[Attention] Update to lastest FA3 code (vllm-project#13111)

Signed-off-by: Lucas Wilkinson <[email protected]>

Author: LucasWilkinson

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG dc9d410b3e2d6534a4c70724c2515f4def670a22
+          GIT_TAG 0a721daebe4fa7149f06ecf3d3eabeb6dcd0f1fa
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

vllm/attention/backends/mla/common.py

@@ -1043,8 +1043,8 @@ def __init__(
         self.q_proj = q_proj
         self.kv_b_proj = kv_b_proj
         self.o_proj = o_proj
-        self.triton_fa_func = triton_attention
 
+        self.triton_fa_func = triton_attention
         # 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
@@ -1055,6 +1055,70 @@ def __init__(
                 functools.partial(flash_attn_varlen_func,
                                   fa_version=self.vllm_flash_attn_version)
 
+        # For MLA the v head dim is smaller than qk head dim so we pad out
+        # v with 0s to match the qk head dim for attention backends that do
+        # not support different headdims
+        # We don't need to pad V if we are on a hopper system with FA3
+        self._pad_v = self.vllm_flash_attn_version is None or not (
+            self.vllm_flash_attn_version == 3
+            and current_platform.get_device_capability()[0] == 9)
+
+    def _flash_attn_varlen_diff_headdims(self, q, k, v, softmax_scale,
+                                         return_softmax_lse, **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_hip and envs.VLLM_USE_TRITON_FLASH_ATTN \
+            and not return_softmax_lse:
+            attn_out = self.triton_fa_func(
+                q,
+                k,
+                maybe_padded_v,
+                **kwargs,
+            )
+        if is_vllm_fa:
+            attn_out = self.flash_attn_varlen_func(
+                q=q,
+                k=k,
+                v=maybe_padded_v,
+                return_softmax_lse=return_softmax_lse,
+                softmax_scale=softmax_scale,
+                **kwargs,
+            )
+        else:
+            # Use return_attn_probs instead of return_softmax_lse for RoCM
+            attn_out = self.flash_attn_varlen_func(
+                q=q,
+                k=k,
+                v=maybe_padded_v,
+                return_attn_probs=return_softmax_lse,
+                softmax_scale=softmax_scale,
+                **kwargs,
+            )
+
+        # Unpack the output if there is multiple results,
+        # triton always returns (output, softmax_lse),
+        # vllm_flash_attn returns (output, softmax_lse) when
+        #  `return_softmax_lse = True`
+        # flash_attn (RoCM) returns (output, softmax_lse, ...) when
+        #  `return_attn_probs = True`
+        rest = None
+        if isinstance(attn_out, tuple):
+            attn_out, *rest = attn_out
+
+        # unpad if necessary
+        if self._pad_v:
+            attn_out = attn_out[..., :v.shape[-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:
+            assert rest is not None
+            return attn_out, rest[0]
+        return attn_out
+
     def _v_up_proj_and_o_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)
@@ -1176,40 +1240,19 @@ def _compute_prefill_context(
             k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
                           dim=-1)
 
-            # 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
-            v_padded = torch.nn.functional.pad(v,
-                                               [0, q.shape[-1] - v.shape[-1]],
-                                               value=0)
-
-            if is_vllm_fa:
-                attn_output, attn_softmax_lse = self.flash_attn_varlen_func(
-                    q=q,
-                    k=k,
-                    v=v_padded,
-                    cu_seqlens_q=prefill_metadata.query_start_loc,
-                    cu_seqlens_k=prefill_metadata.context_chunk_cu_seq_lens[i],
-                    max_seqlen_q=prefill_metadata.max_query_len,
-                    max_seqlen_k=prefill_metadata.
-                    context_chunk_max_seq_lens[i],
-                    softmax_scale=self.scale,
-                    causal=False,  # Context is unmasked
-                    return_softmax_lse=True,
-                )
-            else:
-                attn_output, attn_softmax_lse, _ = self.flash_attn_varlen_func(
-                    q=q,
-                    k=k,
-                    v=v_padded,
-                    cu_seqlens_q=prefill_metadata.query_start_loc,
-                    cu_seqlens_k=prefill_metadata.context_chunk_cu_seq_lens[i],
-                    max_seqlen_q=prefill_metadata.max_query_len,
-                    max_seqlen_k=prefill_metadata.
-                    context_chunk_max_seq_lens[i],
-                    softmax_scale=self.scale,
-                    causal=False,  # Context is unmasked
-                    return_attn_probs=True,
-                )
+            attn_output, attn_softmax_lse = \
+                self._flash_attn_varlen_diff_headdims(
+                q=q,
+                k=k,
+                v=v,
+                cu_seqlens_q=prefill_metadata.query_start_loc,
+                cu_seqlens_k=prefill_metadata.context_chunk_cu_seq_lens[i],
+                max_seqlen_q=prefill_metadata.max_query_len,
+                max_seqlen_k=prefill_metadata.context_chunk_max_seq_lens[i],
+                softmax_scale=self.scale,
+                causal=False,  # Context is unmasked
+                return_softmax_lse=True,
+            )
 
             if output is None:
                 output = attn_output
@@ -1252,58 +1295,22 @@ def _forward_prefill(
 
         k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
 
-        # 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
-        v_padded = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]],
-                                           value=0)
-
-        if is_hip and envs.VLLM_USE_TRITON_FLASH_ATTN and not has_context:
-            output = self.triton_fa_func(
-                q,
-                k,
-                v_padded,
-                None,
-                prefill_metadata.query_start_loc,
-                prefill_metadata.query_start_loc,
-                prefill_metadata.max_prefill_seq_len,
-                prefill_metadata.max_prefill_seq_len,
-                True,  # causal
-                self.scale,
-                None,  # attn_mask is None unless applying ALiBi mask
-            )
-            ## triton flash attention always return 2 objects
-            if not has_context:
-                output = output[0]
-        elif is_vllm_fa:
-            output = self.flash_attn_varlen_func(
-                q=q,
-                k=k,
-                v=v_padded,
-                cu_seqlens_q=prefill_metadata.query_start_loc,
-                cu_seqlens_k=prefill_metadata.query_start_loc,
-                max_seqlen_q=prefill_metadata.max_prefill_seq_len,
-                max_seqlen_k=prefill_metadata.max_prefill_seq_len,
-                softmax_scale=self.scale,
-                causal=True,
-                return_softmax_lse=has_context,
-            )
-        else:
-            output = self.flash_attn_varlen_func(
-                q=q,
-                k=k,
-                v=v_padded,
-                cu_seqlens_q=prefill_metadata.query_start_loc,
-                cu_seqlens_k=prefill_metadata.query_start_loc,
-                max_seqlen_q=prefill_metadata.max_prefill_seq_len,
-                max_seqlen_k=prefill_metadata.max_prefill_seq_len,
-                softmax_scale=self.scale,
-                causal=True,
-                return_attn_probs=has_context,
-            )
+        output = self._flash_attn_varlen_diff_headdims(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=prefill_metadata.query_start_loc,
+            cu_seqlens_k=prefill_metadata.query_start_loc,
+            max_seqlen_q=prefill_metadata.max_prefill_seq_len,
+            max_seqlen_k=prefill_metadata.max_prefill_seq_len,
+            softmax_scale=self.scale,
+            causal=True,
+            return_softmax_lse=has_context,
+        )
 
         if has_context:
             # ROCm flash_attn_varlen_func will return 3 objects instead of 2
-            suffix_output, suffix_lse, *rest = output
+            suffix_output, suffix_lse = output
             context_output, context_lse = self._compute_prefill_context( \
                 q, kv_c_and_k_pe_cache, attn_metadata)
 
@@ -1316,12 +1323,7 @@ def _forward_prefill(
                 suffix_lse=suffix_lse,
             )
 
-        # slice by `:v.shape[-1]` in order to remove v headdim padding
-        output = output\
-            .view(-1, self.num_heads, q.shape[-1])[..., :v.shape[-1]]\
-                .reshape(-1, self.num_heads * v.shape[-1])
-
-        return self.o_proj(output)[0]
+        return self.o_proj(output.flatten(start_dim=-2))[0]
 
     @abstractmethod
     def _forward_decode(

vllm/attention/backends/utils.py

@@ -2,15 +2,18 @@
 """Attention backend utils"""
 from collections import defaultdict
 from contextlib import contextmanager
+from dataclasses import dataclass
 from itertools import accumulate
-from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Type, TypeVar, Union
+from typing import (TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type,
+                    TypeVar, Union)
 
 import numpy as np
 import torch
 
 from vllm.attention import (AttentionMetadata, AttentionMetadataBuilder,
                             AttentionState)
 from vllm.attention.backends.abstract import AttentionType
+from vllm.config import ModelConfig
 from vllm.logger import init_logger
 from vllm.multimodal import MultiModalPlaceholderMap
 from vllm.utils import async_tensor_h2d, make_tensor_with_pad
@@ -583,3 +586,24 @@ def get_num_prefill_decode_query_kv_tokens(
 
     return (num_prefill_query_tokens, num_prefill_kv_tokens,
             num_decode_query_tokens)
+
+
+@dataclass
+class MLADims:
+    q_lora_rank: Optional[int]
+    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,
+    )