ab676413e6
### What this PR does / why we need it?
1) Default enable MLAPO for deepseek MLA Attention W8A8 models on PD
disagregation D Instance, for example: DeepSeekV3-W8A8,
DeepSeek-R1-W8A8.
2) Default enable MLAPO for DeepSeek SFA Attention W8A8 models,
currently is DeepSeek-V3.2-W8A8.
### Does this PR introduce _any_ user-facing change?
Don't need use manully to VLLM_ASCEND_ENABLE_MLAPO=1, to enable MLAPO
feature for deepseek w8a8 model
The effect of enabling MLAPO SFA model deployed on a single A3 Node:
Test
with:tests/e2e/nightly/single_node/models/test_deepseek_v3_2_exp_w8a8.py
dataset: gsm8k-lite，without set MTP, FULL GRAPH, has 19% promote：
未默认开启 MLAPO 时：
├─────────────────────────┤
│ TTFT │ 14055.8836 ms │
├─────────────────────────┤
│ ITL │ 66.8171 ms. │
├─────────────────────────┤
│ Output Token Throughput │ 104.9105 token/s │
├─────────────────────────┤
默认开启 MLAPO 时：
├─────────────────────────┤
│ TTFT │ 3753.1547 ms │
├─────────────────────────┤
│ ITL. │ 61.4236 ms. │
├─────────────────────────┤
│ Output Token Throughput │ 125.2075 token/s│
├─────────────────────────┤
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
1541 lines
67 KiB
Python
1541 lines
67 KiB
Python
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, NamedTuple, Optional, Tuple, Type, TypeVar
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch_npu
|
|
import vllm.envs as envs_vllm
|
|
from vllm.config import VllmConfig, get_current_vllm_config
|
|
from vllm.forward_context import ForwardContext, get_forward_context
|
|
from vllm.logger import logger
|
|
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
|
|
from vllm.utils.math_utils import cdiv, round_down
|
|
from vllm.v1.attention.backends.mla.common import MLACommonMetadataBuilder
|
|
from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec
|
|
|
|
from vllm_ascend import envs
|
|
from vllm_ascend.ascend_config import get_ascend_config
|
|
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
|
|
from vllm_ascend.attention.attention_v1 import AscendAttentionState
|
|
from vllm_ascend.attention.context_parallel.common_cp import (
|
|
AscendPCPMetadata, CPChunkedContextMetadata)
|
|
from vllm_ascend.attention.utils import (
|
|
AscendCommonAttentionMetadata, ascend_chunked_prefill_workspace_size,
|
|
enable_cp, maybe_save_kv_layer_to_connector, split_decodes_and_prefills,
|
|
trans_rope_weight, transdata, wait_for_kv_layer_from_connector,
|
|
enabling_malpo)
|
|
from vllm_ascend.compilation.acl_graph import (
|
|
get_draft_graph_params, get_graph_params,
|
|
update_draft_graph_params_workspaces, update_graph_params_workspaces)
|
|
from vllm_ascend.ops.layer_shard_linear import (
|
|
is_hidden_layer, post_process_after_loading_for_shard_weight_series,
|
|
reach_layer_for_shard_weight_series,
|
|
register_all_layers_to_shard_weight_series)
|
|
from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
|
|
from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
|
|
from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
|
|
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, maybe_trans_nz,
|
|
vllm_version_is, weak_ref_tensors)
|
|
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
|
|
|
|
if TYPE_CHECKING:
|
|
from vllm.v1.core.sched.output import SchedulerOutput
|
|
|
|
# isort: off
|
|
if vllm_version_is('0.13.0'):
|
|
from vllm.v1.attention.backends.utils import AttentionCGSupport
|
|
from vllm.attention.backends.abstract import ( # type: ignore
|
|
AttentionBackend, MLAAttentionImpl)
|
|
from vllm.attention.backends.utils import PAD_SLOT_ID # type: ignore
|
|
else:
|
|
from vllm.v1.attention.backend import ( # type: ignore
|
|
AttentionBackend, AttentionCGSupport, MLAAttentionImpl)
|
|
from vllm.v1.attention.backends.utils import PAD_SLOT_ID # type: ignore
|
|
# isort: on
|
|
|
|
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
|
|
BUILD_METADATA_STEP_PREFILL = 0
|
|
BUILD_METADATA_STEP_DECODE = 1
|
|
# token count limits within the mlapo operator
|
|
MLAPO_MAX_SUPPORTED_TOKENS = 1024
|
|
|
|
|
|
class AscendMLABackend(AttentionBackend):
|
|
accept_output_buffer: bool = True
|
|
|
|
@staticmethod
|
|
def get_name() -> str:
|
|
# HACK(Ronald1995): vllm `initialize_kv_cache` method in model runner v2 make
|
|
# attention name assertion, we just set name to FLASH_ATTN to avoid assertion error.
|
|
# rectify this when vllm disable the assertion.
|
|
return "ASCEND_MLA" if not envs_vllm.VLLM_USE_V2_MODEL_RUNNER else "FLASH_ATTN"
|
|
|
|
@staticmethod
|
|
def get_builder_cls():
|
|
if enable_cp():
|
|
from vllm_ascend.attention.context_parallel.mla_cp import \
|
|
AscendMlaCPMetadataBuilder
|
|
return AscendMlaCPMetadataBuilder
|
|
return AscendMLAMetadataBuilder
|
|
|
|
@staticmethod
|
|
def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int,
|
|
head_size: int) -> tuple[int, ...]:
|
|
return num_blocks, block_size, num_kv_heads, head_size
|
|
|
|
@staticmethod
|
|
def get_impl_cls() -> Type["MLAAttentionImpl"]:
|
|
if enable_cp():
|
|
from vllm_ascend.attention.context_parallel.mla_cp import \
|
|
AscendMlaCPImpl
|
|
return AscendMlaCPImpl
|
|
return AscendMLAImpl
|
|
|
|
|
|
@dataclass
|
|
class ChunkedContextMetadata:
|
|
"""
|
|
Metadata for chunked context handling in MLA attention.
|
|
|
|
Manages sequence boundaries and workspace for chunked prefill processing.
|
|
"""
|
|
cu_seq_lens: torch.Tensor
|
|
starts: torch.Tensor
|
|
seq_tot: list[int]
|
|
max_seq_lens: list[int]
|
|
workspace: torch.Tensor
|
|
chunk_seq_lens: torch.Tensor
|
|
chunk_seq_lens_npu: torch.Tensor
|
|
|
|
|
|
@dataclass
|
|
class AscendMLAPrefillMetadata:
|
|
""" Prefill Specific Metadata for Ascend"""
|
|
attn_mask: torch.Tensor
|
|
query_lens: torch.Tensor
|
|
seq_lens: list[int]
|
|
context_lens: torch.Tensor
|
|
input_positions: torch.Tensor
|
|
query_start_loc: torch.Tensor
|
|
block_table: torch.Tensor
|
|
max_query_len: int
|
|
max_seq_lens: int
|
|
chunked_context: Optional[ChunkedContextMetadata
|
|
| CPChunkedContextMetadata] = None
|
|
sin: torch.Tensor = None
|
|
cos: torch.Tensor = None
|
|
pcp_metadata: Optional[AscendPCPMetadata] = None
|
|
|
|
|
|
@dataclass
|
|
class AscendMLADecodeMetadata:
|
|
""" Decode-specific metadata for Ascend MLA attention."""
|
|
# Input positions for rotary embeddings since for MLA the rotary
|
|
# position embeddings are applied inside the attention backend
|
|
input_positions: torch.Tensor
|
|
block_table: torch.Tensor
|
|
seq_lens: torch.Tensor
|
|
max_seq_lens: int
|
|
seq_lens_list: list[int]
|
|
actual_seq_lengths_q: Optional[list[int]] = None
|
|
attn_mask: Optional[torch.Tensor] = None
|
|
sin: torch.Tensor = None
|
|
cos: torch.Tensor = None
|
|
cp_seq_len: torch.Tensor = None
|
|
batch_seq_mask: torch.Tensor = None
|
|
|
|
|
|
@dataclass
|
|
class AscendMLAMetadata:
|
|
"""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_pcp_padded: int
|
|
num_actual_tokens: int # Number of tokens excluding padding.
|
|
slot_mapping: torch.Tensor
|
|
query_start_loc: torch.Tensor
|
|
seq_lens: torch.Tensor
|
|
block_tables: torch.Tensor
|
|
|
|
# New for MLA (compared to FlashAttention)
|
|
# For handling prefill decode split
|
|
num_decodes: int
|
|
num_decode_tokens: int
|
|
num_prefills: int
|
|
|
|
# For logging.
|
|
num_input_tokens: int = 0 # Number of tokens including padding.
|
|
|
|
query_lens: Optional[list[int]] = None
|
|
# The dimension of the attention heads
|
|
head_dim: Optional[int] = None
|
|
attn_mask: torch.Tensor = None
|
|
# chunked prefill by default if no attn_states passed
|
|
attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
|
|
|
|
decode: Optional[AscendMLADecodeMetadata] = None
|
|
prefill: Optional[AscendMLAPrefillMetadata] = None
|
|
reshape_cache_event: torch.npu.Event = None
|
|
|
|
def __post_init__(self):
|
|
pass
|
|
# supported_head_sizes = AscendMLABackend.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=AscendMLAMetadata)
|
|
|
|
|
|
class AscendMLAMetadataBuilder(MLACommonMetadataBuilder[AscendMLAMetadata]):
|
|
"""
|
|
NOTE: Please read the comment at the top of the file before trying to
|
|
understand this class
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
kv_cache_spec: MLAAttentionSpec,
|
|
layer_names: list[str],
|
|
vllm_config: VllmConfig,
|
|
device: torch.device,
|
|
metadata_cls: type[AscendMLAMetadata] | None = None,
|
|
supports_dcp_with_varlen: bool = False,
|
|
):
|
|
super().__init__(
|
|
kv_cache_spec, layer_names, vllm_config, device,
|
|
metadata_cls if metadata_cls is not None else AscendMLAMetadata,
|
|
supports_dcp_with_varlen)
|
|
|
|
scheduler_config = vllm_config.scheduler_config
|
|
self.block_size = vllm_config.cache_config.block_size
|
|
self.max_blocks = (vllm_config.model_config.max_model_len +
|
|
self.block_size - 1) // self.block_size
|
|
self.chunked_prefill_enabled = scheduler_config.enable_chunked_prefill
|
|
|
|
self.speculative_config = vllm_config.speculative_config
|
|
self.decode_threshold = 1
|
|
if self.speculative_config:
|
|
spec_token_num = self.speculative_config.num_speculative_tokens
|
|
self.decode_threshold += spec_token_num
|
|
assert self.decode_threshold <= 16, f"decode_threshold exceeded \
|
|
npu_fused_infer_attention_score TND layout's limit of 16, \
|
|
got {self.decode_threshold}"
|
|
|
|
self.reorder_batch_threshold = self.decode_threshold
|
|
|
|
self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
|
|
self.cos_cache = None
|
|
self.sin_cache = None
|
|
|
|
self.chunk_seq_lens: torch.Tensor = None
|
|
self.cu_seq_lens_cpu: torch.Tensor = None
|
|
self.num_chunks: torch.Tensor = None
|
|
self.max_context_chunk = 0
|
|
self.num_decodes = 0
|
|
self.num_prefills = 0
|
|
self.num_decode_tokens = 0
|
|
self.num_prefill_tokens = 0
|
|
self.context_lens_cpu: torch.Tensor = None
|
|
self.num_actual_tokens: Optional[int] = None
|
|
self.block_table: torch.Tensor = None
|
|
self.slot_mapping: torch.Tensor = None
|
|
self.graph_pad_size = 0
|
|
self.query_lens: torch.Tensor = None
|
|
self.seq_lens: torch.Tensor = None
|
|
self.attn_mask_builder = AttentionMaskBuilder(self.device)
|
|
|
|
@staticmethod
|
|
def determine_chunked_prefill_workspace_size(
|
|
vllm_config: VllmConfig) -> int:
|
|
return ascend_chunked_prefill_workspace_size(vllm_config)
|
|
|
|
@classmethod
|
|
def get_cudagraph_support(
|
|
cls: type["AscendMLAMetadataBuilder"],
|
|
vllm_config: VllmConfig,
|
|
kv_cache_spec: AttentionSpec,
|
|
) -> AttentionCGSupport:
|
|
# Explicit override in case the underlying builder specialized this getter.
|
|
# @override omitted only because of mypy limitation due to type variable.
|
|
return AttentionCGSupport.UNIFORM_BATCH
|
|
|
|
def reorder_batch(self, input_batch: "NPUInputBatch",
|
|
scheduler_output: "SchedulerOutput") -> bool:
|
|
# We now want to reorder the batch so that the "decode" requests are at
|
|
# 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 = []
|
|
|
|
for i, req_id in enumerate(input_batch.req_ids):
|
|
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
|
|
if num_tokens <= self.decode_threshold:
|
|
decodes.append(i)
|
|
else:
|
|
prefills.append(i)
|
|
|
|
# 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)
|
|
first_prefill = 0
|
|
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
|
|
if decodes[num_decodes - i] >= num_decodes:
|
|
input_batch.swap_states(prefills[first_prefill],
|
|
decodes[num_decodes - i])
|
|
first_prefill += 1
|
|
modified_batch = True
|
|
else:
|
|
break
|
|
|
|
# Save for next `build` call
|
|
# TODO(lucas): this is a bit of a hack, we should probably have a
|
|
# better way of doing this
|
|
return modified_batch
|
|
|
|
def pad_actual_seq_len_q_mtp_enable_pad(self, num_reqs_pad_size, num_reqs,
|
|
actual_seq_lengths_q,
|
|
common_attn_metadata):
|
|
"""
|
|
Pads actual_seq_lengths_q evenly to not exceed 16 tokens per request
|
|
in order to meet the requirement of npu_fused_infer_attention_score.
|
|
|
|
In Torchair scenario, the lengths of the queries must be padded to the same length.
|
|
And npu_fused_infer_attention_score constraint requires the last element must equal to batch_size(num_tokens).
|
|
|
|
For example:
|
|
batch_size=36, num_reqs_pad_size=2, num_reqs=16
|
|
By default, each request should have inference 2 token, which means actual_seq_lengths_q should be
|
|
[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36].
|
|
|
|
However, mtp torchair + PD scenario, the actual_seq_lengths_q may be
|
|
[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] before padding, since the first decode request only has 1 token.
|
|
In order to meet the requirement of npu_fused_infer_attention_score, we need to pad actual_seq_lengths_q evenly to not exceed 16 tokens per request.
|
|
after padding actual_seq_lengths_q should be similar to [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,36]
|
|
"""
|
|
FIA_SEQ_LEN_LIMIT = 16
|
|
need_padding = num_reqs_pad_size != 0 and \
|
|
len(common_attn_metadata.actual_seq_lengths_q) > num_reqs and \
|
|
common_attn_metadata.actual_seq_lengths_q[num_reqs] - actual_seq_lengths_q[
|
|
-1] > FIA_SEQ_LEN_LIMIT
|
|
if need_padding:
|
|
padding_seq_len_q = common_attn_metadata.actual_seq_lengths_q[
|
|
num_reqs:num_reqs + num_reqs_pad_size]
|
|
start_val = actual_seq_lengths_q[-1]
|
|
end_val = padding_seq_len_q[-1]
|
|
|
|
num_step = len(padding_seq_len_q)
|
|
interpolated = np.round(
|
|
np.linspace(start_val, end_val,
|
|
num_step + 1)[1:]).astype(int).tolist()
|
|
assert interpolated[-1] == end_val
|
|
assert len(interpolated) == len(padding_seq_len_q)
|
|
actual_seq_lengths_q = actual_seq_lengths_q + interpolated
|
|
else:
|
|
actual_seq_lengths_q = actual_seq_lengths_q + common_attn_metadata.actual_seq_lengths_q[
|
|
num_reqs:num_reqs + num_reqs_pad_size]
|
|
|
|
return actual_seq_lengths_q
|
|
|
|
def pad_actual_seq_len_q_mtp_disable_pad(self, num_reqs_pad_size, num_reqs,
|
|
actual_seq_lengths_q):
|
|
"""
|
|
Only use for acl full graph mode.
|
|
Pad the last element of the actual_seq_lengths_q equal to the TND(T) and
|
|
the num of dimensions equal to the batch_size of main model.
|
|
|
|
For example:
|
|
batch_size = 8, num_reqs = 4, num_speculative_tokens = 1
|
|
input actual_seq_lengths_q = [1, 2, 4, 5] (the 3rd req was accept a token)
|
|
After padding the actual_seq_lengths_q will be similar to [1, 2, 4, 5, 6, 6, 7, 8]
|
|
"""
|
|
need_padding = num_reqs_pad_size > 0
|
|
if need_padding:
|
|
start_val = actual_seq_lengths_q[-1]
|
|
end_val = num_reqs + num_reqs_pad_size
|
|
num_step = num_reqs_pad_size
|
|
interpolated = np.round(
|
|
np.linspace(start_val, end_val,
|
|
num_step + 1)[1:]).astype(int).tolist()
|
|
assert interpolated[-1] == end_val
|
|
assert len(interpolated) == num_reqs_pad_size
|
|
actual_seq_lengths_q = actual_seq_lengths_q + interpolated
|
|
return actual_seq_lengths_q
|
|
|
|
def set_num_actual_tokens(
|
|
self,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
):
|
|
self.num_actual_tokens = common_attn_metadata.num_actual_tokens
|
|
|
|
def build(
|
|
self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
fast_build: bool = False,
|
|
) -> AscendMLAMetadata:
|
|
num_reqs = common_attn_metadata.num_reqs
|
|
query_start_loc = common_attn_metadata.query_start_loc
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
|
|
|
self.num_decodes, self.num_prefills, self.num_decode_tokens, self.num_prefill_tokens = \
|
|
split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.decode_threshold)
|
|
self.set_num_actual_tokens(common_attn_metadata)
|
|
assert self.num_decodes + self.num_prefills == num_reqs
|
|
assert self.num_decode_tokens + self.num_prefill_tokens == common_attn_metadata.num_actual_tokens
|
|
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
self.slot_mapping = common_attn_metadata.slot_mapping[:self.
|
|
num_actual_tokens]
|
|
|
|
query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
|
|
self.query_lens = query_seq_lens_cpu[:num_reqs]
|
|
self.seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
|
|
|
|
self.graph_pad_size = common_attn_metadata.graph_pad_size
|
|
block_table_size = self.get_block_table_size(
|
|
common_attn_metadata, BUILD_METADATA_STEP_PREFILL)
|
|
self.block_table = common_attn_metadata.block_table_tensor[:
|
|
block_table_size]
|
|
|
|
prefill_metadata = None
|
|
if self.num_prefills > 0:
|
|
prefill_metadata = self.build_prefill_metadata(
|
|
common_prefix_len, common_attn_metadata)
|
|
|
|
decode_metadata = None
|
|
if self.num_decodes > 0:
|
|
decode_metadata = self.build_decode_metadata(
|
|
common_prefix_len, common_attn_metadata)
|
|
return self.metadata_cls( # type: ignore
|
|
num_actual_tokens_pcp_padded=self.num_actual_tokens,
|
|
num_input_tokens=common_attn_metadata.num_input_tokens,
|
|
num_actual_tokens=self.num_actual_tokens,
|
|
query_lens=self.query_lens.tolist(),
|
|
slot_mapping=self.slot_mapping,
|
|
head_dim=self.model_config.get_head_size(),
|
|
num_decodes=self.num_decodes,
|
|
num_decode_tokens=self.num_decode_tokens,
|
|
num_prefills=self.num_prefills,
|
|
attn_mask=self.attn_mask_builder.get_final_mla_mask(
|
|
self.model_config),
|
|
attn_state=common_attn_metadata.attn_state,
|
|
prefill=prefill_metadata,
|
|
decode=decode_metadata,
|
|
query_start_loc=query_start_loc,
|
|
block_tables=self.block_table,
|
|
seq_lens=self.seq_lens,
|
|
)
|
|
|
|
def build_chunked_metadata(
|
|
self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
):
|
|
if not self.chunked_prefill_enabled:
|
|
return None
|
|
num_reqs = common_attn_metadata.num_reqs
|
|
|
|
num_computed_tokens_cpu = (self.seq_lens - self.query_lens)
|
|
reqs_start = self.num_decodes # prefill_start
|
|
|
|
self.context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
|
|
max_context_len_cpu = self.context_lens_cpu.max().item()
|
|
if not max_context_len_cpu > 0:
|
|
return None
|
|
num_prefills_with_context_cpu = (self.context_lens_cpu
|
|
> 0).sum().item()
|
|
self.max_context_chunk = (self.chunked_prefill_workspace_size //
|
|
num_prefills_with_context_cpu)
|
|
self.max_context_chunk = round_down(self.max_context_chunk,
|
|
self.block_size)
|
|
|
|
assert self.max_context_chunk > 0
|
|
self.num_chunks = cdiv(max_context_len_cpu, self.max_context_chunk)
|
|
chunk_starts = torch.arange(self.num_chunks, dtype=torch.int32) \
|
|
.unsqueeze(1).expand(-1, self.num_prefills) * self.max_context_chunk
|
|
chunk_ends = torch.min(self.context_lens_cpu.unsqueeze(0),
|
|
chunk_starts + self.max_context_chunk)
|
|
self.chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
|
|
self.cu_seq_lens_cpu = torch.zeros(self.num_chunks,
|
|
self.num_prefills + 1,
|
|
dtype=torch.int32,
|
|
pin_memory=True)
|
|
torch.cumsum(self.chunk_seq_lens,
|
|
dim=1,
|
|
out=self.cu_seq_lens_cpu[:, 1:],
|
|
dtype=torch.int32)
|
|
return ChunkedContextMetadata(
|
|
cu_seq_lens=self.cu_seq_lens_cpu.pin_memory().to(
|
|
self.device, non_blocking=True),
|
|
starts=chunk_starts.pin_memory().to(self.device,
|
|
non_blocking=True),
|
|
seq_tot=self.chunk_seq_lens.sum(dim=1).tolist(),
|
|
max_seq_lens=self.chunk_seq_lens.max(dim=1).values.tolist(),
|
|
chunk_seq_lens=self.chunk_seq_lens,
|
|
chunk_seq_lens_npu=self.chunk_seq_lens.npu(),
|
|
workspace=self.chunked_prefill_workspace,
|
|
)
|
|
|
|
def get_block_table_size(
|
|
self, common_attn_metadata: AscendCommonAttentionMetadata,
|
|
build_metadata_step: int):
|
|
if build_metadata_step == BUILD_METADATA_STEP_PREFILL:
|
|
# If graph_pad_size > -1, mean is running in fullgraph mode.
|
|
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
|
|
if self.graph_pad_size > common_attn_metadata.num_reqs and self.speculative_config.disable_padded_drafter_batch:
|
|
return self.graph_pad_size
|
|
return common_attn_metadata.num_reqs
|
|
return self.num_decodes
|
|
|
|
def build_prefill_metadata(
|
|
self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
) -> AscendMLAPrefillMetadata:
|
|
query_start_loc = common_attn_metadata.query_start_loc
|
|
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
input_positions = common_attn_metadata.positions[:self.
|
|
num_actual_tokens].long(
|
|
)
|
|
|
|
chunked_context_metadata = self.build_chunked_metadata(
|
|
common_prefix_len, common_attn_metadata)
|
|
reqs_start = self.num_decodes # prefill_start
|
|
tokens_start = self.num_decode_tokens
|
|
max_query_len = self.query_lens[reqs_start:].max().item()
|
|
max_seq_lens = self.seq_lens[reqs_start:].max().item()
|
|
prefill_query_start_loc = query_start_loc[
|
|
reqs_start:] - query_start_loc[reqs_start]
|
|
|
|
prefill_input_positions = input_positions[tokens_start:]
|
|
cos, sin = get_cos_and_sin_mla(prefill_input_positions)
|
|
return AscendMLAPrefillMetadata(
|
|
attn_mask=self.attn_mask_builder.get_final_mla_mask(
|
|
self.model_config),
|
|
query_lens=self.query_lens[reqs_start:].to(torch.int32),
|
|
seq_lens=self.seq_lens,
|
|
context_lens=self.seq_lens[reqs_start:],
|
|
input_positions=prefill_input_positions,
|
|
block_table=self.block_table[reqs_start:, ...],
|
|
max_query_len=max_query_len,
|
|
max_seq_lens=max_seq_lens,
|
|
query_start_loc=prefill_query_start_loc,
|
|
chunked_context=chunked_context_metadata,
|
|
sin=sin,
|
|
cos=cos,
|
|
)
|
|
|
|
def build_decode_metadata(
|
|
self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
) -> AscendMLADecodeMetadata:
|
|
num_reqs = common_attn_metadata.num_reqs
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
|
|
|
input_positions = common_attn_metadata.positions[:self.
|
|
num_actual_tokens].long(
|
|
)
|
|
|
|
# Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario
|
|
actual_seq_lengths_q = query_start_loc_cpu[1:self.num_decodes +
|
|
1].tolist()
|
|
max_seq_lens = self.seq_lens[:self.num_decodes].max().item()
|
|
self.seq_lens = self.seq_lens[:self.num_decodes]
|
|
input_positions = input_positions[:self.num_decode_tokens]
|
|
|
|
block_table_size = self.get_block_table_size(
|
|
common_attn_metadata, BUILD_METADATA_STEP_DECODE)
|
|
self.block_table = self.block_table[:block_table_size]
|
|
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
|
|
if self.graph_pad_size > self.num_decodes and \
|
|
self.speculative_config.disable_padded_drafter_batch:
|
|
self.block_table = self.block_table[:self.graph_pad_size, ...]
|
|
seq_lens_list = self.seq_lens.tolist()
|
|
|
|
cp_seq_len, batch_seq_mask = None, None
|
|
|
|
if self.graph_pad_size > num_reqs:
|
|
if self.speculative_config.disable_padded_drafter_batch:
|
|
num_reqs_pad_size = self.graph_pad_size - num_reqs
|
|
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_disable_pad(
|
|
num_reqs_pad_size, num_reqs, actual_seq_lengths_q)
|
|
seq_lens_list = seq_lens_list + [0] * (self.graph_pad_size -
|
|
self.num_decodes)
|
|
num_block_pad_size = self.graph_pad_size - self.block_table.shape[
|
|
0]
|
|
if num_block_pad_size > 0:
|
|
block_table_padding = torch.zeros(
|
|
(num_block_pad_size, ) + self.block_table.shape[1:],
|
|
dtype=self.block_table.dtype,
|
|
device=self.block_table.device)
|
|
self.block_table = torch.cat(
|
|
[self.block_table, block_table_padding], dim=0)
|
|
else:
|
|
num_token_pad_size = self.graph_pad_size - self.num_decode_tokens
|
|
num_reqs_pad_size = (
|
|
self.graph_pad_size //
|
|
common_attn_metadata.decode_token_per_req - num_reqs)
|
|
num_block_table_pad_size = (
|
|
self.graph_pad_size //
|
|
common_attn_metadata.decode_token_per_req -
|
|
self.num_decodes)
|
|
seq_lens_list = self.seq_lens.tolist() + [0
|
|
] * num_reqs_pad_size
|
|
slot_padding = torch.full((num_token_pad_size, ),
|
|
PAD_SLOT_ID,
|
|
dtype=self.slot_mapping.dtype,
|
|
device=self.slot_mapping.device)
|
|
self.slot_mapping = torch.cat(
|
|
[self.slot_mapping, slot_padding])
|
|
block_table_padding = torch.zeros(
|
|
(num_block_table_pad_size, ) + self.block_table.shape[1:],
|
|
dtype=self.block_table.dtype,
|
|
device=self.block_table.device)
|
|
self.block_table = torch.cat(
|
|
[self.block_table, block_table_padding], dim=0)
|
|
position_padding = torch.zeros(num_token_pad_size,
|
|
dtype=input_positions.dtype,
|
|
device=input_positions.device)
|
|
input_positions = torch.cat(
|
|
[input_positions, position_padding])
|
|
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_enable_pad(
|
|
num_reqs_pad_size, num_reqs, actual_seq_lengths_q,
|
|
common_attn_metadata)
|
|
|
|
cos, sin = get_cos_and_sin_mla(input_positions, use_cache=True)
|
|
decode_metadata = AscendMLADecodeMetadata(
|
|
input_positions=input_positions,
|
|
block_table=self.block_table,
|
|
seq_lens=self.seq_lens,
|
|
seq_lens_list=seq_lens_list,
|
|
max_seq_lens=max_seq_lens,
|
|
attn_mask=self.attn_mask_builder.get_splitfuse_attn_mask(),
|
|
actual_seq_lengths_q=actual_seq_lengths_q,
|
|
sin=sin[:self.num_decode_tokens, ...],
|
|
cos=cos[:self.num_decode_tokens, ...],
|
|
cp_seq_len=cp_seq_len,
|
|
batch_seq_mask=batch_seq_mask)
|
|
return decode_metadata
|
|
|
|
def build_for_graph_capture(
|
|
self,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
|
|
):
|
|
if attn_state in {
|
|
AscendAttentionState.DecodeOnly,
|
|
AscendAttentionState.SpecDecoding
|
|
}:
|
|
attn_metadata = self.build(
|
|
common_prefix_len=0,
|
|
common_attn_metadata=common_attn_metadata,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"Currently we only support building dummy metadata for DecodeOnly and SpecDecoding state"
|
|
)
|
|
|
|
attn_metadata.attn_state = attn_state
|
|
return attn_metadata
|
|
|
|
|
|
class DecodeMLAPreprocessResult(NamedTuple):
|
|
ql_nope: Optional[torch.Tensor] = None
|
|
q_pe: Optional[torch.Tensor] = None
|
|
k_nope: Optional[torch.Tensor] = None
|
|
k_pe: Optional[torch.Tensor] = None
|
|
decode_q_wo_k_up: Optional[torch.Tensor] = None
|
|
|
|
|
|
class PrefillMLAPreprocessResult(NamedTuple):
|
|
q_nope: Optional[torch.Tensor] = None
|
|
q_pe: Optional[torch.Tensor] = None
|
|
k_nope: Optional[torch.Tensor] = None
|
|
k_pe: Optional[torch.Tensor] = None
|
|
value: Optional[torch.Tensor] = None
|
|
|
|
|
|
class AscendMLAImpl(MLAAttentionImpl):
|
|
"""
|
|
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,
|
|
logits_soft_cap: Optional[float],
|
|
attn_type: str,
|
|
kv_sharing_target_layer_name: Optional[str],
|
|
**kwargs,
|
|
):
|
|
self.vllm_config = get_current_vllm_config()
|
|
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
|
|
|
|
# MLA Args
|
|
self.q_lora_rank = kwargs['q_lora_rank']
|
|
self.kv_lora_rank = kwargs['kv_lora_rank']
|
|
self.qk_nope_head_dim = kwargs['qk_nope_head_dim']
|
|
self.qk_rope_head_dim = kwargs['qk_rope_head_dim']
|
|
self.qk_head_dim = kwargs['qk_head_dim']
|
|
self.v_head_dim = kwargs['v_head_dim']
|
|
self.rotary_emb = kwargs['rotary_emb']
|
|
self.fused_qkv_a_proj = kwargs.get('fused_qkv_a_proj', None)
|
|
self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[
|
|
'q_b_proj']
|
|
self.kv_b_proj = kwargs['kv_b_proj']
|
|
self.o_proj = kwargs['o_proj']
|
|
self.vllm_config = get_current_vllm_config()
|
|
self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
|
|
self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
|
|
self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
|
|
self.enable_kv_nz = ascend_config.enable_kv_nz
|
|
|
|
self.ring_mla_mask_size = 512
|
|
|
|
self.speculative_config = self.vllm_config.speculative_config
|
|
self.enable_mlapo = enabling_malpo(self.vllm_config)
|
|
|
|
self.is_kv_producer = self.vllm_config.kv_transfer_config is not None and self.vllm_config.kv_transfer_config.is_kv_producer
|
|
self.layer_sharding_kwargs = []
|
|
for layer_name in (get_ascend_config().layer_sharding or []):
|
|
if layer_name in kwargs:
|
|
self.layer_sharding_kwargs.append(kwargs[layer_name])
|
|
else:
|
|
logger.warning_once(
|
|
f"Layer '{layer_name}' not found in kwargs for layer sharding, skipping sharding configuration"
|
|
)
|
|
register_all_layers_to_shard_weight_series(self.layer_sharding_kwargs)
|
|
|
|
def _v_up_proj(self, x):
|
|
# Convert from (N, B, L)/(N, B, 1, L) to (N, B, L)
|
|
x = x.view(self.num_heads, -1, self.kv_lora_rank)
|
|
# Multiply (N, B, L) x (N, L, V) -> (B, N, V)
|
|
x = torch_npu.npu_transpose_batchmatmul(x, self.W_UV, perm_y=(1, 0, 2))
|
|
# Convert from (B, N, V) to (B, N * V)
|
|
x = x.reshape(-1, self.num_heads * self.v_head_dim)
|
|
return x
|
|
|
|
# Return `ql_nope`, `q_pe`
|
|
def _q_proj_and_k_up_proj(self, x):
|
|
q_nope, q_pe = self.q_proj(x)[0] \
|
|
.view(-1, self.num_heads, self.qk_head_dim) \
|
|
.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)
|
|
return ql_nope.transpose(0, 1), q_pe
|
|
|
|
def process_weights_after_loading(self, act_dtype: torch.dtype):
|
|
# NOTE: We currently do not support quant kv_b_proj.
|
|
assert isinstance(self.kv_b_proj.quant_method, UnquantizedLinearMethod)
|
|
# NOTE: Weight will be reshaped next, we need to revert and transpose it.
|
|
kv_b_proj_weight = torch_npu.npu_format_cast(
|
|
self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_ND).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).contiguous()
|
|
# Convert from (L, N, P) to (N, P, L)
|
|
self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
|
|
|
|
# TODO(zzzzwwjj): Currently, torch.ops._C_ascend.batch_matmul_transpose cannot support weight nz
|
|
# self.W_UV = maybe_trans_nz(self.W_UV)
|
|
|
|
if self.enable_mlapo:
|
|
# Currently mlapo only supports W8A8 quantization in MLA scenario
|
|
# TODO(whx): modify this limitation when mlapo supports floating point
|
|
if self.fused_qkv_a_proj is None or not isinstance(
|
|
getattr(self.fused_qkv_a_proj.quant_method, 'quant_method',
|
|
None), AscendW8A8LinearMethod):
|
|
self.enable_mlapo = False
|
|
logger.warning_once(
|
|
"Currently mlapo only supports W8A8 quantization in MLA scenario."
|
|
"Some layers in your model are not quantized with W8A8,"
|
|
"thus mlapo is disabled for these layers.")
|
|
if self.enable_mlapo:
|
|
self._process_weights_for_fused_mlapo(act_dtype)
|
|
else:
|
|
# if mlapo, W_UK_T can't trans nz
|
|
self.W_UK_T = maybe_trans_nz(self.W_UK_T)
|
|
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
post_process_after_loading_for_shard_weight_series(layer)
|
|
|
|
def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
|
|
kv_a_proj_wt = self.fused_qkv_a_proj.weight.data[
|
|
..., self.q_lora_rank:].contiguous()
|
|
q_a_proj_wt = self.fused_qkv_a_proj.weight.data[
|
|
..., :self.q_lora_rank].contiguous()
|
|
kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
|
|
kv_a_proj_wt = trans_rope_weight(kv_a_proj_wt, self.qk_rope_head_dim)
|
|
kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
|
|
wd_qkv = torch.cat((kv_a_proj_wt, q_a_proj_wt), dim=-1)
|
|
wd_qkv = wd_qkv.t().contiguous()
|
|
wd_qkv = transdata(wd_qkv,
|
|
block_size=(16, 32)).unsqueeze(0).contiguous()
|
|
self.wd_qkv = torch_npu.npu_format_cast(wd_qkv, 29)
|
|
|
|
kv_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[
|
|
self.q_lora_rank:].contiguous()
|
|
q_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[:self.
|
|
q_lora_rank].contiguous(
|
|
)
|
|
kv_a_proj_deq_scl = kv_a_proj_deq_scl.reshape(
|
|
self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
|
|
kv_a_proj_deq_scl = trans_rope_weight(kv_a_proj_deq_scl,
|
|
self.qk_rope_head_dim)
|
|
kv_a_proj_deq_scl = kv_a_proj_deq_scl.view(
|
|
self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
|
|
self.deq_scale_qkv = torch.cat((kv_a_proj_deq_scl, q_a_proj_deq_scl),
|
|
dim=-1).contiguous()
|
|
|
|
kv_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[
|
|
self.q_lora_rank:].contiguous()
|
|
q_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[:self.
|
|
q_lora_rank].contiguous(
|
|
)
|
|
kv_a_proj_qt_bias = kv_a_proj_qt_bias.reshape(
|
|
self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
|
|
kv_a_proj_qt_bias = trans_rope_weight(kv_a_proj_qt_bias,
|
|
self.qk_rope_head_dim)
|
|
kv_a_proj_qt_bias = kv_a_proj_qt_bias.view(
|
|
self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
|
|
self.quant_bias_qkv = torch.cat((kv_a_proj_qt_bias, q_a_proj_qt_bias),
|
|
dim=-1).contiguous()
|
|
|
|
wu_q = self.q_proj.weight.data
|
|
wu_q = wu_q.t().reshape(self.num_heads,
|
|
self.qk_nope_head_dim + self.qk_rope_head_dim,
|
|
-1)
|
|
wu_q = trans_rope_weight(wu_q, self.qk_rope_head_dim)
|
|
wu_q = wu_q.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim),
|
|
-1)
|
|
wu_q = transdata(wu_q, block_size=(16, 32)).unsqueeze(0).contiguous()
|
|
self.wu_q = torch_npu.npu_format_cast(wu_q, 29)
|
|
|
|
qb_deq_scl = self.q_proj.deq_scale.data
|
|
qb_deq_scl = qb_deq_scl.reshape(
|
|
self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
|
|
qb_deq_scl = trans_rope_weight(qb_deq_scl, self.qk_rope_head_dim)
|
|
self.qb_deq_scl = qb_deq_scl.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
|
|
|
|
qb_qt_bias = self.q_proj.quant_bias.data
|
|
qb_qt_bias = qb_qt_bias.reshape(
|
|
self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
|
|
qb_qt_bias = trans_rope_weight(qb_qt_bias, self.qk_rope_head_dim)
|
|
self.qb_qt_bias = qb_qt_bias.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
|
|
|
|
device = self.q_proj.weight.device
|
|
self.gamma1 = self.q_a_layernorm.weight.data
|
|
self.beta1 = torch.zeros_like(self.gamma1) if (
|
|
_bias := self.q_a_layernorm.bias) is None else _bias.data
|
|
self.gamma2 = self.kv_a_layernorm.weight.data
|
|
self.quant_scale0 = self.fused_qkv_a_proj.input_scale.data
|
|
self.quant_offset0 = self.fused_qkv_a_proj.input_offset.data
|
|
self.quant_scale1 = self.q_proj.input_scale.data
|
|
self.quant_offset1 = self.q_proj.input_offset.data
|
|
self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
|
|
self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)
|
|
|
|
# On KV consumers (decode-only) MLAPO uses the transformed weights built above;
|
|
# the original fused_qkv_a_proj/q_proj weights and quant params are no longer
|
|
# referenced, so drop them to save memory.
|
|
if self.vllm_config.kv_transfer_config is not None and \
|
|
self.vllm_config.kv_transfer_config.is_kv_consumer and \
|
|
self.vllm_config.scheduler_config.max_num_batched_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
|
|
self.fused_qkv_a_proj.weight = None
|
|
self.fused_qkv_a_proj.deq_scale = None
|
|
self.fused_qkv_a_proj.quant_bias = None
|
|
self.q_proj.weight = None
|
|
self.q_proj.deq_scale = None
|
|
self.q_proj.quant_bias = None
|
|
torch.npu.empty_cache()
|
|
|
|
def get_context_seq_len_npu(self, index: int,
|
|
attn_metadata: AscendMLAMetadata):
|
|
prefill_metadata = attn_metadata.prefill
|
|
assert prefill_metadata is not None
|
|
assert prefill_metadata.chunked_context is not None
|
|
assert prefill_metadata.chunked_context.chunk_seq_lens_npu is not None
|
|
iters = len(prefill_metadata.chunked_context.seq_tot)
|
|
assert 0 <= index < iters
|
|
return prefill_metadata.chunked_context.chunk_seq_lens_npu[index]
|
|
|
|
def _reorg_kvcache(
|
|
self,
|
|
kv_c_normed: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
chunked_context: CPChunkedContextMetadata,
|
|
chunk_idx: int,
|
|
toks: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
return kv_c_normed, k_pe
|
|
|
|
def _compute_prefill_context(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
rope_dim: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
prefix_output: torch.Tensor,
|
|
prefix_lse: torch.Tensor,
|
|
):
|
|
assert len(kv_c_and_k_pe_cache) > 1
|
|
prefill_metadata = attn_metadata.prefill
|
|
if prefill_metadata is None or prefill_metadata.chunked_context is None:
|
|
return prefix_output, prefix_lse
|
|
|
|
iters = len(prefill_metadata.chunked_context.seq_tot)
|
|
|
|
current_seq_len = torch.tensor(prefill_metadata.query_lens,
|
|
dtype=torch.int32)
|
|
cache_kv_c = kv_c_and_k_pe_cache[0]
|
|
cache_k_pe = kv_c_and_k_pe_cache[1]
|
|
num_heads = cache_k_pe.size(2)
|
|
latent_kv_dim = kv_c_and_k_pe_cache[0].size(-1)
|
|
for i in range(iters):
|
|
toks = prefill_metadata.chunked_context.seq_tot[i]
|
|
# chunk_seq_lens will be padded when pcp&dcp
|
|
context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[
|
|
i]
|
|
seq_len = torch.stack([current_seq_len, context_seq_len])
|
|
context_seq_len_npu = self.get_context_seq_len_npu(
|
|
i, attn_metadata)
|
|
kv_c_normed = torch.empty(toks,
|
|
num_heads,
|
|
latent_kv_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
k_pe = torch.empty(toks,
|
|
num_heads,
|
|
rope_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
|
|
torch_npu.atb.npu_paged_cache_load(
|
|
cache_kv_c,
|
|
cache_k_pe,
|
|
prefill_metadata.block_table,
|
|
context_seq_len_npu,
|
|
seq_starts=prefill_metadata.chunked_context.starts[i],
|
|
key=kv_c_normed,
|
|
value=k_pe,
|
|
)
|
|
kv_c_normed, k_pe = self._reorg_kvcache(
|
|
kv_c_normed,
|
|
k_pe,
|
|
chunked_context=prefill_metadata.chunked_context,
|
|
chunk_idx=i,
|
|
toks=toks,
|
|
)
|
|
kv_c_normed = kv_c_normed.squeeze()
|
|
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_pe = k_pe.expand((*k_nope.shape[:-1], -1))
|
|
|
|
mask = attn_metadata.attn_mask
|
|
torch_npu.atb.npu_ring_mla(
|
|
q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope,
|
|
k_rope=k_pe,
|
|
value=v,
|
|
mask=mask,
|
|
seqlen=seq_len,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=prefix_output,
|
|
prev_lse=prefix_lse,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="no_mask",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_default",
|
|
output=prefix_output,
|
|
softmax_lse=prefix_lse)
|
|
return prefix_output, prefix_lse
|
|
|
|
def _forward_prefill(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata.prefill is not None
|
|
assert len(kv_c_and_k_pe_cache) > 1
|
|
num_tokens = q_nope.size(0)
|
|
attn_output = torch.empty(num_tokens,
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
attn_lse = torch.empty(self.num_heads,
|
|
num_tokens,
|
|
dtype=torch.float32,
|
|
device=q_nope.device)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope,
|
|
k_rope=k_pe,
|
|
value=value,
|
|
mask=attn_metadata.attn_mask,
|
|
seqlen=attn_metadata.prefill.query_lens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=None,
|
|
prev_lse=None,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="mask_type_triu",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_first_ring",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
attn_output, attn_lse = self._compute_prefill_context(
|
|
q_nope, q_pe, kv_c_and_k_pe_cache, self.qk_rope_head_dim,
|
|
attn_metadata, attn_output, attn_lse)
|
|
|
|
attn_output = attn_output.reshape(
|
|
[num_tokens, self.num_heads * self.v_head_dim])
|
|
return attn_output
|
|
|
|
def exec_kv_decode(
|
|
self,
|
|
kv_no_split: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
B = kv_no_split.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv_no_split = kv_no_split.view(
|
|
B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
cache_mode = "PA_NZ" if self.enable_kv_nz else "PA"
|
|
k_pe, k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
kv_no_split,
|
|
self.kv_a_layernorm.weight,
|
|
cos,
|
|
sin,
|
|
slots.to(torch.int64),
|
|
kv_cache[1],
|
|
kv_cache[0],
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode=cache_mode,
|
|
)
|
|
return k_pe, k_nope
|
|
|
|
def exec_kv_prefill(
|
|
self,
|
|
kv_no_split: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
B = kv_no_split.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv_no_split = kv_no_split.view(
|
|
B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
cache_mode = "PA"
|
|
_, _, k_pe, k_nope = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
kv_no_split,
|
|
self.kv_a_layernorm.weight,
|
|
cos,
|
|
sin,
|
|
slots.to(torch.int64),
|
|
kv_cache[1],
|
|
kv_cache[0],
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode=cache_mode,
|
|
is_output_kv=True,
|
|
)
|
|
return k_pe, k_nope
|
|
|
|
def rope_single(
|
|
self,
|
|
x: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
B, N, D = x.shape
|
|
S = 1
|
|
x = x.view(B, N, S, D)
|
|
x = torch_npu.npu_interleave_rope(x, cos, sin)
|
|
return x.view(B, N, D)
|
|
|
|
def _forward_decode(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
block_size: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
decode_meta = attn_metadata.decode
|
|
assert decode_meta is not None
|
|
num_tokens = q_nope.size(0)
|
|
# shape of knope/k_pe for npu graph mode should be:
|
|
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
|
|
actual_seq_lengths = None
|
|
if self.enable_kv_nz:
|
|
nz_fmt_last_dim = 16
|
|
k_nope = k_nope.view(-1, self.num_kv_heads,
|
|
self.kv_lora_rank // nz_fmt_last_dim,
|
|
block_size, nz_fmt_last_dim)
|
|
k_pe = k_pe.view(-1, self.num_kv_heads,
|
|
self.qk_rope_head_dim // nz_fmt_last_dim,
|
|
block_size, nz_fmt_last_dim)
|
|
else:
|
|
k_nope = k_nope.view(-1, self.num_kv_heads, block_size,
|
|
self.kv_lora_rank)
|
|
k_pe = k_pe.view(-1, self.num_kv_heads, block_size,
|
|
self.qk_rope_head_dim)
|
|
|
|
attn_output_shape: tuple | None = None
|
|
if attn_metadata.attn_state in [
|
|
AscendAttentionState.SpecDecoding,
|
|
AscendAttentionState.ChunkedPrefill,
|
|
AscendAttentionState.DecodeOnly,
|
|
] and self.speculative_config is not None:
|
|
# The right part layout indicates the layout of the attention
|
|
# output. It is set to NTD to avoid the need for a transpose
|
|
# operation after attention.
|
|
input_layout = "TND_NTD"
|
|
# TODO: If the driver is upgraded later, the contiguous function can be deleted.
|
|
# Input shape: [num_tokens, num_heads, dim]
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, -1).contiguous()
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, -1)
|
|
# Output shape: [num_heads, num_tokens, dim]
|
|
attn_output_shape = (self.num_heads, num_tokens, self.kv_lora_rank)
|
|
sparse_mode = 3
|
|
attn_mask = attn_metadata.decode.attn_mask # type:ignore
|
|
actual_seq_lengths = decode_meta.actual_seq_lengths_q
|
|
else:
|
|
# The output layout is set to NBSD to eliminate the need for a
|
|
# transpose operation after attention.
|
|
if self.enable_kv_nz:
|
|
# Input shape: [num_tokens, seq_len, num_heads, dim]
|
|
input_layout = "BSND_NBSD"
|
|
q_nope = q_nope.view(num_tokens, 1, self.num_heads,
|
|
-1).contiguous()
|
|
q_pe = q_pe.view(num_tokens, 1, self.num_heads, -1)
|
|
else:
|
|
# Input shape: [num_tokens, num_heads, seq_len, dim]
|
|
input_layout = "BNSD_NBSD"
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, 1,
|
|
-1).contiguous()
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1)
|
|
# Output shape: [num_heads, num_tokens, seq_len, dim]
|
|
attn_output_shape = (self.num_heads, num_tokens, 1,
|
|
self.kv_lora_rank)
|
|
sparse_mode = 0
|
|
attn_mask = None
|
|
|
|
common_kwargs = {
|
|
'query_rope': q_pe,
|
|
'key_rope': k_pe,
|
|
'num_heads': self.num_heads,
|
|
'num_key_value_heads': self.num_kv_heads,
|
|
'input_layout': input_layout,
|
|
'atten_mask': attn_mask,
|
|
'sparse_mode': sparse_mode,
|
|
'scale': self.scale,
|
|
'antiquant_mode': 0,
|
|
'antiquant_scale': None,
|
|
'block_table': decode_meta.block_table,
|
|
'block_size': block_size,
|
|
"actual_seq_lengths": actual_seq_lengths,
|
|
"actual_seq_lengths_kv": decode_meta.seq_lens_list,
|
|
}
|
|
forward_context: ForwardContext = get_forward_context()
|
|
if forward_context.is_draft_model:
|
|
graph_params = get_draft_graph_params()
|
|
else:
|
|
graph_params = get_graph_params()
|
|
if forward_context.capturing:
|
|
stream = torch_npu.npu.current_stream()
|
|
|
|
event = torch.npu.ExternalEvent()
|
|
event.wait(stream)
|
|
event.reset(stream)
|
|
graph_params.events[num_tokens].append(event)
|
|
|
|
workspace = graph_params.workspaces.get(num_tokens)
|
|
if workspace is None:
|
|
workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
|
|
q_nope, k_nope, k_nope, **common_kwargs)
|
|
if forward_context.is_draft_model:
|
|
update_draft_graph_params_workspaces(num_tokens, workspace)
|
|
else:
|
|
update_graph_params_workspaces(num_tokens, workspace)
|
|
|
|
attn_output = torch.empty(attn_output_shape,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
softmax_lse = torch.empty(num_tokens,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
|
|
graph_params.attn_params[num_tokens].append(
|
|
(weak_ref_tensors(q_nope), weak_ref_tensors(k_nope),
|
|
weak_ref_tensors(q_pe), weak_ref_tensors(k_pe),
|
|
self.num_heads, self.num_kv_heads, input_layout,
|
|
weak_ref_tensors(attn_mask) if attn_mask is not None else
|
|
None, sparse_mode, self.scale, decode_meta.block_table,
|
|
block_size, decode_meta.seq_lens_list, actual_seq_lengths,
|
|
weak_ref_tensors(attn_output), weak_ref_tensors(softmax_lse)))
|
|
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.npu_fused_infer_attention_score.out(
|
|
q_nope,
|
|
k_nope,
|
|
k_nope,
|
|
**common_kwargs,
|
|
workspace=workspace,
|
|
out=[attn_output, softmax_lse])
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
else:
|
|
attn_output, _ = torch_npu.npu_fused_infer_attention_score(
|
|
q_nope, k_nope, k_nope, **common_kwargs)
|
|
|
|
return self._v_up_proj(attn_output)
|
|
|
|
def reorg_decode_q(self, decode_q_nope, decode_q_pe):
|
|
return decode_q_nope, decode_q_pe
|
|
|
|
def _mla_preprocess_only_decode(self, hidden_states, kv_cache,
|
|
attn_metadata):
|
|
bsz = attn_metadata.num_decode_tokens
|
|
hidden_states = hidden_states[:bsz]
|
|
|
|
cos_shape = attn_metadata.decode.cos.shape
|
|
cos = attn_metadata.decode.cos.view(cos_shape[0], cos_shape[-1])
|
|
sin = attn_metadata.decode.sin.view(cos_shape[0], cos_shape[-1])
|
|
|
|
decode_k_nope, decode_k_pe = kv_cache[0], kv_cache[1]
|
|
decode_q_nope = torch.empty(
|
|
(hidden_states.shape[0], self.W_UK_T.shape[0],
|
|
decode_k_nope.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
decode_q_pe = torch.empty(
|
|
(hidden_states.shape[0], self.W_UK_T.shape[0],
|
|
decode_k_pe.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
|
|
torch.ops._C_ascend.mla_preprocess(
|
|
hidden_states,
|
|
self.wd_qkv,
|
|
self.deq_scale_qkv,
|
|
self.gamma1,
|
|
self.beta1,
|
|
self.wu_q,
|
|
self.qb_deq_scl,
|
|
self.gamma2,
|
|
cos,
|
|
sin,
|
|
self.W_UK_T,
|
|
decode_k_nope,
|
|
decode_k_pe,
|
|
attn_metadata.slot_mapping[:bsz],
|
|
quant_scale0=self.quant_scale0,
|
|
quant_offset0=self.quant_offset0,
|
|
bias0=self.quant_bias_qkv,
|
|
quant_scale1=self.quant_scale1,
|
|
quant_offset1=self.quant_offset1,
|
|
bias1=self.qb_qt_bias,
|
|
ctkv_scale=self.ctkv_scale,
|
|
q_nope_scale=self.q_nope_scale,
|
|
cache_mode="nzcache" if self.enable_kv_nz else "krope_ctkv",
|
|
quant_mode="per_tensor_quant_asymm",
|
|
q_out0=decode_q_nope,
|
|
kv_cache_out0=decode_k_nope,
|
|
q_out1=decode_q_pe,
|
|
kv_cache_out1=decode_k_pe,
|
|
enable_inner_out=False,
|
|
inner_out=torch.tensor([], device=hidden_states.device))
|
|
decode_q_nope = decode_q_nope.view(bsz, self.num_heads,
|
|
self.kv_lora_rank)
|
|
decode_q_pe = decode_q_pe.view(bsz, self.num_heads, -1)
|
|
|
|
decode_q_nope, decode_q_pe = self.reorg_decode_q(
|
|
decode_q_nope, decode_q_pe)
|
|
|
|
decode_preprocess_res = DecodeMLAPreprocessResult(
|
|
decode_q_nope, decode_q_pe, decode_k_nope, decode_k_pe)
|
|
return decode_preprocess_res, None
|
|
|
|
def mla_preprocess_prefill(self, q_c, kv_no_split, kv_cache,
|
|
attn_metadata):
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
prefill_kv_no_split = kv_no_split[num_decode_tokens:num_actual_tokens]
|
|
prefill_q_c = q_c[num_decode_tokens:num_actual_tokens]
|
|
prefill_q = self.q_proj(prefill_q_c)[0] \
|
|
.view(-1, self.num_heads, self.qk_head_dim)
|
|
prefill_q_pe = prefill_q[..., self.qk_nope_head_dim:]
|
|
prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
|
|
cos = attn_metadata.prefill.cos
|
|
sin = attn_metadata.prefill.sin
|
|
prefill_slots = attn_metadata.slot_mapping[
|
|
num_decode_tokens:num_actual_tokens]
|
|
prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
|
|
if self.is_kv_producer:
|
|
attn_metadata.reshape_cache_event = torch.npu.Event()
|
|
prefill_k_pe, prefill_k_c_normed = self.exec_kv_prefill(
|
|
prefill_kv_no_split, cos, sin, kv_cache, prefill_slots)
|
|
if self.is_kv_producer:
|
|
attn_metadata.reshape_cache_event.record()
|
|
prefill_k_nope, prefill_value = self.kv_b_proj(
|
|
prefill_k_c_normed)[0].view(
|
|
-1, self.num_heads,
|
|
self.qk_nope_head_dim + self.v_head_dim).split(
|
|
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
prefill_k_pe = prefill_k_pe.view(prefill_q_c.shape[0],
|
|
self.num_kv_heads, -1)
|
|
prefill_k_pe = prefill_k_pe.expand((*prefill_k_nope.shape[:-1], -1))
|
|
return PrefillMLAPreprocessResult(prefill_q_nope, prefill_q_pe,
|
|
prefill_k_nope, prefill_k_pe,
|
|
prefill_value)
|
|
|
|
def mla_preprocess_decode(self, q_c, kv_no_split, kv_cache, attn_metadata):
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
decode_q_c = q_c[:num_decode_tokens]
|
|
cos = attn_metadata.decode.cos
|
|
sin = attn_metadata.decode.sin
|
|
decode_ql_nope, decode_q_pe = \
|
|
self._q_proj_and_k_up_proj(decode_q_c)
|
|
decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
|
|
decode_slots = attn_metadata.slot_mapping[:num_decode_tokens:1]
|
|
decode_kv_no_split = kv_no_split[:num_decode_tokens]
|
|
decode_k_pe, decode_k_nope = self.exec_kv_decode(
|
|
decode_kv_no_split, cos, sin, kv_cache, decode_slots)
|
|
return DecodeMLAPreprocessResult(decode_ql_nope, decode_q_pe,
|
|
decode_k_nope, decode_k_pe)
|
|
|
|
def _mla_preprocess(self, layer_name, hidden_states, kv_cache,
|
|
attn_metadata, need_gather_q_kv):
|
|
# MLA Preprocess:
|
|
# 1. Perform fused_qkv_a_proj and q_a_layernorm to obtain q_c and kv_no_split
|
|
# or
|
|
# Perform kv_a_proj_with_mqa to obtain kv_no_split
|
|
# 2. If need_gather_q_kv, perform all_gather.
|
|
# 3. Preprocess decode tokens, write kv cache and get:
|
|
# decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope
|
|
# 4. Preprocess prefill tokens, write kv cache and get:
|
|
# prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value
|
|
has_decode = attn_metadata.num_decodes > 0
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
if self.fused_qkv_a_proj is not None:
|
|
maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight,
|
|
dependency=hidden_states,
|
|
enabled=self.enable_prefetch)
|
|
qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
|
|
q_c, kv_no_split = qkv_lora.split(
|
|
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
|
|
dim=-1,
|
|
)
|
|
q_c = self.q_a_layernorm(q_c)
|
|
# allgather need contiguous data
|
|
kv_no_split = kv_no_split.contiguous()
|
|
else:
|
|
q_c = hidden_states
|
|
kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
|
|
# Process for Flash Comm V1
|
|
q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
q_c.contiguous(), need_gather_q_kv)
|
|
kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
kv_no_split.contiguous(), need_gather_q_kv)
|
|
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
reach_layer_for_shard_weight_series(layer)
|
|
|
|
decode_preprocess_res = None
|
|
prefill_preprocess_res = None
|
|
if has_prefill:
|
|
wait_for_kv_layer_from_connector(layer_name)
|
|
# Preprocess for decode tokens
|
|
if has_decode:
|
|
decode_preprocess_res = self.mla_preprocess_decode(
|
|
q_c, kv_no_split, kv_cache, attn_metadata)
|
|
# Preprocess for prefill tokens
|
|
if has_prefill:
|
|
prefill_preprocess_res = self.mla_preprocess_prefill(
|
|
q_c, kv_no_split, kv_cache, attn_metadata)
|
|
return decode_preprocess_res, prefill_preprocess_res
|
|
|
|
def get_num_actual_tokens(self, attn_metadata: M):
|
|
return attn_metadata.num_actual_tokens
|
|
|
|
def forward(
|
|
self,
|
|
layer_name,
|
|
hidden_states: torch.Tensor, # query in unified attn
|
|
kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: M,
|
|
need_gather_q_kv: bool = False,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
assert output is not None, "Output tensor must be provided."
|
|
if attn_metadata is None:
|
|
# Profiling run.
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
reach_layer_for_shard_weight_series(layer)
|
|
return output.fill_(0)
|
|
|
|
forward_context = get_forward_context()
|
|
num_actual_tokens = self.get_num_actual_tokens(attn_metadata)
|
|
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_prefill = attn_metadata.num_prefills > 0
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
output_padded = output
|
|
o_proj_input_shape = (forward_context.num_tokens,
|
|
self.num_heads * self.v_head_dim)
|
|
o_proj_input = torch.empty(o_proj_input_shape,
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device)
|
|
|
|
# MLA Preprocess
|
|
if self.enable_mlapo and \
|
|
attn_metadata.num_decode_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
|
|
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
hidden_states.contiguous(), need_gather_q_kv)
|
|
decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess_only_decode(
|
|
hidden_states, kv_cache, attn_metadata)
|
|
else:
|
|
decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess(
|
|
layer_name, hidden_states, kv_cache, attn_metadata,
|
|
need_gather_q_kv)
|
|
if decode_preprocess_res is not None:
|
|
# MLA Preprocess for decoding
|
|
output_decode = self._forward_decode(decode_preprocess_res.ql_nope,
|
|
decode_preprocess_res.q_pe,
|
|
decode_preprocess_res.k_nope,
|
|
decode_preprocess_res.k_pe,
|
|
kv_cache[0].shape[1],
|
|
attn_metadata)
|
|
|
|
o_proj_input[:num_decode_tokens] = output_decode
|
|
|
|
if prefill_preprocess_res is not None:
|
|
# FIX: aicore move should be also placed on the comm stream in dbo,
|
|
# otherwise it may affect the accuracy
|
|
# TODO: use an elegant way to overlap
|
|
output_prefill = self._forward_prefill(
|
|
prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe,
|
|
prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe,
|
|
prefill_preprocess_res.value, kv_cache, attn_metadata)
|
|
|
|
o_proj_input[num_decode_tokens:num_actual_tokens] = output_prefill
|
|
# O proj
|
|
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
|
|
maybe_npu_prefetch(inputs=self.o_proj.weight,
|
|
dependency=o_proj_input,
|
|
max_size=MAX_O_PROJ_PREFETCH_SIZE,
|
|
enabled=self.enable_prefetch)
|
|
|
|
output[...] = self.o_proj(o_proj_input,
|
|
is_prefill=prefill_preprocess_res
|
|
is not None)[0]
|
|
|
|
del o_proj_input
|
|
|
|
if has_prefill:
|
|
maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))
|
|
return output_padded
|