enginex-vllm-bi100-qwen36/qwen3_6_scripts/paged_attn.py

from dataclasses import dataclass
from typing import List, Optional, Tuple
import sys
import torch
import traceback
from vllm import _custom_ops as ops

# from vllm.attention.ops.prefix_prefill import context_attention_fwd
# NOTE: context_attention_fwd (Triton kernel from prefix_prefill.py) is NOT
# imported here.  On Iluvatar BI-V100 that kernel hangs the GPU card
# permanently.  Chunked-prefill / prefix-caching attention is handled by
# _forward_prefix_pytorch below (pure PyTorch, no Triton dependency).

# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
_PARTITION_SIZE = 512


@dataclass
class PagedAttentionMetadata:
    """Metadata for PagedAttention."""
    # (batch_size,). The length of sequences (entire tokens seen so far) per
    # sequence.
    seq_lens_tensor: Optional[torch.Tensor]
    # Maximum sequence length in the batch. 0 if it is prefill-only batch.
    max_decode_seq_len: int
    # (batch_size, max_blocks_per_seq).
    # Block addresses per sequence. (Seq id -> list of physical block)
    # E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
    # in the kv cache. Each block can contain up to block_size tokens.
    # 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
    # captured.
    block_tables: Optional[torch.Tensor]


class PagedAttention:

    @staticmethod
    def get_supported_head_sizes() -> List[int]:
        return [64, 80, 96, 112, 120, 128, 192, 256]

    @staticmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[int, ...]:
        return (2, num_blocks, block_size * num_kv_heads * head_size)

    @staticmethod
    def split_kv_cache(
        kv_cache: torch.Tensor,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        x = 16 // kv_cache.element_size()
        num_blocks = kv_cache.shape[1]

        key_cache = kv_cache[0]
        key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
                                   -1, x)
        value_cache = kv_cache[1]
        value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
        return key_cache, value_cache

    @staticmethod
    def write_to_paged_cache(
        key: torch.Tensor,
        value: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
        kv_cache_dtype: str,
        k_scale: float,
        v_scale: float,
    ) -> None:
        ops.reshape_and_cache(
            key,
            value,
            key_cache,
            value_cache,
            slot_mapping.flatten(),
            kv_cache_dtype,
            k_scale,
            v_scale,
        )

    @staticmethod
    def _forward_decode_pytorch(
        query: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        block_tables: torch.Tensor,
        seq_lens: torch.Tensor,
        scale: float,
    ) -> torch.Tensor:
        """Pure-PyTorch decode attention for long contexts (no hardware kernel).

        paged_attention_v1 hangs on BI-V100 when max_seq_len > ~32K due to
        shared memory limits. For decode, q_len=1 per sequence so no Q-tiling
        is needed — the attention weight tensor is [H, 1, seq_len] which is
        trivially small (~5 MB at 50K).

        Shapes
        ------
        query       : [num_seqs, num_heads, head_dim]
        key_cache   : [num_blocks, num_kv_heads, head_dim//x, block_size, x]
        value_cache : [num_blocks, num_kv_heads, head_dim,    block_size]
        block_tables: [num_seqs, max_blocks_per_seq]
        seq_lens    : [num_seqs]
        """
        num_seqs, num_heads, head_dim = query.shape
        num_kv_heads = key_cache.shape[1]
        block_size = value_cache.shape[3]
        gqa_ratio = num_heads // num_kv_heads
        orig_dtype = query.dtype

        output = torch.empty_like(query)

        try:
            for i in range(num_seqs):
                seq_len = int(seq_lens[i].item())
                num_blocks = (seq_len + block_size - 1) // block_size
                blk_ids = block_tables[i, :num_blocks]

                # Gather K: [kv_h, head_dim, seq_len] fp32 — no GQA expansion.
                # With kv_h=1 and seq_len=100K this is 98 MB vs 586 MB if expanded.
                k_t = (key_cache[blk_ids]
                       .permute(0, 3, 1, 2, 4)
                       .contiguous()
                       .view(-1, num_kv_heads, head_dim))[:seq_len] \
                      .permute(1, 2, 0).contiguous().float()  # [kv_h, d, seq_len]

                # Gather V: [kv_h, seq_len, head_dim] fp32
                v_t = (value_cache[blk_ids]
                       .permute(0, 3, 1, 2)
                       .contiguous()
                       .view(-1, num_kv_heads, head_dim))[:seq_len] \
                      .permute(1, 0, 2).contiguous().float()  # [kv_h, seq_len, d]

                # Reshape Q for lazy GQA: [kv_h, gqa_ratio, 1, d]
                q_grouped = (query[i].float()
                             .view(num_kv_heads, gqa_ratio, head_dim)
                             .unsqueeze(2))

                # [kv_h, gqa_ratio, 1, seq_len]
                attn_w = torch.matmul(
                    q_grouped * scale,       # [kv_h, gqa, 1, d]
                    k_t.unsqueeze(1))        # [kv_h, 1, d, seq_len]
                attn_w = torch.softmax(attn_w, dim=-1)

                # [kv_h, gqa_ratio, 1, d] → [num_heads, head_dim]
                out_i = torch.matmul(attn_w, v_t.unsqueeze(1))
                output[i] = out_i.view(num_heads, head_dim).to(orig_dtype)

        except Exception as e:
            print(f"[decode_pytorch ERROR] {type(e).__name__}: {e}",
                  file=sys.stderr, flush=True)
            traceback.print_exc(file=sys.stderr)
            raise

        return output

    # paged_attention_v1 on BI-V100 fails for long contexts.
    # Route on actual sequence length (seq_lens.max()), not the max_seq_len
    # parameter which is inflated to max_model_len in CUDA graph mode.
    _PYTORCH_DECODE_THRESHOLD = 32768

    @staticmethod
    def forward_decode(
        query: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        block_tables: torch.Tensor,
        seq_lens: torch.Tensor,
        max_seq_len: int,
        kv_cache_dtype: str,
        num_kv_heads: int,
        scale: float,
        alibi_slopes: Optional[torch.Tensor],
        k_scale: float,
        v_scale: float,
        tp_rank: int = 0,
        blocksparse_local_blocks: int = 0,
        blocksparse_vert_stride: int = 0,
        blocksparse_block_size: int = 64,
        blocksparse_head_sliding_step: int = 0,
    ) -> torch.Tensor:
        actual_max = int(seq_lens.max().item()) if seq_lens.numel() > 0 else max_seq_len
        if actual_max > PagedAttention._PYTORCH_DECODE_THRESHOLD:
            return PagedAttention._forward_decode_pytorch(
                query, key_cache, value_cache, block_tables, seq_lens, scale)

        if blocksparse_vert_stride is not None and blocksparse_vert_stride > 1:
            # use blocksparse paged attention
            block_size = value_cache.size(-1)
            assert (blocksparse_block_size > 0 and
                    blocksparse_block_size % block_size == 0), \
                (f"{blocksparse_block_size=} needs to be a multiple of"
                 f"{block_size=} used in block_tables.")

        output = torch.empty_like(query)
        block_size = value_cache.shape[3]
        num_seqs, num_heads, head_size = query.shape
        max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
                              _PARTITION_SIZE)
        # NOTE(woosuk): We use a simple heuristic to decide whether to use
        # PagedAttention V1 or V2. If the number of partitions is 1, we use
        # V1 to avoid the overhead of reduction. Also, if the number of
        # sequences or heads is large, we use V1 since there is enough work
        # to parallelize.
        # TODO(woosuk): Tune this heuristic.
        # For context len > 8192, use V2 kernel to avoid shared memory shortage.
        use_v1 = (max_seq_len <= 8192
                  and (max_num_partitions == 1 or num_seqs * num_heads > 512))
        use_v1 = True
        if use_v1:
            # Run PagedAttention V1.
            ops.paged_attention_v1(
                output,
                query,
                key_cache,
                value_cache,
                num_kv_heads,
                scale,
                block_tables,
                seq_lens,
                block_size,
                max_seq_len,
                alibi_slopes,
            )
        else:
            # Run PagedAttention V2.
            assert _PARTITION_SIZE % block_size == 0
            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)
            ops.paged_attention_v2(
                output,
                exp_sums,
                max_logits,
                tmp_output,
                query,
                key_cache,
                value_cache,
                num_kv_heads,
                scale,
                block_tables,
                seq_lens,
                block_size,
                max_seq_len,
                alibi_slopes,
                kv_cache_dtype,
                k_scale,
                v_scale,
                tp_rank,
                blocksparse_local_blocks,
                blocksparse_vert_stride,
                blocksparse_block_size,
                blocksparse_head_sliding_step,
            )
        return output

    @staticmethod
    def forward_prefix(
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache_dtype: str,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        block_tables: torch.Tensor,
        query_start_loc: torch.Tensor,
        seq_lens_tensor: torch.Tensor,
        context_lens: torch.Tensor,
        max_query_len: int,
        alibi_slopes: Optional[torch.Tensor],
        sliding_window: Optional[int],
        k_scale: float,
        v_scale: float,
    ) -> torch.Tensor:
        # NOTE: The Triton context_attention_fwd kernel hangs on Iluvatar
        # BI-V100 hardware (same class of issue as cudnnFlashAttnForward).
        # Use a pure-PyTorch fallback that reads the paged KV cache directly.
        return PagedAttention._forward_prefix_pytorch(
            query, key, value,
            key_cache, value_cache,
            block_tables, query_start_loc,
            seq_lens_tensor, context_lens,
        )

    @staticmethod
    def _forward_prefix_pytorch(
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        block_tables: torch.Tensor,
        query_start_loc: torch.Tensor,
        seq_lens_tensor: torch.Tensor,
        context_lens: torch.Tensor,
    ) -> torch.Tensor:
        """Pure-PyTorch prefix-attention with query-chunking (no Triton).

        For each sequence, gathers the context KV from the paged KV cache,
        concatenates with the current-chunk K/V, then computes scaled-dot-
        product attention with a causal mask.

        Memory optimisation — GQA-aware Q-tiling
        -----------------------------------------
        Two complementary tricks keep peak activation memory well below 1 GB
        even for 100K context on TP=4 (kv_h=1, q_h=6):

        1. No GQA pre-expansion: K/V are kept at native [kv_h, kv_len, d]
           resolution and GQA grouping is handled via 4D reshape+broadcast
           inside the matmul.  With kv_h=1 and kv_len=100K this saves ~6×
           vs the old expand-then-float32 approach:
             Old: [6, 100K, 256] fp32 = 586 MB each for K and V
             New: [1, 100K, 256] fp32 =  98 MB each for K and V

        2. Q-tiling (_ATTN_Q_CHUNK=64): attn_w [kv_h, gqa, Q, kv_len] fp32
           is bounded to ~148 MB at 100K instead of growing with q_len.

        Combined peak per layer (100K): ~352 MB vs ~1200 MB previously.

        Shapes
        ------
        query          : [total_q_tokens, num_q_heads,  head_dim]
        key            : [total_q_tokens, num_kv_heads, head_dim]
        value          : [total_q_tokens, num_kv_heads, head_dim]
        key_cache      : [num_blocks, num_kv_heads, head_dim//x, block_size, x]
        value_cache    : [num_blocks, num_kv_heads, head_dim,    block_size]
        block_tables   : [batch_size, max_blocks_per_seq]
        query_start_loc: [batch_size + 1]
        seq_lens_tensor: [batch_size]  total length (context + query)
        context_lens   : [batch_size]  tokens already in KV cache
        """
        # Memory-efficient query-chunked attention.
        # Key optimisation: do NOT expand KV heads for GQA before materialising
        # k_t / v_t.  With kv_h=1 (Qwen3.6 TP=4), keeping K/V at native kv_h
        # resolution saves ~6× memory vs expanding to q_h first:
        #   Old path (expand then float32): [6, 100K, 256] fp32 = 586 MB
        #   New path (keep kv_h, float32):  [1, 100K, 256] fp32 = 98 MB
        # GQA grouping is handled lazily inside the Q-tile matmul via 4D
        # reshaping, so no extra tensors are created.
        try:
            _ATTN_Q_CHUNK = 64  # [kv_h, gqa, Q_CHUNK, kv_len] fp32 ≤ 150 MB

            batch_size   = seq_lens_tensor.shape[0]
            num_q_heads  = query.shape[1]
            num_kv_heads = key_cache.shape[1]
            head_dim     = query.shape[2]
            gqa_ratio    = num_q_heads // num_kv_heads

            # value_cache: [num_blocks, num_kv_heads, head_dim, block_size]
            block_size = value_cache.shape[3]

            scale      = 1.0 / (head_dim ** 0.5)
            output     = torch.empty_like(query)
            orig_dtype = query.dtype

            for i in range(batch_size):
                ctx_len = int(context_lens[i].item())
                q_start = int(query_start_loc[i].item())
                q_end   = int(query_start_loc[i + 1].item())
                q_len   = q_end - q_start

                q_i = query[q_start:q_end]  # [q_len, num_q_heads,  head_dim]
                k_i = key  [q_start:q_end]  # [q_len, num_kv_heads, head_dim]
                v_i = value[q_start:q_end]

                # --- Build full K/V (context from cache + current chunk) ----
                if ctx_len > 0:
                    num_ctx_blocks = (ctx_len + block_size - 1) // block_size
                    blk_ids = block_tables[i, :num_ctx_blocks]

                    # key_cache[blk_ids]: [n, kv_h, d//x, blk_sz, x]
                    # → permute(0,3,1,2,4) → contiguous → view → [:ctx_len]
                    k_ctx = (key_cache[blk_ids]
                            .permute(0, 3, 1, 2, 4)
                            .contiguous()
                            .view(-1, num_kv_heads, head_dim))[:ctx_len]

                    # value_cache[blk_ids]: [n, kv_h, d, blk_sz]
                    # → permute(0,3,1,2) → contiguous → view → [:ctx_len]
                    v_ctx = (value_cache[blk_ids]
                            .permute(0, 3, 1, 2)
                            .contiguous()
                            .view(-1, num_kv_heads, head_dim))[:ctx_len]

                    k_full = torch.cat([k_ctx, k_i], dim=0)  # [kv_len, kv_h, d]
                    v_full = torch.cat([v_ctx, v_i], dim=0)
                    del k_ctx, v_ctx
                else:
                    k_full = k_i
                    v_full = v_i

                kv_len = k_full.shape[0]  # ctx_len + q_len

                # Transpose to [kv_h, kv_len, d], keep original dtype (fp16/bf16).
                # Do NOT cast to fp32 here — k/v stay in fp16 to halve memory.
                # attn_w is computed in fp32 (q cast to fp32 before matmul, then
                # k cast inline) so softmax precision is unaffected.
                # Do NOT expand GQA heads here either — gqa_ratio x memory savings.
                k_t = k_full.permute(1, 0, 2).contiguous()  # [kv_h, kv_len, d] fp16
                del k_full
                v_t = v_full.permute(1, 0, 2).contiguous()  # [kv_h, kv_len, d] fp16
                del v_full

                # k_pos used for causal mask: shape [kv_len]
                k_pos = torch.arange(kv_len, device=query.device)

                # --- Query-chunked attention with lazy GQA grouping ----------
                # q_i reshaped to [kv_h, gqa_ratio, qc, d] so matmul with
                # k_t [kv_h, kv_len, d] (broadcast over gqa_ratio dim) gives
                # attn_w [kv_h, gqa_ratio, qc, kv_len] without extra K copies.
                for qc_start in range(0, q_len, _ATTN_Q_CHUNK):
                    qc_end = min(qc_start + _ATTN_Q_CHUNK, q_len)
                    qc = qc_end - qc_start

                    # [kv_h, gqa_ratio, qc, d]
                    q_t_chunk = (q_i[qc_start:qc_end]
                                .permute(1, 0, 2)        # [q_h, qc, d]
                                .float()
                                .view(num_kv_heads, gqa_ratio, qc, head_dim))

                    # [kv_h, gqa_ratio, qc, kv_len]
                    # k_t unsqueezed to [kv_h, 1, kv_len, d] broadcasts over gqa_ratio.
                    # Cast k slice to fp32 inline; the temporary is freed after matmul.
                    attn_w = torch.matmul(q_t_chunk * scale,
                                          k_t.unsqueeze(1).transpose(-1, -2).float())

                    # Causal mask for this sub-chunk:
                    # query absolute position = ctx_len + qc_start..qc_end-1
                    qc_q_pos = torch.arange(qc_start, qc_end,
                                            device=query.device)
                    # mask[j, k] = True  →  future key, block it
                    mask = k_pos.unsqueeze(0) > (ctx_len + qc_q_pos.unsqueeze(1))
                    attn_w.masked_fill_(
                        mask.unsqueeze(0).unsqueeze(0), float('-inf'))

                    # In-place numerically stable softmax — avoids allocating a
                    # new 150 MB tensor (same size as attn_w) that torch.softmax
                    # would create, which exhausts the fragmented GPU pool.
                    attn_w -= attn_w.amax(dim=-1, keepdim=True)
                    attn_w.exp_()
                    attn_w /= attn_w.sum(dim=-1, keepdim=True)
                    # [kv_h, gqa_ratio, qc, d]; v_t cast to fp32 inline
                    out_c = torch.matmul(attn_w,
                                         v_t.unsqueeze(1).float())
                    # reshape to [q_h, qc, d] then [qc, q_h, d]
                    out_c = out_c.view(num_q_heads, qc, head_dim)

                    output[q_start + qc_start : q_start + qc_end] = (
                        out_c.to(orig_dtype).permute(1, 0, 2))
        except Exception as e:
            print(f"[paged_attn ERROR] {type(e).__name__}: {e}", file=sys.stderr, flush=True)
            traceback.print_exc(file=sys.stderr)
            raise
        return output

    @staticmethod
    def swap_blocks(
        src_kv_cache: torch.Tensor,
        dst_kv_cache: torch.Tensor,
        src_to_dst: torch.Tensor,
    ) -> None:
        src_key_cache = src_kv_cache[0]
        dst_key_cache = dst_kv_cache[0]
        ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)

        src_value_cache = src_kv_cache[1]
        dst_value_cache = dst_kv_cache[1]
        ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst)

    @staticmethod
    def copy_blocks(
        kv_caches: List[torch.Tensor],
        src_to_dists: torch.Tensor,
    ) -> None:
        key_caches = [kv_cache[0] for kv_cache in kv_caches]
        value_caches = [kv_cache[1] for kv_cache in kv_caches]
        ops.copy_blocks(key_caches, value_caches, src_to_dists)