545 lines
20 KiB
Python
545 lines
20 KiB
Python
|
# SPDX-License-Identifier: Apache-2.0
|
|||
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|||
|
|
import math
|
|||
|
|
from dataclasses import dataclass
|
|||
|
|
from typing import TYPE_CHECKING, ClassVar, Optional
|
|||
|
|
|
|||
|
|
import numpy as np
|
|||
|
|
import torch
|
|||
|
|
|
|||
|
|
from vllm import _custom_ops as ops
|
|||
|
|
from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
|
|||
|
|
AttentionMetadata)
|
|||
|
|
from vllm.attention.backends.utils import get_mla_dims
|
|||
|
|
from vllm.attention.ops.flashmla import (flash_mla_sparse_prefill,
|
|||
|
|
flash_mla_with_kvcache,
|
|||
|
|
get_mla_metadata)
|
|||
|
|
from vllm.config import VllmConfig
|
|||
|
|
from vllm.logger import init_logger
|
|||
|
|
from vllm.platforms import current_platform
|
|||
|
|
from vllm.triton_utils import tl, triton
|
|||
|
|
from vllm.utils import cdiv
|
|||
|
|
from vllm.v1.attention.backends.mla.common import MLACommonBaseImpl
|
|||
|
|
from vllm.v1.attention.backends.utils import (AttentionCGSupport,
|
|||
|
|
AttentionMetadataBuilder,
|
|||
|
|
CommonAttentionMetadata)
|
|||
|
|
from vllm.v1.kv_cache_interface import AttentionSpec
|
|||
|
|
|
|||
|
|
if TYPE_CHECKING:
|
|||
|
|
from vllm.model_executor.models.deepseek_v2 import Indexer
|
|||
|
|
|
|||
|
|
logger = init_logger(__name__)
|
|||
|
|
"""
|
|||
|
|
NOTE: FlashMLA Sparse uses an fp8 cache with the following format
|
|||
|
|
|
|||
|
|
In the "FP8 with scale" format, each token's KV cache is 656 Bytes,
|
|||
|
|
structured as:
|
|||
|
|
- **First 512 bytes:** The "quantized NoPE" part, containing 512
|
|||
|
|
`float8_e4m3` values.
|
|||
|
|
- **Next 16 bytes:** Scale factors, containing 4 `float32` values.
|
|||
|
|
The first `float32` is the scale for the first 128 `float8_e4m3` values,
|
|||
|
|
the second for the next 128, and so on.
|
|||
|
|
- **Last 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This
|
|||
|
|
part is not quantized for accuracy.
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _lse2_to_lse(lse_base2: torch.Tensor) -> torch.Tensor:
|
|||
|
|
# Convert base-2 LSE to natural-log LSE
|
|||
|
|
# Keep FP32 for numerical stability during the merge.
|
|||
|
|
return (lse_base2.to(torch.float32) * math.log(2.0))
|
|||
|
|
|
|||
|
|
|
|||
|
|
class FlashMLASparseBackend(AttentionBackend):
|
|||
|
|
|
|||
|
|
accept_output_buffer: bool = True
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_name() -> str:
|
|||
|
|
return "FLASHMLA_SPARSE_VLLM_V1"
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_metadata_cls() -> type[AttentionMetadata]:
|
|||
|
|
return FlashMLASparseMetadata
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_builder_cls() -> type["FlashMLASparseMetadataBuilder"]:
|
|||
|
|
return FlashMLASparseMetadataBuilder
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_impl_cls() -> type["FlashMLASparseImpl"]:
|
|||
|
|
return FlashMLASparseImpl
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_kv_cache_shape(
|
|||
|
|
num_blocks: int,
|
|||
|
|
block_size: int,
|
|||
|
|
num_kv_heads: int, # assumed to be 1 for MLA
|
|||
|
|
head_size: int,
|
|||
|
|
cache_dtype_str: str = "auto",
|
|||
|
|
) -> tuple[int, ...]:
|
|||
|
|
if cache_dtype_str == "fp8_ds_mla":
|
|||
|
|
# custom storage fromat is 656 bytes
|
|||
|
|
# see FlashMLA readme.md for details
|
|||
|
|
return (num_blocks, block_size, 656)
|
|||
|
|
else:
|
|||
|
|
return (num_blocks, block_size, head_size)
|
|||
|
|
|
|||
|
|
@classmethod
|
|||
|
|
def get_supported_dtypes(cls) -> list[torch.dtype]:
|
|||
|
|
return [torch.bfloat16]
|
|||
|
|
|
|||
|
|
@classmethod
|
|||
|
|
def get_supported_head_sizes(cls) -> list[int]:
|
|||
|
|
return [576]
|
|||
|
|
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class MLASparsePrefillMetadata:
|
|||
|
|
# NOTE(Chen): not call it "FlashMLASparsePrefillMetadata" because
|
|||
|
|
# the kernel is not from flashmla
|
|||
|
|
block_table: torch.Tensor
|
|||
|
|
has_context: bool = False
|
|||
|
|
context_lens: Optional[torch.Tensor] = None
|
|||
|
|
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class FlashMLASparseDecodeAndContextMetadata:
|
|||
|
|
scheduler_metadata: torch.Tensor = None
|
|||
|
|
num_splits: torch.Tensor = None
|
|||
|
|
cache_lens: torch.Tensor = None
|
|||
|
|
prefill_context_lengths: Optional[torch.Tensor] = None
|
|||
|
|
prefill_new_k_start_locs: Optional[torch.Tensor] = None
|
|||
|
|
dummy_block_table: torch.Tensor = None
|
|||
|
|
|
|||
|
|
def filter_prefill_indices(
|
|||
|
|
self, indices: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
|||
|
|
assert self.prefill_context_lengths is not None
|
|||
|
|
prefill_context_lengths = self.prefill_context_lengths.unsqueeze(-1)
|
|||
|
|
context_indices = torch.where(indices < prefill_context_lengths,
|
|||
|
|
indices, -1)
|
|||
|
|
new_token_indices = torch.where(indices >= prefill_context_lengths,
|
|||
|
|
indices - prefill_context_lengths, -1)
|
|||
|
|
return context_indices, new_token_indices
|
|||
|
|
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class FlashMLASparseMetadata:
|
|||
|
|
num_reqs: int
|
|||
|
|
max_query_len: int
|
|||
|
|
max_seq_len: int
|
|||
|
|
|
|||
|
|
num_actual_tokens: int # Number of tokens excluding padding.
|
|||
|
|
query_start_loc: torch.Tensor
|
|||
|
|
slot_mapping: torch.Tensor
|
|||
|
|
|
|||
|
|
block_table: torch.Tensor
|
|||
|
|
req_id_per_token: torch.Tensor
|
|||
|
|
block_size: int = 64
|
|||
|
|
topk_tokens: int = 2048
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class FP8KernelMetadata:
|
|||
|
|
scheduler_metadata: Optional[torch.Tensor]
|
|||
|
|
num_splits: torch.Tensor
|
|||
|
|
dummy_block_table: torch.Tensor
|
|||
|
|
cache_lens: torch.Tensor
|
|||
|
|
|
|||
|
|
fp8_extra_metadata: Optional[FP8KernelMetadata] = None
|
|||
|
|
|
|||
|
|
|
|||
|
|
@triton.jit
|
|||
|
|
def _convert_req_index_to_global_index_kernel(
|
|||
|
|
req_id_ptr, # int32 [num_tokens]
|
|||
|
|
block_table_ptr, # int32 [num_requests, max_num_blocks_per_req]
|
|||
|
|
token_indices_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS]
|
|||
|
|
out_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS]
|
|||
|
|
# shapes (compile-time where possible)
|
|||
|
|
max_num_blocks_per_req: tl.constexpr,
|
|||
|
|
BLOCK_SIZE: tl.constexpr,
|
|||
|
|
BLOCK_N: tl.constexpr, # tile width along columns
|
|||
|
|
# strides (in elements)
|
|||
|
|
bt_stride0,
|
|||
|
|
bt_stride1,
|
|||
|
|
ti_stride0,
|
|||
|
|
ti_stride1,
|
|||
|
|
out_stride0,
|
|||
|
|
out_stride1,
|
|||
|
|
):
|
|||
|
|
# program_id(0) -> token_id (row)
|
|||
|
|
# program_id(1) -> tile index along columns
|
|||
|
|
token_id = tl.program_id(0)
|
|||
|
|
tile_id = tl.program_id(1)
|
|||
|
|
|
|||
|
|
# Each program covers BLOCK_N consecutive columns
|
|||
|
|
indice_id = tile_id * BLOCK_N + tl.arange(0, BLOCK_N)
|
|||
|
|
|
|||
|
|
# Load request id for this token (no mask: grid is exact)
|
|||
|
|
req = tl.load(req_id_ptr + token_id)
|
|||
|
|
|
|||
|
|
# Load token indices for this tile
|
|||
|
|
ti_ptr = token_indices_ptr + token_id * ti_stride0 + indice_id * ti_stride1
|
|||
|
|
tok = tl.load(ti_ptr) # int32
|
|||
|
|
|
|||
|
|
# Only token == -1 should propagate as -1
|
|||
|
|
is_invalid_tok = tok < 0
|
|||
|
|
|
|||
|
|
# Compute block id and in-block offset
|
|||
|
|
block_id = tok // BLOCK_SIZE
|
|||
|
|
inblock_off = tok % BLOCK_SIZE
|
|||
|
|
|
|||
|
|
# Guard block_table access
|
|||
|
|
valid_block = block_id < max_num_blocks_per_req
|
|||
|
|
bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1
|
|||
|
|
base = tl.load(bt_ptr, mask=valid_block, other=0)
|
|||
|
|
|
|||
|
|
# If token == -1 OR block_id OOB, output -1; else base * BLOCK_SIZE + offset
|
|||
|
|
out_val = tl.where(is_invalid_tok | (~valid_block), -1,
|
|||
|
|
base * BLOCK_SIZE + inblock_off)
|
|||
|
|
|
|||
|
|
# Store results
|
|||
|
|
out_ptr_ij = out_ptr + token_id * out_stride0 + indice_id * out_stride1
|
|||
|
|
tl.store(out_ptr_ij, out_val)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def triton_convert_req_index_to_global_index(
|
|||
|
|
req_id: torch.Tensor, # int32 [num_tokens]
|
|||
|
|
block_table: torch.
|
|||
|
|
Tensor, # int32 [num_requests, max_num_blocks_per_req]
|
|||
|
|
token_indices: torch.Tensor, # int32 [num_tokens, NUM_TOPK_TOKENS]
|
|||
|
|
BLOCK_SIZE: int = 64,
|
|||
|
|
NUM_TOPK_TOKENS: int = 2048,
|
|||
|
|
BLOCK_N: int = 128, # tile width along columns
|
|||
|
|
):
|
|||
|
|
"""
|
|||
|
|
out[token_id, indice_id] =
|
|||
|
|
block_table[req_id[token_id],
|
|||
|
|
token_indices[token_id, indice_id] // BLOCK_SIZE] * BLOCK_SIZE
|
|||
|
|
+ token_indices[token_id, indice_id] % BLOCK_SIZE
|
|||
|
|
|
|||
|
|
Only when token_indices[token_id, indice_id] == -1 do we output -1.
|
|||
|
|
For safety, we also output -1 if the derived block_id would be
|
|||
|
|
out-of-bounds.
|
|||
|
|
"""
|
|||
|
|
assert req_id.dtype == torch.int32
|
|||
|
|
assert block_table.dtype == torch.int32
|
|||
|
|
assert token_indices.dtype == torch.int32
|
|||
|
|
assert token_indices.shape[1] == NUM_TOPK_TOKENS
|
|||
|
|
assert NUM_TOPK_TOKENS % BLOCK_N == 0, \
|
|||
|
|
f"NUM_TOPK_TOKENS ({NUM_TOPK_TOKENS}) must be divisible by" \
|
|||
|
|
f"BLOCK_N ({BLOCK_N})"
|
|||
|
|
|
|||
|
|
num_tokens = req_id.shape[0]
|
|||
|
|
num_requests, max_num_blocks_per_req = block_table.shape
|
|||
|
|
tiles_per_row = NUM_TOPK_TOKENS // BLOCK_N
|
|||
|
|
|
|||
|
|
# Ensure contiguous tensors on the same device
|
|||
|
|
req_id_c = req_id.contiguous()
|
|||
|
|
block_table_c = block_table.contiguous()
|
|||
|
|
token_indices_c = token_indices.contiguous()
|
|||
|
|
out = torch.empty_like(token_indices_c)
|
|||
|
|
|
|||
|
|
# Strides in elements
|
|||
|
|
bt_stride0, bt_stride1 = block_table_c.stride()
|
|||
|
|
ti_stride0, ti_stride1 = token_indices_c.stride()
|
|||
|
|
out_stride0, out_stride1 = out.stride()
|
|||
|
|
|
|||
|
|
# Exact 2D grid: tokens × column tiles
|
|||
|
|
grid = (num_tokens, tiles_per_row)
|
|||
|
|
|
|||
|
|
_convert_req_index_to_global_index_kernel[grid](
|
|||
|
|
req_id_c,
|
|||
|
|
block_table_c,
|
|||
|
|
token_indices_c,
|
|||
|
|
out,
|
|||
|
|
# shapes / constexprs
|
|||
|
|
max_num_blocks_per_req,
|
|||
|
|
BLOCK_SIZE,
|
|||
|
|
BLOCK_N,
|
|||
|
|
# strides
|
|||
|
|
bt_stride0,
|
|||
|
|
bt_stride1,
|
|||
|
|
ti_stride0,
|
|||
|
|
ti_stride1,
|
|||
|
|
out_stride0,
|
|||
|
|
out_stride1,
|
|||
|
|
)
|
|||
|
|
return out
|
|||
|
|
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class FlashMLASparseMetadataBuilder(
|
|||
|
|
AttentionMetadataBuilder[FlashMLASparseMetadata]):
|
|||
|
|
cudagraph_support: ClassVar[AttentionCGSupport] = \
|
|||
|
|
AttentionCGSupport.UNIFORM_BATCH
|
|||
|
|
|
|||
|
|
def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
|
|||
|
|
vllm_config: VllmConfig, device: torch.device):
|
|||
|
|
|
|||
|
|
cache_config = vllm_config.cache_config
|
|||
|
|
self.kv_cache_spec = kv_cache_spec
|
|||
|
|
self.model_config = vllm_config.model_config
|
|||
|
|
parallel_config = vllm_config.parallel_config
|
|||
|
|
self.device = device
|
|||
|
|
|
|||
|
|
props = torch.cuda.get_device_properties(device)
|
|||
|
|
sm_count = props.multi_processor_count
|
|||
|
|
|
|||
|
|
self.num_heads = self.model_config.get_num_attention_heads(
|
|||
|
|
parallel_config)
|
|||
|
|
self.mla_dims = get_mla_dims(self.model_config)
|
|||
|
|
self.topk_tokens = vllm_config.model_config.hf_config.index_topk
|
|||
|
|
self.use_fp8_kv_cache = cache_config.cache_dtype == "fp8_ds_mla"
|
|||
|
|
self.topk_tokens_tensor = torch.tensor([self.topk_tokens],
|
|||
|
|
device=device,
|
|||
|
|
dtype=torch.int32)
|
|||
|
|
self.max_model_len_tensor = torch.tensor(
|
|||
|
|
[self.model_config.max_model_len],
|
|||
|
|
device=device,
|
|||
|
|
dtype=torch.int32)
|
|||
|
|
# this is ignored by `flash_mla_with_kvcache` if indices not None
|
|||
|
|
self.dummy_block_table = torch.empty((1, 1),
|
|||
|
|
dtype=torch.int32,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
# Equation taken from FlashMLA/csrc/pybind.cpp
|
|||
|
|
h_q, h_k = self.num_heads, 1
|
|||
|
|
s_q = 1 # inversely proportional to s_q, so s_q = 1 is the largest
|
|||
|
|
max_num_sm_parts = int(
|
|||
|
|
max((sm_count // 2) / h_k // (cdiv(h_q // h_k, 2 * 64) * s_q), 1))
|
|||
|
|
if current_platform.is_device_capability(100):
|
|||
|
|
max_num_sm_parts *= 2
|
|||
|
|
self.tile_scheduler_metadata_buffer = torch.empty(
|
|||
|
|
# TileSchedulerMetaDataSize = 8
|
|||
|
|
# see: FlashMLA/csrc/params.h
|
|||
|
|
(max_num_sm_parts, 8),
|
|||
|
|
dtype=torch.int32,
|
|||
|
|
device=device)
|
|||
|
|
self.num_splits_buffer = torch.empty(
|
|||
|
|
# We pack all the tokens into one batch for sparse attention.
|
|||
|
|
# Otherwise, we can exceed the sm of `get_mla_metadata`.
|
|||
|
|
(
|
|||
|
|
2, ),
|
|||
|
|
dtype=torch.int32,
|
|||
|
|
device=device)
|
|||
|
|
self.req_id_per_token_buffer = torch.empty(
|
|||
|
|
(vllm_config.scheduler_config.max_num_batched_tokens, ),
|
|||
|
|
dtype=torch.int32,
|
|||
|
|
device=device)
|
|||
|
|
|
|||
|
|
def build(self,
|
|||
|
|
common_prefix_len: int,
|
|||
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|||
|
|
fast_build: bool = False) -> FlashMLASparseMetadata:
|
|||
|
|
|
|||
|
|
num_tokens = common_attn_metadata.num_actual_tokens
|
|||
|
|
starts = np.asarray(common_attn_metadata.query_start_loc_cpu,
|
|||
|
|
dtype=np.int32)
|
|||
|
|
seg_lengths = np.diff(starts)
|
|||
|
|
req_id_per_token = np.repeat(
|
|||
|
|
np.arange(seg_lengths.shape[0], dtype=np.int32), seg_lengths)
|
|||
|
|
# Zero-fill for cudagraphs
|
|||
|
|
self.req_id_per_token_buffer.fill_(0)
|
|||
|
|
self.req_id_per_token_buffer[:req_id_per_token.shape[0]]\
|
|||
|
|
.copy_(torch.from_numpy(req_id_per_token), non_blocking=True)
|
|||
|
|
req_id_per_token = self.req_id_per_token_buffer[:num_tokens]
|
|||
|
|
|
|||
|
|
fp8_extra_metadata = None
|
|||
|
|
if self.use_fp8_kv_cache:
|
|||
|
|
tile_scheduler_metadata, num_splits = get_mla_metadata(
|
|||
|
|
cache_seqlens=self.topk_tokens_tensor,
|
|||
|
|
num_q_tokens_per_head_k=num_tokens * self.num_heads,
|
|||
|
|
topk=self.topk_tokens,
|
|||
|
|
num_heads_q=self.num_heads,
|
|||
|
|
num_heads_k=1,
|
|||
|
|
is_fp8_kvcache=True,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
num_sm_parts = tile_scheduler_metadata.size(0)
|
|||
|
|
# Copy to persistent buffer for full-CG support
|
|||
|
|
tile_scheduler_metadata_buffer = \
|
|||
|
|
self.tile_scheduler_metadata_buffer[:num_sm_parts]
|
|||
|
|
tile_scheduler_metadata_buffer.copy_(tile_scheduler_metadata)
|
|||
|
|
self.num_splits_buffer.copy_(num_splits)
|
|||
|
|
|
|||
|
|
fp8_extra_metadata = FlashMLASparseMetadata.FP8KernelMetadata(
|
|||
|
|
scheduler_metadata=tile_scheduler_metadata_buffer,
|
|||
|
|
num_splits=self.num_splits_buffer,
|
|||
|
|
# cache_lens and block_table are basically unused in sparse case
|
|||
|
|
# but the decode kernel will treat -1 and indices >= cache_lens
|
|||
|
|
# as invalid so we make sure cache_lens is large enough to not
|
|||
|
|
# accidentally mark indices invalid, we will use -1 exclusively
|
|||
|
|
# to mark invalid indices
|
|||
|
|
cache_lens=self.max_model_len_tensor,
|
|||
|
|
dummy_block_table=self.dummy_block_table)
|
|||
|
|
|
|||
|
|
metadata = FlashMLASparseMetadata(
|
|||
|
|
num_reqs=common_attn_metadata.num_reqs,
|
|||
|
|
max_query_len=common_attn_metadata.max_query_len,
|
|||
|
|
max_seq_len=common_attn_metadata.max_seq_len,
|
|||
|
|
num_actual_tokens=common_attn_metadata.num_actual_tokens,
|
|||
|
|
query_start_loc=common_attn_metadata.query_start_loc,
|
|||
|
|
slot_mapping=common_attn_metadata.slot_mapping,
|
|||
|
|
block_table=common_attn_metadata.block_table_tensor,
|
|||
|
|
req_id_per_token=req_id_per_token,
|
|||
|
|
block_size=self.kv_cache_spec.block_size,
|
|||
|
|
topk_tokens=self.topk_tokens,
|
|||
|
|
fp8_extra_metadata=fp8_extra_metadata,
|
|||
|
|
)
|
|||
|
|
return metadata
|
|||
|
|
|
|||
|
|
|
|||
|
|
class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
topk_indice_buffer: Optional[torch.Tensor] = None,
|
|||
|
|
indexer: Optional["Indexer"] = None,
|
|||
|
|
**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, **mla_args)
|
|||
|
|
self.softmax_scale = scale
|
|||
|
|
assert indexer is not None
|
|||
|
|
self.topk_indices_buffer = indexer.topk_indices_buffer
|
|||
|
|
self.padding = 128 if current_platform.is_device_capability(
|
|||
|
|
100) else 64
|
|||
|
|
|
|||
|
|
def _forward_bf16_kv(
|
|||
|
|
self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor,
|
|||
|
|
topk_indices: torch.Tensor,
|
|||
|
|
attn_metadata: FlashMLASparseMetadata) -> torch.Tensor:
|
|||
|
|
num_tokens = q.shape[0]
|
|||
|
|
kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.view(
|
|||
|
|
-1, 1, kv_c_and_k_pe_cache.shape[-1])
|
|||
|
|
|
|||
|
|
# NOTE(Chen): kernel requires num_local_head to be a multiple of
|
|||
|
|
# 64 on hopper and 128 on blackwell
|
|||
|
|
if self.num_heads % self.padding != 0:
|
|||
|
|
assert self.padding % self.num_heads == 0
|
|||
|
|
logger.warning_once(f"padding num_heads to {self.padding} \
|
|||
|
|
due to sparse attn kernel requirement")
|
|||
|
|
q_padded = q.new_empty((q.shape[0], self.padding, q.shape[2]))
|
|||
|
|
q_padded[:, :self.num_heads, :] = q
|
|||
|
|
q = q_padded
|
|||
|
|
|
|||
|
|
topk_indices = topk_indices.view(num_tokens, 1, -1)
|
|||
|
|
output = flash_mla_sparse_prefill(q, kv_c_and_k_pe_cache, topk_indices,
|
|||
|
|
self.softmax_scale)[0]
|
|||
|
|
output = output[:, :self.num_heads, :]
|
|||
|
|
return output
|
|||
|
|
|
|||
|
|
def _forward_fp8_kv(self, q: torch.Tensor,
|
|||
|
|
kv_c_and_k_pe_cache: torch.Tensor,
|
|||
|
|
topk_indices: torch.Tensor,
|
|||
|
|
attn_metadata: FlashMLASparseMetadata) -> torch.Tensor:
|
|||
|
|
|
|||
|
|
assert attn_metadata.fp8_extra_metadata is not None
|
|||
|
|
extra_metadata = attn_metadata.fp8_extra_metadata
|
|||
|
|
|
|||
|
|
_attn_out, _ = flash_mla_with_kvcache(
|
|||
|
|
q=q.unsqueeze(0), # unsqueeze to add batch_dim
|
|||
|
|
k_cache=kv_c_and_k_pe_cache.view(torch.uint8).unsqueeze(-2),
|
|||
|
|
block_table=extra_metadata.dummy_block_table,
|
|||
|
|
head_dim_v=512,
|
|||
|
|
cache_seqlens=extra_metadata.cache_lens,
|
|||
|
|
tile_scheduler_metadata=extra_metadata.scheduler_metadata,
|
|||
|
|
num_splits=extra_metadata.num_splits,
|
|||
|
|
is_fp8_kvcache=True,
|
|||
|
|
indices=topk_indices.unsqueeze(0), # unsqueeze to add batch_dim
|
|||
|
|
softmax_scale=self.softmax_scale,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return _attn_out
|
|||
|
|
|
|||
|
|
def forward(
|
|||
|
|
self,
|
|||
|
|
layer: AttentionLayer,
|
|||
|
|
q: torch.Tensor,
|
|||
|
|
k_c_normed: torch.Tensor, # key in unified attn
|
|||
|
|
k_pe: torch.Tensor, # value in unified attn
|
|||
|
|
kv_cache: torch.Tensor,
|
|||
|
|
attn_metadata: FlashMLASparseMetadata,
|
|||
|
|
output: Optional[torch.Tensor] = None,
|
|||
|
|
output_scale: Optional[torch.Tensor] = None,
|
|||
|
|
output_block_scale: Optional[torch.Tensor] = None,
|
|||
|
|
) -> torch.Tensor:
|
|||
|
|
# NOTE(lucas): for the sparse FlashMLA kernels the kernels want to use
|
|||
|
|
# MQA 576/512 approach for both prefill and decode
|
|||
|
|
|
|||
|
|
assert output is not None, "Output tensor must be provided."
|
|||
|
|
|
|||
|
|
if output_scale is not None or output_block_scale is not None:
|
|||
|
|
raise NotImplementedError(
|
|||
|
|
"fused output quantization is not yet supported"
|
|||
|
|
" for MLACommonImpl")
|
|||
|
|
|
|||
|
|
if attn_metadata is None:
|
|||
|
|
# The zero fill is required when used with DP + EP
|
|||
|
|
# to ensure all ranks within a DP group compute the
|
|||
|
|
# same expert outputs.
|
|||
|
|
return output.fill_(0)
|
|||
|
|
|
|||
|
|
num_actual_toks = attn_metadata.num_actual_tokens
|
|||
|
|
|
|||
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|||
|
|
|
|||
|
|
q = q[:num_actual_toks, ...]
|
|||
|
|
k_c_normed = k_c_normed[:num_actual_toks, ...]
|
|||
|
|
k_pe = k_pe[:num_actual_toks, ...]
|
|||
|
|
|
|||
|
|
q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
|
|||
|
|
dim=-1)
|
|||
|
|
# Convert from (B, N, P) to (N, B, P)
|
|||
|
|
q_nope = q_nope.transpose(0, 1)
|
|||
|
|
# Multiply (N, B, P) x (N, P, L) -> (N, B, L)
|
|||
|
|
ql_nope = torch.bmm(q_nope, self.W_UK_T)
|
|||
|
|
# Convert from (N, B, L) to (B, N, L)
|
|||
|
|
ql_nope = ql_nope.transpose(0, 1)
|
|||
|
|
|
|||
|
|
topk_indices = self.topk_indices_buffer[:num_actual_toks]
|
|||
|
|
|
|||
|
|
# TODO: handle index / kv_cache correctly
|
|||
|
|
topk_indices_global = triton_convert_req_index_to_global_index(
|
|||
|
|
attn_metadata.req_id_per_token,
|
|||
|
|
attn_metadata.block_table,
|
|||
|
|
topk_indices,
|
|||
|
|
BLOCK_SIZE=attn_metadata.block_size,
|
|||
|
|
NUM_TOPK_TOKENS=attn_metadata.topk_tokens,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
q = torch.cat([ql_nope, q_pe], dim=-1)
|
|||
|
|
|
|||
|
|
# write the latent and rope to kv cache
|
|||
|
|
if kv_cache.numel() > 0:
|
|||
|
|
ops.concat_and_cache_mla(
|
|||
|
|
k_c_normed,
|
|||
|
|
k_pe.squeeze(1),
|
|||
|
|
kv_cache,
|
|||
|
|
attn_metadata.slot_mapping.flatten(),
|
|||
|
|
kv_cache_dtype=self.kv_cache_dtype,
|
|||
|
|
scale=layer._k_scale,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if self.kv_cache_dtype != "fp8_ds_mla":
|
|||
|
|
attn_out = self._forward_bf16_kv(q, kv_cache, topk_indices_global,
|
|||
|
|
attn_metadata)
|
|||
|
|
else:
|
|||
|
|
attn_out = self._forward_fp8_kv(q, kv_cache, topk_indices_global,
|
|||
|
|
attn_metadata)
|
|||
|
|
|
|||
|
|
self._v_up_proj(attn_out, out=output[:num_actual_toks])
|
|||
|
|
return output
|