3007 lines
118 KiB
Python
3007 lines
118 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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# MLA Common Components
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This file implements common components for MLA implementations.
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First we define:
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Sq as Q sequence length
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Skv as KV sequence length
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MLA has two possible ways of computing, a data-movement friendly approach and a
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compute friendly approach, we generally want to use the compute friendly
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approach for "prefill" (i.e. the ratio Sq / Skv is "small", is near 1)
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and the data-movement friendly approach for "decode" (i.e. the ratio
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Sq / Skv is "large").
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NOTE what we deem small and large is currently determined by if its labelled
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prefill or decode by the scheduler, but this is something we should probably
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tune.
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Main reference: DeepseekV2 paper, and FlashInfer Implementation
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(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
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Deepseek's MLA attention works the following way:
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* Use a single latent vector to represent the per-token entry of the KV cache.
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* For decode (i.e. the memory friendly approach) the attention "simulates" a
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multi-head attention, while the compute is similar to multi-query attention.
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Below is example of both paths assuming batchsize = 1
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## More Extent Definitions:
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C Context length, `Skv - Sq`
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H hidden size
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N number of attention heads
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Lq latent dimension for Q 1536 in DSV3
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Lkv latent dimension for K/V 512 in DSV3
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P nope dimension, no rope. 128 in DSV3
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R rope dimension, goes through rope. 64 in DSV3
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V V head dim. 128 in DSV3
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## Vector/Matrix Definitions
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h_t hidden states (input to attention) shape [Sq, H]
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q_c latent/compressed Q shape [Sq, Lq]
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q_nope uncompressed Q (no-rope) shape [Sq, N, P]
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q_pe uncompressed Q (rope) shape [Sq, N, R]
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kv_c latent/compressed KV shape [Skv, Lkv]
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k_pe decoupled k position embeddings shape [Skv, R]
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new_kv_c new kv_c from current iter shape [Sq, Lkv]
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new_k_pe new k_pe from current iter shape [Sq, R]
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cache_kv_c cached k_c from previous iters shape [C, Lkv]
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cache_k_pe cached k_pe from previous iters shape [C, R]
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W_DQ project h_t to q_c shape [H, Lq]
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W_UQ project q_c to q_nope shape [Lq, N * P]
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W_QR project q_c to q_pe shape [Lq, N * R]
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W_DKV project h_t to kv_c shape [H, Lkv]
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W_UK project kv_c to k_nope shape [Lkv, N, P]
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W_KR project h_t to k_pe shape [H, R]
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W_UV project kv_c to v shape [Lkv, N, V]
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W_O project v to h_t shape [N * V, H]
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## Compute Friendly Approach (i.e. "forward_mha"):
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q_c = h_t @ W_DQ
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q_nope = (q_c @ W_UQ).view(Sq, N, P)
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q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
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new_kv_c = h_t @ W_DKV
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new_k_pe = RoPE(h_t @ W_KR)
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kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0)
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k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0)
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k_nope = (kv_c @ W_UK.view(Lkv, N * P)).view(Skv, N, P)
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v = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V)
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// MHA with QK headdim = P + R
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// V headdim = V
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// spda_o shape [Sq, N, V]
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spda_o = scaled_dot_product_attention(
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torch.cat([q_nope, q_pe], dim=-1),
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torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
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v
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)
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return spda_o @ W_O
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NOTE: in the actual code,
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`kv_b_proj` is [W_UK; W_UV] concatenated per head
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`q_b_proj` is [W_UQ; W_QR] concatenated per head
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`out_proj` is W_O
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## Data-Movement Friendly Approach (i.e. "forward_mqa"):
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Runtime
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q_c = h_t @ W_DQ
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q_nope = (q_c @ W_UQ).view(-1, N, P)
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ql_nope = einsum("snh,lnh->snl", q, W_UK)
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q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
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new_kv_c = h_t @ W_DKV
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new_k_pe = RoPE(h_t @ W_KR)
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kv_c = torch.cat([new_kv_c, cache_kv_c], dim=0)
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k_pe = torch.cat([new_k_pe, cache_k_pe], dim=0)
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// MQA with QK headdim = Lkv + R
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// V headdim = Lkv
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// spda_o shape [Sq, N, Lkv]
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// NOTE: this is less compute-friendly since Lkv > P
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// but is more data-movement friendly since its MQA vs MHA
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spda_o = scaled_dot_product_attention(
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torch.cat([ql_nope, q_pe], dim=-1),
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torch.cat([kv_c, k_pe], dim=-1),
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kv_c
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)
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o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
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return o.view(-1, N * V) @ self.num_heads @ W_O
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## Chunked Prefill
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For chunked prefill we want to use the compute friendly algorithm. We are
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assuming sufficiently large Sq / Skv ratio, in the future may want to switch to
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the data-movement friendly approach if the chunk (i.e. `Sq`) is small.
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However, the compute-friendly approach can potentially run out of memory if Skv
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is large due to: `k_nope = (kv_c @ W_UK).view(Skv, N, P)`
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To mitigate this, we chunk the computation of attention with respect to the
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current context (i.e. `cache_kv_c` and `cache_k_pe`) so that we can used a
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fixed workspace size.
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The chunked prefill approach is as follows:
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MCC Max chunk of context to process per iter, computed dynamically,
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used to bound the memory usage
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q_c = h_t @ W_DQ
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q_nope = (q_c @ W_UQ).view(Sq, N, P)
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q_pe = RoPE(q_c @ W_QR).view(Sq, N, R)
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new_kv_c = h_t @ W_DKV
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new_k_pe = RoPE(h_t @ W_KR)
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new_k_nope = (new_kv_c @ W_UK.view(Lkv, N * P)).view(Sq, N, P)
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new_v = (new_kv_c @ W_UV.view(Lkv, N * V)).view(Sq, N, V)
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// MHA between queries and new KV
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// with QK headdim = P + R
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// V headdim = V
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// curr_o shape [Sq, N, V]
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// curr_lse shape [N, Sq], this is just order FA returns
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curr_o, curr_lse = scaled_dot_product_attention(
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torch.cat([q_nope, q_pe], dim=-1),
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torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
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new_v,
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casual=True,
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return_softmax_lse=True
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)
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// Compute attention with the already existing context
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for chunk_idx in range(cdiv(C, MCC)):
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chunk_start = chunk_idx * MCC
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chunk_end = min(chunk_start + MCC, C)
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Sc = chunk_end - chunk_start
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cache_kv_c_chunk = cache_kv_c[chunk_start:chunk_end]
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cache_k_pe_chunk = cache_k_pe[chunk_start:chunk_end]
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cache_k_nope_chunk = (cache_kv_c_chunk @ W_UK).view(-1, N, P)
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cache_v_chunk = (cache_kv_c_chunk @ W_UV).view(-1, N, V)
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chunk_o, chunk_lse = scaled_dot_product_attention(
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torch.cat([q_nope, q_pe], dim=-1),
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torch.cat([cache_k_nope_chunk,
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cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)],
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dim=-1),
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cache_v_chunk,
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casual=False,
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return_softmax_lse=True
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)
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curr_o, curr_lse = merge_attn_states(
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suffix_output=curr_o,
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suffix_lse=curr_lse,
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prefix_output=chunk_o,
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prefix_lse=chunk_lse,
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)
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return curr_o @ W_O
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"""
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import functools
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from abc import abstractmethod
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar, cast
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if TYPE_CHECKING:
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from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
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import torch
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import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.config import CacheConfig, ModelConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed.parallel_state import get_dcp_group, is_global_first_rank
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.attention.attention import (
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_init_kv_cache_quant,
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get_attention_context,
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set_default_quant_scales,
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should_load_quant_weights,
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)
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from vllm.model_executor.layers.attention.kv_transfer_utils import (
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maybe_transfer_kv_layer,
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)
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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UnquantizedLinearMethod,
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LinearBase,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape,
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get_and_maybe_dequant_weights,
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)
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from vllm.platforms import current_platform
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from vllm.utils.flashinfer import has_flashinfer, has_nvidia_artifactory
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from vllm.utils.math_utils import cdiv, round_down
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from vllm.utils.torch_utils import (
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direct_register_custom_op,
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kv_cache_dtype_str_to_dtype,
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)
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from vllm.v1.attention.backend import (
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AttentionBackend,
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AttentionLayer,
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AttentionMetadata,
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AttentionMetadataBuilder,
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AttentionType,
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CommonAttentionMetadata,
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MLAAttentionImpl,
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)
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from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
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from vllm.v1.attention.backends.utils import (
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get_dcp_local_seq_lens,
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get_per_layer_parameters,
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infer_global_hyperparameters,
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split_decodes_and_prefills,
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)
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from vllm.v1.attention.ops.common import cp_lse_ag_out_rs
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from vllm.v1.attention.ops.merge_attn_states import merge_attn_states
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from vllm.v1.attention.selector import get_attn_backend
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from vllm.v1.kv_cache_interface import (
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AttentionSpec,
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KVCacheSpec,
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MLAAttentionSpec,
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)
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from vllm.v1.attention.backend import AttentionCGSupport
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logger = init_logger(__name__)
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class MLAAttention(nn.Module, AttentionLayerBase):
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"""Multi-Head Latent Attention layer.
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This class takes query, and compressed key/value tensors as input.
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The class does the following:
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1. Store the input key and value tensors in the KV cache.
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2. Perform (multi-head/multi-query/grouped-query) attention.
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3. Return the output tensor.
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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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scale: float,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int | None,
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kv_lora_rank: int,
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kv_b_proj: ColumnParallelLinear,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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use_sparse: bool = False,
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indexer: object | None = None,
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**extra_impl_args,
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):
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super().__init__()
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self.num_heads = num_heads
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self.scale = scale
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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.v_head_dim = v_head_dim
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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.head_size = kv_lora_rank + qk_rope_head_dim
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self.layer_name = prefix
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if cache_config is not None:
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kv_cache_dtype = cache_config.cache_dtype
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block_size = cache_config.block_size
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calculate_kv_scales = cache_config.calculate_kv_scales
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else:
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kv_cache_dtype = "auto"
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block_size = 16
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calculate_kv_scales = False
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self.quant_config = quant_config
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# Initialize KV cache quantization attributes
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self.kv_cache_dtype = kv_cache_dtype
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self.calculate_kv_scales = calculate_kv_scales
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_init_kv_cache_quant(self, quant_config, prefix)
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dtype = torch.get_default_dtype()
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self.attn_backend = get_attn_backend(
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self.head_size,
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dtype,
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kv_cache_dtype,
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block_size,
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use_mla=True,
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use_sparse=use_sparse,
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)
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if (
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cache_config is not None
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and cache_config.enable_prefix_caching
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and vllm_is_batch_invariant()
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and (
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self.attn_backend.get_name() == "TRITON_MLA"
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or self.attn_backend.get_name() == "FLASHINFER"
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)
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):
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logger.warning_once(
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"Disabling prefix caching for TRITON_MLA / FLASHINFER "
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"with batch invariance, as it is not yet supported.",
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scope="local",
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)
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cache_config.enable_prefix_caching = False
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impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
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self.impl = impl_cls(
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num_heads=self.num_heads,
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head_size=self.head_size,
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scale=self.scale,
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num_kv_heads=1,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype=self.kv_cache_dtype,
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logits_soft_cap=None,
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attn_type=AttentionType.DECODER,
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kv_sharing_target_layer_name=None,
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# MLA Args
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q_lora_rank=self.q_lora_rank,
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kv_lora_rank=self.kv_lora_rank,
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qk_nope_head_dim=self.qk_nope_head_dim,
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qk_rope_head_dim=self.qk_rope_head_dim,
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qk_head_dim=self.qk_nope_head_dim + self.qk_rope_head_dim,
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v_head_dim=self.v_head_dim,
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kv_b_proj=kv_b_proj,
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indexer=indexer,
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**extra_impl_args,
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)
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# self.use_direct_call = not current_platform.opaque_attention_op()
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self.use_direct_call = True
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError(f"Duplicate layer name: {prefix}")
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compilation_config.static_forward_context[prefix] = self
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self.kv_cache = [
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torch.tensor([])
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for _ in range(
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get_current_vllm_config().parallel_config.pipeline_parallel_size
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)
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]
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self.use_sparse = use_sparse
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# Initialize q/k/v range constants.
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self.q_range = torch.tensor(envs.Q_SCALE_CONSTANT, dtype=torch.float32)
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self.k_range = torch.tensor(envs.K_SCALE_CONSTANT, dtype=torch.float32)
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self.v_range = torch.tensor(envs.V_SCALE_CONSTANT, dtype=torch.float32)
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def forward_corex(
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self,
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q: torch.Tensor,
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kv_c_normed: torch.Tensor,
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k_pe: torch.Tensor,
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output_shape: torch.Size | None = None,
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) -> torch.Tensor:
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if self.calculate_kv_scales:
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torch.ops.vllm.maybe_calc_kv_scales(q, kv_c_normed, k_pe, self.layer_name)
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optional_args = {}
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output_shape = output_shape if output_shape is not None else q.shape
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output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
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if self.use_direct_call:
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forward_context: ForwardContext = get_forward_context()
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||
attn_metadata = forward_context.attn_metadata
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||
if isinstance(attn_metadata, dict):
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attn_metadata = attn_metadata[self.layer_name]
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self_kv_cache = self.kv_cache[forward_context.virtual_engine]
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||
if self.attn_backend.accept_output_buffer:
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output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
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output = self.impl.forward(
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||
self,
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q,
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kv_c_normed,
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k_pe,
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self_kv_cache,
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attn_metadata,
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output=output,
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**optional_args,
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||
)
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||
else:
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||
output = self.impl.forward(
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||
self,
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||
q,
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||
kv_c_normed,
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k_pe,
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||
self_kv_cache,
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||
attn_metadata,
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||
**optional_args,
|
||
)
|
||
return output
|
||
|
||
def forward(
|
||
self,
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||
q: torch.Tensor,
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||
kv_c_normed: torch.Tensor,
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||
k_pe: torch.Tensor,
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||
output_shape: torch.Size | None = None,
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||
) -> torch.Tensor:
|
||
return self.forward_corex(q, kv_c_normed, k_pe, output_shape)
|
||
if self.calculate_kv_scales:
|
||
torch.ops.vllm.maybe_calc_kv_scales(q, kv_c_normed, k_pe, self.layer_name)
|
||
|
||
if self.use_direct_call:
|
||
forward_context: ForwardContext = get_forward_context()
|
||
attn_metadata = forward_context.attn_metadata
|
||
if isinstance(attn_metadata, dict):
|
||
attn_metadata = attn_metadata[self.layer_name]
|
||
self_kv_cache = self.kv_cache[forward_context.virtual_engine]
|
||
|
||
if self.attn_backend.accept_output_buffer:
|
||
output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
|
||
self.impl.forward(
|
||
self,
|
||
q,
|
||
kv_c_normed,
|
||
k_pe,
|
||
self_kv_cache,
|
||
attn_metadata,
|
||
output=output,
|
||
)
|
||
return output
|
||
else:
|
||
return self.impl.forward(
|
||
self, q, kv_c_normed, k_pe, self_kv_cache, attn_metadata
|
||
)
|
||
else:
|
||
if self.attn_backend.accept_output_buffer:
|
||
output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
|
||
torch.ops.vllm.unified_mla_attention_with_output(
|
||
q,
|
||
kv_c_normed,
|
||
k_pe,
|
||
output,
|
||
self.layer_name,
|
||
)
|
||
return output
|
||
else:
|
||
return torch.ops.vllm.unified_mla_attention(
|
||
q,
|
||
kv_c_normed,
|
||
k_pe,
|
||
self.layer_name,
|
||
)
|
||
|
||
def process_weights_after_loading(self, act_dtype: torch.dtype):
|
||
if hasattr(self.impl, "process_weights_after_loading"):
|
||
self.impl.process_weights_after_loading(act_dtype)
|
||
|
||
# If we should not load quant weights, we initialize the scales to 1.0
|
||
# as the default value. See [Note: Register q/k/v/prob scales in state dict]
|
||
# for more details.
|
||
quant_method = (
|
||
self.quant_config.get_quant_method(self, prefix=self.layer_name)
|
||
if self.quant_config
|
||
else None
|
||
)
|
||
if not should_load_quant_weights(quant_method):
|
||
set_default_quant_scales(self, register_buffer=False)
|
||
|
||
def calc_kv_scales(
|
||
self, q: torch.Tensor, kv_c_normed: torch.Tensor, k_pe: torch.Tensor
|
||
) -> None:
|
||
"""Optional scale calculation for MLA inputs.
|
||
|
||
Mirrors Attention.calc_kv_scales. Not all MLA backends require this
|
||
"""
|
||
# Use safe defaults if ranges are not present
|
||
q_range = getattr(self, "q_range", torch.tensor(1.0))
|
||
k_range = getattr(self, "k_range", torch.tensor(1.0))
|
||
v_range = getattr(self, "v_range", torch.tensor(1.0))
|
||
|
||
self._q_scale.copy_(torch.abs(q).max() / q_range)
|
||
# kv_c_normed is the compressed KV representation; use it for k/v
|
||
kv_abs_max = torch.abs(kv_c_normed).max()
|
||
self._k_scale.copy_(kv_abs_max / k_range)
|
||
self._v_scale.copy_(kv_abs_max / v_range)
|
||
self._q_scale_float = self._q_scale.item()
|
||
self._k_scale_float = self._k_scale.item()
|
||
self._v_scale_float = self._v_scale.item()
|
||
self.calculate_kv_scales = False
|
||
|
||
def get_attn_backend(self) -> type[AttentionBackend]:
|
||
return self.attn_backend
|
||
|
||
def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
|
||
kv_cache_dtype = kv_cache_dtype_str_to_dtype(
|
||
self.kv_cache_dtype, vllm_config.model_config
|
||
)
|
||
return MLAAttentionSpec(
|
||
block_size=vllm_config.cache_config.block_size,
|
||
num_kv_heads=1,
|
||
head_size=self.head_size,
|
||
dtype=kv_cache_dtype,
|
||
cache_dtype_str=vllm_config.cache_config.cache_dtype,
|
||
)
|
||
|
||
|
||
@maybe_transfer_kv_layer
|
||
def unified_mla_attention(
|
||
q: torch.Tensor,
|
||
kv_c_normed: torch.Tensor,
|
||
k_pe: torch.Tensor,
|
||
layer_name: str,
|
||
) -> torch.Tensor:
|
||
attn_metadata, self, kv_cache = get_attention_context(layer_name)
|
||
output = self.impl.forward(self, q, kv_c_normed, k_pe, kv_cache, attn_metadata)
|
||
|
||
return output
|
||
|
||
|
||
def unified_mla_attention_fake(
|
||
q: torch.Tensor,
|
||
kv_c_normed: torch.Tensor,
|
||
k_pe: torch.Tensor,
|
||
layer_name: str,
|
||
) -> torch.Tensor:
|
||
return torch.empty_like(q).contiguous()
|
||
|
||
|
||
direct_register_custom_op(
|
||
op_name="unified_mla_attention",
|
||
op_func=unified_mla_attention,
|
||
mutates_args=[],
|
||
fake_impl=unified_mla_attention_fake,
|
||
dispatch_key=current_platform.dispatch_key,
|
||
)
|
||
|
||
|
||
@maybe_transfer_kv_layer
|
||
def unified_mla_attention_with_output(
|
||
q: torch.Tensor,
|
||
kv_c_normed: torch.Tensor,
|
||
k_pe: torch.Tensor,
|
||
output: torch.Tensor,
|
||
layer_name: str,
|
||
output_scale: torch.Tensor | None = None,
|
||
output_block_scale: torch.Tensor | None = None,
|
||
) -> None:
|
||
attn_metadata, self, kv_cache = get_attention_context(layer_name)
|
||
self.impl.forward(
|
||
self,
|
||
q,
|
||
kv_c_normed,
|
||
k_pe,
|
||
kv_cache,
|
||
attn_metadata,
|
||
output=output,
|
||
output_scale=output_scale,
|
||
output_block_scale=output_block_scale,
|
||
)
|
||
|
||
|
||
def unified_mla_attention_with_output_fake(
|
||
q: torch.Tensor,
|
||
kv_c_normed: torch.Tensor,
|
||
k_pe: torch.Tensor,
|
||
output: torch.Tensor,
|
||
layer_name: str,
|
||
output_scale: torch.Tensor | None = None,
|
||
output_block_scale: torch.Tensor | None = None,
|
||
) -> None:
|
||
return
|
||
|
||
|
||
direct_register_custom_op(
|
||
op_name="unified_mla_attention_with_output",
|
||
op_func=unified_mla_attention_with_output,
|
||
mutates_args=["output", "output_block_scale"],
|
||
fake_impl=unified_mla_attention_with_output_fake,
|
||
dispatch_key=current_platform.dispatch_key,
|
||
)
|
||
|
||
|
||
class QueryLenSupport(Enum):
|
||
"""Defines the level of query length support for an attention backend's
|
||
decode pipeline.
|
||
|
||
- SINGLE_ONLY: Decode pipeline only supports single-token queries
|
||
(query_len=1)
|
||
- UNIFORM: Decode pipeline supports uniform multi-token queries
|
||
(all requests must have same query_len > 1)
|
||
- VARLEN: Decode pipeline supports variable-length queries
|
||
(mixed query lengths in same batch)
|
||
"""
|
||
|
||
SINGLE_ONLY = "single_only"
|
||
UNIFORM = "uniform"
|
||
VARLEN = "varlen"
|
||
|
||
|
||
try:
|
||
# from vllm.vllm_flash_attn import ( # type: ignore[attr-defined]
|
||
# flash_attn_varlen_func,
|
||
# )
|
||
from ixformer.contrib.vllm_flash_attn import (
|
||
flash_attn_varlen_func,
|
||
merge_attn_states,
|
||
)
|
||
|
||
is_vllm_fa = True
|
||
except ImportError:
|
||
# For rocm use upstream flash attention
|
||
if current_platform.is_rocm():
|
||
from flash_attn import flash_attn_varlen_func # type: ignore[no-redef]
|
||
is_vllm_fa = False
|
||
|
||
|
||
def dynamic_per_batched_tensor_quant(
|
||
x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn
|
||
):
|
||
DTYPE_MAX = torch.finfo(dtype).max
|
||
min_val, max_val = x.aminmax()
|
||
amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-10)
|
||
scale = DTYPE_MAX / amax
|
||
x_scl_sat = (x * scale).clamp(min=-DTYPE_MAX, max=DTYPE_MAX)
|
||
return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
|
||
|
||
|
||
logger = init_logger(__name__)
|
||
|
||
|
||
@CustomOp.register("mla_decode_concat_quant_fp8")
|
||
class _DecodeConcatQuantFP8(QuantFP8):
|
||
"""
|
||
QuantFP8 variant that concatenates decode_ql_nope and decode_q_pe before
|
||
quantization. When disabled, forward_native is compiled via torch.compile,
|
||
fusing cat/reshape/quant/view together.
|
||
"""
|
||
|
||
def _make_forward(quant_fn): # noqa: N805
|
||
"""Factory to create forward methods that concat before quantization."""
|
||
|
||
def forward(
|
||
self,
|
||
decode_ql_nope: torch.Tensor,
|
||
decode_q_pe: torch.Tensor,
|
||
scale: torch.Tensor,
|
||
scale_ub: torch.Tensor | None = None,
|
||
) -> torch.Tensor:
|
||
decode_q0 = torch.cat((decode_ql_nope, decode_q_pe), dim=-1)
|
||
decode_q_flat = decode_q0.reshape(decode_q0.shape[0], -1)
|
||
decode_q, _ = quant_fn(self, decode_q_flat, scale, scale_ub)
|
||
return decode_q.view(decode_q0.shape)
|
||
|
||
return forward
|
||
|
||
forward_native = _make_forward(QuantFP8.forward_native) # type: ignore[arg-type]
|
||
forward_cuda = _make_forward(QuantFP8.forward_cuda) # type: ignore[arg-type]
|
||
forward_hip = _make_forward(QuantFP8.forward_hip) # type: ignore[arg-type]
|
||
|
||
|
||
CUDNN_WORKSPACE_SIZE = 12800
|
||
|
||
from vllm import envs
|
||
from vllm.model_executor.layers.quantization.utils.quant_utils import scaled_dequantize
|
||
import ixformer.inference.functions as ixf_ops
|
||
import numpy as np
|
||
|
||
|
||
class MLACommonBackend(AttentionBackend):
|
||
accept_output_buffer: bool = True
|
||
|
||
@staticmethod
|
||
def get_name() -> str:
|
||
return "TRITON_MLA"
|
||
|
||
@staticmethod
|
||
def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
|
||
return MLACommonMetadataBuilder
|
||
|
||
@staticmethod
|
||
def get_kv_cache_shape(
|
||
num_blocks: int,
|
||
block_size: int,
|
||
num_kv_heads: int, # assumed to be 1 for MLA
|
||
head_size: int,
|
||
cache_dtype_str: str = "auto",
|
||
) -> tuple[int, ...]:
|
||
return (num_blocks, block_size, head_size)
|
||
|
||
@staticmethod
|
||
def get_kv_cache_stride_order(
|
||
include_num_layers_dimension: bool = False,
|
||
) -> tuple[int, ...]:
|
||
# `stride_order` indicates the permutation that gets
|
||
# us from `get_kv_cache_shape` to the actual memory layout we want.
|
||
# (num_blocks, num_layers, block_size, head_size)
|
||
return (1, 0, 2, 3) if include_num_layers_dimension else (0, 1, 2)
|
||
|
||
@classmethod
|
||
def get_supported_head_sizes(cls) -> list[int]:
|
||
return [576]
|
||
|
||
@classmethod
|
||
def is_mla(cls) -> bool:
|
||
return True
|
||
|
||
|
||
@dataclass
|
||
class MLACommonPrefillMetadata:
|
||
"""Prefill Specific Metadata"""
|
||
|
||
@dataclass
|
||
class ChunkedContextMetadata:
|
||
# New for MLA (compared to FlashAttention)
|
||
# For handling chunked prefill
|
||
cu_seq_lens: torch.Tensor
|
||
starts: torch.Tensor
|
||
seq_tot: list[int]
|
||
max_seq_lens: list[int]
|
||
seq_lens: torch.Tensor
|
||
workspace: torch.Tensor
|
||
token_to_seq: torch.Tensor
|
||
chunk_total_token: list[int]
|
||
|
||
# for mla DCP
|
||
padded_local_chunk_seq_lens: list[list[int]] | None = None
|
||
local_context_lens_allranks: list[list[int]] | None = None
|
||
padded_local_cu_seq_lens: torch.Tensor | None = None
|
||
cu_seq_lens_lst: list[list[int]] | None = None
|
||
chunk_size: int | None = None
|
||
|
||
block_table: torch.Tensor
|
||
query_start_loc: torch.Tensor
|
||
max_query_len: int
|
||
chunked_context: ChunkedContextMetadata | None = None
|
||
query_seq_lens: torch.Tensor | None = None
|
||
workspace_buffer: torch.Tensor | None = None
|
||
q_data_type: torch.dtype | None = None
|
||
|
||
|
||
@dataclass
|
||
class FlashInferPrefillMetadata(MLACommonPrefillMetadata):
|
||
prefill_main: "BatchPrefillWithRaggedKVCacheWrapper | None" = None
|
||
prefill_chunks: "list[BatchPrefillWithRaggedKVCacheWrapper]" = field(
|
||
default_factory=list
|
||
)
|
||
|
||
|
||
@dataclass
|
||
class CudnnPrefillMetadata(MLACommonPrefillMetadata):
|
||
class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata):
|
||
seq_lens: torch.Tensor
|
||
|
||
cudnn_workspace: torch.Tensor | None = None
|
||
|
||
|
||
@dataclass
|
||
class MLACommonDecodeMetadata:
|
||
block_table: torch.Tensor
|
||
seq_lens: torch.Tensor
|
||
dcp_tot_seq_lens: torch.Tensor | None
|
||
max_decode_seq_len: int
|
||
use_cuda_graph: bool
|
||
|
||
|
||
D = TypeVar("D", bound=MLACommonDecodeMetadata)
|
||
|
||
|
||
@dataclass
|
||
class MLACommonMetadata(AttentionMetadata, Generic[D]):
|
||
"""Metadata for MLACommon.
|
||
|
||
NOTE: Please read the comment at the top of the file before trying to
|
||
understand this class
|
||
"""
|
||
|
||
# NOTE(sang): Definition of context_len, query_len, and seq_len.
|
||
# |---------- N-1 iteration --------|
|
||
# |---------------- N iteration ---------------------|
|
||
# |- tokenA -|......................|-- newTokens ---|
|
||
# |---------- context_len ----------|
|
||
# |-------------------- seq_len ---------------------|
|
||
# |-- query_len ---|
|
||
|
||
num_reqs: int
|
||
max_query_len: int
|
||
max_seq_len: int
|
||
|
||
num_actual_tokens: int # Number of tokens excluding padding.
|
||
query_start_loc: torch.Tensor
|
||
slot_mapping: torch.Tensor
|
||
|
||
# New for MLA (compared to FlashAttention)
|
||
# For handling prefill decode split
|
||
num_decodes: int
|
||
num_decode_tokens: int
|
||
num_prefills: int
|
||
|
||
# The dimension of the attention heads
|
||
head_dim: int | None = None
|
||
|
||
decode: D | None = None
|
||
prefill: (
|
||
MLACommonPrefillMetadata
|
||
| FlashInferPrefillMetadata
|
||
| CudnnPrefillMetadata
|
||
| None
|
||
) = None
|
||
|
||
def __post_init__(self):
|
||
if self.head_dim is not None and not MLACommonBackend.supports_head_size(
|
||
self.head_dim
|
||
):
|
||
raise ValueError(f"Head dimension {self.head_dim} is not supported by MLA.")
|
||
|
||
|
||
M = TypeVar("M", bound=MLACommonMetadata)
|
||
A = TypeVar("A", bound=AttentionMetadata)
|
||
|
||
|
||
def is_deepseek_r1_mla_compatible(vllm_config: VllmConfig) -> bool:
|
||
# Check if model has DeepSeek R1 compatible MLA dimensions:
|
||
# qk_nope_head_dim = 128, qk_rope_head_dim = 64, v_head_dim = 128
|
||
# which results in query/key head dim = 192.
|
||
if vllm_config.model_config is None:
|
||
return False
|
||
hf_text_config = vllm_config.model_config.hf_text_config
|
||
qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1)
|
||
qk_rope_head_dim = getattr(hf_text_config, "qk_rope_head_dim", 1)
|
||
v_head_dim = getattr(hf_text_config, "v_head_dim", 1)
|
||
return qk_nope_head_dim == 128 and qk_rope_head_dim == 64 and v_head_dim == 128
|
||
|
||
|
||
def use_flashinfer_prefill() -> bool:
|
||
# For blackwell default to flashinfer prefill if it's available since
|
||
# it is faster than FA2.
|
||
from vllm.config import get_current_vllm_config
|
||
|
||
vllm_config = get_current_vllm_config()
|
||
if not (
|
||
not vllm_config.attention_config.disable_flashinfer_prefill
|
||
and has_flashinfer()
|
||
and not vllm_config.attention_config.use_cudnn_prefill
|
||
and current_platform.is_device_capability_family(100)
|
||
):
|
||
return False
|
||
|
||
return is_deepseek_r1_mla_compatible(vllm_config)
|
||
|
||
|
||
def use_cudnn_prefill() -> bool:
|
||
from vllm.config import get_current_vllm_config
|
||
|
||
vllm_config = get_current_vllm_config()
|
||
return (
|
||
has_flashinfer()
|
||
and vllm_config.attention_config.use_cudnn_prefill
|
||
and current_platform.is_device_capability_family(100)
|
||
and has_nvidia_artifactory()
|
||
)
|
||
|
||
|
||
def use_trtllm_ragged_deepseek_prefill() -> bool:
|
||
"""Check if TRT-LLM ragged DeepSeek prefill should be used."""
|
||
from vllm.config import get_current_vllm_config
|
||
|
||
vllm_config = get_current_vllm_config()
|
||
if not (
|
||
has_flashinfer()
|
||
and vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
|
||
and current_platform.is_device_capability_family(100)
|
||
):
|
||
return False
|
||
|
||
return is_deepseek_r1_mla_compatible(vllm_config)
|
||
|
||
|
||
@dataclass
|
||
class MLADims:
|
||
q_lora_rank: int | None
|
||
kv_lora_rank: int
|
||
qk_nope_head_dim: int
|
||
qk_rope_head_dim: int
|
||
v_head_dim: int
|
||
|
||
|
||
def get_mla_dims(model_config: ModelConfig) -> MLADims:
|
||
hf_text_config = model_config.hf_text_config
|
||
|
||
return MLADims(
|
||
q_lora_rank=getattr(hf_text_config, "q_lora_rank", None),
|
||
kv_lora_rank=hf_text_config.kv_lora_rank,
|
||
qk_nope_head_dim=hf_text_config.qk_nope_head_dim,
|
||
qk_rope_head_dim=hf_text_config.qk_rope_head_dim,
|
||
v_head_dim=hf_text_config.v_head_dim,
|
||
)
|
||
|
||
|
||
class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
|
||
"""
|
||
NOTE: Please read the comment at the top of the file before trying to
|
||
understand this class
|
||
"""
|
||
|
||
# Defines the level of query length support for this backend.
|
||
# - SINGLE_ONLY: Only single-token queries (no spec decode support)
|
||
# - UNIFORM: Supports uniform multi-token queries (spec decode with uniform lengths)
|
||
# - VARLEN: Supports variable-length queries (spec decode with mixed lengths)
|
||
# If set to UNIFORM or VARLEN, this will increase `reorder_batch_threshold` when
|
||
# speculative decoding is enabled.
|
||
query_len_support: ClassVar[QueryLenSupport] = QueryLenSupport.SINGLE_ONLY
|
||
|
||
# The threshold for reordering the batch into decode and prefill requests.
|
||
# If > 1, the batch will be reordered such that requests with
|
||
# query length <= threshold are classified as decode requests.
|
||
# Use `query_len_support` (above) to set this automatically
|
||
# when speculative decoding is enabled.
|
||
reorder_batch_threshold: int = 1
|
||
_cudagraph_support: ClassVar[AttentionCGSupport] = \
|
||
AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
|
||
|
||
@staticmethod
|
||
def determine_chunked_prefill_workspace_size(vllm_config: VllmConfig) -> int:
|
||
scheduler_config = vllm_config.scheduler_config
|
||
cache_config = vllm_config.cache_config
|
||
model_config = vllm_config.model_config
|
||
|
||
chunked_prefill_workspace_size = min(
|
||
# Try for 8 full length request or at least 4 pages per-request
|
||
max(
|
||
8 * model_config.max_model_len,
|
||
4 * scheduler_config.max_num_seqs * cache_config.block_size,
|
||
),
|
||
# For long-context models try not to over-allocate limiting
|
||
# kv-cache space, limiting it to 64k tokens,
|
||
# which would result in the workspace being:
|
||
# 2*(576)*(64*1024) = 144mb
|
||
# (assuming 576 MLA head dim, and fp16)
|
||
# which would result in up-projected context being
|
||
# 2*(192*128)*(64*1024) = 3gb
|
||
# (assuming 192 QK head dim, 128 heads, and fp16)
|
||
64 * 1024,
|
||
)
|
||
|
||
# Enforce that we enough for at least 1 page per request
|
||
chunked_prefill_workspace_size = max(
|
||
chunked_prefill_workspace_size,
|
||
scheduler_config.max_num_seqs * cache_config.block_size,
|
||
)
|
||
|
||
return chunked_prefill_workspace_size
|
||
|
||
def __init__(
|
||
self,
|
||
kv_cache_spec: AttentionSpec,
|
||
layer_names: list[str],
|
||
vllm_config: VllmConfig,
|
||
device: torch.device,
|
||
metadata_cls: type[M] | None = None,
|
||
supports_dcp_with_varlen: bool = False,
|
||
):
|
||
self.metadata_cls = (
|
||
metadata_cls if metadata_cls is not None else MLACommonMetadata
|
||
)
|
||
self.kv_cache_spec = kv_cache_spec
|
||
scheduler_config = vllm_config.scheduler_config
|
||
self.model_config = vllm_config.model_config
|
||
parallel_config = vllm_config.parallel_config
|
||
self.compilation_config = vllm_config.compilation_config
|
||
self.vllm_config = vllm_config
|
||
self.device = device
|
||
|
||
self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
|
||
self.mla_dims = get_mla_dims(self.model_config)
|
||
self.aot_schedule = current_platform.is_cuda()
|
||
try:
|
||
self.dcp_world_size = get_dcp_group().world_size
|
||
self.dcp_rank = get_dcp_group().rank_in_group
|
||
except AssertionError:
|
||
# DCP might not be initialized in testing
|
||
self.dcp_world_size = 1
|
||
self.dcp_rank = 0
|
||
self.dcp_local_block_size = parallel_config.cp_kv_cache_interleave_size
|
||
self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size
|
||
self.cp_kv_cache_interleave_size = parallel_config.cp_kv_cache_interleave_size
|
||
|
||
# Don't try to access the runner on AMD
|
||
if self.aot_schedule:
|
||
self.page_size = self.kv_cache_spec.block_size
|
||
|
||
self.chunked_prefill_workspace_size = (
|
||
self.determine_chunked_prefill_workspace_size(vllm_config)
|
||
)
|
||
|
||
if self.dcp_world_size > 1:
|
||
# Note(hc): The local kvcache is incomplete when DCP is triggered,
|
||
# an additional kvcache allgather across the DCP group is therefore
|
||
# required, so the workspace has to be enlarged by 1/DCP relative
|
||
# to the original TP allocation.
|
||
assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0
|
||
self.chunked_prefill_workspace = torch.empty(
|
||
(
|
||
self.chunked_prefill_workspace_size
|
||
+ self.chunked_prefill_workspace_size // self.dcp_world_size,
|
||
self.model_config.get_head_size(),
|
||
),
|
||
dtype=self.model_config.dtype,
|
||
device=device,
|
||
)
|
||
else:
|
||
self.chunked_prefill_workspace = torch.empty(
|
||
(
|
||
self.chunked_prefill_workspace_size,
|
||
self.model_config.get_head_size(),
|
||
),
|
||
dtype=self.model_config.dtype,
|
||
device=device,
|
||
)
|
||
|
||
self._use_cudnn_prefill = use_cudnn_prefill()
|
||
self._use_fi_prefill = use_flashinfer_prefill()
|
||
self._use_trtllm_ragged_prefill = use_trtllm_ragged_deepseek_prefill()
|
||
self.prefill_metadata_cls = (
|
||
FlashInferPrefillMetadata
|
||
if self._use_fi_prefill
|
||
else CudnnPrefillMetadata
|
||
if self._use_cudnn_prefill
|
||
else MLACommonPrefillMetadata
|
||
)
|
||
|
||
if self._use_fi_prefill:
|
||
self._workspace_buffer = torch.empty(
|
||
envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
|
||
dtype=torch.uint8,
|
||
device=device,
|
||
)
|
||
|
||
self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
|
||
self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []
|
||
|
||
self._global_hyperparameters = infer_global_hyperparameters(
|
||
get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl) # type: ignore[type-abstract]
|
||
)
|
||
|
||
if self._use_trtllm_ragged_prefill:
|
||
self._workspace_buffer = torch.empty(
|
||
envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
|
||
dtype=torch.uint8,
|
||
device=device,
|
||
)
|
||
|
||
if self._use_cudnn_prefill:
|
||
self.cudnn_workspace = torch.empty(
|
||
CUDNN_WORKSPACE_SIZE * scheduler_config.max_num_seqs,
|
||
dtype=torch.int8,
|
||
device=device,
|
||
)
|
||
|
||
supports_spec_decode = self.query_len_support != QueryLenSupport.SINGLE_ONLY
|
||
self._init_reorder_batch_threshold(
|
||
self.reorder_batch_threshold, supports_spec_decode, supports_dcp_with_varlen
|
||
)
|
||
|
||
# Validate consistency between query_len_support and reorder_batch_threshold
|
||
if self.query_len_support == QueryLenSupport.SINGLE_ONLY:
|
||
assert self.reorder_batch_threshold == 1, (
|
||
f"reorder_batch_threshold must be 1 when query_len_support is "
|
||
f"SINGLE_ONLY, got {self.reorder_batch_threshold}"
|
||
)
|
||
|
||
def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata):
|
||
qo_indptr = prefill.query_start_loc
|
||
|
||
has_context = False
|
||
if prefill.chunked_context is not None:
|
||
chunked_context = prefill.chunked_context
|
||
has_context = True
|
||
|
||
if self._fi_prefill_main is None:
|
||
from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
|
||
|
||
self._fi_prefill_main = BatchPrefillWithRaggedKVCacheWrapper(
|
||
self._workspace_buffer, "NHD", backend="cutlass"
|
||
)
|
||
|
||
if has_context:
|
||
num_chunks = chunked_context.cu_seq_lens.shape[0]
|
||
# Allocate more prefill chunk wrappers if needed
|
||
if len(self._fi_prefill_chunks) < num_chunks:
|
||
from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
|
||
|
||
for _ in range(len(self._fi_prefill_chunks), num_chunks):
|
||
self._fi_prefill_chunks.append(
|
||
BatchPrefillWithRaggedKVCacheWrapper(
|
||
self._workspace_buffer, "NHD", backend="cutlass"
|
||
)
|
||
)
|
||
assert num_chunks <= len(self._fi_prefill_chunks)
|
||
|
||
# In MLA, the non-latent num_qo_heads == num_kv_heads
|
||
num_qo_heads = self.num_heads
|
||
num_kv_heads = num_qo_heads
|
||
|
||
# Sanity: Verify that num_kv_heads == 1 since it is latent space
|
||
assert self.kv_cache_spec.num_kv_heads == 1
|
||
|
||
# Get non-latent head_dim_qk and head_dim_vo
|
||
head_dim_qk = self.mla_dims.qk_nope_head_dim + self.mla_dims.qk_rope_head_dim
|
||
head_dim_vo = self.mla_dims.v_head_dim
|
||
|
||
# For main run, qo_indptr == kv_indptr
|
||
kv_indptr = qo_indptr.clone()
|
||
|
||
# Prepare main prefill
|
||
self._fi_prefill_main.plan(
|
||
qo_indptr=qo_indptr,
|
||
kv_indptr=kv_indptr,
|
||
num_qo_heads=num_qo_heads,
|
||
num_kv_heads=num_kv_heads,
|
||
head_dim_qk=head_dim_qk,
|
||
head_dim_vo=head_dim_vo,
|
||
causal=True, # This is main run
|
||
sm_scale=self._global_hyperparameters.sm_scale,
|
||
window_left=self._global_hyperparameters.window_left,
|
||
logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
|
||
q_data_type=self.model_config.dtype,
|
||
)
|
||
|
||
# Prepare context prefills
|
||
if has_context:
|
||
for i in range(num_chunks):
|
||
kv_indptr_chunk = chunked_context.cu_seq_lens[i]
|
||
|
||
self._fi_prefill_chunks[i].plan(
|
||
qo_indptr=qo_indptr,
|
||
kv_indptr=kv_indptr_chunk,
|
||
num_qo_heads=num_qo_heads,
|
||
num_kv_heads=num_kv_heads,
|
||
head_dim_qk=head_dim_qk,
|
||
head_dim_vo=head_dim_vo,
|
||
causal=False, # This is context run
|
||
sm_scale=self._global_hyperparameters.sm_scale,
|
||
window_left=self._global_hyperparameters.window_left,
|
||
logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
|
||
q_data_type=self.model_config.dtype,
|
||
)
|
||
|
||
prefill.prefill_main = self._fi_prefill_main
|
||
prefill.prefill_chunks = self._fi_prefill_chunks
|
||
|
||
def _build_decode(
|
||
self,
|
||
block_table_tensor: torch.Tensor,
|
||
seq_lens_device: torch.Tensor,
|
||
max_seq_len: int,
|
||
query_start_loc_cpu: torch.Tensor,
|
||
query_start_loc_device: torch.Tensor,
|
||
num_decode_tokens: int,
|
||
max_decode_seq_len: int,
|
||
use_cuda_graph: bool,
|
||
dcp_tot_seq_lens_device: torch.Tensor | None,
|
||
) -> MLACommonDecodeMetadata:
|
||
return MLACommonDecodeMetadata(
|
||
block_table=block_table_tensor,
|
||
seq_lens=seq_lens_device,
|
||
max_decode_seq_len=max_decode_seq_len,
|
||
use_cuda_graph=use_cuda_graph,
|
||
dcp_tot_seq_lens=dcp_tot_seq_lens_device,
|
||
)
|
||
|
||
def build_for_cudagraph_capture(
|
||
self, common_attn_metadata: CommonAttentionMetadata
|
||
) -> M:
|
||
"""
|
||
This method builds the metadata for full cudagraph capture.
|
||
Currently, only decode is supported for full cudagraphs with MLA.
|
||
"""
|
||
m = common_attn_metadata
|
||
assert m.num_reqs <= (m.num_actual_tokens * self.reorder_batch_threshold), (
|
||
"MLA only supports decode-only full CUDAGraph capture. "
|
||
"Make sure all cudagraph capture sizes <= max_num_seq."
|
||
)
|
||
|
||
assert m.max_query_len <= self.reorder_batch_threshold # decode only
|
||
|
||
return self.build(0, m)
|
||
|
||
def build(
|
||
self,
|
||
common_prefix_len: int,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
fast_build: bool = False,
|
||
) -> M:
|
||
num_reqs = common_attn_metadata.num_reqs
|
||
num_tokens = common_attn_metadata.num_actual_tokens
|
||
max_query_len = common_attn_metadata.max_query_len
|
||
max_seq_len = common_attn_metadata.max_seq_len
|
||
|
||
# Note(simon): be careful about the CPU <> GPU memory movement in this
|
||
# function. We should avoid GPU -> CPU sync as much as possible because
|
||
# it blocks on all previous kernels.
|
||
device = self.device
|
||
block_table_tensor = common_attn_metadata.block_table_tensor
|
||
slot_mapping = common_attn_metadata.slot_mapping
|
||
|
||
query_start_loc = common_attn_metadata.query_start_loc
|
||
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
||
seq_lens = common_attn_metadata.seq_lens
|
||
dcp_local_seq_lens = common_attn_metadata.dcp_local_seq_lens
|
||
|
||
num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
|
||
split_decodes_and_prefills(
|
||
common_attn_metadata,
|
||
decode_threshold=self.reorder_batch_threshold,
|
||
require_uniform=(self.query_len_support != QueryLenSupport.VARLEN),
|
||
)
|
||
)
|
||
|
||
assert num_decodes + num_prefills == num_reqs
|
||
assert num_decode_tokens + num_prefill_tokens == num_tokens
|
||
|
||
prefill_metadata = None
|
||
if num_prefills > 0:
|
||
num_computed_tokens_cpu = (
|
||
common_attn_metadata.compute_num_computed_tokens().cpu()
|
||
)
|
||
|
||
reqs_start = num_decodes # prefill_start
|
||
|
||
context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
|
||
max_context_len_cpu = context_lens_cpu.max().item()
|
||
num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
|
||
prefill_query_start_loc = (
|
||
query_start_loc[reqs_start:] - query_start_loc[reqs_start]
|
||
)
|
||
|
||
chunked_context_metadata = None
|
||
if max_context_len_cpu > 0:
|
||
# NOTE: it is recommend you read the `Chunked Prefill` section
|
||
# in the comment at the top of the file before trying to
|
||
# understand the following code
|
||
|
||
# currently we allocate an equal amount of workspace for each
|
||
# prefill in the batch, we could probably use a more advanced
|
||
# algorithm here and allocate more workspace to prefills with
|
||
# longer context lengths
|
||
max_context_chunk = (
|
||
self.chunked_prefill_workspace_size // num_prefills_with_context_cpu
|
||
)
|
||
|
||
if self.aot_schedule:
|
||
# align max_context_chunk to page_size by rounding down,
|
||
# currently the `gather_and_maybe_dequant_cache` kernel
|
||
# cannot handle `context_chunk_starts` that are not aligned
|
||
# to page_size
|
||
max_context_chunk = round_down(max_context_chunk, self.page_size)
|
||
|
||
assert max_context_chunk > 0
|
||
num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
|
||
|
||
# if `max_context_chunk = 256`, `num_chunks = 3`, and
|
||
# `num_prefills_with_context = 4`, create a tensor that looks
|
||
# like
|
||
# [[0, 0, 0, 0], [256, 256, 256, 256], [512, 512, 512, 512]]
|
||
# Note(simon): this is done in CPU because of downstream's
|
||
# of `to_list`.
|
||
chunk_starts = (
|
||
torch.arange(num_chunks, dtype=torch.int32)
|
||
.unsqueeze(1)
|
||
.expand(-1, num_prefills)
|
||
* max_context_chunk
|
||
)
|
||
chunk_ends = torch.min(
|
||
context_lens_cpu.unsqueeze(0), chunk_starts + max_context_chunk
|
||
)
|
||
chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
|
||
|
||
cu_seq_lens_cpu = torch.zeros(
|
||
num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
|
||
)
|
||
torch.cumsum(
|
||
chunk_seq_lens, dim=1, out=cu_seq_lens_cpu[:, 1:], dtype=torch.int32
|
||
)
|
||
chunk_total_token = cu_seq_lens_cpu[:, -1]
|
||
|
||
max_token_num_over_chunk = chunk_total_token.max().item()
|
||
token_to_seq_tensor_cpu = torch.zeros(
|
||
[num_chunks, max_token_num_over_chunk], dtype=torch.int32
|
||
)
|
||
range_idx = torch.arange(num_prefills, dtype=torch.int32)
|
||
for i in range(num_chunks):
|
||
chunk_token_to_seq_tensor = torch.repeat_interleave(
|
||
range_idx, chunk_seq_lens[i]
|
||
)
|
||
chunk_len = chunk_token_to_seq_tensor.shape[0]
|
||
token_to_seq_tensor_cpu[i, :chunk_len] = chunk_token_to_seq_tensor
|
||
|
||
if self.dcp_world_size > 1:
|
||
local_context_lens_allranks = get_dcp_local_seq_lens(
|
||
context_lens_cpu,
|
||
self.dcp_world_size,
|
||
None,
|
||
self.dcp_local_block_size,
|
||
)
|
||
# Note(qcs): The max local context lengths
|
||
# padded to `dcp_local_block_size`.
|
||
padded_local_context_lens_cpu: torch.Tensor = (
|
||
cdiv(
|
||
context_lens_cpu,
|
||
self.dcp_virtual_block_size,
|
||
)
|
||
* self.dcp_local_block_size
|
||
)
|
||
# Note(hc): The above max_context_chunk already enforces
|
||
# block_size alignment, DCP just need the block_size can
|
||
# be divisible by dcp_world_size, because DCP use
|
||
# cp_gather_cache which not require `cp_chunk_starts`
|
||
# aligned to page_size.
|
||
assert max_context_chunk % self.dcp_world_size == 0
|
||
padded_local_max_context_chunk_across_ranks = (
|
||
cdiv(
|
||
max_context_chunk,
|
||
self.dcp_virtual_block_size,
|
||
)
|
||
* self.dcp_local_block_size
|
||
)
|
||
local_chunk_starts = (
|
||
torch.arange(num_chunks, dtype=torch.int32)
|
||
.unsqueeze(1)
|
||
.expand(-1, num_prefills)
|
||
* padded_local_max_context_chunk_across_ranks
|
||
)
|
||
local_chunk_ends = torch.min(
|
||
padded_local_context_lens_cpu.unsqueeze(0),
|
||
local_chunk_starts
|
||
+ padded_local_max_context_chunk_across_ranks,
|
||
)
|
||
padded_local_chunk_seq_lens = (
|
||
local_chunk_ends - local_chunk_starts
|
||
).clamp(min=0)
|
||
|
||
padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
|
||
num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
|
||
)
|
||
torch.cumsum(
|
||
padded_local_chunk_seq_lens,
|
||
dim=1,
|
||
out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
|
||
dtype=torch.int32,
|
||
)
|
||
|
||
chunked_context_metadata_cls = (
|
||
CudnnPrefillMetadata.ChunkedContextMetadata
|
||
if self._use_cudnn_prefill
|
||
else MLACommonPrefillMetadata.ChunkedContextMetadata
|
||
)
|
||
if self.dcp_world_size > 1:
|
||
chunked_context_metadata = chunked_context_metadata_cls(
|
||
cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
|
||
starts=local_chunk_starts.to(device, non_blocking=True),
|
||
seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
|
||
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
|
||
seq_lens=chunk_seq_lens,
|
||
token_to_seq=token_to_seq_tensor_cpu.to(
|
||
device, non_blocking=True
|
||
),
|
||
chunk_total_token=chunk_total_token.tolist(),
|
||
workspace=self.chunked_prefill_workspace,
|
||
padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(),
|
||
local_context_lens_allranks=local_context_lens_allranks.tolist(),
|
||
padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu.to(
|
||
device, non_blocking=True
|
||
),
|
||
cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
|
||
chunk_size=padded_local_max_context_chunk_across_ranks,
|
||
)
|
||
else:
|
||
chunked_context_metadata = chunked_context_metadata_cls(
|
||
cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
|
||
starts=chunk_starts.to(device, non_blocking=True),
|
||
seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
|
||
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
|
||
seq_lens=chunk_seq_lens,
|
||
token_to_seq=token_to_seq_tensor_cpu.to(
|
||
device, non_blocking=True
|
||
),
|
||
chunk_total_token=chunk_total_token,
|
||
workspace=self.chunked_prefill_workspace,
|
||
)
|
||
|
||
if self._use_cudnn_prefill:
|
||
chunked_context_metadata.seq_lens = chunk_seq_lens
|
||
|
||
assert (
|
||
max(chunked_context_metadata.max_seq_lens)
|
||
<= self.chunked_prefill_workspace_size
|
||
)
|
||
|
||
prefill_metadata = self.prefill_metadata_cls(
|
||
block_table=block_table_tensor[reqs_start:, ...],
|
||
query_start_loc=prefill_query_start_loc,
|
||
max_query_len=max_query_len,
|
||
chunked_context=chunked_context_metadata,
|
||
)
|
||
|
||
if self._use_cudnn_prefill:
|
||
assert isinstance(prefill_metadata, CudnnPrefillMetadata)
|
||
prefill_metadata.query_seq_lens = (
|
||
prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
|
||
)
|
||
prefill_metadata.cudnn_workspace = self.cudnn_workspace
|
||
|
||
if self._use_trtllm_ragged_prefill:
|
||
prefill_metadata.query_seq_lens = (
|
||
prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
|
||
)
|
||
prefill_metadata.workspace_buffer = self._workspace_buffer
|
||
|
||
decode_metadata = None
|
||
if num_decodes > 0:
|
||
dcp_tot_seq_lens_device = None
|
||
if self.dcp_world_size > 1:
|
||
dcp_tot_seq_lens_device = seq_lens[:num_decodes]
|
||
seq_lens = dcp_local_seq_lens
|
||
|
||
# After DCP distribution, the maximum number of tokens for any rank is
|
||
# ceil(L / (N * I)) * I, where L is max_seq_len, N is dcp_world_size,
|
||
# and I is cp_kv_cache_interleave_size.
|
||
# This eliminates GPU->CPU sync while minimizing workspace
|
||
# over-allocation.
|
||
num_partitions = self.dcp_world_size * self.cp_kv_cache_interleave_size
|
||
max_seq_len = (
|
||
(max_seq_len + num_partitions - 1) // num_partitions
|
||
) * self.cp_kv_cache_interleave_size
|
||
|
||
decode_metadata = self._build_decode(
|
||
block_table_tensor=block_table_tensor[:num_decodes, ...],
|
||
seq_lens_device=seq_lens[:num_decodes],
|
||
max_seq_len=max_seq_len,
|
||
query_start_loc_cpu=query_start_loc_cpu[: num_decodes + 1],
|
||
query_start_loc_device=query_start_loc[: num_decodes + 1],
|
||
num_decode_tokens=num_decode_tokens,
|
||
max_decode_seq_len=torch.max(seq_lens[:num_decodes]).item(),
|
||
use_cuda_graph=False,
|
||
dcp_tot_seq_lens_device=dcp_tot_seq_lens_device,
|
||
)
|
||
|
||
attn_metadata = self.metadata_cls(
|
||
num_reqs=common_attn_metadata.num_reqs,
|
||
max_query_len=common_attn_metadata.max_query_len,
|
||
max_seq_len=max_seq_len,
|
||
num_actual_tokens=num_tokens,
|
||
query_start_loc=query_start_loc,
|
||
slot_mapping=slot_mapping,
|
||
head_dim=self.model_config.get_head_size(),
|
||
# MLACommonMetadata Chunk prefill specific
|
||
num_decodes=num_decodes,
|
||
num_decode_tokens=num_decode_tokens,
|
||
num_prefills=num_prefills,
|
||
prefill=prefill_metadata,
|
||
decode=decode_metadata,
|
||
)
|
||
|
||
if self._use_fi_prefill and num_prefills > 0:
|
||
assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
|
||
self._build_fi_prefill_wrappers(attn_metadata.prefill)
|
||
|
||
return attn_metadata
|
||
|
||
|
||
def reorg_kvcache(
|
||
allgatered_kv_c_normed: torch.Tensor,
|
||
allgatered_k_pe: torch.Tensor,
|
||
padded_local_chunk_seq_lens_lst: list[int],
|
||
local_context_lens_allranks: list[list[int]],
|
||
sum_seq_len: int,
|
||
max_seq_len: int,
|
||
chunk_size: int,
|
||
chunk_idx: int,
|
||
toks: int,
|
||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||
"""
|
||
reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
|
||
e.g.
|
||
allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
|
||
T0_4, T0_5, pad, pad, T1_2, pad, ...]
|
||
-> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
|
||
T1_0, T1_1, T1_2, ...]
|
||
Args:
|
||
padded_local_chunk_seq_lens_lst: local chunk context lengths
|
||
under current CP rank.
|
||
local_context_lens_allranks: local context lengths on each CP rank.
|
||
sum_seq_len: the sum of cp_chunk_seq_lens_lst.
|
||
max_seq_len: the max value of cp_chunk_seq_lens_lst.
|
||
chunk_size: the local padded max context chunk from
|
||
chunked_context_metadata building.
|
||
chunk_idx: chunk idx of chunked_prefill.
|
||
toks: the number of tokens for local gather cache.
|
||
"""
|
||
kv_c_segments = []
|
||
k_pe_segments = []
|
||
src_token_idx = 0
|
||
max_seq_len_check = 0
|
||
for padded_local_chunk_seq_len, local_context_lens in zip(
|
||
padded_local_chunk_seq_lens_lst, local_context_lens_allranks
|
||
):
|
||
cur_seq_len = 0
|
||
for rank, local_context_len in enumerate(local_context_lens):
|
||
# Note(qcs): We split the context into multiple chunks,
|
||
# depending on the size of the workspace.
|
||
# local_context in dcp0: |-----------------|
|
||
# local_context in dcp1: |--------------|
|
||
# n*padded_local_chunk: |-----|-----|-----|
|
||
# local_chunk_len in dcp1: |-----|-----|--|
|
||
# so we need update the last chunk length in dcp1.
|
||
local_chunk_len = min(
|
||
max(0, local_context_len - chunk_idx * chunk_size),
|
||
padded_local_chunk_seq_len,
|
||
)
|
||
if local_chunk_len != 0:
|
||
kv_c_segment = allgatered_kv_c_normed[
|
||
rank * toks + src_token_idx : rank * toks
|
||
+ src_token_idx
|
||
+ local_chunk_len
|
||
]
|
||
k_pe_segment = allgatered_k_pe[
|
||
rank * toks + src_token_idx : rank * toks
|
||
+ src_token_idx
|
||
+ local_chunk_len
|
||
]
|
||
kv_c_segments.append(kv_c_segment)
|
||
k_pe_segments.append(k_pe_segment)
|
||
cur_seq_len += local_chunk_len
|
||
max_seq_len_check = max(max_seq_len_check, cur_seq_len)
|
||
src_token_idx += padded_local_chunk_seq_len
|
||
reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
|
||
reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
|
||
assert reorganized_kv_c_normed.shape[0] == sum_seq_len
|
||
assert reorganized_k_pe.shape[0] == sum_seq_len
|
||
assert max_seq_len_check == max_seq_len
|
||
return reorganized_kv_c_normed, reorganized_k_pe
|
||
|
||
|
||
# TODO(Lucas): rename MLACommonBaseImpl -> MLACommonImpl,
|
||
# and MLACommonImpl -> MLACommonDenseImpl or somthing like that
|
||
class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
|
||
"""
|
||
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: list[float] | None,
|
||
sliding_window: int | None,
|
||
kv_cache_dtype: str,
|
||
logits_soft_cap: float | None,
|
||
attn_type: str,
|
||
kv_sharing_target_layer_name: str | None,
|
||
# MLA Specific Arguments
|
||
q_lora_rank: int | None,
|
||
kv_lora_rank: int,
|
||
qk_nope_head_dim: int,
|
||
qk_rope_head_dim: int,
|
||
qk_head_dim: int,
|
||
v_head_dim: int,
|
||
kv_b_proj: ColumnParallelLinear,
|
||
indexer=None,
|
||
q_pad_num_heads: int | None = None,
|
||
rotary_emb = None,
|
||
) -> None:
|
||
if kv_sharing_target_layer_name is not None:
|
||
raise NotImplementedError("KV sharing is not supported for MLA")
|
||
|
||
self.num_heads = num_heads
|
||
self.head_size = head_size
|
||
self.scale = float(scale)
|
||
self.num_kv_heads = num_kv_heads
|
||
self.kv_cache_dtype = kv_cache_dtype
|
||
|
||
self.q_lora_rank = q_lora_rank
|
||
self.kv_lora_rank = kv_lora_rank
|
||
self.qk_nope_head_dim = qk_nope_head_dim
|
||
self.qk_rope_head_dim = qk_rope_head_dim
|
||
self.qk_head_dim = qk_head_dim
|
||
self.v_head_dim = v_head_dim
|
||
self.kv_b_proj = kv_b_proj
|
||
self.indexer = indexer
|
||
self.q_pad_num_heads = q_pad_num_heads
|
||
self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()
|
||
self.rotary_emb = rotary_emb
|
||
|
||
# If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
|
||
self.is_aiter_triton_fp4_bmm_enabled = (
|
||
rocm_aiter_ops.is_fp4bmm_enabled()
|
||
and self.kv_b_proj.weight.dtype == torch.bfloat16
|
||
)
|
||
|
||
def process_weights_after_loading(self, act_dtype: torch.dtype):
|
||
# we currently do not have quantized bmm's which are needed for
|
||
# `W_UV` and `W_UK_T`, 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, out_dtype=act_dtype
|
||
# ).T
|
||
|
||
def get_layer_weight(layer):
|
||
WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
|
||
for attr in WEIGHT_NAMES:
|
||
if hasattr(layer, attr):
|
||
return getattr(layer, attr)
|
||
raise AttributeError(
|
||
f"Layer '{layer}' has no recognized weight attribute: {WEIGHT_NAMES}."
|
||
)
|
||
|
||
def get_and_maybe_dequant_weights(layer: LinearBase):
|
||
if not isinstance(layer.quant_method, UnquantizedLinearMethod):
|
||
# we already prepare a dequant weight for GGUF, skip it.
|
||
if layer.quant_method.__class__.__name__ == "GGUFLinearMethod":
|
||
weight = layer.weight.clone()
|
||
del layer.weight
|
||
return weight
|
||
if layer.quant_method.__class__.__name__ == "AWQMarlinLinearMethod":
|
||
from ixformer.inference.functions import ref_wui4a16
|
||
return ref_wui4a16(None, layer.qweight, layer.scales, layer.qzeros, None, layer.quant_method.quant_config.group_size, only_return_weight=True)
|
||
# for W8A8, we directly dequantize it here to avoiding quantization errors
|
||
if hasattr(layer, "scheme") and layer.scheme.__class__.__name__ == "CompressedTensorsW8A8Int8" and not layer.scheme.is_static_input_scheme:
|
||
quant_weight = layer.weight.T # output, input
|
||
scales = layer.weight_scale
|
||
return scaled_dequantize(quant_weight, scales, (1, -1), act_dtype)
|
||
# NOTE: This should only be used offline, since it's O(N^3)
|
||
eye = torch.eye(
|
||
layer.input_size_per_partition,
|
||
dtype=act_dtype,
|
||
device=get_layer_weight(layer).device,
|
||
)
|
||
dequant_weights = layer.quant_method.apply(layer, eye, bias=None)
|
||
del eye
|
||
# standardize to (output, input)
|
||
return dequant_weights.T
|
||
return layer.weight
|
||
|
||
# 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
|
||
# )
|
||
back_to_vllm = False
|
||
if envs.VLLM_MLA_CUSTOMIZE:
|
||
layer = self.kv_b_proj
|
||
quant_method = layer.quant_method.__class__.__name__
|
||
if hasattr(layer, "scheme") and layer.scheme.__class__.__name__ == "CompressedTensorsW8A8Int8":
|
||
kv_b_proj_weight = self.kv_b_proj.weight # [i，o]
|
||
kv_b_proj_weight_scale = self.kv_b_proj.weight_scale # [o, 1]
|
||
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)
|
||
kv_b_proj_weight_scale = kv_b_proj_weight_scale.view(
|
||
self.num_heads,
|
||
self.qk_nope_head_dim + self.v_head_dim,
|
||
1,
|
||
)
|
||
W_UK_S, W_UV_S = kv_b_proj_weight_scale.split(
|
||
[self.qk_nope_head_dim, self.v_head_dim], dim=1)
|
||
|
||
# self.W_UK = W_UK # [kv_lora_rank, n, qk_nope]
|
||
self.W_UK = ixf_ops.marlin_w8_weight_repack(
|
||
weights=W_UK.permute(1, 2, 0).contiguous(),
|
||
weight_format="int8",
|
||
reformat="k16n16_grouped_n",
|
||
)
|
||
self.W_UK_S = W_UK_S.contiguous() # [n, qk_nope, 1]
|
||
# self.W_UV = W_UV # [kv_lora_rank, n, v_head_dim]
|
||
self.W_UV = ixf_ops.marlin_w8_weight_repack(
|
||
weights=W_UV.permute(1, 0, 2).contiguous(),
|
||
weight_format="int8",
|
||
reformat="k16n16",
|
||
)
|
||
self.W_UV_S = W_UV_S.contiguous() # [n, v_head_dim, 1]
|
||
|
||
def _v_up_proj_w8a8(self, x):
|
||
# x: b, n, kv_lora
|
||
# W_UV = (self.W_UV * self.W_UV_S.view(1, self.num_heads, self.v_head_dim)).to(x.dtype)
|
||
# x = torch.einsum("bnl,lnv->bnv", x, W_UV)
|
||
return ixf_ops.marlin_w8a16(
|
||
x,
|
||
self.W_UV,
|
||
self.W_UV_S,
|
||
group_size=-1,
|
||
format="k16n16",
|
||
batch_first=False,
|
||
)
|
||
|
||
def _k_up_proj_w8a8(self, x_nope):
|
||
# x_nope: b, n, qk_nope_head_dim
|
||
# W_UK = (self.W_UK * self.W_UK_S.view(1, self.num_heads, self.qk_nope_head_dim)).to(x.dtype)
|
||
# return torch.einsum("bnp,lnp->bnl", x_nope, W_UK).view(-1, self.num_heads, self.kv_lora_rank)
|
||
return ixf_ops.marlin_w8a16(
|
||
x_nope,
|
||
self.W_UK,
|
||
self.W_UK_S,
|
||
group_size=-1,
|
||
format="k16n16_grouped_n",
|
||
batch_first=False,
|
||
)
|
||
|
||
self._v_up_proj = functools.partial(_v_up_proj_w8a8, self)
|
||
self._k_up_proj = functools.partial(_k_up_proj_w8a8, self)
|
||
elif quant_method == "AWQMarlinLinearMethod":
|
||
# ===== W_UK & W_UV use W4A16 =====
|
||
def split_4bit_matrix(qweight, scales, qzeros, num_heads, head_dim_1, head_dim_2):
|
||
assert head_dim_1 % 8 == 0
|
||
assert head_dim_2 % 8 == 0
|
||
qweight = qweight.view(-1, num_heads, (head_dim_1 + head_dim_2)//8)
|
||
w1, w2 = qweight.split([head_dim_1//8, head_dim_2//8], dim=-1)
|
||
scales = scales.view(-1, num_heads, head_dim_1 + head_dim_2)
|
||
s1, s2 = scales.split([head_dim_1, head_dim_2], dim=-1)
|
||
qzeros = qzeros.view(-1, num_heads, (head_dim_1 + head_dim_2)//8)
|
||
z1, z2 = qzeros.split([head_dim_1//8, head_dim_2//8], dim=-1)
|
||
return (w1.contiguous().view(w1.shape[0], -1),
|
||
s1.contiguous().view(s1.shape[0], -1),
|
||
z1.contiguous().view(z1.shape[0], -1),
|
||
w2.contiguous().view(w2.shape[0], -1),
|
||
s2.contiguous().view(s2.shape[0], -1),
|
||
z2.contiguous().view(z2.shape[0], -1))
|
||
# split W_UK and W_UV
|
||
(
|
||
W_UK, W_UK_S, W_UK_Z,
|
||
W_UV, W_UV_S, W_UV_Z
|
||
) = split_4bit_matrix(
|
||
self.kv_b_proj.qweight, self.kv_b_proj.scales, self.kv_b_proj.qzeros,
|
||
self.num_heads, self.qk_nope_head_dim, self.v_head_dim
|
||
)
|
||
# repack W_UK
|
||
W_UK = W_UK.reshape(self.kv_lora_rank, self.num_heads, -1).permute(1, 2, 0).contiguous()
|
||
W_UK_S = W_UK_S.reshape(W_UK_S.shape[0], self.num_heads, -1).permute(1, 0, 2).contiguous()
|
||
W_UK_Z = W_UK_Z.reshape(W_UK_Z.shape[0], self.num_heads, -1).permute(1, 0, 2).contiguous()
|
||
(
|
||
self.W_UK,
|
||
self.W_UK_S,
|
||
self.W_UK_Z
|
||
) = ixf_ops.marlin_w4_weight_repack(
|
||
weights = W_UK,
|
||
scales = W_UK_S,
|
||
zeros = W_UK_Z,
|
||
weight_format = "gptq_grouped_n",
|
||
reformat = "k16n32_grouped_n",
|
||
)
|
||
# repack W_UV
|
||
W_UV = W_UV.reshape(self.kv_lora_rank, self.num_heads, -1).permute(1, 0, 2).contiguous()
|
||
W_UV_S = W_UV_S.reshape(W_UV_S.shape[0], self.num_heads, -1).permute(1, 0, 2).contiguous()
|
||
W_UV_Z = W_UV_Z.reshape(W_UV_Z.shape[0], self.num_heads, -1).permute(1, 0, 2).contiguous()
|
||
(
|
||
self.W_UV,
|
||
self.W_UV_S,
|
||
self.W_UV_Z
|
||
) = ixf_ops.marlin_w4_weight_repack(
|
||
weights = W_UV,
|
||
scales = W_UV_S,
|
||
zeros = W_UV_Z,
|
||
weight_format = "awq",
|
||
reformat = "k16n32",
|
||
)
|
||
self.w4_group_size = self.kv_b_proj.quant_method.quant_config.group_size
|
||
|
||
# ===== W_UK & W_UV use W16A16 =====
|
||
# kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
|
||
# 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,
|
||
# )
|
||
# self.W_UK_fp16, self.W_UV_fp16 = kv_b_proj_weight.split(
|
||
# [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||
|
||
def _v_up_proj_w4a16(self, x):
|
||
# W16A16
|
||
# res = torch.einsum("bnl,lnv->bnv", x, self.W_UV_fp16)
|
||
|
||
# W4A16
|
||
return ixf_ops.marlin_w4a16(
|
||
x, self.W_UV, self.W_UV_S, self.W_UV_Z,
|
||
group_size=self.w4_group_size,
|
||
format="k16n32",
|
||
batch_first=False)
|
||
|
||
def _k_up_proj_w4a16(self, x_nope):
|
||
# W16A16
|
||
# return torch.einsum("bnp,lnp->bnl", x_nope, self.W_UK_fp16).view(-1, self.num_heads, self.kv_lora_rank), x_pe
|
||
|
||
# W4A16
|
||
return ixf_ops.marlin_w4a16(
|
||
x_nope, self.W_UK, self.W_UK_S, self.W_UK_Z,
|
||
group_size=self.w4_group_size,
|
||
format="k16n32_grouped_n",
|
||
batch_first=False,
|
||
)
|
||
|
||
self._v_up_proj = functools.partial(_v_up_proj_w4a16, self)
|
||
self._k_up_proj = functools.partial(_k_up_proj_w4a16, self)
|
||
else:
|
||
print(f"Custom MLA for quant method: {layer.quant_method.__class__.__name__} is not supported, will use the vllm official impl.")
|
||
back_to_vllm = True
|
||
else:
|
||
back_to_vllm = True
|
||
|
||
if back_to_vllm:
|
||
# `W_UV` and `W_UK_T`, 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
|
||
)
|
||
|
||
if self.is_aiter_triton_fp8_bmm_enabled:
|
||
W_K = W_UK.transpose(0, 1) # 16 512 128
|
||
W_V = W_UV.permute(1, 2, 0) # 16 128 512
|
||
self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
|
||
W_K, dtype=current_platform.fp8_dtype()
|
||
)
|
||
self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
|
||
W_V, dtype=current_platform.fp8_dtype()
|
||
)
|
||
|
||
# The kernel operates on non-padded inputs. Hence, pre-compiling
|
||
# triton kernel to avoid runtime compilation for unseen batch sizes
|
||
# Pre-compile for batch sizes 1 to 1024 to cover most use-cases.
|
||
# On DS-R1, this step adds roughly 50s to the model loading time.
|
||
max_batch_size = 1024 # [ToDo] Find the optimal upper limit
|
||
pre_compilation_list = list(range(1, max_batch_size + 1))
|
||
if is_global_first_rank():
|
||
pre_compilation_list = tqdm(
|
||
pre_compilation_list,
|
||
desc="[Aiter Triton] Pre-compiling fp8 BMM kernel",
|
||
total=max_batch_size,
|
||
)
|
||
|
||
for m in pre_compilation_list:
|
||
x = torch.empty(
|
||
(self.W_K.shape[0], m, self.W_K.shape[2]),
|
||
dtype=torch.bfloat16,
|
||
device=self.W_K.device,
|
||
)
|
||
rocm_aiter_ops.triton_fp8_bmm(
|
||
x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True
|
||
)
|
||
|
||
x = torch.empty(
|
||
(self.W_V.shape[0], m, self.W_V.shape[2]),
|
||
dtype=torch.bfloat16,
|
||
device=self.W_V.device,
|
||
)
|
||
rocm_aiter_ops.triton_fp8_bmm(
|
||
x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
|
||
)
|
||
else:
|
||
# # Convert from (L, N, V) to (N, L, V)
|
||
# self.W_UV = W_UV.transpose(0, 1)
|
||
# # Convert from (L, N, P) to (N, P, L)
|
||
# self.W_UK_T = W_UK.permute(1, 2, 0)
|
||
self.W_UV = W_UV
|
||
self.W_UK = W_UK
|
||
|
||
# # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
|
||
# if self.is_aiter_triton_fp4_bmm_enabled:
|
||
# from vllm.model_executor.layers.quantization.quark.utils import (
|
||
# quark_quantize_weight_to_mxfp4,
|
||
# )
|
||
|
||
# self.W_K, self.W_K_scale = quark_quantize_weight_to_mxfp4(W_UK)
|
||
# # Convert from (L, N, P) to (N, L, P)
|
||
# self.W_K = self.W_K.transpose(0, 1)
|
||
# self.W_K_scale = self.W_K_scale.transpose(0, 1)
|
||
|
||
# self.W_V, self.W_V_scale = quark_quantize_weight_to_mxfp4(
|
||
# W_UV.permute(1, 2, 0)
|
||
# )
|
||
# elif self.is_aiter_triton_fp8_bmm_enabled:
|
||
# W_K = W_UK.transpose(0, 1) # 16 512 128
|
||
# W_V = W_UV.permute(1, 2, 0) # 16 128 512
|
||
# self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
|
||
# W_K, dtype=current_platform.fp8_dtype()
|
||
# )
|
||
# self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
|
||
# W_V, dtype=current_platform.fp8_dtype()
|
||
# )
|
||
|
||
# # The kernel operates on non-padded inputs. Hence, pre-compiling
|
||
# # triton kernel to avoid runtime compilation for unseen batch sizes
|
||
# # Pre-compile for batch sizes 1 to 1024 to cover most use-cases.
|
||
# # On DS-R1, this step adds roughly 50s to the model loading time.
|
||
# max_batch_size = 1024 # [ToDo] Find the optimal upper limit
|
||
# pre_compilation_list = list(range(1, max_batch_size + 1))
|
||
# if is_global_first_rank():
|
||
# pre_compilation_list = tqdm(
|
||
# pre_compilation_list,
|
||
# desc="[Aiter Triton] Pre-compiling fp8 BMM kernel",
|
||
# total=max_batch_size,
|
||
# )
|
||
|
||
# for m in pre_compilation_list:
|
||
# x = torch.empty(
|
||
# (self.W_K.shape[0], m, self.W_K.shape[2]),
|
||
# dtype=torch.bfloat16,
|
||
# device=self.W_K.device,
|
||
# )
|
||
# rocm_aiter_ops.triton_fp8_bmm(
|
||
# x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True
|
||
# )
|
||
|
||
# x = torch.empty(
|
||
# (self.W_V.shape[0], m, self.W_V.shape[2]),
|
||
# dtype=torch.bfloat16,
|
||
# device=self.W_V.device,
|
||
# )
|
||
# rocm_aiter_ops.triton_fp8_bmm(
|
||
# x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
|
||
# )
|
||
# else:
|
||
# # Convert from (L, N, V) to (N, L, V)
|
||
# self.W_UV = W_UV.transpose(0, 1)
|
||
# # Convert from (L, N, P) to (N, P, L)
|
||
# self.W_UK_T = W_UK.permute(1, 2, 0)
|
||
|
||
def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor):
|
||
# Convert from (B, N, L) to (N, B, L)
|
||
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
|
||
out = out.view(-1, self.num_heads, self.v_head_dim)
|
||
if self.is_aiter_triton_fp4_bmm_enabled:
|
||
out = rocm_aiter_ops.batched_gemm_a16wfp4(
|
||
x,
|
||
self.W_V,
|
||
self.W_V_scale,
|
||
out,
|
||
transpose_bm=True,
|
||
prequant=True,
|
||
y_scale=None,
|
||
)
|
||
x = out.view(-1, self.num_heads * self.v_head_dim)
|
||
elif self.is_aiter_triton_fp8_bmm_enabled:
|
||
# Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
|
||
x = rocm_aiter_ops.triton_fp8_bmm(
|
||
x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out
|
||
)
|
||
else:
|
||
# Convert from (B, N * V) to (N, B, V)
|
||
out = out.transpose(0, 1)
|
||
|
||
# Multiply (N, B, L) x (N, L, V) -> (N, B, V)
|
||
torch.bmm(x, self.W_UV, out=out) # Reuse "out" to make it "hot"
|
||
|
||
# Convert from (N, B, V) to (B, N * V)
|
||
out_new = out.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
|
||
|
||
# Adjust output buffer shape back to the original (B, N * V)
|
||
N, B, V = out.shape
|
||
out.resize_((B, N * V))
|
||
out.copy_(out_new) # Copy result
|
||
|
||
|
||
class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
|
||
"""
|
||
NOTE: Please read the comment at the top of the file before trying to
|
||
understand this class
|
||
"""
|
||
|
||
def __init__(self, *args, **kwargs) -> None:
|
||
super().__init__(*args, **kwargs)
|
||
|
||
if use_trtllm_ragged_deepseek_prefill():
|
||
logger.info_once(
|
||
"Using TRT-LLM ragged DeepSeek prefill for MLA", scope="local"
|
||
)
|
||
self._run_prefill_context_chunk = (
|
||
self._run_prefill_context_chunk_trtllm_ragged
|
||
)
|
||
self._run_prefill_new_tokens = self._run_prefill_new_tokens_trtllm_ragged
|
||
self._pad_v = False
|
||
elif use_flashinfer_prefill():
|
||
logger.info_once("Using FlashInfer prefill for MLA", scope="local")
|
||
self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi
|
||
self._run_prefill_new_tokens = self._run_prefill_new_tokens_fi
|
||
self._pad_v = False
|
||
elif use_cudnn_prefill():
|
||
logger.info_once("Using CUDNN prefill for MLA", scope="local")
|
||
self._run_prefill_context_chunk = self._run_prefill_context_chunk_cudnn
|
||
self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn
|
||
self._pad_v = False
|
||
else: # Use FlashAttention
|
||
logger.info_once("Using FlashAttention prefill for MLA", scope="local")
|
||
self._run_prefill_context_chunk = self._run_prefill_context_chunk_fa
|
||
self._run_prefill_new_tokens = self._run_prefill_new_tokens_fa
|
||
|
||
# Handle the differences between the flash_attn_varlen from
|
||
# flash_attn and the one from vllm_flash_attn. The former is used on
|
||
# RoCM and the latter has an additional parameter to control
|
||
# FA2 vs FA3
|
||
self.flash_attn_varlen_func = flash_attn_varlen_func
|
||
self.vllm_flash_attn_version = get_flash_attn_version()
|
||
# if self.vllm_flash_attn_version is not None:
|
||
# self.flash_attn_varlen_func = functools.partial(
|
||
# flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version
|
||
# )
|
||
|
||
# For MLA the v head dim is smaller than qk head dim so we pad out
|
||
# v with 0s to match the qk head dim for attention backends that do
|
||
# not support different headdims
|
||
# We don't need to pad V if we are on a hopper system with FA3
|
||
# device_capability = current_platform.get_device_capability()
|
||
# self._pad_v = self.vllm_flash_attn_version is None or not (
|
||
# self.vllm_flash_attn_version == 3
|
||
# and device_capability is not None
|
||
# and device_capability[0] == 9
|
||
# )
|
||
self._pad_v = False
|
||
|
||
self.dcp_world_size: int = -1
|
||
|
||
self.chunked_prefill_workspace_size = (
|
||
MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
|
||
get_current_vllm_config()
|
||
)
|
||
)
|
||
self.cp_kv_cache_interleave_size: int = (
|
||
get_current_vllm_config().parallel_config.cp_kv_cache_interleave_size
|
||
)
|
||
self._decode_concat_quant_fp8_op = _DecodeConcatQuantFP8(
|
||
static=True,
|
||
group_shape=GroupShape.PER_TENSOR,
|
||
compile_native=True,
|
||
)
|
||
|
||
def _flash_attn_varlen_diff_headdims(
|
||
self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
|
||
):
|
||
maybe_padded_v = v
|
||
if self._pad_v:
|
||
maybe_padded_v = torch.nn.functional.pad(
|
||
v, [0, q.shape[-1] - v.shape[-1]], value=0
|
||
)
|
||
|
||
if is_vllm_fa:
|
||
kwargs["return_softmax_lse"] = return_softmax_lse
|
||
else:
|
||
# ROCm leverages the upstream flash_attn, which takes a parameter
|
||
# called "return_attn_probs" instead of return_softmax_lse
|
||
kwargs["return_attn_probs"] = return_softmax_lse
|
||
if vllm_is_batch_invariant():
|
||
kwargs["num_splits"] = 1
|
||
|
||
attn_out = self.flash_attn_varlen_func(
|
||
q=q,
|
||
k=k,
|
||
v=maybe_padded_v,
|
||
softmax_scale=softmax_scale,
|
||
**kwargs,
|
||
)
|
||
|
||
# Unpack the output if there is multiple results
|
||
lse = None
|
||
if isinstance(attn_out, tuple):
|
||
attn_out, lse = attn_out[0], attn_out[1]
|
||
|
||
# Remain consistent with old `flash_attn_varlen_func` where there
|
||
# is only one output tensor if `return_softmax_lse` is False.
|
||
if return_softmax_lse:
|
||
return attn_out, lse
|
||
return attn_out
|
||
|
||
def _run_prefill_new_tokens_fa(
|
||
self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse, out
|
||
):
|
||
return self._flash_attn_varlen_diff_headdims(
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
cu_seqlens_q=prefill.query_start_loc,
|
||
cu_seqlens_k=prefill.query_start_loc,
|
||
max_seqlen_q=prefill.max_query_len,
|
||
max_seqlen_k=prefill.max_query_len,
|
||
softmax_scale=self.scale,
|
||
causal=True,
|
||
return_softmax_lse=return_softmax_lse,
|
||
out=out
|
||
)
|
||
|
||
def _run_prefill_new_tokens_fi(
|
||
self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
|
||
):
|
||
assert isinstance(prefill, FlashInferPrefillMetadata)
|
||
assert prefill.prefill_main is not None
|
||
|
||
ret = prefill.prefill_main.run(
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
return_lse=return_softmax_lse,
|
||
)
|
||
|
||
if isinstance(ret, tuple):
|
||
return ret[0], ret[1].transpose(0, 1).contiguous()
|
||
return ret
|
||
|
||
def _run_prefill_new_tokens_cudnn(
|
||
self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
|
||
):
|
||
assert isinstance(prefill, CudnnPrefillMetadata)
|
||
assert prefill.query_seq_lens is not None
|
||
from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
|
||
|
||
output, lse = cudnn_batch_prefill_with_kv_cache(
|
||
q=q,
|
||
k_cache=k,
|
||
v_cache=v,
|
||
scale=self.scale,
|
||
workspace_buffer=prefill.cudnn_workspace,
|
||
max_token_per_sequence=prefill.max_query_len,
|
||
max_sequence_kv=prefill.max_query_len,
|
||
actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
|
||
actual_seq_lens_kv=prefill.query_seq_lens.view(-1, 1, 1, 1),
|
||
causal=True,
|
||
# Do not support False for now
|
||
return_lse=True,
|
||
# Indicates actual_seq_lens are on GPU or CPU.
|
||
is_cuda_graph_compatible=True,
|
||
)
|
||
if return_softmax_lse:
|
||
return output, lse
|
||
return output
|
||
|
||
def _run_prefill_context_chunk_fa(
|
||
self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v, out
|
||
):
|
||
assert prefill.chunked_context is not None
|
||
return self._flash_attn_varlen_diff_headdims(
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
cu_seqlens_q=prefill.query_start_loc,
|
||
cu_seqlens_k=prefill.chunked_context.cu_seq_lens[chunk_idx],
|
||
max_seqlen_q=prefill.max_query_len,
|
||
max_seqlen_k=prefill.chunked_context.max_seq_lens[chunk_idx],
|
||
softmax_scale=self.scale,
|
||
causal=False, # Context is unmasked
|
||
return_softmax_lse=True,
|
||
out=out
|
||
)
|
||
|
||
def _run_prefill_context_chunk_fi(
|
||
self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
|
||
):
|
||
assert isinstance(prefill, FlashInferPrefillMetadata)
|
||
|
||
attn_out, lse = prefill.prefill_chunks[chunk_idx].run(
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
return_lse=True,
|
||
)
|
||
|
||
# Convert from (q_len, num_heads) to (num_heads, q_len)
|
||
return attn_out, lse.transpose(0, 1).contiguous()
|
||
|
||
def _run_prefill_context_chunk_cudnn(
|
||
self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
|
||
):
|
||
assert isinstance(prefill, CudnnPrefillMetadata)
|
||
assert prefill.chunked_context is not None
|
||
assert prefill.chunked_context.seq_lens[chunk_idx] is not None
|
||
assert prefill.query_seq_lens is not None
|
||
from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
|
||
|
||
return cudnn_batch_prefill_with_kv_cache(
|
||
q=q,
|
||
k_cache=k,
|
||
v_cache=v,
|
||
scale=self.scale,
|
||
workspace_buffer=prefill.cudnn_workspace,
|
||
max_token_per_sequence=prefill.max_query_len,
|
||
max_sequence_kv=prefill.chunked_context.max_seq_lens[chunk_idx],
|
||
actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
|
||
actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].view(
|
||
-1, 1, 1, 1
|
||
),
|
||
causal=False,
|
||
return_lse=True,
|
||
# Indicates actual_seq_lens are on GPU or CPU.
|
||
is_cuda_graph_compatible=True,
|
||
)
|
||
|
||
def _v_up_proj(self, x):
|
||
# Convert from (B, N, L) to (N, B, L)
|
||
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
|
||
if is_rocm_aiter_fp8bmm_enabled():
|
||
# Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
|
||
x = aiter_triton_fp8_bmm(
|
||
x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
|
||
)
|
||
# Convert from (B, N, V) to (B, N * V)
|
||
x = x.reshape(-1, self.num_heads * self.v_head_dim)
|
||
else:
|
||
# Multiply (N, B, L) x (N, L, V) -> (N, B, V)
|
||
x = torch.bmm(x, self.W_UV)
|
||
# Convert from (N, B, V) to (B, N * V)
|
||
x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
|
||
return x
|
||
|
||
def _run_prefill_new_tokens_trtllm_ragged(
|
||
self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
|
||
):
|
||
"""TRT-LLM ragged attention for new tokens (causal)."""
|
||
from flashinfer.prefill import trtllm_ragged_attention_deepseek
|
||
|
||
assert prefill.query_seq_lens is not None
|
||
assert prefill.workspace_buffer is not None
|
||
|
||
ret = trtllm_ragged_attention_deepseek(
|
||
query=q,
|
||
key=k,
|
||
value=v,
|
||
workspace_buffer=prefill.workspace_buffer,
|
||
seq_lens=prefill.query_seq_lens,
|
||
max_q_len=prefill.max_query_len,
|
||
max_kv_len=prefill.max_query_len,
|
||
bmm1_scale=self.scale,
|
||
bmm2_scale=1.0,
|
||
o_sf_scale=1.0,
|
||
batch_size=prefill.query_seq_lens.shape[0],
|
||
window_left=-1,
|
||
cum_seq_lens_q=prefill.query_start_loc,
|
||
cum_seq_lens_kv=prefill.query_start_loc,
|
||
enable_pdl=False,
|
||
is_causal=True,
|
||
return_lse=return_softmax_lse,
|
||
)
|
||
|
||
if isinstance(ret, tuple):
|
||
# Convert from (q_len, num_heads) to (num_heads, q_len)
|
||
return ret[0], ret[1].transpose(0, 1).contiguous()
|
||
return ret
|
||
|
||
def _run_prefill_context_chunk_trtllm_ragged(
|
||
self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
|
||
):
|
||
"""TRT-LLM ragged attention for context chunks (non-causal)."""
|
||
from flashinfer.prefill import trtllm_ragged_attention_deepseek
|
||
|
||
assert prefill.chunked_context is not None
|
||
assert prefill.chunked_context.seq_lens[chunk_idx] is not None
|
||
assert prefill.workspace_buffer is not None
|
||
|
||
out = torch.zeros(
|
||
q.shape[0],
|
||
q.shape[1],
|
||
v.shape[2],
|
||
device=q.device,
|
||
dtype=q.dtype,
|
||
)
|
||
prefill.workspace_buffer.fill_(0)
|
||
|
||
attn_out, lse = trtllm_ragged_attention_deepseek(
|
||
query=q,
|
||
key=k,
|
||
value=v,
|
||
workspace_buffer=prefill.workspace_buffer,
|
||
seq_lens=prefill.chunked_context.seq_lens[chunk_idx],
|
||
max_q_len=prefill.max_query_len,
|
||
max_kv_len=prefill.chunked_context.max_seq_lens[chunk_idx],
|
||
bmm1_scale=self.scale,
|
||
bmm2_scale=1.0,
|
||
o_sf_scale=1.0,
|
||
batch_size=prefill.chunked_context.seq_lens[chunk_idx].shape[0],
|
||
window_left=-1,
|
||
cum_seq_lens_q=prefill.query_start_loc,
|
||
cum_seq_lens_kv=prefill.chunked_context.cu_seq_lens[chunk_idx],
|
||
enable_pdl=False,
|
||
is_causal=False,
|
||
return_lse=True,
|
||
out=out,
|
||
)
|
||
|
||
# Convert from (q_len, num_heads) to (num_heads, q_len)
|
||
return attn_out, lse.transpose(0, 1).contiguous()
|
||
|
||
def _concat_k_nope_k_pe(
|
||
self, k_nope: torch.Tensor, k_pe: torch.Tensor
|
||
) -> torch.Tensor:
|
||
"""
|
||
Efficiently concatenate k_nope and k_pe tensors along the last dimension.
|
||
|
||
This function avoids the performance penalty of torch.cat with expanded
|
||
non-contiguous tensors by pre-allocating the output and using direct copies.
|
||
|
||
Args:
|
||
k_nope: Tensor of shape [..., nope_dim]
|
||
k_pe: Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim]
|
||
or [..., pe_dim]
|
||
|
||
Returns:
|
||
Tensor of shape [..., nope_dim + pe_dim]
|
||
"""
|
||
k = torch.empty(
|
||
(*k_nope.shape[:-1], k_nope.shape[-1] + k_pe.shape[-1]),
|
||
dtype=k_nope.dtype,
|
||
device=k_nope.device,
|
||
)
|
||
# Direct copies with efficient broadcasting
|
||
k[..., : k_nope.shape[-1]] = k_nope
|
||
k[..., k_nope.shape[-1] :] = k_pe
|
||
return k
|
||
|
||
def _compute_prefill_context(
|
||
self,
|
||
q: torch.Tensor,
|
||
kv_c_and_k_pe_cache: torch.Tensor,
|
||
kv_c_and_k_pe_cache_scale: torch.Tensor,
|
||
attn_metadata: MLACommonMetadata,
|
||
k_scale: torch.Tensor | None = None,
|
||
):
|
||
assert attn_metadata.prefill is not None
|
||
prefill_metadata = attn_metadata.prefill
|
||
assert prefill_metadata.chunked_context is not None
|
||
|
||
output = None
|
||
iters = len(prefill_metadata.chunked_context.seq_tot)
|
||
workspace = prefill_metadata.chunked_context.workspace
|
||
for i in range(iters):
|
||
toks = prefill_metadata.chunked_context.seq_tot[i]
|
||
# ops.gather_and_maybe_dequant_cache(
|
||
# src_cache=kv_c_and_k_pe_cache,
|
||
# dst=workspace,
|
||
# block_table=prefill_metadata.block_table,
|
||
# cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
|
||
# token_to_seq=prefill_metadata.chunked_context.token_to_seq[i],
|
||
# num_tokens=prefill_metadata.chunked_context.chunk_total_token[i],
|
||
# kv_cache_dtype=self.kv_cache_dtype,
|
||
# scale=k_scale,
|
||
# seq_starts=prefill_metadata.chunked_context.starts[i],
|
||
# )
|
||
|
||
if envs.VLLM_USE_INT8_MLA:
|
||
ops.gather_cache_int8(
|
||
src_cache=kv_c_and_k_pe_cache,
|
||
src_cache_scale=kv_c_and_k_pe_cache_scale,
|
||
kv_lora_rank=self.kv_lora_rank,
|
||
dst=workspace,
|
||
block_table=prefill_metadata.block_table,
|
||
cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
|
||
batch_size=attn_metadata.num_prefills,
|
||
seq_starts=prefill_metadata.chunked_context.starts[i],
|
||
)
|
||
else:
|
||
ops.gather_cache(
|
||
src_cache=kv_c_and_k_pe_cache,
|
||
dst=workspace,
|
||
block_table=prefill_metadata.block_table,
|
||
cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
|
||
batch_size=attn_metadata.num_prefills,
|
||
seq_starts=prefill_metadata.chunked_context.starts[i],
|
||
)
|
||
|
||
kv_c_normed = workspace[:toks][..., : self.kv_lora_rank].contiguous()
|
||
k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
|
||
|
||
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
|
||
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
|
||
)
|
||
k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||
|
||
# k = self._concat_k_nope_k_pe(k_nope, k_pe)
|
||
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
|
||
attn_output = torch.empty(
|
||
q.shape[0],
|
||
self.num_heads,
|
||
self.v_head_dim,
|
||
dtype=q.dtype,
|
||
device=q.device,
|
||
)
|
||
|
||
attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
|
||
prefill=prefill_metadata,
|
||
chunk_idx=i,
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
out=attn_output,
|
||
)
|
||
|
||
if output is None:
|
||
output = attn_output
|
||
output_lse = attn_softmax_lse
|
||
else:
|
||
# output_tmp = torch.empty_like(output)
|
||
# output_lse_tmp = torch.empty_like(output_lse)
|
||
# merge_attn_states(
|
||
# output=output_tmp,
|
||
# output_lse=output_lse_tmp,
|
||
# prefix_output=output,
|
||
# prefix_lse=output_lse,
|
||
# suffix_output=attn_output,
|
||
# suffix_lse=attn_softmax_lse,
|
||
# )
|
||
# output = output_tmp
|
||
# output_lse = output_lse_tmp
|
||
merge_attn_states(
|
||
prefix_output=output,
|
||
prefix_lse=output_lse,
|
||
suffix_output=attn_output,
|
||
suffix_lse=attn_softmax_lse,
|
||
return_lse=True
|
||
)
|
||
|
||
return output, output_lse
|
||
|
||
def _context_parallel_compute_prefill_context(
|
||
self,
|
||
q: torch.Tensor,
|
||
kv_c_and_k_pe_cache: torch.Tensor,
|
||
attn_metadata: MLACommonMetadata,
|
||
k_scale: torch.Tensor,
|
||
dcp_world_size: int,
|
||
):
|
||
assert k_scale is None, "DCP not support scaled kvcache now."
|
||
assert attn_metadata.prefill is not None
|
||
prefill_metadata = attn_metadata.prefill
|
||
assert prefill_metadata.chunked_context is not None
|
||
assert prefill_metadata.chunked_context.padded_local_chunk_seq_lens is not None
|
||
assert prefill_metadata.chunked_context.local_context_lens_allranks is not None
|
||
assert prefill_metadata.chunked_context.padded_local_cu_seq_lens is not None
|
||
assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None
|
||
assert prefill_metadata.chunked_context.chunk_size is not None
|
||
|
||
output = None
|
||
iters = len(prefill_metadata.chunked_context.seq_tot)
|
||
workspace = prefill_metadata.chunked_context.workspace
|
||
|
||
for i in range(iters):
|
||
toks = prefill_metadata.chunked_context.seq_tot[i]
|
||
ops.cp_gather_cache(
|
||
src_cache=kv_c_and_k_pe_cache,
|
||
dst=workspace,
|
||
block_table=prefill_metadata.block_table,
|
||
cu_seq_lens=prefill_metadata.chunked_context.padded_local_cu_seq_lens[
|
||
i
|
||
],
|
||
batch_size=attn_metadata.num_prefills,
|
||
seq_starts=prefill_metadata.chunked_context.starts[i],
|
||
)
|
||
# workspace
|
||
# |------- N tokens --------|--------- N*dcp_size tokens ----------|
|
||
# |<- use for loca_gather ->|<--------- use for allgather -------->|
|
||
allgather_offset = workspace.shape[0] // (dcp_world_size + 1)
|
||
assert allgather_offset * (dcp_world_size + 1) == workspace.shape[0]
|
||
assert toks <= allgather_offset
|
||
local_gathered_kvcache = workspace[:toks]
|
||
cur_allgather_workspace = workspace[
|
||
allgather_offset : allgather_offset * (1 + dcp_world_size)
|
||
]
|
||
assert toks * dcp_world_size <= cur_allgather_workspace.shape[0]
|
||
cur_allgather_kvcache = cur_allgather_workspace[: toks * dcp_world_size]
|
||
cur_allgather_kvcache.copy_(
|
||
get_dcp_group().all_gather(local_gathered_kvcache, dim=0)
|
||
)
|
||
assert (
|
||
cur_allgather_kvcache.shape[-1]
|
||
== self.kv_lora_rank + self.qk_rope_head_dim
|
||
)
|
||
allgatered_kv_c_normed, allgatered_k_pe = cur_allgather_kvcache.unsqueeze(
|
||
1
|
||
).split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
||
|
||
kv_c_normed, k_pe = reorg_kvcache(
|
||
allgatered_kv_c_normed,
|
||
allgatered_k_pe,
|
||
padded_local_chunk_seq_lens_lst=prefill_metadata.chunked_context.padded_local_chunk_seq_lens[
|
||
i
|
||
],
|
||
local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks,
|
||
sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1],
|
||
max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
|
||
chunk_size=prefill_metadata.chunked_context.chunk_size,
|
||
chunk_idx=i,
|
||
toks=toks,
|
||
)
|
||
|
||
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 = self._concat_k_nope_k_pe(k_nope, k_pe)
|
||
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
|
||
|
||
attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
|
||
prefill=prefill_metadata,
|
||
chunk_idx=i,
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
)
|
||
|
||
if output is None:
|
||
output = attn_output
|
||
output_lse = attn_softmax_lse
|
||
else:
|
||
output_tmp = torch.empty_like(output)
|
||
output_lse_tmp = torch.empty_like(output_lse)
|
||
merge_attn_states(
|
||
output=output_tmp,
|
||
output_lse=output_lse_tmp,
|
||
prefix_output=output,
|
||
prefix_lse=output_lse,
|
||
suffix_output=attn_output,
|
||
suffix_lse=attn_softmax_lse,
|
||
)
|
||
output = output_tmp
|
||
output_lse = output_lse_tmp
|
||
|
||
return output, output_lse
|
||
|
||
def forward_mha(
|
||
self,
|
||
q: torch.Tensor,
|
||
kv_c_normed: torch.Tensor,
|
||
# k_pe: torch.Tensor,
|
||
k: torch.Tensor,
|
||
kv_c_and_k_pe_cache: torch.Tensor,
|
||
attn_metadata: MLACommonMetadata,
|
||
# k_scale: torch.Tensor,
|
||
# output: torch.Tensor,
|
||
kv_c_and_k_pe_cache_scale: torch.Tensor,
|
||
output: torch.Tensor | None = None,
|
||
) -> None:
|
||
# TODO (zyongye): Prefill function here
|
||
assert attn_metadata.prefill is not None
|
||
assert self.dcp_world_size != -1
|
||
|
||
has_context = attn_metadata.prefill.chunked_context is not None
|
||
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
|
||
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
|
||
)
|
||
# k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||
|
||
# k = self._concat_k_nope_k_pe(k_nope, k_pe)
|
||
|
||
# output_prefill = self._run_prefill_new_tokens(
|
||
# prefill=attn_metadata.prefill,
|
||
# q=q,
|
||
# k=k,
|
||
# v=v,
|
||
# return_softmax_lse=has_context,
|
||
# )
|
||
k_nope, v_nope = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||
v = v_nope
|
||
k[..., : self.qk_nope_head_dim] = k_nope
|
||
|
||
attn_output = torch.empty(
|
||
q.shape[0], self.num_heads, self.v_head_dim, dtype=q.dtype, device=q.device
|
||
)
|
||
|
||
output = self._run_prefill_new_tokens(
|
||
prefill=attn_metadata.prefill,
|
||
q=q,
|
||
k=k,
|
||
v=v,
|
||
return_softmax_lse=has_context,
|
||
out=attn_output,
|
||
)
|
||
|
||
if has_context:
|
||
# suffix_output, suffix_lse = output_prefill
|
||
suffix_output, suffix_lse = output
|
||
if self.dcp_world_size > 1:
|
||
context_output, context_lse = (
|
||
self._context_parallel_compute_prefill_context(
|
||
q,
|
||
kv_c_and_k_pe_cache,
|
||
attn_metadata,
|
||
k_scale=None,
|
||
dcp_world_size=self.dcp_world_size,
|
||
)
|
||
)
|
||
else:
|
||
# context_output, context_lse = self._compute_prefill_context(
|
||
# q, kv_c_and_k_pe_cache, attn_metadata, k_scale
|
||
# )
|
||
context_output, context_lse = self._compute_prefill_context(
|
||
q, kv_c_and_k_pe_cache, kv_c_and_k_pe_cache_scale, attn_metadata
|
||
)
|
||
|
||
# unpad if necessary
|
||
if self._pad_v:
|
||
context_output = context_output[..., : v.shape[-1]]
|
||
suffix_output = suffix_output[..., : v.shape[-1]]
|
||
|
||
# output = output.view(-1, self.num_heads, self.v_head_dim)
|
||
# merge_attn_states(
|
||
# output=output,
|
||
# prefix_output=context_output,
|
||
# prefix_lse=context_lse,
|
||
# suffix_output=suffix_output,
|
||
# suffix_lse=suffix_lse,
|
||
# )
|
||
output = torch.empty_like(suffix_output)
|
||
output = merge_attn_states(
|
||
output=output,
|
||
prefix_output=context_output,
|
||
prefix_lse=context_lse,
|
||
suffix_output=suffix_output,
|
||
suffix_lse=suffix_lse,
|
||
)
|
||
# else:
|
||
# output_prefill = output_prefill[..., : v.shape[-1]].flatten(start_dim=-2)
|
||
# output.copy_(output_prefill)
|
||
return output
|
||
|
||
@abstractmethod
|
||
def forward_mqa(
|
||
self,
|
||
q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
|
||
kv_c_and_k_pe_cache: torch.Tensor,
|
||
attn_metadata: M,
|
||
layer: AttentionLayer,
|
||
) -> tuple[torch.Tensor, torch.Tensor | None]:
|
||
raise NotImplementedError
|
||
|
||
def forward_prepare(
|
||
self,
|
||
positions: torch.Tensor,
|
||
) -> None:
|
||
self.positions = positions
|
||
|
||
def forward(
|
||
self,
|
||
layer: AttentionLayer,
|
||
q: torch.Tensor,
|
||
k_c_normed: torch.Tensor, # key in unified attn
|
||
k_pe: torch.Tensor, # value in unified attn
|
||
kv_cache: torch.Tensor,
|
||
attn_metadata: M,
|
||
output: torch.Tensor | None = None,
|
||
kv_cache_scale: torch.Tensor | None = None,
|
||
output_scale: torch.Tensor | None = None,
|
||
output_block_scale: torch.Tensor | None = None,
|
||
) -> torch.Tensor:
|
||
assert output is not None, "Output tensor must be provided."
|
||
|
||
if output_scale is not None or output_block_scale is not None:
|
||
raise NotImplementedError(
|
||
"fused output quantization is not yet supported for MLACommonImpl"
|
||
)
|
||
|
||
if attn_metadata is None:
|
||
# During the profile run try to simulate to worse case output size
|
||
# for `self.kv_b_proj(kv_c_normed)` in `_compute_prefill_context`
|
||
# since this can be large
|
||
_ = torch.empty(
|
||
(
|
||
self.chunked_prefill_workspace_size,
|
||
self.num_heads,
|
||
self.qk_nope_head_dim + self.v_head_dim,
|
||
),
|
||
device=k_c_normed.device,
|
||
dtype=k_c_normed.dtype,
|
||
)
|
||
|
||
# The zero fill is required when used with DP + EP
|
||
# to ensure all ranks within a DP group compute the
|
||
# same expert outputs.
|
||
# return output.fill_(0)
|
||
output = torch.empty(
|
||
output.shape[0],
|
||
self.v_head_dim * self.num_heads,
|
||
device=q.device,
|
||
dtype=q.dtype,
|
||
)
|
||
return output
|
||
|
||
if self.dcp_world_size == -1:
|
||
self.dcp_world_size = get_dcp_group().world_size
|
||
|
||
fp8_attention = self.kv_cache_dtype.startswith("fp8")
|
||
|
||
# num_actual_toks = attn_metadata.num_actual_tokens
|
||
|
||
# Inputs and outputs may be padded for CUDA graphs
|
||
# output_padded = output
|
||
# output = output[:num_actual_toks, ...]
|
||
# q = q[:num_actual_toks, ...]
|
||
# k_c_normed = k_c_normed[:num_actual_toks, ...]
|
||
# k_pe = k_pe[:num_actual_toks, ...]
|
||
|
||
assert (
|
||
attn_metadata.num_decodes is not None
|
||
and attn_metadata.num_prefills is not None
|
||
and attn_metadata.num_decode_tokens is not None
|
||
)
|
||
|
||
has_decode = attn_metadata.num_decodes > 0
|
||
has_prefill = attn_metadata.num_prefills > 0
|
||
num_decode_tokens = attn_metadata.num_decode_tokens
|
||
|
||
decode_q = q[:num_decode_tokens]
|
||
|
||
k_pe = k_pe.unsqueeze(1)
|
||
prefill_q = q[num_decode_tokens:]
|
||
prefill_k_pe = k_pe[num_decode_tokens:]
|
||
prefill_k_c_normed = k_c_normed[num_decode_tokens:]
|
||
prefill_k = torch.empty_like(prefill_q)
|
||
|
||
# write the latent and rope to kv cache
|
||
write_kv_cache = (None, None)
|
||
if kv_cache.numel() > 0:
|
||
# ops.concat_and_cache_mla(
|
||
# k_c_normed,
|
||
# k_pe.squeeze(1),
|
||
# kv_cache,
|
||
# attn_metadata.slot_mapping.flatten(),
|
||
# kv_cache_dtype=self.kv_cache_dtype,
|
||
# scale=layer._k_scale,
|
||
# )
|
||
if has_decode:
|
||
decode_q_pe, decode_k_pe = self.rotary_emb(
|
||
self.positions[:num_decode_tokens],
|
||
decode_q[..., self.qk_nope_head_dim :],
|
||
k_pe[:num_decode_tokens],
|
||
)
|
||
if envs.VLLM_USE_INT8_MLA:
|
||
write_kv_cache = (None, None)
|
||
k_c_normed_int8, k_c_normed_scale, _ = ops.scaled_int8_quant(
|
||
k_c_normed[:num_decode_tokens]
|
||
)
|
||
decode_k_pe_int8, decode_k_pe_scale, _ = ops.scaled_int8_quant(
|
||
decode_k_pe
|
||
)
|
||
ops.concat_and_cache_mla_int8(
|
||
kv_c_int8=k_c_normed_int8,
|
||
kv_c_scale=k_c_normed_scale[..., 0],
|
||
k_pe_int8=decode_k_pe_int8,
|
||
k_pe_scale=decode_k_pe_scale[..., 0].view(
|
||
-1, decode_k_pe_int8.shape[-2]
|
||
),
|
||
kv_cache=kv_cache,
|
||
kv_cache_scale=kv_cache_scale,
|
||
slot_mapping=attn_metadata.slot_mapping.flatten()[
|
||
:num_decode_tokens
|
||
],
|
||
kv_cache_dtype=self.kv_cache_dtype,
|
||
scale=layer._k_scale,
|
||
)
|
||
else:
|
||
# write_kv_cache = (k_c_normed[:num_decode_tokens], decode_k_pe)
|
||
if self.dcp_world_size > 1:
|
||
ops.concat_and_cache_mla(
|
||
k_c_normed[:num_decode_tokens],
|
||
decode_k_pe,
|
||
kv_cache,
|
||
attn_metadata.slot_mapping.flatten()[:num_decode_tokens],
|
||
kv_cache_dtype=self.kv_cache_dtype,
|
||
scale=layer._k_scale,
|
||
)
|
||
else:
|
||
write_kv_cache = (k_c_normed[:num_decode_tokens], decode_k_pe)
|
||
if has_prefill:
|
||
ixf_ops.mla_rope(
|
||
self.positions[num_decode_tokens:],
|
||
prefill_q[..., self.qk_nope_head_dim :],
|
||
prefill_k_pe.squeeze(1),
|
||
prefill_k[..., self.qk_nope_head_dim :],
|
||
self.rotary_emb.cos_sin_cache,
|
||
)
|
||
if envs.VLLM_USE_INT8_MLA:
|
||
prefill_k_c_normed_int8, prefill_k_c_normed_scale, _ = (
|
||
ops.scaled_int8_quant(prefill_k_c_normed)
|
||
)
|
||
prefill_k_pe_int8, prefill_k_pe_scale, _ = ops.scaled_int8_quant(
|
||
prefill_k[..., self.qk_nope_head_dim :].contiguous()
|
||
)
|
||
|
||
ops.concat_and_cache_mla_int8(
|
||
prefill_k_c_normed_int8,
|
||
prefill_k_c_normed_scale[..., 0],
|
||
prefill_k_pe_int8,
|
||
prefill_k_pe_scale[..., 0].view(
|
||
-1, prefill_k_pe_int8.shape[-2]
|
||
),
|
||
kv_cache,
|
||
kv_cache_scale,
|
||
attn_metadata.slot_mapping.flatten()[num_decode_tokens:],
|
||
kv_cache_dtype=self.kv_cache_dtype,
|
||
scale=layer._k_scale,
|
||
)
|
||
else:
|
||
ops.concat_and_cache_mla(
|
||
prefill_k_c_normed,
|
||
prefill_k[..., self.qk_nope_head_dim :],
|
||
kv_cache,
|
||
attn_metadata.slot_mapping.flatten()[num_decode_tokens:],
|
||
kv_cache_dtype=self.kv_cache_dtype,
|
||
scale=layer._k_scale,
|
||
)
|
||
output = torch.empty(
|
||
output.shape[0],
|
||
self.num_heads,
|
||
self.v_head_dim,
|
||
device=q.device,
|
||
dtype=q.dtype,
|
||
)
|
||
|
||
if fp8_attention:
|
||
kv_cache = kv_cache.view(current_platform.fp8_dtype())
|
||
|
||
if has_prefill:
|
||
# self.forward_mha(
|
||
# prefill_q,
|
||
# prefill_k_c_normed,
|
||
# prefill_k_pe,
|
||
# kv_cache,
|
||
# attn_metadata,
|
||
# layer._k_scale,
|
||
# output=output[num_decode_tokens:],
|
||
# )
|
||
output[num_decode_tokens:] = self.forward_mha(
|
||
prefill_q,
|
||
prefill_k_c_normed,
|
||
prefill_k,
|
||
kv_cache,
|
||
attn_metadata,
|
||
kv_c_and_k_pe_cache_scale=kv_cache_scale,
|
||
)
|
||
|
||
if has_decode:
|
||
assert attn_metadata.decode is not None
|
||
|
||
# decode_q_nope, decode_q_pe = decode_q.split(
|
||
# [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
|
||
# )
|
||
|
||
# # Convert from (B, N, P) to (N, B, P)
|
||
# decode_q_nope = decode_q_nope.transpose(0, 1)
|
||
|
||
# if self.q_pad_num_heads is not None:
|
||
# B, N, L = decode_q_pe.shape
|
||
# decode_pe_padded = decode_q_pe.new_empty((B, self.q_pad_num_heads, L))
|
||
# decode_pe_padded.resize_((B, N, L))
|
||
# decode_pe_padded.copy_(decode_q_pe)
|
||
# decode_q_pe = decode_pe_padded
|
||
|
||
# if self.is_aiter_triton_fp4_bmm_enabled:
|
||
# from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4
|
||
|
||
# decode_ql_nope = batched_gemm_a16wfp4(
|
||
# decode_q_nope,
|
||
# self.W_K,
|
||
# self.W_K_scale,
|
||
# transpose_bm=True,
|
||
# prequant=True,
|
||
# y_scale=layer._q_scale if fp8_attention else None,
|
||
# )
|
||
# elif self.is_aiter_triton_fp8_bmm_enabled:
|
||
# # Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L)
|
||
# decode_ql_nope = rocm_aiter_ops.triton_fp8_bmm(
|
||
# decode_q_nope,
|
||
# self.W_K,
|
||
# self.W_K_scale,
|
||
# group_size=128,
|
||
# transpose_bm=True,
|
||
# )
|
||
# else:
|
||
# # Pads the head_dim if necessary (for the underlying kernel)
|
||
# N, B, P = decode_q_nope.shape
|
||
# _, _, L = self.W_UK_T.shape
|
||
|
||
# if self.q_pad_num_heads is not None:
|
||
# decode_ql_nope = decode_q_nope.new_empty(
|
||
# (self.q_pad_num_heads, B, L)
|
||
# )
|
||
# decode_ql_nope.resize_((N, B, L))
|
||
# else:
|
||
# decode_ql_nope = decode_q_nope.new_empty((N, B, L))
|
||
|
||
# # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
|
||
# torch.bmm(decode_q_nope, self.W_UK_T, out=decode_ql_nope)
|
||
|
||
# # Convert from (N, B, L) to (B, N, L)
|
||
# decode_ql_nope = decode_ql_nope.transpose(0, 1)
|
||
|
||
# if fp8_attention:
|
||
# assert decode_ql_nope.shape[0] == decode_q_pe.shape[0]
|
||
# assert decode_ql_nope.shape[1] == decode_q_pe.shape[1]
|
||
# decode_q = self._decode_concat_quant_fp8_op(
|
||
# decode_ql_nope, decode_q_pe, layer._q_scale
|
||
# )
|
||
# else:
|
||
# decode_q = (decode_ql_nope, decode_q_pe)
|
||
# if self.dcp_world_size > 1:
|
||
# assert not fp8_attention, "DCP not support fp8 kvcache now."
|
||
# # concatenate decode_ql_nope and decode_q_pe -> (B, N, L + P)
|
||
# decode_q = torch.cat(decode_q, dim=-1)
|
||
# # decode_q do allgather in head dim.
|
||
# decode_q = get_dcp_group().all_gather(decode_q, dim=1)
|
||
|
||
# call decode attn
|
||
# decode_q, kv_cache, attn_metadata, layer
|
||
|
||
attn_out, lse = self.forward_mqa(
|
||
decode_q[..., :self.qk_nope_head_dim], decode_q_pe, kv_cache, attn_metadata,
|
||
*write_kv_cache, kv_c_and_k_pe_cache_scale=kv_cache_scale
|
||
)
|
||
|
||
# correct dcp attn_out with lse.
|
||
if self.dcp_world_size > 1:
|
||
assert lse is not None
|
||
attn_out = cp_lse_ag_out_rs(attn_out, lse, get_dcp_group())
|
||
output[:num_decode_tokens] = self._v_up_proj(attn_out)
|
||
else:
|
||
assert lse is None
|
||
output[:num_decode_tokens] = attn_out
|
||
|
||
return output.view(output.shape[0], self.v_head_dim * self.num_heads)
|
||
# attn_out = cp_lse_ag_out_rs(
|
||
# attn_out,
|
||
# lse,
|
||
# get_dcp_group(),
|
||
# is_lse_base_on_e=not getattr(self, "_use_fi_prefill", False),
|
||
# )
|
||
|
||
# # v_up projection
|
||
# self._v_up_proj(attn_out, out=output[:num_decode_tokens])
|
||
# return output_padded
|