,
out=query_start_loc[1:])
if len(self.paged_kv_indptr) > 0:
# extend to the maximum number of blocks as returned by the
# scheduler
self.paged_kv_indices.extend(
[0] * (self.total_blocks - len(self.paged_kv_indices)))
paged_kv_indices_tensor = torch.tensor(self.paged_kv_indices,
device="cpu",
dtype=torch.int)
paged_kv_indptr_tensor = torch.tensor(self.paged_kv_indptr,
device="cpu",
dtype=torch.int)
paged_kv_last_page_len_tensor = torch.tensor(
self.paged_kv_last_page_len, device="cpu", dtype=torch.int)
block_table_bound_tensor = torch.zeros(len(self.paged_kv_indptr) -
1,
device="cpu",
dtype=torch.int)
else:
paged_kv_indices_tensor = None
paged_kv_indptr_tensor = None
paged_kv_last_page_len_tensor = None
block_table_bound_tensor = None
if self.runner.kv_cache_dtype.startswith("fp8"):
kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
self.runner.kv_cache_dtype)
else:
kv_cache_dtype = get_kv_cache_torch_dtype(
self.runner.kv_cache_dtype, self.runner.model_config.dtype)
return FlashInferMetadata(
decode_query_len=decode_query_len,
num_prefills=self.num_prefills,
slot_mapping=slot_mapping_tensor,
multi_modal_placeholder_index_maps=placeholder_index_maps,
enable_kv_scales_calculation=False,
num_prefill_tokens=self.num_prefill_tokens,
num_decode_tokens=num_decode_tokens,
max_prefill_seq_len=max_prefill_seq_len,
max_decode_seq_len=max_decode_seq_len,
block_tables=block_tables,
paged_kv_indptr=paged_kv_indptr_tensor,
paged_kv_indices=paged_kv_indices_tensor,
paged_kv_last_page_len=paged_kv_last_page_len_tensor,
block_table_bound=block_table_bound_tensor,
seq_lens_tensor=seq_lens_tensor,
num_qo_heads=self.runner.model_config.get_num_attention_heads(
self.runner.parallel_config),
num_kv_heads=self.runner.model_config.get_num_kv_heads(
self.runner.parallel_config),
head_dim=self.runner.model_config.get_head_size(),
page_size=self.block_size,
seq_start_loc=seq_start_loc,
query_start_loc=query_start_loc,
device=device,
data_type=kv_cache_dtype,
q_data_type=self.runner.model_config.dtype,
use_cuda_graph=use_captured_graph,
is_profile_run=self.is_profile_run,
window_left=self.window_left,
logits_soft_cap=self.logits_soft_cap,
sm_scale=self.sm_scale,
)
class FlashInferImpl(AttentionImpl):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
) -> None:
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported in V0 "
"FLASHINFER backend.")
if use_irope:
logger.warning_once(
"Using irope in FlashInfer is not supported yet, it will fall"
" back to global attention for long context.")
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_kv_heads
if alibi_slopes is not None:
alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
self.alibi_slopes = alibi_slopes
self.sliding_window = ((sliding_window - 1,
0) if sliding_window is not None else (-1, -1))
self.kv_cache_dtype = kv_cache_dtype
self.logits_soft_cap = logits_soft_cap
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"FlashInferImpl")
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: FlashInferMetadata,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if output_scale is not None:
raise NotImplementedError(
"fused output quantization is not yet supported"
" for FlashInferImpl")
# TODO: directly write to output tensor
num_heads: int = self.num_heads
head_size: int = self.head_size
num_kv_heads: int = self.num_kv_heads
kv_cache_dtype: str = self.kv_cache_dtype
softmax_scale: float = self.scale
window_size = self.sliding_window
alibi_slopes = self.alibi_slopes
logits_soft_cap = self.logits_soft_cap
num_tokens, hidden_size = query.shape
query = query.view(-1, num_heads, head_size)
key = key.view(-1, num_kv_heads, head_size)
value = value.view(-1, num_kv_heads, head_size)
if kv_cache.numel() > 0:
# Use the same reshape and cache kernel as flash attention.
ops.reshape_and_cache_flash(
key,
value,
kv_cache[:, 0],
kv_cache[:, 1],
attn_metadata.slot_mapping.flatten(),
kv_cache_dtype,
layer._k_scale,
layer._v_scale,
)
# The FlashInfer api requires data to be in fp8_e4m3 or fp8_e5m2
# to process the cache when the kv_cache_dtype is fp8
if kv_cache_dtype.startswith("fp8"):
torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
kv_cache_dtype)
kv_cache = kv_cache.view(torch_dtype)
num_prefill_tokens = attn_metadata.num_prefill_tokens
num_decode_tokens = attn_metadata.num_decode_tokens
assert key.shape[0] == num_prefill_tokens + num_decode_tokens, \
f"key : {key.shape} : #prefill tokens {num_prefill_tokens} : #decode tokens {num_decode_tokens}" # noqa
assert value.shape[0] == num_prefill_tokens + num_decode_tokens, \
f"value : {value.shape} : #prefill toks {num_prefill_tokens} : #decode toks {num_decode_tokens}" # noqa
query = query.contiguous(
) # Flashinfer requires query to be contiguous
# Query for decode. KV is not needed because it is already cached.
# QKV for prefill.
decode_query = query[num_prefill_tokens:]
query = query[:num_prefill_tokens]
key = key[:num_prefill_tokens]
value = value[:num_prefill_tokens]
assert query.shape[0] == num_prefill_tokens
assert decode_query.shape[0] == num_decode_tokens
window_left = window_size[0] if window_size is not None else -1
prefill_output: Optional[torch.Tensor] = None
if num_decode_tokens > 0:
decode_output = torch.empty(decode_query.shape,
dtype=decode_query.dtype,
device=decode_query.device)
else:
decode_output = None
stride_order = FlashInferBackend.get_kv_cache_stride_order()
if prefill_meta := attn_metadata.prefill_metadata:
# We will use flash attention for prefill
# when kv_cache is not provided.
# This happens when vllm runs the profiling to
# determine the number of blocks.
if kv_cache.numel() == 0:
prefill_output = flash_attn_varlen_func(
q=query,
k=key,
v=value,
cu_seqlens_q=prefill_meta.seq_start_loc,
cu_seqlens_k=prefill_meta.seq_start_loc,
max_seqlen_q=prefill_meta.max_prefill_seq_len,
max_seqlen_k=prefill_meta.max_prefill_seq_len,
softmax_scale=softmax_scale,
causal=True,
window_size=window_size,
alibi_slopes=alibi_slopes,
)
else:
assert prefill_meta is not None
assert prefill_meta.prefill_wrapper is not None
assert prefill_meta.prefill_wrapper._causal
assert prefill_meta.prefill_wrapper._window_left == window_left
assert prefill_meta.prefill_wrapper._logits_soft_cap == (
logits_soft_cap or 0.0)
assert prefill_meta.prefill_wrapper._sm_scale == softmax_scale
prefill_output = prefill_meta.prefill_wrapper.run(
query,
kv_cache.permute(*stride_order),
k_scale=layer._k_scale_float,
v_scale=layer._v_scale_float,
)
if decode_meta := attn_metadata.decode_metadata:
assert decode_meta is not None
assert decode_meta.decode_wrapper is not None
assert decode_meta.decode_wrapper._window_left == window_left
assert decode_meta.decode_wrapper._logits_soft_cap == (
logits_soft_cap or 0.0)
assert decode_meta.decode_wrapper._sm_scale == softmax_scale
# TODO: @pavanimajety Remove this once the switch happens
# inside flashinfer.
if not use_trtllm_attention(
num_decode_tokens, attn_metadata.max_decode_seq_len,
kv_cache_dtype, attn_metadata.num_qo_heads,
attn_metadata.num_kv_heads, attn_metadata.head_dim):
decode_meta.decode_wrapper.run(
decode_query,
kv_cache.permute(*stride_order),
k_scale=layer._k_scale_float,
v_scale=layer._v_scale_float,
out=decode_output,
)
else:
workspace_buffer = (
decode_meta.decode_wrapper._float_workspace_buffer)
assert FlashInferState.get_kv_cache_layout() == "HND"
trtllm_batch_decode_with_kv_cache(
query=decode_query,
kv_cache=kv_cache.permute(*stride_order),
workspace_buffer=workspace_buffer,
block_tables=attn_metadata.block_tables,
seq_lens=decode_meta.seq_lens_tensor,
max_seq_len=attn_metadata.max_decode_seq_len,
bmm1_scale=layer._k_scale_float * softmax_scale,
bmm2_scale=layer._v_scale_float,
out=decode_output,
)
if prefill_output is None and decode_output is not None:
# Decode only batch.
output, num_tokens = decode_output, num_decode_tokens
elif decode_output is None and prefill_output is not None:
# Prefill only batch.
output, num_tokens = prefill_output, num_prefill_tokens
else:
# Chunked prefill batch does not work with speculative decoding in
# FlashInfer backend, so the query length for decode should be 1.
assert prefill_output is not None
assert decode_output is not None
assert decode_meta is not None
assert decode_meta.decode_query_len == 1
decode_output = decode_output.squeeze(1)
output = torch.cat([prefill_output, decode_output], dim=0)
return output.view(num_tokens, hidden_size)
vllm/platforms/cuda.py
View file @ 14006840
......@@ -350,17 +350,7 @@ class CudaPlatformBase(Platform):
return FLEX_ATTENTION_V1
# Backends for V0 engine
if selected_backend == _Backend.FLASHINFER:
logger.info("Using FlashInfer backend.")
if cls.has_device_capability(100):
from vllm.v1.attention.backends.utils import (
set_kv_cache_layout)
logger.info_once(
"Using HND KV cache layout on V1 engine by default for "
"Blackwell (SM 10.0) GPUs.")
set_kv_cache_layout("HND")
return "vllm.attention.backends.flashinfer.FlashInferBackend"
elif selected_backend == _Backend.XFORMERS:
if selected_backend == _Backend.XFORMERS:
logger.info("Using XFormers backend.")
return "vllm.attention.backends.xformers.XFormersBackend"
elif selected_backend == _Backend.DUAL_CHUNK_FLASH_ATTN:
......@@ -416,10 +406,6 @@ class CudaPlatformBase(Platform):
if (fp8_kv_cache and not flash_attn_supports_fp8()):
logger.info(
"Cannot use FlashAttention backend for FP8 KV cache.")
logger.warning(
"Please use FlashInfer backend with FP8 KV Cache for "
"better performance by setting environment variable "
"VLLM_ATTENTION_BACKEND=FLASHINFER")
target_backend = _Backend.XFORMERS
except ImportError:
logger.info(
......
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