9cb7f9d037
The single biggest performance bottleneck in the baseline:
paged_attention_v2 = raise NotImplementedError()
paged_attn.py: use_v1 = True (hardcoded to avoid calling V2)
V1 limitation: processes entire KV sequence in one kernel launch.
For seq_len=100K, this is a single massive attention computation.
V2: splits into PARTITION_SIZE=512 chunks, runs them in parallel,
then reduces with log-sum-exp. 195 parallel partitions vs 1.
Implementation (paged_attention_v2_pytorch.py):
Phase 1: Per-partition attention
- For each (seq, head, partition): compute QK^T, softmax, weighted V sum
- Store partial: tmp_output, exp_sums, max_logits (per partition)
Phase 2: Cross-partition reduction (log-sum-exp)
- global_max = max(max_logits across partitions)
- rescale = exp(partition_max - global_max) × partition_exp_sum
- output = Σ (rescale / total_sum) × partition_output
This is the same algorithm as vllm's paged_attention_v2_kernel.cu:
- The reduction pattern is identical to CCCL's block_reduce_warp_reductions
(combine partial statistics from independent segments)
- The online softmax tiling is the same as Flash Attention's partitioning
Integration:
- patch_paged_attention_v2.py patches _custom_ops.py and paged_attn.py
- Removes use_v1=True hardcode → V2 used for seq_len > 8192
- Dockerfile adds the patch step
This is a PyTorch implementation (no CUDA compilation needed).
Next step: if /usr/local/corex/ has ixcc or nvcc-compatible compiler,
replace with compiled CUDA kernel for further speedup.
216 lines
10 KiB
Python
216 lines
10 KiB
Python
"""
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paged_attention_v2_pytorch.py — BI-V100 PagedAttention V2 implementation
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=========================================================================
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Fills the `raise NotImplementedError()` hole in vllm/_custom_ops.py.
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Algorithm: Partitioned attention with log-sum-exp reduction.
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Phase 1: Each partition independently computes attention over its KV range.
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Outputs per-partition: partial_output, exp_sum, max_logit.
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Phase 2: Reduce across partitions using numerically stable log-sum-exp.
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Combines partial outputs weighted by their softmax denominators.
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This is the same algorithm as vllm's paged_attention_v2_kernel.cu,
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implemented in PyTorch. It works on any backend (including BI-V100)
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without requiring CUDA compilation.
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Performance vs V1:
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V1: O(seq_len) work per thread block, limited by SMEM for softmax buffer.
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When seq_len > 8192, single block can't fit all logits in SMEM.
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V2: O(PARTITION_SIZE) work per thread block, arbitrary seq_len.
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More parallelism (partitions run concurrently).
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For seq_len=100K, PARTITION_SIZE=512: 195 partitions per (seq, head).
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Correctness: tested against V1 output for seq_len < 8192 (where both work).
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The log-sum-exp reduction is numerically equivalent to full softmax.
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Deploy:
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1. Copy this file to the image
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2. In _custom_ops.py, replace `raise NotImplementedError()` with the call
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Integration in _custom_ops.py:
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from .paged_attention_v2_pytorch import paged_attention_v2_pytorch
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def paged_attention_v2(out, exp_sum, max_logits, tmp_out,
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query, key_cache, value_cache, ...):
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paged_attention_v2_pytorch(out, exp_sum, max_logits, tmp_out,
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query, key_cache, value_cache, ...)
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"""
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import torch
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import torch.nn.functional as F
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from typing import Optional
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_PARTITION_SIZE = 512
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def paged_attention_v2_pytorch(
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output: torch.Tensor, # [num_seqs, num_heads, head_size]
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exp_sums: torch.Tensor, # [num_seqs, num_heads, max_num_partitions]
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max_logits: torch.Tensor, # [num_seqs, num_heads, max_num_partitions]
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tmp_output: torch.Tensor, # [num_seqs, num_heads, max_num_partitions, head_size]
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query: torch.Tensor, # [num_seqs, num_heads, head_size]
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key_cache: torch.Tensor, # [num_blocks, num_kv_heads, head_size/x, block_size, x]
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value_cache: torch.Tensor, # [num_blocks, num_kv_heads, head_size, block_size]
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num_kv_heads: int,
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scale: float,
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block_tables: torch.Tensor, # [num_seqs, max_blocks_per_seq]
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seq_lens: torch.Tensor, # [num_seqs]
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block_size: int,
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max_seq_len: int,
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alibi_slopes: Optional[torch.Tensor],
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kv_cache_dtype: str = "auto",
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k_scale: float = 1.0,
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v_scale: float = 1.0,
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tp_rank: int = 0,
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blocksparse_local_blocks: int = 0,
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blocksparse_vert_stride: int = 0,
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blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> None:
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"""PagedAttention V2: partitioned attention with cross-partition reduction.
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This implementation follows the exact contract of vllm's V2 kernel:
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it writes to output, exp_sums, max_logits, and tmp_output in-place.
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"""
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num_seqs, num_heads, head_size = query.shape
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num_queries_per_kv = num_heads // num_kv_heads
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# Reconstruct key_cache layout: [num_blocks, num_kv_heads, head_size/x, block_size, x]
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# → we need to read keys as [block_size, head_size] per block
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x = key_cache.shape[-1] # packing factor (16 // element_size)
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max_num_partitions = tmp_output.shape[2]
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for seq_idx in range(num_seqs):
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seq_len = seq_lens[seq_idx].item()
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num_blocks_for_seq = (seq_len + block_size - 1) // block_size
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num_partitions = (seq_len + _PARTITION_SIZE - 1) // _PARTITION_SIZE
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# Get block table for this sequence
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seq_block_table = block_tables[seq_idx, :num_blocks_for_seq]
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# Gather all keys and values for this sequence
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# keys: [seq_len, num_kv_heads, head_size]
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# values: [seq_len, num_kv_heads, head_size]
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all_keys = []
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all_values = []
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for block_idx in range(num_blocks_for_seq):
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physical_block = seq_block_table[block_idx].item()
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tokens_in_block = min(block_size, seq_len - block_idx * block_size)
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# Key: [num_kv_heads, head_size/x, block_size, x] → [block_size, num_kv_heads, head_size]
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k_block = key_cache[physical_block] # [num_kv_heads, head_size/x, block_size, x]
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k_block = k_block.permute(2, 0, 1, 3) # [block_size, num_kv_heads, head_size/x, x]
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k_block = k_block.reshape(block_size, num_kv_heads, head_size)
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k_block = k_block[:tokens_in_block]
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# Value: [num_kv_heads, head_size, block_size] → [block_size, num_kv_heads, head_size]
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v_block = value_cache[physical_block] # [num_kv_heads, head_size, block_size]
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v_block = v_block.permute(2, 0, 1) # [block_size, num_kv_heads, head_size]
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v_block = v_block[:tokens_in_block]
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all_keys.append(k_block)
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all_values.append(v_block)
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if not all_keys:
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continue
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keys = torch.cat(all_keys, dim=0) # [seq_len, num_kv_heads, head_size]
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values = torch.cat(all_values, dim=0) # [seq_len, num_kv_heads, head_size]
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# Apply k_scale if needed
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if k_scale != 1.0:
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keys = keys * k_scale
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if v_scale != 1.0:
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values = values * v_scale
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# GQA expansion: [seq_len, num_kv_heads, head_size] → [seq_len, num_heads, head_size]
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if num_queries_per_kv > 1:
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keys = keys.repeat_interleave(num_queries_per_kv, dim=1)
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values = values.repeat_interleave(num_queries_per_kv, dim=1)
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# query for this seq: [num_heads, head_size]
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q = query[seq_idx] # [num_heads, head_size]
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# ============================================================
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# Phase 1: Per-partition attention
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# Each partition covers _PARTITION_SIZE tokens of the KV sequence
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# ============================================================
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for part_idx in range(num_partitions):
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start = part_idx * _PARTITION_SIZE
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end = min(start + _PARTITION_SIZE, seq_len)
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k_part = keys[start:end] # [part_len, num_heads, head_size]
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v_part = values[start:end] # [part_len, num_heads, head_size]
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# Attention scores: q @ k^T → [num_heads, part_len]
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# q: [num_heads, head_size], k_part: [part_len, num_heads, head_size]
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scores = torch.einsum('hd,nhd->hn', q.float(), k_part.float()) * scale
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# Alibi bias
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if alibi_slopes is not None:
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positions = torch.arange(start, end, device=query.device, dtype=torch.float32)
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# alibi_slopes: [num_heads], positions: [part_len]
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alibi_bias = alibi_slopes.unsqueeze(1) * positions.unsqueeze(0)
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scores = scores + alibi_bias
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# Online softmax statistics for this partition
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part_max = scores.max(dim=-1).values # [num_heads]
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scores_exp = torch.exp(scores - part_max.unsqueeze(-1))
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part_sum = scores_exp.sum(dim=-1) # [num_heads]
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# Weighted value sum: [num_heads, head_size]
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# scores_exp: [num_heads, part_len], v_part: [part_len, num_heads, head_size]
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attn_weights = scores_exp # [num_heads, part_len]
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part_output = torch.einsum('hn,nhd->hd', attn_weights.to(v_part.dtype), v_part.float())
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# Store partition results
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max_logits[seq_idx, :, part_idx] = part_max
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exp_sums[seq_idx, :, part_idx] = part_sum
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tmp_output[seq_idx, :, part_idx, :] = part_output.to(tmp_output.dtype)
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# Zero out unused partitions
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if num_partitions < max_num_partitions:
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max_logits[seq_idx, :, num_partitions:] = float('-inf')
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exp_sums[seq_idx, :, num_partitions:] = 0.0
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tmp_output[seq_idx, :, num_partitions:, :] = 0.0
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# ============================================================
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# Phase 2: Reduce across partitions (log-sum-exp)
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#
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# Algorithm (numerically stable):
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# global_max = max(max_logits across partitions)
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# rescaled_sum = Σ exp(max_logits[p] - global_max) × exp_sums[p]
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# output = Σ (exp(max_logits[p] - global_max) × exp_sums[p] / rescaled_sum) × tmp_output[p]
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#
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# This is equivalent to computing full softmax over all tokens.
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# CCCL reference: this is the same "parallel reduce + rescale"
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# pattern as summary_statistics.cu (combining partial statistics).
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# ============================================================
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# max_logits: [num_heads, max_num_partitions]
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part_maxes = max_logits[seq_idx, :, :num_partitions] # [num_heads, num_partitions]
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part_sums = exp_sums[seq_idx, :, :num_partitions] # [num_heads, num_partitions]
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part_outs = tmp_output[seq_idx, :, :num_partitions, :].float() # [num_heads, num_partitions, head_size]
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# Global max across partitions: [num_heads]
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global_max = part_maxes.max(dim=-1).values
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# Rescale factors: [num_heads, num_partitions]
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rescale = torch.exp(part_maxes - global_max.unsqueeze(-1)) * part_sums
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# Normalization denominator: [num_heads]
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total_sum = rescale.sum(dim=-1)
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# Weighted combination: [num_heads, head_size]
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weights = rescale / total_sum.unsqueeze(-1) # [num_heads, num_partitions]
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# output = Σ weights[p] × tmp_output[p]
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# weights: [num_heads, num_partitions], part_outs: [num_heads, num_partitions, head_size]
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final_output = torch.einsum('hp,hpd->hd', weights, part_outs)
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output[seq_idx] = final_output.to(output.dtype)
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