249 lines
8.6 KiB
Python
249 lines
8.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import os
|
|
from typing import ClassVar, Optional, Union
|
|
|
|
import torch
|
|
|
|
import vllm._custom_ops as ops
|
|
from vllm.attention.backends.abstract import (AttentionLayer, AttentionType,
|
|
is_quantized_kv_cache)
|
|
from vllm.logger import init_logger
|
|
from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
|
|
MLACommonImpl,
|
|
MLACommonMetadata,
|
|
MLACommonMetadataBuilder)
|
|
from vllm.v1.attention.backends.utils import AttentionCGSupport
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class CutlassMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
|
|
# enable full CUDA Graph support for decode-only capture
|
|
cudagraph_support: ClassVar[
|
|
AttentionCGSupport] = AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
|
|
|
|
|
|
class CutlassMLABackend(MLACommonBackend):
|
|
|
|
@staticmethod
|
|
def get_name() -> str:
|
|
return "CUTLASS_MLA"
|
|
|
|
@staticmethod
|
|
def get_impl_cls() -> type["CutlassMLAImpl"]:
|
|
return CutlassMLAImpl
|
|
|
|
@staticmethod
|
|
def get_builder_cls() -> type["CutlassMLAMetadataBuilder"]:
|
|
return CutlassMLAMetadataBuilder
|
|
|
|
|
|
class SM100Workspace:
|
|
|
|
def __init__(self, initial_workspace_size):
|
|
self._workspace_buf = torch.empty(initial_workspace_size,
|
|
device="cuda",
|
|
dtype=torch.uint8)
|
|
|
|
self._block_size = 128 # Forced to 128
|
|
|
|
# Pre-compute sm_count to avoid recomputing it. Use device 0 as a proxy
|
|
# (assumes all devices are similar)
|
|
properties = torch.cuda.get_device_properties(torch.device("cuda:0"))
|
|
self._sm_count = properties.multi_processor_count
|
|
|
|
def get_buf(self):
|
|
return self._workspace_buf
|
|
|
|
def ensure_size(self, attn_metadata: MLACommonMetadata,
|
|
num_kv_splits: int):
|
|
batch_size = attn_metadata.num_reqs
|
|
max_seq_len = attn_metadata.max_query_len
|
|
|
|
workspace_size = ops.sm100_cutlass_mla_get_workspace_size(
|
|
max_seq_len * self._block_size,
|
|
batch_size,
|
|
self._sm_count,
|
|
num_kv_splits=num_kv_splits)
|
|
|
|
if self._workspace_buf.shape[0] < workspace_size:
|
|
self._workspace_buf.resize_(workspace_size)
|
|
|
|
|
|
g_sm100_workspace = SM100Workspace(128 * 1024 * 1024) # 128MB
|
|
|
|
MAX_HEADS = 128
|
|
|
|
|
|
class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
|
|
can_return_lse_for_decode: bool = True
|
|
|
|
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],
|
|
attn_type: str,
|
|
kv_sharing_target_layer_name: Optional[str],
|
|
# MLA Specific Arguments
|
|
**mla_args) -> None:
|
|
super().__init__(num_heads,
|
|
head_size,
|
|
scale,
|
|
num_kv_heads,
|
|
alibi_slopes,
|
|
sliding_window,
|
|
kv_cache_dtype,
|
|
logits_soft_cap,
|
|
attn_type,
|
|
kv_sharing_target_layer_name,
|
|
q_pad_num_heads=MAX_HEADS,
|
|
**mla_args)
|
|
|
|
unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
|
|
if any(unsupported_features):
|
|
raise NotImplementedError(
|
|
"CutlassMLAImpl does not support one of the following: "
|
|
"alibi_slopes, sliding_window, logits_soft_cap")
|
|
|
|
if attn_type != AttentionType.DECODER:
|
|
raise NotImplementedError("Encoder self-attention and "
|
|
"encoder/decoder cross-attention "
|
|
"are not implemented for "
|
|
"CutlassMLAImpl")
|
|
|
|
# TODO: Currently, num_kv_splits is limited to 16 to avoid hanging
|
|
# issues. In case the code hangs, use:
|
|
# FORCE_NUM_KV_SPLITS=1
|
|
force_num_kv_splits = os.environ.get("FORCE_NUM_KV_SPLITS", None)
|
|
if force_num_kv_splits:
|
|
logger.warning_once("Forcing num_kv_splits to %d",
|
|
int(force_num_kv_splits))
|
|
self._num_kv_splits = int(force_num_kv_splits)
|
|
else:
|
|
self._num_kv_splits = -1 # => Auto-detect
|
|
|
|
# Share workspace buffer across all executions
|
|
self._workspace = g_sm100_workspace
|
|
|
|
def _sm100_cutlass_mla_decode(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
kv_c_and_k_pe_cache: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
page_table: torch.Tensor,
|
|
workspace: torch.Tensor,
|
|
sm_scale: float,
|
|
num_kv_splits: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
assert (q_nope.ndim == 3
|
|
), f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
|
|
assert (
|
|
q_pe.ndim == 3), f"q_pe must be a 3D tensor, but got {q_pe.ndim}"
|
|
assert (
|
|
kv_c_and_k_pe_cache.ndim == 3
|
|
), "kv_c_and_k_pe_cache must be a 3D tensor, but got {}".format(
|
|
kv_c_and_k_pe_cache.ndim)
|
|
|
|
B_q, H, D_q_nope = q_nope.shape
|
|
B_q_2, H_2, D_q_pe = q_pe.shape
|
|
assert (B_q == B_q_2) and (H == H_2)
|
|
|
|
_, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape
|
|
|
|
D_latent = 512
|
|
D_rope = 64
|
|
assert D_q_nope == D_latent
|
|
assert D_q_pe == D_rope
|
|
assert D_ckv == D_latent + D_rope
|
|
|
|
MAX_HEADS = 128
|
|
assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}"
|
|
|
|
assert len(page_table.shape) == 2
|
|
B_block_table, block_num = page_table.shape
|
|
assert B_block_table == B_q
|
|
assert (block_num
|
|
> 0), f"block num must be greater than 0, got {block_num}"
|
|
assert block_num % (128 / PAGE_SIZE) == 0
|
|
|
|
assert q_nope.dtype in (
|
|
torch.float16, torch.bfloat16, torch.float8_e4m3fn), (
|
|
f"q_nope.dtype needs to be fp16 or bf16 or e4m3 but got "
|
|
f"{q_nope.dtype}.")
|
|
assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype
|
|
assert (
|
|
seq_lens.dtype == torch.int32
|
|
), f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}."
|
|
assert (
|
|
page_table.dtype == torch.int32
|
|
), f"page_table.dtype needs to be int32 but got {page_table.dtype}."
|
|
|
|
dtype = (torch.bfloat16 if is_quantized_kv_cache(self.kv_cache_dtype)
|
|
else q_nope.dtype)
|
|
out = q_nope.new_empty((B_q, MAX_HEADS, D_latent), dtype=dtype)
|
|
lse = (torch.empty(
|
|
(B_q, MAX_HEADS), dtype=torch.float32, device=q_nope.device)
|
|
if self.need_to_return_lse_for_decode else torch.Tensor())
|
|
|
|
ops.sm100_cutlass_mla_decode(
|
|
out,
|
|
lse,
|
|
q_nope,
|
|
q_pe,
|
|
kv_c_and_k_pe_cache,
|
|
seq_lens,
|
|
page_table,
|
|
workspace,
|
|
sm_scale,
|
|
num_kv_splits,
|
|
)
|
|
|
|
if H < MAX_HEADS:
|
|
# Extract the subsets of the outputs
|
|
lse = lse[:, :H] if self.need_to_return_lse_for_decode else lse
|
|
out = out[:, :H]
|
|
|
|
return out, lse
|
|
|
|
def _forward_decode(
|
|
self,
|
|
q: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
|
|
kv_c_and_k_pe_cache: torch.Tensor,
|
|
attn_metadata: MLACommonMetadata,
|
|
layer: AttentionLayer,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
assert kv_c_and_k_pe_cache.numel() > 0
|
|
assert attn_metadata.decode is not None
|
|
|
|
if type(q) is tuple:
|
|
q_nope, q_pe = q
|
|
else:
|
|
q_nope, q_pe = torch.split(
|
|
q, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
|
|
# Adjust workspace size (if necessary)
|
|
self._workspace.ensure_size(attn_metadata, self._num_kv_splits)
|
|
|
|
# Run MLA
|
|
o, lse = self._sm100_cutlass_mla_decode(
|
|
q_nope,
|
|
q_pe,
|
|
kv_c_and_k_pe_cache,
|
|
attn_metadata.decode.seq_lens,
|
|
attn_metadata.decode.block_table,
|
|
self._workspace.get_buf(),
|
|
self.scale,
|
|
self._num_kv_splits,
|
|
)
|
|
|
|
return o, (lse if self.need_to_return_lse_for_decode else None)
|