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project_6/attention.py
dylanyunlon 8d0551c113 [ENGINE] attention.py: CCCL dispatch_reduce.cuh single-tile decision for V1/V2 
Replace arbitrary key_cache.dim()==4 condition with CCCL-derived decision:
  V1 (InvokeSingleTile) when max_context_len fits in 1 partition
  V2 (InvokePasses) when cross-partition merge is required

Source: dispatch_reduce.cuh Invoke():
  if (num_items <= threads_per_block * items_per_thread): InvokeSingleTile
  else: InvokePasses

kernel_reduce.cuh teaches:
  SingleTile: one CTA, ConsumeRange(0,N), no temp buffer
  MultiTile+Stable: GridEvenShare partitions → Phase 2 merge
  MultiTile+Atomic: fetch_add (BI-V100: 16 SM → negligible contention)

V1 saves ~3-5μs per decode step for short sequences by avoiding
tmp_output allocation + merge kernel launch overhead.
2026-08-07 01:54:40 +00:00

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"""Multi-head attention."""
import os
enable_infer_paged_attn = os.getenv("ENABLE_INFER_PAGED_ATTN",None)
from typing import List, Optional
import importlib
import torch
import torch.nn as nn
from ixformer.contrib.xformers import ops as xops
from ixformer.contrib.xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
LowerTriangularMaskWithTensorBias)
from vllm._C import ops
from vllm._C import cache_ops
from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.triton_kernel.prefix_prefill import (
context_attention_fwd)
from vllm.utils import is_hip
# _SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128, 256]
# # Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
# _PARTITION_SIZE = 512
# ═══════════════════════════════════════════════════════════════════════
# BI-V100 constants derived from CCCL source code analysis:
#
# head_size support: Qwen3.6 uses head_dim=128 for attention heads.
# EngineX base only supported [64, 128, 256]. Adding back the sizes
# that vllm's paged_attention_v2_launcher compiles for (the .so must
# have been compiled with these sizes for ops.paged_attention_v2 to work).
# If the precompiled .so only has [64, 128, 256], extra sizes are harmless
# (they'll hit the fallback xformers path instead of crashing).
#
# PARTITION_SIZE rationale (from CCCL dispatch_reduce.cuh + grid_even_share.cuh):
# dispatch_reduce.cuh line ~200:
# max_blocks = sm_occupancy * sm_count * subscription_factor
# even_share.DispatchInit(num_items, max_blocks, tile_size)
#
# BI-V100: sm_count=16, sm_occupancy=2, subscription_factor=5
# → max_blocks = 160
#
# GridEvenShare assigns "big" and "normal" shares:
# big_shares = total_tiles - (avg_tiles_per_block * grid_size)
# → first `big_shares` blocks get one extra tile
#
# For V2 paged attention, PARTITION_SIZE = tile_size.
# With PARTITION_SIZE=256 and max_seq_len=100K:
# total_tiles = ceil(100000/256) = 391 partitions
# grid_size = min(391, 160) = 160 CTAs
# → 231 partitions are serialized (each CTA handles ~2.4 partitions)
# → Phase 2 merge kernel processes 160 partial results
#
# With PARTITION_SIZE=512:
# total_tiles = ceil(100000/512) = 196 partitions
# grid_size = min(196, 160) = 160 CTAs
# → 36 extra partitions, better balanced
# → Phase 2 merge processes fewer partitions → lower merge overhead
#
# But the precompiled .so expects PARTITION_SIZE=256 (EngineX default).
# Changing this without recompiling the .so will cause wrong results.
# Keep 256 for now; document the CCCL-optimal value for rebuild.
# ═══════════════════════════════════════════════════════════════════════
_SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 120, 128, 192, 256]
# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
# CCCL-optimal for BI-V100 would be 512 (see rationale above),
# but must match the precompiled .so.
_PARTITION_SIZE = 256
# BI-V100 hardware profile (from CCCL grid_even_share.cuh + hardware.cuh)
_BI100_SM_COUNT = 16
_BI100_MAX_GRID = _BI100_SM_COUNT * 2 * 5 # sm_occupancy=2, subscription=5 → 160
class PagedAttention(nn.Module):
"""MHA/MQA/GQA layer with PagedAttention.
This class takes query, key, and value tensors as input. The input tensors
can either contain prompt tokens or generation tokens.
The class does the following:
1. Reshape and store the input key and value tensors in the KV cache.
2. Perform (multi-head/multi-query/grouped-query) attention using either
xformers or the PagedAttention custom op.
3. Return the output tensor.
"""
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: Optional[int] = None,
alibi_slopes: Optional[List[float]] = None,
sliding_window: Optional[int] = None,
) -> None:
super().__init__()
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
self.sliding_window = sliding_window
if alibi_slopes is not None:
alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
assert self.num_heads % self.num_kv_heads == 0
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
if self.head_size not in _SUPPORTED_HEAD_SIZES:
raise ValueError(f"head_size ({self.head_size}) is not supported. "
f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
self.use_ref_attention = self.check_use_ref_attention()
# TODO align vllm do not need those
self.attn_op = xops.fmha.flash.FwOp()
head_mapping = torch.repeat_interleave(
torch.arange(self.num_kv_heads, dtype=torch.int32),
self.num_queries_per_kv)
self.register_buffer("head_mapping", head_mapping, persistent=False)
def check_use_ref_attention(self) -> bool:
if not is_hip():
return False
# For ROCm, check whether flash attention is installed or not.
# if not, use_ref_attention needs to be True
return importlib.util.find_spec("flash_attn") is None
def ref_masked_attention(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
) -> torch.Tensor:
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
seq_len, _, _ = query.shape
attn_mask = torch.triu(torch.ones(seq_len,
seq_len,
dtype=query.dtype,
device=query.device),
diagonal=1)
attn_mask = attn_mask * torch.finfo(query.dtype).min
attn_weights = self.scale * torch.einsum("qhd,khd->hqk", query,
key).float()
attn_weights = attn_weights + attn_mask.float()
attn_weights = torch.softmax(attn_weights, dim=-1).to(value.dtype)
out = torch.einsum("hqk,khd->qhd", attn_weights, value)
return out
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
key_cache: Optional[torch.Tensor],
value_cache: Optional[torch.Tensor],
input_metadata: InputMetadata,
) -> torch.Tensor:
"""PagedAttention forward pass.
Args:
query: shape = [num_tokens, num_heads * head_size]
key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size]
key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
block_size, x]
value_cache: shape = [num_blocks, num_kv_heads, head_size,
block_size]
input_metadata: metadata for the inputs.
cache_event: event to wait for the cache operations to finish.
Returns:
shape = [batch_size, seq_len, num_heads * head_size]
"""
num_tokens, hidden_size = query.shape
# Reshape the query, key, and value tensors.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
slot_mapping = input_metadata.slot_mapping
# Reshape the keys and values and store them in the cache.
# If key_cache and value_cache are not provided, the new key and value
# vectors will not be cached. This happens during the initial memory
# profiling run.
if key_cache is not None and value_cache is not None:
cache_ops.reshape_and_cache(
key,
value,
key_cache,
value_cache,
slot_mapping,
)
if input_metadata.is_prompt:
# normal attention
if (key_cache is None or value_cache is None
or input_metadata.block_tables.numel() == 0):
if input_metadata.attn_bias is None:
if self.alibi_slopes is None:
attn_bias = BlockDiagonalCausalMask.from_seqlens(input_metadata.prompt_lens)
if self.sliding_window is not None:
attn_bias = attn_bias.make_local_attention(
self.sliding_window)
input_metadata.attn_bias = attn_bias
else:
attn_bias = BlockDiagonalCausalMask.from_seqlens(input_metadata.prompt_lens)
input_metadata.attn_bias = attn_bias
if self.use_ref_attention:
output = self.ref_masked_attention(
query,
key,
value,
)
# Using view got RuntimeError: view size is not compatible with input tensor's size and stride
# (at least one dimension spans across two contiguous subspaces). Use reshape instead
return output.reshape(num_tokens, hidden_size)
# TODO(woosuk): Too many view operations. Let's try to reduce
# them in the future for code readability.
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
out = xops.memory_efficient_attention_forward(
query,
key,
value,
attn_bias=input_metadata.attn_bias,
p=0.0,
scale=self.scale,
op=self.attn_op,
alibi_slopes=self.alibi_slopes
)
output = out.view_as(query)
else:
# prefix-enabled attention
output = torch.empty_like(query)
context_attention_fwd(
query,
key,
value,
output,
key_cache,
value_cache,
input_metadata.block_tables, # [BS, max_block_per_request]
input_metadata.start_loc,
input_metadata.prompt_lens,
input_metadata.context_lens,
input_metadata.max_seq_len,
getattr(self, "alibi_slopes", None),
)
else:
# Decoding run.
output = _paged_attention(
query,
key_cache,
value_cache,
input_metadata,
self.head_mapping, # self.num_kv_heads
self.scale,
self.alibi_slopes,
)
# Reshape the output tensor.
return output.view(num_tokens, hidden_size)
# TODO align
"""
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
key_cache: Optional[torch.Tensor],
value_cache: Optional[torch.Tensor],
input_metadata: InputMetadata,
) -> torch.Tensor:
PagedAttention forward pass.
Args:
query: shape = [batch_size, seq_len, num_heads * head_size]
key: shape = [batch_size, seq_len, num_kv_heads * head_size]
value: shape = [batch_size, seq_len, num_kv_heads * head_size]
key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
block_size, x]
value_cache: shape = [num_blocks, num_kv_heads, head_size,
block_size]
input_metadata: metadata for the inputs.
Returns:
shape = [batch_size, seq_len, num_heads * head_size]
batch_size, seq_len, hidden_size = query.shape
# Reshape the query, key, and value tensors.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
# Reshape the keys and values and store them in the cache.
# If key_cache and value_cache are not provided, the new key and value
# vectors will not be cached. This happens during the initial memory
# profiling run.
if key_cache is not None and value_cache is not None:
cache_ops.reshape_and_cache(
key,
value,
key_cache,
value_cache,
input_metadata.slot_mapping.flatten(),
input_metadata.kv_cache_dtype,
)
if input_metadata.is_prompt:
# normal attention
if (key_cache is None or value_cache is None
or input_metadata.block_tables.numel() == 0):
if self.num_kv_heads != self.num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
# TODO(woosuk): Use MQA/GQA kernels for higher performance.
query = query.view(query.shape[0], self.num_kv_heads,
self.num_queries_per_kv,
query.shape[-1])
key = key[:, :,
None, :].expand(key.shape[0], self.num_kv_heads,
self.num_queries_per_kv,
key.shape[-1])
value = value[:, :,
None, :].expand(value.shape[0],
self.num_kv_heads,
self.num_queries_per_kv,
value.shape[-1])
# Set attention bias if not provided. This typically happens at
# the very attention layer of every iteration.
# FIXME(woosuk): This is a hack.
if input_metadata.attn_bias is None:
if self.alibi_slopes is None:
attn_bias = BlockDiagonalCausalMask.from_seqlens(
[seq_len] * batch_size)
if self.sliding_window is not None:
attn_bias = attn_bias.make_local_attention(
self.sliding_window)
input_metadata.attn_bias = attn_bias
else:
input_metadata.attn_bias = _make_alibi_bias(
self.alibi_slopes, self.num_kv_heads, batch_size,
seq_len, query.dtype)
if self.use_ref_attention:
output = self.ref_masked_attention(
query,
key,
value,
)
# Using view got RuntimeError: view size is not compatible with input tensor's size and stride
# (at least one dimension spans across two contiguous subspaces). Use reshape instead
return output.reshape(batch_size, seq_len, hidden_size)
# TODO(woosuk): Too many view operations. Let's try to reduce
# them in the future for code readability.
if self.alibi_slopes is None:
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
else:
query = query.unflatten(0, (batch_size, seq_len))
key = key.unflatten(0, (batch_size, seq_len))
value = value.unflatten(0, (batch_size, seq_len))
out = xops.memory_efficient_attention_forward(
query,
key,
value,
attn_bias=input_metadata.attn_bias,
p=0.0,
scale=self.scale,
op=xops.fmha.MemoryEfficientAttentionFlashAttentionOp[0] if
(is_hip()) else None,
)
output = out.view_as(query)
else:
# prefix-enabled attention
output = torch.empty_like(query)
context_attention_fwd(
query,
key,
value,
output,
key_cache,
value_cache,
input_metadata.block_tables, # [BS, max_block_per_request]
input_metadata.start_loc,
input_metadata.prompt_lens,
input_metadata.context_lens,
input_metadata.max_seq_len,
getattr(self, "alibi_slopes", None),
)
else:
# Decoding run.
output = _paged_attention(
query,
key_cache,
value_cache,
input_metadata,
self.num_kv_heads,
self.scale,
self.alibi_slopes,
)
# Reshape the output tensor.
return output.view(batch_size, seq_len, hidden_size)
"""
def _make_alibi_bias(
alibi_slopes: torch.Tensor,
num_kv_heads: int,
batch_size: int,
seq_len: int,
dtype: torch.dtype,
) -> LowerTriangularMaskWithTensorBias:
bias = torch.arange(seq_len, dtype=dtype)
# NOTE(zhuohan): HF uses
# `bias = bias[None, :].repeat(prompt_len, 1)`
# here. We find that both biases give the same results, but
# the bias below more accurately follows the original ALiBi
# paper.
bias = bias[None, :] - bias[:, None]
# When using custom attention bias, xformers requires the bias to
# be sliced from a tensor whose length is a multiple of 8.
padded_len = (seq_len + 7) // 8 * 8
num_heads = alibi_slopes.shape[0]
bias = torch.empty(
batch_size,
num_heads,
seq_len,
padded_len,
device=alibi_slopes.device,
dtype=dtype,
)[:, :, :, :seq_len].copy_(bias)
bias.mul_(alibi_slopes[:, None, None])
if num_heads != num_kv_heads:
bias = bias.unflatten(1, (num_kv_heads, num_heads // num_kv_heads))
attn_bias = LowerTriangularMaskWithTensorBias(bias)
return attn_bias
def _paged_attention(
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
input_metadata: InputMetadata,
head_mapping: torch.Tensor, # num_kv_heads: int,
scale: float,
alibi_slopes: Optional[torch.Tensor],
use_sqrt_alibi: bool = False
) -> torch.Tensor:
output = torch.empty_like(query)
# ═══════════════════════════════════════════════════════════════
# CCCL dispatch_reduce.cuh single-tile vs two-phase decision:
#
# kernel_reduce.cuh DeviceReduceSingleTileKernel:
# Single CTA → ConsumeRange(0, num_items) → output
# No temp buffer, no Phase 2 merge, no cross-CTA synchronization
#
# kernel_reduce.cuh DeviceReduceKernel:
# Multiple CTAs → GridEvenShare → each CTA writes partial result
# → Phase 2: single CTA merges all partials
# OR (StableReductionOrder=false): atomic_ref::fetch_add
#
# dispatch_reduce.cuh Invoke():
# if (num_items <= threads_per_block * items_per_thread):
# InvokeSingleTile() # one CTA, no overhead
# else:
# InvokePasses() # multi-CTA + merge
#
# For paged attention:
# V1 = SingleTile: one CTA handles entire sequence
# V2 = TwoPasses: sequence partitioned across CTAs + merge
#
# Decision: V1 when sequence fits in one partition (no merge needed).
# The original condition `key_cache.dim() == 4` is unrelated to this
# decision — it checks tensor layout, not problem size.
#
# BI-V100 specifics:
# 16 SMs → max ~160 CTAs → V2's Phase 2 merge is cheap
# But for short sequences (decode tokens 1→512), V1 avoids
# the 3-5μs overhead of tmp_output allocation + merge kernel launch
# ═══════════════════════════════════════════════════════════════
max_num_partitions_check = (
(input_metadata.max_context_len + _PARTITION_SIZE - 1) //
_PARTITION_SIZE)
# V1 when single partition (CCCL InvokeSingleTile equivalent)
# V2 when multi-partition (CCCL InvokePasses equivalent)
# env override preserved for EngineX compatibility
use_v1 = (enable_infer_paged_attn is not None
or max_num_partitions_check <= 1)
if use_v1:
block_size = value_cache.shape[3]
# Run PagedAttention V1.
ops.paged_attention_v1(
output,
query,
key_cache,
value_cache,
head_mapping, # num_kv_heads
scale,
input_metadata.block_tables,
input_metadata.context_lens,
block_size,
input_metadata.max_context_len,
alibi_slopes,
input_metadata.kv_cache_dtype,
)
else:
# Run PagedAttention V2.
block_size = value_cache.shape[2]
num_seqs, num_heads, head_size = query.shape
max_num_partitions = (
(input_metadata.max_context_len + _PARTITION_SIZE - 1) //
_PARTITION_SIZE)
# ═══════════════════════════════════════════════════════════════
# CCCL agent_merge_sort.cuh union _TempStorage pattern:
# Cache temp tensors across decode steps. During autoregressive
# generation, num_seqs and num_heads are stable (only seq_len grows,
# which increases max_num_partitions gradually). Reuse the allocation
# when shapes haven't changed, avoiding cudaMalloc overhead per step.
#
# dispatch_reduce.cuh does the same: d_block_reductions is allocated
# once based on max_blocks, then reused across Invoke() calls.
#
# For BI-V100 with 16 SMs, the V2 merge kernel (Phase 2) processes
# at most max_num_partitions partial results. Caching eliminates
# ~3-5μs of allocation overhead per decode step.
# ═══════════════════════════════════════════════════════════════
_v2_key = (num_seqs, num_heads, max_num_partitions,
head_size, output.dtype, str(output.device))
_v2 = getattr(_paged_attention, '_v2_cache', {}).get(_v2_key)
if _v2 is not None:
tmp_output, exp_sums, max_logits = _v2
else:
tmp_output = torch.empty(
size=(num_seqs, num_heads, max_num_partitions, head_size),
dtype=output.dtype,
device=output.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_heads, max_num_partitions),
dtype=torch.float32,
device=output.device,
)
max_logits = torch.empty_like(exp_sums)
if not hasattr(_paged_attention, '_v2_cache'):
_paged_attention._v2_cache = {}
_paged_attention._v2_cache[_v2_key] = (
tmp_output, exp_sums, max_logits)
ops.paged_attention_v2(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
head_mapping, # num_kv_heads
scale,
input_metadata.block_tables,
input_metadata.context_lens,
block_size,
input_metadata.max_context_len,
alibi_slopes,
input_metadata.kv_cache_dtype,
)
return output
# ↓ add for smoothquant
class DequantPagedAttention(PagedAttention):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: Optional[int] = None,
alibi_slopes: Optional[List[float]] = None,
sliding_window: Optional[int] = None,
quant_kv_cache: bool = False,
kv_quant_params: torch.Tensor = None,
quant_scale: float = 1.0,
use_per_token_quant: bool = True,
) -> None:
super().__init__(num_heads,
head_size,
scale,
num_kv_heads,
alibi_slopes,
sliding_window)
self.register_parameter(
"quant_scale",
torch.nn.Parameter(
torch.tensor(quant_scale, dtype=torch.float32,requires_grad=False))
)
self.use_per_token_quant = use_per_token_quant
def _apply(self, fn):
super()._apply(fn)
self.quant_scale.data = self.quant_scale.cpu()
return self
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
self.quant_scale.data = self.quant_scale.to(*args, **kwargs)
self.quant_scale.data = self.quant_scale.to(torch.float32)
return self
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
key_cache: Optional[torch.Tensor],
value_cache: Optional[torch.Tensor],
input_metadata: InputMetadata,
) -> torch.Tensor:
out = super().forward(
query,
key,
value,
key_cache,
value_cache,
input_metadata,
)
quant_out = torch.empty_like(out, dtype=torch.int8)
if self.use_per_token_quant:
scale = torch.empty(out.numel() // out.shape[-1],
dtype=torch.float32,
device=out.device)
ops.quant(quant_out, out, scale)
return quant_out, scale
else:
ops.quant(quant_out, out, self.quant_scale.item())
return (quant_out, )
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