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 K-tiling (Flash-Attention online softmax).

        Memory complexity: O(q_len), independent of kv_len.
        With chunked prefill (q_len ≤ max_num_batched_tokens = 4096) peak
        per layer ≈ 96 MB regardless of context length.

        Algorithm: Flash Attention online softmax.
        Q is reshaped once to [kv_h, gqa, q_len, d] (24 MB) and held for all
        K-tiles.  For each tile a running (m, l, o) accumulator is updated —
        the [q_len × kv_len] attention matrix is NEVER materialised in full.

        Tile budget (kv_h=1, gqa=6, q_len=4096, tile=256 tokens):
            q_seq   [1, 6, 4096, 256] fp32  24 MB  (held all tiles)
            o_acc   same shape               24 MB  (held all tiles)
            s       same shape               24 MB  (per tile, freed before exp_s)
            exp_s   same shape               24 MB  (per tile, brief overlap with s)
            Peak ≈ 96 MB  (s and exp_s briefly coexist during update).

        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
        """
        try:
            # Paged-block tiles for context phase.
            # tile_sz = _BLOCKS_PER_TILE × block_size  (e.g. 16×16 = 256 tokens).
            # Score tensor [kv_h, gqa, q_len, tile_sz] fp32 = 24 MB per tile.
            # Same tile size reused for the current-chunk phase.
            _BLOCKS_PER_TILE = 32

            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
            block_size   = value_cache.shape[3]
            tile_sz      = _BLOCKS_PER_TILE * block_size
            scale        = head_dim ** -0.5
            orig_dtype   = query.dtype
            output       = torch.empty_like(query)
            dev          = query.device

            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, q_h,  d]
                k_i = key  [q_start:q_end]   # [q_len, kv_h, d]
                v_i = value[q_start:q_end]

                # Q reshaped and scaled once; held for all K-tiles.
                # [kv_h, gqa, q_len, d] fp32  —  24 MB for q_len=4096, d=256
                q_seq = (q_i.permute(1, 0, 2)
                           .float()
                           .view(num_kv_heads, gqa_ratio, q_len, head_dim)
                           .mul_(scale))

                # Flash-Attention online-softmax accumulators.
                # m, l : [kv_h, gqa, q_len]     fp32  — <0.1 MB
                # o    : [kv_h, gqa, q_len, d]  fp32  — 24 MB
                m = torch.full((num_kv_heads, gqa_ratio, q_len),
                               float('-inf'), dtype=torch.float32, device=dev)
                l = torch.zeros_like(m)
                o = torch.zeros((num_kv_heads, gqa_ratio, q_len, head_dim),
                                dtype=torch.float32, device=dev)

                # --------------------------------------------------------------
                # Phase 1 — context tokens (positions 0 … ctx_len-1).
                #
                # Every context key has absolute position < ctx_len; every
                # query has position ≥ ctx_len.  k_pos < q_pos is always True
                # → no causal mask needed for pure context tiles.
                # --------------------------------------------------------------
                if ctx_len > 0:
                    num_ctx_blocks = (ctx_len + block_size - 1) // block_size
                    for tile_blk in range(0, num_ctx_blocks, _BLOCKS_PER_TILE):
                        blk_end = min(tile_blk + _BLOCKS_PER_TILE, num_ctx_blocks)
                        blk_ids = block_tables[i, tile_blk:blk_end]

                        # Gather K/V for this tile.
                        # key_cache  [blk_ids]: [n, kv_h, d//x, blk_sz, x]
                        # value_cache[blk_ids]: [n, kv_h, d,    blk_sz]
                        k_tile = (key_cache[blk_ids]
                                  .permute(0, 3, 1, 2, 4)
                                  .contiguous()
                                  .view(-1, num_kv_heads, head_dim))
                        v_tile = (value_cache[blk_ids]
                                  .permute(0, 3, 1, 2)
                                  .contiguous()
                                  .view(-1, num_kv_heads, head_dim))

                        # Trim padding in the last block of the tile.
                        valid = (min(blk_end * block_size, ctx_len)
                                 - tile_blk * block_size)
                        k_tile = k_tile[:valid]   # [valid, kv_h, d]
                        v_tile = v_tile[:valid]

                        # k_t: [kv_h, 1, d, valid]   (broadcast over gqa_ratio)
                        # v_t: [kv_h, 1, valid, d]
                        k_t = (k_tile.permute(1, 0, 2)
                                     .unsqueeze(1)
                                     .transpose(-1, -2)
                                     .float())
                        v_t = (v_tile.permute(1, 0, 2)
                                     .unsqueeze(1)
                                     .float())
                        del k_tile, v_tile

                        # Scores: [kv_h, gqa, q_len, valid]
                        s = torch.matmul(q_seq, k_t)
                        del k_t
                        # No causal mask: all context keys precede all queries.

                        # Online softmax update — Flash-Attention Algorithm 1.
                        # exp_s = s - new_max  (in-place exp after del s)
                        m_blk = s.amax(dim=-1)
                        m_new = torch.maximum(m, m_blk)
                        exp_s = s - m_new.unsqueeze(-1)
                        del s
                        exp_s.exp_()
                        corr  = torch.exp(m - m_new)
                        m.copy_(m_new)
                        del m_blk, m_new
                        l.mul_(corr).add_(exp_s.sum(dim=-1))
                        o.mul_(corr.unsqueeze(-1)).add_(
                            torch.matmul(exp_s, v_t))
                        del exp_s, v_t, corr

                # --------------------------------------------------------------
                # Phase 2 — current-chunk tokens (positions ctx_len … ctx_len+q_len-1).
                #
                # Causal mask: query at relative position j sees key at relative
                # position k only when k ≤ j.  Tiles of tile_sz tokens each.
                # --------------------------------------------------------------
                for kc_start in range(0, q_len, tile_sz):
                    kc_end = min(kc_start + tile_sz, q_len)
                    kc_len = kc_end - kc_start

                    k_blk = k_i[kc_start:kc_end]   # [kc_len, kv_h, d]
                    v_blk = v_i[kc_start:kc_end]

                    k_t = (k_blk.permute(1, 0, 2)
                                 .unsqueeze(1)
                                 .transpose(-1, -2)
                                 .float())   # [kv_h, 1, d, kc_len]
                    v_t = (v_blk.permute(1, 0, 2)
                                 .unsqueeze(1)
                                 .float())   # [kv_h, 1, kc_len, d]

                    s = torch.matmul(q_seq, k_t)   # [kv_h, gqa, q_len, kc_len]
                    del k_t

                    # Causal mask: key at (kc_start+k) must not exceed query j.
                    k_rel = torch.arange(kc_start, kc_end, device=dev)
                    q_rel = torch.arange(q_len, device=dev)
                    mask  = k_rel.unsqueeze(0) > q_rel.unsqueeze(1)  # [q_len, kc_len]
                    s.masked_fill_(mask.unsqueeze(0).unsqueeze(0), float('-inf'))
                    del mask, k_rel, q_rel

                    # Online softmax update (identical to context phase).
                    m_blk = s.amax(dim=-1)
                    m_new = torch.maximum(m, m_blk)
                    exp_s = s - m_new.unsqueeze(-1)
                    del s
                    exp_s.exp_()
                    corr  = torch.exp(m - m_new)
                    m.copy_(m_new)
                    del m_blk, m_new
                    l.mul_(corr).add_(exp_s.sum(dim=-1))
                    o.mul_(corr.unsqueeze(-1)).add_(
                        torch.matmul(exp_s, v_t))
                    del exp_s, v_t, corr

                # --------------------------------------------------------------
                # Finalize: normalize running output by normalization factor.
                # o: [kv_h, gqa, q_len, d]  →  [q_len, q_h, d]
                # --------------------------------------------------------------
                o.div_(l.unsqueeze(-1))
                output[q_start:q_end] = (
                    o.view(num_q_heads, q_len, head_dim)
                     .permute(1, 0, 2)
                     .to(orig_dtype)
                )

        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)