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[gpt-oss] Add gpt-oss bf16 support

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wangjing
2025-08-13 21:25:57 +08:00
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
# MLA Common Components
This file implements common components for MLA implementations.
First we define:
Sq as Q sequence length
Skv as KV sequence length
MLA has two possible ways of computing, a data-movement friendly approach and a
compute friendly approach, we generally want to use the compute friendly
approach for "prefill" (i.e. the ratio Sq / Skv is "small", is near 1)
and the data-movement friendly approach for "decode" (i.e. the ratio
Sq / Skv is "large").
NOTE what we deem small and large is currently determined by if its labelled
prefill or decode by the scheduler, but this is something we should probably
tune.
Main reference: DeepseekV2 paper, and FlashInfer Implementation
(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
Deepseek's MLA attention works the following way:
* Use a single latent vector to represent the per-token entry of the KV cache.
* For decode (i.e. the memory friendly approach) the attention "simulates" a
multi-head attention, while the compute is similar to multi-query attention.
Below is example of both paths assuming batchsize = 1
## More Extent Definitions:
C Context length, `Skv - Sq`
H hidden size
N number of attention heads
Lq latent dimension for Q 1536 in DSV3
Lkv latent dimension for K/V 512 in DSV3
P nope dimension, no rope. 128 in DSV3
R rope dimension, goes through rope. 64 in DSV3
V V head dim. 128 in DSV3
## Vector/Matrix Definitions
h_t hidden states (input to attention) shape [Sq, H]
q_c latent/compressed Q shape [Sq, Lq]
q_nope uncompressed Q (no-rope) shape [Sq, N, P]
q_pe uncompressed Q (rope) shape [Sq, N, R]
kv_c latent/compressed KV shape [Skv, Lkv]
k_pe decoupled k position embeddings shape [Skv, R]
new_kv_c new kv_c from current iter shape [Sq, Lkv]
new_k_pe new k_pe from current iter shape [Sq, R]
cache_kv_c cached k_c from previous iters shape [C, Lkv]
cache_k_pe cached k_pe from previous iters shape [C, R]
W_DQ project h_t to q_c shape [H, Lq]
W_UQ project q_c to q_nope shape [Lq, N * P]
W_QR project q_c to q_pe shape [Lq, N * R]
W_DKV project h_t to kv_c shape [H, Lkv]
W_UK project kv_c to k_nope shape [Lkv, N, P]
W_KR project h_t to k_pe shape [H, R]
W_UV project kv_c to v shape [Lkv, N, V]
W_O project v to h_t shape [N * V, H]
## Compute Friendly Approach (i.e. "_forward_prefill"):
q_c = h_t @ W_DQ
q_nope = (q_c @ W_UQ).view(Sq, N, P)
q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c = h_t @ W_DKV
new_k_pe = RoPE(h_t @ W_KR)
kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0)
k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0)
k_nope = (kv_c @ W_UK.view(Lkv, N * P)).view(Skv, N, P)
v = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V)
// MHA with QK headdim = P + R
// V headdim = V
// spda_o shape [Sq, N, V]
spda_o = scaled_dot_product_attention(
torch.cat([q_nope, q_pe], dim=-1),
torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
v
)
return spda_o @ W_O
NOTE: in the actual code,
`kv_b_proj` is [W_UK; W_UV] concatenated per head
`q_b_proj` is [W_UQ; W_QR] concatenated per head
`out_proj` is W_O
## Data-Movement Friendly Approach (i.e. "_forward_decode"):
Runtime
q_c = h_t @ W_DQ
q_nope = (q_c @ W_UQ).view(-1, N, P)
ql_nope = einsum("snh,lnh->snl", q, W_UK)
q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c = h_t @ W_DKV
new_k_pe = RoPE(h_t @ W_KR)
kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0)
k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0)
// MQA with QK headdim = Lkv + R
// V headdim = Lkv
// spda_o shape [Sq, N, Lkv]
// NOTE: this is less compute-friendly since Lkv > P
// but is more data-movement friendly since its MQA vs MHA
spda_o = scaled_dot_product_attention(
torch.cat([ql_nope, q_pe], dim=-1),
torch.cat([kv_c, k_pe], dim=-1),
kv_c
)
o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
return o.view(-1, N * V) @ self.num_heads @ W_O
## Chunked Prefill
For chunked prefill we want to use the compute friendly algorithm. We are
assuming sufficiently large Sq / Skv ratio, in the future may want to switch to
the data-movement friendly approach if the chunk (i.e. `Sq`) is small.
However, the compute-friendly approach can potentially run out of memory if Skv
is large due to: `k_nope = (kv_c @ W_UK).view(Skv, N, P)`
To mitigate this, we chunk the computation of attention with respect to the
current context (i.e. `cache_kv_c` and `cache_k_pe`) so that we can used a
fixed workspace size.
The chunked prefill approach is as follows:
MCC Max chunk of context to process per iter, computed dynamically,
used to bound the memory usage
q_c = h_t @ W_DQ
q_nope = (q_c @ W_UQ).view(Sq, N, P)
q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c = h_t @ W_DKV
new_k_pe = RoPE(h_t @ W_KR)
new_k_nope = (new_kv_c @ W_UK.view(Lkv, N * P)).view(Sq, N, P)
new_v = (new_kv_c @ W_UV.view(Lkv, N * V)).view(Sq, N, V)
// MHA between queries and new KV
// with QK headdim = P + R
// V headdim = V
// curr_o shape [Sq, N, V]
// curr_lse shape [N, Sq], this is just order FA returns
curr_o, curr_lse = scaled_dot_product_attention(
torch.cat([q_nope, q_pe], dim=-1),
torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
new_v,
casual=True,
return_softmax_lse=True
)
// Compute attention with the already existing context
for chunk_idx in range(cdiv(C, MCC)):
chunk_start = chunk_idx * MCC
chunk_end = min(chunk_start + MCC, C)
Sc = chunk_end - chunk_start
cache_kv_c_chunk = cache_kv_c[chunk_start:chunk_end]
cache_k_pe_chunk = cache_k_pe[chunk_start:chunk_end]
cache_k_nope_chunk = (cache_kv_c_chunk @ W_UK).view(-1, N, P)
cache_v_chunk = (cache_kv_c_chunk @ W_UV).view(-1, N, V)
chunk_o, chunk_lse = scaled_dot_product_attention(
torch.cat([q_nope, q_pe], dim=-1),
torch.cat([cache_k_nope_chunk,
cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)],
dim=-1),
cache_v_chunk,
casual=False,
return_softmax_lse=True
)
curr_o, curr_lse = merge_attn_states(
suffix_output=curr_o,
suffix_lse=curr_lse,
prefix_output=chunk_o,
prefix_lse=chunk_lse,
)
return curr_o @ W_O
"""
import functools
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Generic, Optional, TypeVar
import torch
from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
AttentionMetadata,
MLAAttentionImpl)
from vllm.attention.backends.utils import get_mla_dims
from vllm.attention.ops.merge_attn_states import merge_attn_states
# from vllm.attention.utils.fa_utils import get_flash_attn_version
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
LinearBase,
UnquantizedLinearMethod)
from vllm.platforms import current_platform
from vllm.utils import cdiv, round_down
from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
CommonAttentionMetadata)
from vllm.v1.kv_cache_interface import AttentionSpec
from vllm.v1.worker.block_table import BlockTable
try:
from vllm.vllm_flash_attn import flash_attn_varlen_func
is_vllm_fa = True
except ImportError:
# For rocm use upstream flash attention
from flash_attn import flash_attn_varlen_func
is_vllm_fa = False
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.worker.gpu_input_batch import InputBatch
from vllm.v1.worker.gpu_model_runner import GPUModelRunner
from vllm import envs
logger = init_logger(__name__)
def get_flash_attn_version():
return None
class MLACommonBackend(AttentionBackend):
accept_output_buffer: bool = True
@staticmethod
def get_name() -> str:
return "TRITON_MLA_VLLM_V1"
@staticmethod
def get_metadata_cls() -> type["AttentionMetadata"]:
return MLACommonMetadata
@staticmethod
def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
return MLACommonMetadataBuilder
@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,
) -> tuple[int, ...]:
return (num_blocks, block_size, head_size)
@staticmethod
def get_supported_head_sizes() -> list[int]:
return [576]
@dataclass
class MLACommonPrefillMetadata:
""" Prefill Specific Metadata """
@dataclass
class ChunkedContextMetadata:
# New for MLA (compared to FlashAttention)
# For handling chunked prefill
cu_seq_lens: torch.Tensor
starts: torch.Tensor
seq_tot: list[int]
max_seq_lens: list[int]
workspace: torch.Tensor
block_table: torch.Tensor
query_start_loc: torch.Tensor
max_query_len: int
chunked_context: Optional[ChunkedContextMetadata] = None
@dataclass
class MLACommonDecodeMetadata:
block_table: torch.Tensor
seq_lens: torch.Tensor
D = TypeVar("D", bound=MLACommonDecodeMetadata)
@dataclass
class MLACommonMetadata(Generic[D]):
"""Metadata for MLACommon.
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
# NOTE(sang): Definition of context_len, query_len, and seq_len.
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ---------------------|
# |-- query_len ---|
num_actual_tokens: int # Number of tokens excluding padding.
query_start_loc: torch.Tensor
slot_mapping: torch.Tensor
# New for MLA (compared to FlashAttention)
# For handling prefill decode split
num_decodes: int
num_decode_tokens: int
num_prefills: int
# The dimension of the attention heads
head_dim: Optional[int] = None
decode: Optional[D] = None
prefill: Optional[MLACommonPrefillMetadata] = None
def __post_init__(self):
supported_head_sizes = MLACommonBackend.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}.")
M = TypeVar("M", bound=MLACommonMetadata)
class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
"""
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
def __init__(self,
runner: "GPUModelRunner",
kv_cache_spec: AttentionSpec,
block_table: BlockTable,
metadata_cls: Optional[type[M]] = None):
self.metadata_cls = metadata_cls \
if metadata_cls is not None else MLACommonMetadata
self.runner = runner
scheduler_config = runner.scheduler_config
model_config = runner.model_config
cache_config = runner.cache_config
self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
self.num_heads = model_config.get_num_attention_heads(
runner.parallel_config)
self.mla_dims = get_mla_dims(model_config)
self.aot_schedule = False # current_platform.is_cuda()
self.kv_cache_spec = kv_cache_spec
# Dont try to access the runner on AMD
if self.aot_schedule:
self.page_size = self.kv_cache_spec.block_size
if self.chunked_prefill_enabled:
self.chunked_prefill_workspace_size = min(
# Max sure there is enough for 8 full length request or at least
# 4 pages of cache per request
max(
8 * model_config.max_model_len, 4 *
scheduler_config.max_num_seqs * cache_config.block_size),
# For long-context models try not to over-allocate limiting
# kv-cache space, limiting it to 64k tokens,
# which would result in the workspace being:
# 2*(576)*(64*1024) = 144mb
# (assuming 576 MLA head dim, and fp16)
# which would result in up-projected context being
# 2*(192*128)*(64*1024) = 3gb
# (assuming 192 QK head dim, 128 heads, and fp16)
128 * 1024)
assert self.chunked_prefill_workspace_size >= \
scheduler_config.max_num_seqs * cache_config.block_size
self.chunked_prefill_workspace = torch.empty(
(self.chunked_prefill_workspace_size,
model_config.get_head_size()),
dtype=model_config.dtype,
device=runner.device,
)
self.block_table = block_table
def reorder_batch(self, input_batch: "InputBatch",
scheduler_output: "SchedulerOutput") -> bool:
# We now want to reorder the batch so that the "decode" requests are and
# the front and the "prefill" requests are at the using the least amount
# swaps possible. (NOTE for now we loosely use "decode" to mean requests
# where attention is likely memory-bound and "prefill" to mean requests
# where attention is likely compute-bound, TODO(lucas): figure out a
# better naming here)
decodes = []
prefills = []
num_decode_tokens = 0
num_prefill_tokens = 0
for i, req_id in enumerate(input_batch.req_ids):
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
# for now treat 1 scheduled token as "decode" even if its not,
# we should update this to something like < 8 in the future but
# currently the TritonMLA._forward_decode only supports
# num_tokens = 1
if num_tokens == 1:
decodes.append(i)
num_decode_tokens += num_tokens
else:
prefills.append(i)
num_prefill_tokens += num_tokens
# We hope that this is fairly minimal since decodes
# should be around for a number of iterations so hopefully they are
# relatively stationary (and new request are generally appended to the
# persistent batch so already should be at the back)
# To achieve this we loop over the decodes in descending order and
# the prefills in ascending order. We swap decodes from the "back"
# i.e. past where the last decode should be in the reodorered with
# prefills from the front of the batch.
# `decodes` and `prefills` are already in ascending order just based on
# the above loop
num_decodes = len(decodes)
num_prefills = len(prefills)
modified_batch = False
for i in range(1, min(num_decodes, num_prefills) + 1):
# If the decode is at the "back" of the batch, i, we can swap it
# with the prefill closest to the front of the batch
decode_idx = decodes[num_decodes - i]
if decode_idx < num_decodes:
break
input_batch.swap_states(prefills[i - 1], decode_idx)
modified_batch = True
# Save for next `build` call
# TODO(lucas): this is a bit of a hack, we should probably have a
# better way of doing this
self._num_decodes = num_decodes
self._num_prefills = num_prefills
self._num_decode_tokens = num_decode_tokens
self._num_prefill_tokens = num_prefill_tokens
return modified_batch
def _build_decode(self, block_table_tensor: torch.Tensor,
seq_lens: torch.Tensor):
return MLACommonDecodeMetadata(
block_table=block_table_tensor,
seq_lens=seq_lens,
)
def build_for_cudagraph_capture(
self, common_attn_metadata: CommonAttentionMetadata) -> M:
"""
This method builds the metadata for full cudagraph capture.
Currently, only decode is supported for full cudagraphs with MLA.
"""
m = common_attn_metadata
assert m.num_reqs == m.num_actual_tokens, \
"MLA only supports decode-only full CUDAGraph capture. " \
"Make sure all cudagraph capture sizes <= max_num_seq."
m.max_query_len = 1 # decode-only
# Update state usually set in reorder_batch.
self._num_decodes = m.num_reqs
self._num_decode_tokens = m.num_actual_tokens
self._num_prefills = 0
self._num_prefill_tokens = 0
return self.build(0, m)
def build(self, common_prefix_len: int,
common_attn_metadata: CommonAttentionMetadata) -> M:
num_reqs = common_attn_metadata.num_reqs
num_actual_tokens = common_attn_metadata.num_actual_tokens
max_query_len = common_attn_metadata.max_query_len
assert self._num_decodes + self._num_prefills == num_reqs
# Note(simon): be careful about the CPU <> GPU memory movement in this
# function. We should avoid GPU -> CPU sync as much as possible because
# it blocks on all previous kernels.
device = self.runner.device
block_table = self.block_table
block_table_tensor = block_table.get_device_tensor()[:num_reqs]
block_table.slot_mapping[:num_actual_tokens].copy_(
block_table.slot_mapping_cpu[:num_actual_tokens],
non_blocking=True)
block_table.slot_mapping[num_actual_tokens:].fill_(-1)
slot_mapping = block_table.slot_mapping[:num_actual_tokens]
query_start_loc = common_attn_metadata.query_start_loc
seq_lens = common_attn_metadata.seq_lens
prefill_metadata = None
if self._num_prefills > 0:
reqs_start = self._num_decodes # prefill_start
context_lens_cpu = self.runner.input_batch.\
num_computed_tokens_cpu_tensor[reqs_start:num_reqs]
max_context_len_cpu = context_lens_cpu.max().item()
num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
prefill_query_start_loc = query_start_loc[
reqs_start:] - query_start_loc[reqs_start]
chunked_context_metadata = None
if self.chunked_prefill_enabled and self._num_prefills > 0 \
and max_context_len_cpu > 0:
# NOTE: it is recommend you read the `Chunked Prefill` section
# in the comment at the top of the file before trying to
# understand the following code
# currently we allocate an equal amount of workspace for each
# prefill in the batch, we could probably use a more advanced
# algorithm here and allocate more workspace to prefills with
# longer context lengths
max_context_chunk = (self.chunked_prefill_workspace_size //
num_prefills_with_context_cpu)
if self.aot_schedule:
# align max_context_chunk to page_size by rounding down,
# currently the `gather_cache` kernel cannot handle
# `context_chunk_starts` that are not aligned to page_size
max_context_chunk = round_down(max_context_chunk,
self.page_size)
assert max_context_chunk > 0
num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
# if `max_context_chunk = 256`, `num_chunks = 3`, and
# `num_prefills_with_context = 4`, create a tensor that looks
# like
# [[0, 0, 0, 0], [256, 256, 256, 256], [512, 512, 512, 512]]
# Note(simon): this is done in CPU because of downstream's
# of `to_list`.
chunk_starts = \
torch.arange(num_chunks, dtype=torch.int32) \
.unsqueeze(1).expand(-1, self._num_prefills) \
* max_context_chunk
chunk_ends = torch.min(context_lens_cpu.unsqueeze(0),
chunk_starts + max_context_chunk)
chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
cu_seq_lens_cpu = torch.zeros(num_chunks,
self._num_prefills + 1,
dtype=torch.int32,
pin_memory=True)
torch.cumsum(chunk_seq_lens,
dim=1,
out=cu_seq_lens_cpu[:, 1:],
dtype=torch.int32)
chunked_context_metadata = \
MLACommonPrefillMetadata.ChunkedContextMetadata(
cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
starts=chunk_starts.to(device, non_blocking=True),
seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
workspace=self.chunked_prefill_workspace,
)
assert max(chunked_context_metadata.max_seq_lens) <= \
self.chunked_prefill_workspace_size
prefill_metadata = MLACommonPrefillMetadata(
block_table=block_table_tensor[reqs_start:, ...],
query_start_loc=prefill_query_start_loc,
max_query_len=max_query_len,
chunked_context=chunked_context_metadata,
)
decode_metadata = None
if self._num_decodes > 0:
decode_metadata = self._build_decode(
block_table_tensor=block_table_tensor[:self._num_decodes, ...],
seq_lens=seq_lens[:self._num_decodes],
)
return self.metadata_cls(
num_actual_tokens=num_actual_tokens,
query_start_loc=query_start_loc,
slot_mapping=slot_mapping,
head_dim=self.runner.model_config.get_head_size(),
# MLACommonMetadata Chunk prefill specific
num_decodes=self._num_decodes,
num_decode_tokens=self._num_decode_tokens,
num_prefills=self._num_prefills,
prefill=prefill_metadata,
decode=decode_metadata,
)
def can_run_in_cudagraph(
self, common_attn_metadata: CommonAttentionMetadata) -> bool:
return common_attn_metadata.max_query_len == 1
class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
"""
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
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,
blocksparse_params: Optional[dict[str, Any]],
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str],
# MLA Specific Arguments
q_lora_rank: Optional[int],
kv_lora_rank: int,
qk_nope_head_dim: int,
qk_rope_head_dim: int,
qk_head_dim: int,
v_head_dim: int,
kv_b_proj: ColumnParallelLinear,
) -> None:
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported for MLA")
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_kv_heads
self.kv_cache_dtype = kv_cache_dtype
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_head_dim = qk_head_dim
self.v_head_dim = v_head_dim
self.kv_b_proj = kv_b_proj
self.vllm_flash_attn_version = get_flash_attn_version()
# Handle the differences between the flash_attn_varlen from flash_attn
# and the one from vllm_flash_attn. The former is used on RoCM and the
# latter has an additional parameter to control FA2 vs FA3
self.flash_attn_varlen_func = flash_attn_varlen_func
self.vllm_flash_attn_version = get_flash_attn_version()
if self.vllm_flash_attn_version is not None:
self.flash_attn_varlen_func = \
functools.partial(flash_attn_varlen_func,
fa_version=self.vllm_flash_attn_version)
# For MLA the v head dim is smaller than qk head dim so we pad out
# v with 0s to match the qk head dim for attention backends that do
# not support different headdims
# We don't need to pad V if we are on a hopper system with FA3
self._pad_v = self.vllm_flash_attn_version is None or not (
self.vllm_flash_attn_version == 3
and current_platform.get_device_capability()[0] == 9)
def _flash_attn_varlen_diff_headdims(self,
q,
k,
v,
return_softmax_lse=False,
softmax_scale=None,
**kwargs):
maybe_padded_v = v
if self._pad_v:
maybe_padded_v = torch.nn.functional.pad(
v, [0, q.shape[-1] - v.shape[-1]], value=0)
if is_vllm_fa:
attn_out = self.flash_attn_varlen_func(
q=q,
k=k,
v=maybe_padded_v,
return_softmax_lse=return_softmax_lse,
softmax_scale=softmax_scale,
**kwargs,
)
else:
# Use return_attn_probs instead of return_softmax_lse for RoCM
attn_out = self.flash_attn_varlen_func(
q=q,
k=k,
v=maybe_padded_v,
return_attn_probs=return_softmax_lse,
softmax_scale=softmax_scale,
**kwargs,
)
# Unpack the output if there is multiple results
lse = None
if isinstance(attn_out, tuple):
attn_out, lse = attn_out[0], attn_out[1]
# Remain consistent with old `flash_attn_varlen_func` where there
# is only one output tensor if `return_softmax_lse` is False.
if return_softmax_lse:
return attn_out, lse
return attn_out
def _v_up_proj(self, x):
# Convert from (B, N, L) to (N, B, L)
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
# Multiply (N, B, L) x (N, L, V) -> (N, B, V)
x = torch.bmm(x, self.W_UV)
# Convert from (N, B, V) to (B, N * V)
return x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
def process_weights_after_loading(self, act_dtype: torch.dtype):
def get_layer_weight(layer):
WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
for attr in WEIGHT_NAMES:
if hasattr(layer, attr):
return getattr(layer, attr)
raise AttributeError(
f"Layer '{layer}' has no recognized weight attribute:"
f" {WEIGHT_NAMES}.")
def get_and_maybe_dequant_weights(layer: LinearBase):
if not isinstance(layer.quant_method, UnquantizedLinearMethod):
# NOTE: This should only be used offline, since it's O(N^3)
eye = torch.eye(layer.input_size_per_partition,
dtype=act_dtype,
device=get_layer_weight(layer).device)
dequant_weights = layer.quant_method.apply(layer,
eye,
bias=None)
del eye
# standardize to (output, input)
return dequant_weights.T
return layer.weight if not envs.MACA_VLLM_USE_TN_2_NN else layer.weight.T
# we currently do not have quantized bmm's which are needed for
# `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
# the bmm's in 16-bit, the extra memory overhead of this is fairly low
kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
assert kv_b_proj_weight.shape == (
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
f"{kv_b_proj_weight.shape=}, "
f"{self.kv_lora_rank=}, "
f"{self.num_heads=}, "
f"{self.qk_nope_head_dim=}, "
f"{self.v_head_dim=}")
kv_b_proj_weight = kv_b_proj_weight.view(
self.kv_lora_rank,
self.num_heads,
self.qk_nope_head_dim + self.v_head_dim,
)
W_UK, W_UV = kv_b_proj_weight.split(
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
# Convert from (L, N, V) to (N, L, V)
self.W_UV = W_UV.transpose(0, 1)
# Convert from (L, N, P) to (N, P, L)
self.W_UK_T = W_UK.permute(1, 2, 0)
def _compute_prefill_context(
self,
q: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: MLACommonMetadata,
):
assert attn_metadata.prefill is not None
prefill_metadata = attn_metadata.prefill
assert prefill_metadata.chunked_context is not None
output = None
iters = len(prefill_metadata.chunked_context.seq_tot)
workspace = prefill_metadata.chunked_context.workspace
for i in range(iters):
toks = prefill_metadata.chunked_context.seq_tot[i]
ops.gather_cache(
src_cache=kv_c_and_k_pe_cache,
dst=workspace,
block_table=prefill_metadata.block_table,
cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
batch_size=attn_metadata.num_prefills,
seq_starts=prefill_metadata.chunked_context.starts[i],
)
kv_c_normed = workspace[:toks]\
[..., :self.kv_lora_rank]
k_pe = workspace[:toks]\
[..., self.kv_lora_rank:].unsqueeze(1)
kv_nope = self.kv_b_proj(kv_c_normed)[0].view( \
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv_nope\
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
dim=-1)
attn_output, attn_softmax_lse = \
self._flash_attn_varlen_diff_headdims(
q=q,
k=k,
v=v,
cu_seqlens_q=prefill_metadata.query_start_loc,
cu_seqlens_k=prefill_metadata.chunked_context.cu_seq_lens[i],
max_seqlen_q=prefill_metadata.max_query_len,
max_seqlen_k=prefill_metadata.chunked_context.max_seq_lens[i],
softmax_scale=self.scale,
causal=False, # Context is unmasked
return_softmax_lse=True,
)
if output is None:
output = attn_output
output_lse = attn_softmax_lse
else:
output_tmp = torch.empty_like(output)
output_lse_tmp = torch.empty_like(output_lse)
merge_attn_states(
output=output_tmp,
output_lse=output_lse_tmp,
prefix_output=output,
prefix_lse=output_lse,
suffix_output=attn_output,
suffix_lse=attn_softmax_lse,
)
output = output_tmp
output_lse = output_lse_tmp
return output, output_lse
def _forward_prefill(
self,
q: torch.Tensor,
kv_c_normed: torch.Tensor,
k_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: MLACommonMetadata,
) -> torch.Tensor:
assert attn_metadata.prefill is not None
has_context = False # attn_metadata.prefill.chunked_context is not None
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(\
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv_nope\
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
output = self._flash_attn_varlen_diff_headdims(
q=q,
k=k,
v=v,
cu_seqlens_q=attn_metadata.prefill.query_start_loc,
cu_seqlens_k=attn_metadata.prefill.query_start_loc,
max_seqlen_q=attn_metadata.prefill.max_query_len,
max_seqlen_k=attn_metadata.prefill.max_query_len,
softmax_scale=self.scale,
causal=True,
# return_softmax_lse=has_context,
)
if has_context:
suffix_output, suffix_lse = output
context_output, context_lse = self._compute_prefill_context( \
q, kv_c_and_k_pe_cache, attn_metadata)
output = torch.empty_like(suffix_output)
merge_attn_states(
output=output,
prefix_output=context_output,
prefix_lse=context_lse,
suffix_output=suffix_output,
suffix_lse=suffix_lse,
)
# unpad if necessary
if self._pad_v:
output = output.view(-1, self.num_heads, q.shape[-1])[..., :v.shape[-1]] \
.reshape(-1, self.num_heads * v.shape[-1])
return output
@abstractmethod
def _forward_decode(
self,
ql_nope: torch.Tensor,
q_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: M,
) -> torch.Tensor:
raise NotImplementedError
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: M,
output: Optional[torch.Tensor] = None,
) -> torch.Tensor:
assert output is not None, "Output tensor must be provided."
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
output_padded = output
output = output[:num_actual_toks, ...]
q = q[:num_actual_toks, ...]
k_c_normed = k_c_normed[:num_actual_toks, ...]
k_pe = k_pe[:num_actual_toks, ...]
assert attn_metadata.num_decodes is not None and \
attn_metadata.num_prefills is not None and \
attn_metadata.num_decode_tokens is not None
has_decode = attn_metadata.num_decodes > 0
has_prefill = attn_metadata.num_prefills > 0
num_decode_tokens = attn_metadata.num_decode_tokens
decode_q = q[:num_decode_tokens]
prefill_q = q[num_decode_tokens:]
prefill_k_pe = k_pe[num_decode_tokens:]
prefill_k_c_normed = k_c_normed[num_decode_tokens:]
# 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 has_prefill:
output[num_decode_tokens:] = self._forward_prefill(
prefill_q, prefill_k_c_normed, prefill_k_pe, kv_cache,
attn_metadata)
if has_decode:
assert attn_metadata.decode is not None
decode_q_nope, decode_q_pe = decode_q.split(
[self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
# Convert from (B, N, P) to (N, B, P)
decode_q_nope = decode_q_nope.transpose(0, 1)
# Multiply (N, B, P) x (N, P, L) -> (N, B, L)
decode_ql_nope = torch.bmm(decode_q_nope, self.W_UK_T)
# Convert from (N, B, L) to (B, N, L)
decode_ql_nope = decode_ql_nope.transpose(0, 1)
output[:num_decode_tokens] = self._forward_decode(
decode_ql_nope, decode_q_pe, kv_cache, attn_metadata)
return output_padded