f6e5decc10
Upgrade to vllm 0.9.0. 0.8.5 will not be supported any more. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
784 lines
33 KiB
Python
784 lines
33 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
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import numpy as np
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import torch
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import torch_npu
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
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AttentionMetadata,
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MLAAttentionImpl)
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from vllm.attention.backends.utils import PAD_SLOT_ID
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from vllm.config import get_current_vllm_config
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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LinearBase, RowParallelLinear,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.ops.attention import vanilla_chunked_prefill_mla
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.worker.gpu_input_batch import InputBatch
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class AscendMLABackend(AttentionBackend):
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accept_output_buffer: bool = True
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@staticmethod
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def get_name() -> str:
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return "VLLM_ASCEND_MLA"
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return AscendMLAMetadata
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@staticmethod
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def get_builder_cls():
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return AscendMLAMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int,
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head_size: int) -> tuple[int, ...]:
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return (num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_impl_cls() -> Type["MLAAttentionImpl"]:
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return AscendMLAImpl
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@dataclass
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class AscendMLAPrefillMetadata:
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""" Prefill Specific Metadata for Ascend"""
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attn_mask: torch.Tensor
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query_lens: list[int]
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seq_lens: list[int]
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context_lens: torch.Tensor
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input_positions: torch.Tensor
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block_table: torch.Tensor
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max_query_len: int
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max_seq_lens: int
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@dataclass
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class AscendMLADecodeMetadata:
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# Input positions for rotrary embeddings since for MLA the rotary
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# position embeddings are applied inside the attention backend
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input_positions: torch.Tensor
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block_table: torch.Tensor
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seq_lens: torch.Tensor
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max_seq_lens: int
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seq_lens_list: list[int]
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@dataclass
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class AscendMLAMetadata:
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"""Metadata for MLACommon.
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# NOTE(sang): Definition of context_len, query_len, and seq_len.
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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num_actual_tokens: int # Number of tokens excluding padding.
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slot_mapping: torch.Tensor
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# New for MLA (compared to FlashAttention)
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# For handling prefill decode split
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num_decodes: int
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num_decode_tokens: int
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num_prefills: int
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# For logging.
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num_input_tokens: int = 0 # Number of tokens including padding.
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# The dimension of the attention heads
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head_dim: Optional[int] = None
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attn_mask: torch.Tensor = None
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# chunked prefill by default if no attn_states passed
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attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
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decode: Optional[AscendMLADecodeMetadata] = None
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prefill: Optional[AscendMLAPrefillMetadata] = None
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def __post_init__(self):
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pass
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# supported_head_sizes = AscendMLABackend.get_supported_head_sizes()
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# if self.head_dim is not None and self.head_dim \
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# not in supported_head_sizes:
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# raise ValueError(
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# f"Only {supported_head_sizes} are supported for head_dim,",
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# f"received {self.head_dim}.")
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M = TypeVar("M", bound=AscendMLAMetadata)
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class AscendMLAMetadataBuilder:
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# _attn_mask_builder = None
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def __init__(self,
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runner: "NPUModelRunner",
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metadata_cls: Optional[AscendMLAMetadata] = None):
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self.metadata_cls: Optional[AscendMLAMetadata] = metadata_cls \
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if metadata_cls is not None else AscendMLAMetadata # type: ignore
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self.runner = runner
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scheduler_config = runner.scheduler_config
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self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
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def reorder_batch(self, input_batch: "InputBatch",
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scheduler_output: "SchedulerOutput") -> bool:
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# We now want to reorder the batch so that the "decode" requests are at
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# the front and the "prefill" requests are at the using the least amount
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# swaps possible. (NOTE for now we loosely use "decode" to mean requests
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# where attention is likely memory-bound and "prefill" to mean requests
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# where attention is likely compute-bound, TODO(lucas): figure out a
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# better naming here)
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decodes = []
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prefills = []
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num_decode_tokens = 0
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num_prefill_tokens = 0
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for i, req_id in enumerate(input_batch.req_ids):
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num_tokens = scheduler_output.num_scheduled_tokens[req_id]
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# for now treat 1 scheduled token as "decode" even if its not,
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# we should update this to something like < 8 in the future but
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# currently the TritonMLA._forward_decode only supports
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# num_tokens = 1
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if num_tokens == 1:
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decodes.append(i)
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num_decode_tokens += num_tokens
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else:
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prefills.append(i)
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num_prefill_tokens += num_tokens
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# We hope that this is fairly minimal since decodes
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# should be around for a number of iterations so hopefully they are
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# relatively stationary (and new request are generally appended to the
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# persistent batch so already should be at the back)
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# To achieve this we loop over the decodes in descending order and
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# the prefills in ascending order. We swap decodes from the "back"
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# i.e. past where the last decode should be in the reodorered with
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# prefills from the front of the batch.
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# `decodes` and `prefills` are already in ascending order just based on
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# the above loop
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num_decodes = len(decodes)
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num_prefills = len(prefills)
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first_prefill = 0
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modified_batch = False
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for i in range(1, min(num_decodes, num_prefills) + 1):
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# If the decode is at the "back" of the batch, i, we can swap it
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# with the prefill closest to the front of the batch
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if decodes[num_decodes - i] >= num_decodes:
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input_batch.swap_states(prefills[first_prefill],
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decodes[num_decodes - i])
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first_prefill += 1
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modified_batch = True
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else:
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break
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# Save for next `build` call
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# TODO(lucas): this is a bit of a hack, we should probably have a
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# better way of doing this
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self._num_decodes = num_decodes
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self._num_prefills = num_prefills
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self._num_decode_tokens = num_decode_tokens
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self._num_prefill_tokens = num_prefill_tokens
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return modified_batch
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def _get_graph_runner_block_tables(
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self, num_seqs: int, block_tables: torch.Tensor) -> torch.Tensor:
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max_batch_size, max_blocks = self.runner.graph_block_tables.shape
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assert max_batch_size >= num_seqs
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if isinstance(self.runner.graph_block_tables, np.ndarray):
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graph_block_tables = torch.zeros((max_batch_size, max_blocks),
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dtype=block_tables.dtype,
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device=block_tables.device)
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else:
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graph_block_tables = self.runner.graph_block_tables.to(
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device=block_tables.device, dtype=block_tables.dtype)
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num_blocks = block_tables.size(1)
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if num_blocks <= max_blocks:
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graph_block_tables[:num_seqs, :
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num_blocks] = block_tables[:num_seqs, :
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num_blocks]
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else:
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graph_block_tables[:num_seqs, :
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max_blocks] = block_tables[:num_seqs, :
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max_blocks]
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return graph_block_tables
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def build(self,
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num_reqs: int,
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num_actual_tokens: int,
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max_query_len: int,
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common_prefix_len: Optional[int] = None,
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graph_pad_size: int = -1) -> AscendMLAMetadata:
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assert self._num_decodes + self._num_prefills == num_reqs
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# Note(simon): be careful about the CPU <> GPU memory movement in this
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# function. We should avoid GPU -> CPU sync as much as possible because
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# it blocks on all previous kernels.
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device = self.runner.device
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block_table = self.runner.input_batch.block_table[0].get_device_tensor(
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)
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block_table[:num_reqs, :self.runner.max_num_blocks_per_req] = (
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block_table[:num_reqs])
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slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to(
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device, non_blocking=True)
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input_positions = self.runner.positions_cpu[:num_actual_tokens].to(
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device, non_blocking=True).long()
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seq_lens_cpu = self.runner.seq_lens_cpu[:num_reqs]
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query_lens = seq_lens_cpu - self.runner.input_batch.num_computed_tokens_cpu_tensor[:
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num_reqs]
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seq_lens = seq_lens_cpu
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max_query_len = query_lens.max().item()
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max_seq_lens = seq_lens.max().item()
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prefill_metadata = None
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if self._num_prefills > 0:
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reqs_start = self._num_decodes # prefill_start
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tokens_start = self._num_decode_tokens
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max_query_len = query_lens[tokens_start:].max().item()
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max_seq_lens = seq_lens[tokens_start:].max().item()
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prefill_metadata = AscendMLAPrefillMetadata(
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attn_mask=self.runner.attn_mask,
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query_lens=query_lens[tokens_start:],
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seq_lens=seq_lens,
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context_lens=seq_lens[tokens_start:],
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input_positions=input_positions[tokens_start:],
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block_table=block_table[reqs_start:, ...],
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max_query_len=max_query_len,
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max_seq_lens=max_seq_lens,
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)
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decode_metadata = None
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use_torchair_graph = graph_pad_size != -1
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if self._num_decodes > 0:
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max_seq_lens = seq_lens[:self._num_decodes].max().item()
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seq_lens = seq_lens[:self._num_decode_tokens]
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input_positions = input_positions[:self._num_decode_tokens]
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block_table = block_table[:self._num_decode_tokens, ...]
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if use_torchair_graph and self.runner.attn_state == AscendAttentionState.DecodeOnly:
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num_seqs = len(seq_lens)
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if graph_pad_size != 0:
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pad_value = 1
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padded_seq_lens = seq_lens.tolist() + [pad_value
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] * graph_pad_size
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else:
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padded_seq_lens = seq_lens.tolist()
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seq_lens = torch.from_numpy(
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np.array(padded_seq_lens).astype(np.int32))
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padding = torch.full((graph_pad_size, ),
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PAD_SLOT_ID,
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dtype=slot_mapping.dtype,
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device=slot_mapping.device)
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slot_mapping = torch.cat([slot_mapping, padding])
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block_table_padding = torch.zeros(
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(graph_pad_size, ) + block_table.shape[1:],
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dtype=block_table.dtype,
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device=block_table.device)
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block_table = torch.cat([block_table, block_table_padding],
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dim=0)
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block_table = self._get_graph_runner_block_tables(
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num_seqs, block_table)
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padding_0 = torch.zeros(graph_pad_size,
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dtype=input_positions.dtype,
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device=input_positions.device)
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input_positions = torch.cat([input_positions, padding_0])
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decode_metadata = AscendMLADecodeMetadata(
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input_positions=input_positions,
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block_table=block_table,
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seq_lens=seq_lens,
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seq_lens_list=seq_lens.tolist(),
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max_seq_lens=max_seq_lens)
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return self.metadata_cls( # type: ignore
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num_actual_tokens=num_actual_tokens,
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slot_mapping=slot_mapping,
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head_dim=self.runner.model_config.get_head_size(),
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num_decodes=self._num_decodes,
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num_decode_tokens=self._num_decode_tokens,
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num_prefills=self._num_prefills,
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attn_mask=self.runner.attn_mask,
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attn_state=self.runner.attn_state,
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prefill=prefill_metadata,
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decode=decode_metadata,
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)
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class AscendMLAImpl(MLAAttentionImpl):
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[dict[str, Any]],
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logits_soft_cap: Optional[float],
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attn_type: str,
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# MLA Specific Arguments
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q_lora_rank: Optional[int],
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kv_lora_rank: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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qk_head_dim: int,
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v_head_dim: int,
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rotary_emb: RotaryEmbedding,
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# q_proj should be q_b_proj if q_lora_rank is not None, but from an
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# attention backend perspective we rely on the layer to pass in the
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# correct matrix
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q_proj: ColumnParallelLinear,
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kv_b_proj: ColumnParallelLinear,
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o_proj: RowParallelLinear,
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**kwargs,
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) -> None:
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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self.kv_cache_dtype = kv_cache_dtype
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_head_dim
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self.v_head_dim = v_head_dim
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# Hack for V1 for now to avoid torch library overhead (since we are
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# already inside an attention custom op), pull out the forward
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# method from the rotary embedding and call it directly
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# TODO(lucas): we should probably find a cleaner way to do this
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self.rotary_emb = rotary_emb
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self.q_proj = q_proj
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self.kv_b_proj = kv_b_proj
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self.o_proj = o_proj
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self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
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self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
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# Handle the differences between the flash_attn_varlen from flash_attn
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# and the one from vllm_flash_attn. The former is used on RoCM and the
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# latter has an additional parameter to control FA2 vs FA3
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# self.flash_attn_varlen_func = flash_attn_varlen_func
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# if self.vllm_flash_attn_version is not None:
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# self.flash_attn_varlen_func = \
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# functools.partial(flash_attn_varlen_func,
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# fa_version=self.vllm_flash_attn_version)
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self.enable_graph_mode = False
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additional_config = get_current_vllm_config().additional_config
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if additional_config:
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self.enable_graph_mode = additional_config.get(
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"enable_graph_mode", False)
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def _v_up_proj_and_o_proj(self, x):
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# Convert from (B, N, L) to (N, B, L)
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x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
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# Multiply (N, B, L) x (N, L, V) -> (N, B, V)
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x = torch.bmm(x, self.W_UV)
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# Convert from (N, B, V) to (B, N * V)
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x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
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return self.o_proj(x)[0]
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# Return `ql_nope`, `q_pe`
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def _q_proj_and_k_up_proj(self, x):
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q_nope, q_pe = self.q_proj(x)[0]\
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.view(-1, self.num_heads, self.qk_head_dim)\
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.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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# Convert from (B, N, P) to (N, B, P)
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q_nope = q_nope.transpose(0, 1)
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# Multiply (N, B, P) x (N, P, L) -> (N, B, L)
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ql_nope = torch.bmm(q_nope, self.W_UK_T)
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# Convert from (N, B, L) to (B, N, L)
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return ql_nope.transpose(0, 1), q_pe
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def process_weights_after_loading(self, act_dtype: torch.dtype):
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def get_layer_weight(layer):
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WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
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for attr in WEIGHT_NAMES:
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if hasattr(layer, attr):
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return getattr(layer, attr)
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raise AttributeError(
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f"Layer '{layer}' has no recognized weight attribute:"
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f" {WEIGHT_NAMES}.")
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def get_and_maybe_dequant_weights(layer: LinearBase):
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if not isinstance(layer.quant_method, UnquantizedLinearMethod):
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# NOTE: This should only be used offline, since it's O(N^3)
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eye = torch.eye(layer.input_size_per_partition,
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dtype=act_dtype,
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device=get_layer_weight(layer).device)
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dequant_weights = layer.quant_method.apply(layer,
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eye,
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bias=None)
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del eye
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# standardize to (output, input)
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return dequant_weights.T
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return layer.weight
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# 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).contiguous()
|
|
# Convert from (L, N, P) to (N, P, L)
|
|
self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
|
|
self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29)
|
|
self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29)
|
|
|
|
def _forward_prefill(
|
|
self,
|
|
query: torch.Tensor,
|
|
kv_c_normed: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
kv_c_and_k_pe_cache: torch.Tensor,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata.prefill is not None
|
|
|
|
num_tokens = query.size(0)
|
|
attn_output = None
|
|
# Here is only 2 possibility of input, ChunkedPrefill or PrefillNoCache
|
|
if attn_metadata.attn_state == AscendAttentionState.ChunkedPrefill:
|
|
attn_output = torch.empty(num_tokens,
|
|
self.num_heads * self.v_head_dim,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
# current requests is chunked in prefill, disable flash attention with chunked prefill
|
|
vanilla_chunked_prefill_mla(
|
|
output=attn_output,
|
|
query=query,
|
|
kv_cache=kv_c_and_k_pe_cache,
|
|
block_tables=attn_metadata.prefill.block_table,
|
|
query_lens=attn_metadata.prefill.query_lens,
|
|
context_lens=attn_metadata.prefill.context_lens,
|
|
kv_b_proj=self.kv_b_proj,
|
|
max_query_len=attn_metadata.prefill.max_query_len,
|
|
max_context_len=attn_metadata.prefill.max_seq_lens,
|
|
nope_dim=self.qk_nope_head_dim,
|
|
rope_dim=self.qk_rope_head_dim,
|
|
v_head_dim=self.v_head_dim,
|
|
scale=self.scale,
|
|
alibi_slopes=None,
|
|
causal=True)
|
|
elif attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
|
|
attn_output = torch.empty(num_tokens,
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
k_nope, value = self.kv_b_proj(kv_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)
|
|
key = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
|
|
dim=-1)
|
|
torch_npu._npu_flash_attention(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
mask=attn_metadata.attn_mask,
|
|
seq_len=attn_metadata.prefill.context_lens,
|
|
scale_value=self.scale,
|
|
num_heads=self.num_heads,
|
|
num_kv_heads=self.num_heads,
|
|
out=attn_output)
|
|
attn_output = attn_output.view(-1, self.num_heads, self.v_head_dim)
|
|
else:
|
|
raise RuntimeError(
|
|
"Unexpected path reached, AscendMLAImpl should only have PrefillNoCache and ChunkedPrefill scenario in forward prefill, please file a bug to vllm-ascend !"
|
|
)
|
|
attn_output = attn_output.reshape(
|
|
[num_tokens, self.num_heads * self.v_head_dim])
|
|
return self.o_proj(attn_output)[0]
|
|
|
|
def exec_kv(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
|
|
B = hidden_states.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
kv = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv = kv.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
k_pe, k_nope, _, _ = torch.ops.npu_inference.npu_kv_rmsnorm_rope_cache(
|
|
kv,
|
|
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="PA",
|
|
)
|
|
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.ops.npu_inference.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,
|
|
kv_c_and_k_pe_cache: torch.Tensor,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
decode_meta = attn_metadata.decode
|
|
assert decode_meta is not None
|
|
|
|
q = torch.cat([q_nope, q_pe], dim=-1)
|
|
num_tokens = q.size(0)
|
|
attn_output = torch.empty(
|
|
[num_tokens, self.num_heads, self.kv_lora_rank],
|
|
dtype=q.dtype,
|
|
device=q.device)
|
|
if self.running_in_graph:
|
|
# TorchAir's shape is [bs, num_heads_per_rank, seq_len, dim]
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, 1, -1)
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1)
|
|
# 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]
|
|
block_size = kv_c_and_k_pe_cache[0].shape[1]
|
|
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, _ = torch.ops.npu.npu_fused_infer_attention_score(
|
|
q_nope,
|
|
k_nope,
|
|
k_nope,
|
|
query_rope=q_pe,
|
|
key_rope=k_pe,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="BNSD",
|
|
atten_mask=attn_metadata.attn_mask,
|
|
scale=self.scale,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
block_table=decode_meta.block_table,
|
|
block_size=block_size,
|
|
actual_seq_lengths_kv=decode_meta.seq_lens_list,
|
|
)
|
|
else:
|
|
torch_npu._npu_paged_attention_mla(
|
|
query=q,
|
|
key_cache=kv_c_and_k_pe_cache,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
block_table=attn_metadata.decode.block_table, # type:ignore
|
|
context_lens=attn_metadata.decode.seq_lens, # type:ignore
|
|
mla_vheadsize=self.kv_lora_rank,
|
|
out=attn_output)
|
|
return self._v_up_proj_and_o_proj(attn_output)
|
|
|
|
def forward(
|
|
self,
|
|
layer: AttentionLayer,
|
|
hidden_states_or_q_c: torch.Tensor, # query in unified attn
|
|
hidden_states_or_kv_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:
|
|
# Profiling run.
|
|
return output
|
|
self.running_in_graph = self.enable_graph_mode and attn_metadata.attn_state == AscendAttentionState.DecodeOnly
|
|
num_actual_toks = attn_metadata.num_actual_tokens
|
|
if k_pe is None and not self.running_in_graph:
|
|
kv_c, k_pe = self.kv_a_proj_with_mqa(
|
|
hidden_states_or_kv_c_normed)[0].split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
|
|
else:
|
|
kv_c_normed = hidden_states_or_kv_c_normed
|
|
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
|
|
if not self.running_in_graph:
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
output_padded = output
|
|
output = output[:num_actual_toks, ...]
|
|
kv_c_normed = kv_c_normed[:num_actual_toks, ...]
|
|
prefill_k_c_normed = kv_c_normed[num_decode_tokens:]
|
|
if not self.running_in_graph:
|
|
hidden_states_or_q_c = hidden_states_or_q_c[:num_actual_toks, ...]
|
|
decode_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens]
|
|
prefill_hs_or_q_c = hidden_states_or_q_c[num_decode_tokens:]
|
|
k_pe = k_pe[:num_actual_toks, ...]
|
|
k_pe = k_pe.unsqueeze(1)
|
|
decode_k_pe = k_pe[:num_decode_tokens]
|
|
prefill_k_pe = k_pe[num_decode_tokens:]
|
|
else:
|
|
decode_hs_or_q_c = hidden_states_or_q_c
|
|
if has_decode:
|
|
decode_k_nope = None
|
|
assert attn_metadata.decode is not None
|
|
decode_ql_nope, decode_q_pe = \
|
|
self._q_proj_and_k_up_proj(decode_hs_or_q_c)
|
|
if self.running_in_graph:
|
|
seq_len = self.rotary_emb.max_position_embeddings
|
|
cos = self.rotary_emb.cos_cached[:seq_len].to(
|
|
dtype=decode_q_pe.dtype)
|
|
sin = self.rotary_emb.sin_cached[:seq_len].to(
|
|
dtype=decode_q_pe.dtype)
|
|
cos = cos[attn_metadata.decode.input_positions]
|
|
sin = sin[attn_metadata.decode.input_positions]
|
|
cos = cos[:, None, None, :]
|
|
sin = sin[:, None, None, :]
|
|
decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
|
|
decode_k_pe, decode_k_nope = self.exec_kv(
|
|
hidden_states_or_kv_c_normed, cos, sin, kv_cache,
|
|
attn_metadata.slot_mapping)
|
|
else:
|
|
decode_q_pe[...], decode_k_pe[...] = self.rotary_emb(
|
|
attn_metadata.decode.input_positions,
|
|
decode_q_pe.contiguous(),
|
|
decode_k_pe,
|
|
max_seq_len=attn_metadata.decode.max_seq_lens)
|
|
if has_prefill:
|
|
assert attn_metadata.prefill is not None
|
|
prefill_q = self.q_proj(prefill_hs_or_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]
|
|
if self.enable_graph_mode:
|
|
num_tokens = prefill_hs_or_q_c.shape[0]
|
|
prefill_k_pe = prefill_k_pe.view(num_tokens, self.num_kv_heads,
|
|
-1)
|
|
if self.rotary_emb.__class__.__name__ == 'RotaryEmbedding':
|
|
# NOTE: When scaling not specified
|
|
ori_q_pe_shape, ori_k_pe_shape = prefill_q_pe.shape, prefill_k_pe.shape
|
|
prefill_q_pe = prefill_q_pe.reshape(num_tokens, -1)
|
|
prefill_k_pe = prefill_k_pe.reshape(num_tokens, -1)
|
|
prefill_q_pe, prefill_k_pe = self.rotary_emb(
|
|
attn_metadata.prefill.input_positions, prefill_q_pe,
|
|
prefill_k_pe)
|
|
prefill_q_pe = prefill_q_pe.view(ori_q_pe_shape)
|
|
prefill_k_pe = prefill_k_pe.view(ori_k_pe_shape)
|
|
else:
|
|
prefill_q_pe, prefill_k_pe = self.rotary_emb(
|
|
attn_metadata.prefill.input_positions, prefill_q_pe,
|
|
prefill_k_pe)
|
|
prefill_q = torch.cat([prefill_q_nope, prefill_q_pe], dim=-1)
|
|
else:
|
|
prefill_q_pe[...], prefill_k_pe[...] = self.rotary_emb(
|
|
attn_metadata.prefill.input_positions,
|
|
prefill_q_pe.contiguous(),
|
|
prefill_k_pe,
|
|
max_seq_len=attn_metadata.prefill.max_seq_lens)
|
|
if self.enable_graph_mode:
|
|
if len(kv_cache) > 0 and kv_cache[0].numel(
|
|
) > 0 and attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
|
|
slots = attn_metadata.slot_mapping
|
|
# NOTE: Separate the kv cache in advance to avoid OOM or other issues
|
|
torch_npu._npu_reshape_and_cache(key=kv_c_normed.view(
|
|
num_tokens, self.num_kv_heads, -1),
|
|
value=prefill_k_pe,
|
|
key_cache=kv_cache[0],
|
|
value_cache=kv_cache[1],
|
|
slot_indices=slots)
|
|
elif kv_cache.numel() > 0:
|
|
key = torch.cat([
|
|
kv_c_normed.view([num_actual_toks, self.num_kv_heads, -1]),
|
|
k_pe
|
|
],
|
|
dim=2)
|
|
torch_npu._npu_reshape_and_cache_siso(
|
|
key=key,
|
|
key_cache=kv_cache,
|
|
slot_indices=attn_metadata.slot_mapping.flatten())
|
|
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:
|
|
if self.running_in_graph:
|
|
return self._forward_decode(decode_ql_nope, decode_q_pe,
|
|
decode_k_nope, decode_k_pe,
|
|
kv_cache, attn_metadata)
|
|
else:
|
|
output[:num_decode_tokens] = self._forward_decode(
|
|
decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe,
|
|
kv_cache, attn_metadata)
|
|
return output_padded
|