[Kernel] Add Split-KV Support to Unified Triton Attention Kernel (#19152)

Signed-off-by: Jan van Lunteren <jvl@zurich.ibm.com>

vllm/attention/ops/triton_unified_attention.py

@@ -7,6 +7,7 @@
 #  - Chih-Chieh Yang <chih.chieh.yang@ibm.com>
 #  - Thomas Parnell <tpa@zurich.ibm.com>
 
+import torch
 import triton
 import triton.language as tl
 
@@ -28,6 +29,24 @@ def apply_softcap(S, x):
     return x * (p1 - p2) / (p1 + p2)
 
 
+@triton.jit
+def find_seq_idx(query_start_len_ptr, target_idx, num_seqs,
+                 BLOCK_Q: tl.constexpr, use_q_block_mode: tl.constexpr):
+    left: tl.int32 = 0
+    right = num_seqs
+    while left < right:
+        mid = (left + right) // 2
+        val = tl.load(query_start_len_ptr + mid)
+        mid_val = val // BLOCK_Q + mid if use_q_block_mode else val
+
+        if mid_val <= target_idx:
+            left = mid + 1
+        else:
+            right = mid
+
+    return left - 1
+
+
 @triton.jit
 def kernel_unified_attention_2d(
         output_ptr,  # [num_tokens, num_query_heads, head_size]
@@ -67,21 +86,12 @@ def kernel_unified_attention_2d(
         num_seqs: tl.int32,
         BLOCK_M: tl.constexpr,  # int
 ):
-
     q_block_global_idx = tl.program_id(0)
     kv_head_idx = tl.program_id(1)
 
-    left: tl.int32 = 0
-    right = num_seqs
-    while left < right:
-        mid = (left + right) // 2
-        mid_val = tl.load(query_start_len_ptr + mid) // BLOCK_Q + mid
-        if mid_val <= q_block_global_idx:
-            left = mid + 1
-        else:
-            right = mid
+    seq_idx = find_seq_idx(query_start_len_ptr, q_block_global_idx, num_seqs,
+                           BLOCK_Q, True)
 
-    seq_idx = left - 1
     q_block_start_idx = tl.load(query_start_len_ptr +
                                 seq_idx) // BLOCK_Q + seq_idx
 
@@ -242,6 +252,311 @@ def kernel_unified_attention_2d(
     )
 
 
+@triton.jit
+def kernel_unified_attention_3d(
+        segm_output_ptr,
+        # [num_tokens, num_query_heads, num_segments, head_size]
+        segm_max_ptr,  # [num_tokens, num_query_heads, num_segments]
+        segm_expsum_ptr,  # [num_tokens, num_query_heads, num_segments]
+        query_ptr,  # [num_tokens, num_query_heads, head_size]
+        key_cache_ptr,  # [num_blks, num_kv_heads, head_size // x, blk_size, x]
+        value_cache_ptr,  # [num_blks, num_kv_heads, head_size, blk_size]
+        block_tables_ptr,  # [num_seqs, max_num_blocks_per_seq]
+        seq_lens_ptr,  # [num_seqs]
+        alibi_slopes_ptr,  # [num_query_heads]
+        scale,  # float32
+        k_scale,  # float32
+        v_scale,  # float32
+        softcap,  # float32
+        num_query_heads: tl.constexpr,  # int
+        num_queries_per_kv: tl.constexpr,  # int
+        block_table_stride: tl.int64,  # int
+        query_stride_0: tl.int64,  # int
+        query_stride_1: tl.int64,  # int, should be equal to head_size
+        BLOCK_SIZE: tl.constexpr,  # int
+        HEAD_SIZE: tl.constexpr,  # int
+        HEAD_SIZE_PADDED: tl.constexpr,  # int, must be power of 2
+        USE_ALIBI_SLOPES: tl.constexpr,  # bool
+        USE_SOFTCAP: tl.constexpr,  # bool
+        SLIDING_WINDOW: tl.constexpr,  # int
+        stride_k_cache_0: tl.int64,  # int
+        stride_k_cache_1: tl.int64,  # int
+        stride_k_cache_2: tl.int64,  # int
+        stride_k_cache_3: tl.constexpr,  # int
+        stride_v_cache_0: tl.int64,  # int
+        stride_v_cache_1: tl.int64,  # int
+        stride_v_cache_2: tl.int64,  # int
+        stride_v_cache_3: tl.constexpr,  # int
+        query_start_len_ptr,  # [num_seqs+1]
+        BLOCK_Q: tl.constexpr,  # int
+        num_seqs: tl.int32,
+        BLOCK_M: tl.constexpr,  # int
+        NUM_SEGMENTS_PER_SEQ: tl.constexpr,  # int
+):
+    q_block_global_idx = tl.program_id(0)
+    kv_head_idx = tl.program_id(1)
+    segm_idx = tl.program_id(2)
+
+    seq_idx = find_seq_idx(query_start_len_ptr, q_block_global_idx, num_seqs,
+                           BLOCK_Q, True)
+
+    q_block_start_idx = tl.load(query_start_len_ptr +
+                                seq_idx) // BLOCK_Q + seq_idx
+
+    q_block_local_idx = q_block_global_idx - q_block_start_idx
+
+    cur_batch_in_all_start_index = tl.load(query_start_len_ptr + seq_idx)
+    cur_batch_in_all_stop_index = tl.load(query_start_len_ptr + seq_idx + 1)
+
+    cur_batch_query_len = cur_batch_in_all_stop_index \
+        - cur_batch_in_all_start_index
+
+    if q_block_local_idx * BLOCK_Q >= cur_batch_query_len:
+        return
+
+    # sequence len for this particular sequence
+    seq_len = tl.load(seq_lens_ptr + seq_idx)
+
+    # number of segments for this particular sequence
+    num_segments = NUM_SEGMENTS_PER_SEQ
+    blocks_per_segment = cdiv_fn(seq_len, num_segments * BLOCK_SIZE)
+
+    if segm_idx * blocks_per_segment * BLOCK_SIZE >= seq_len:
+        return
+
+    offs_m = tl.arange(0, BLOCK_M)
+    offs_d = tl.arange(0, HEAD_SIZE_PADDED)
+
+    query_pos = q_block_local_idx * BLOCK_Q + offs_m // num_queries_per_kv
+
+    query_offset_0 = cur_batch_in_all_start_index + query_pos
+    query_offset_1 = kv_head_idx * num_queries_per_kv + \
+        offs_m % num_queries_per_kv
+
+    query_offset = (query_offset_0[:, None] * query_stride_0 +
+                    query_offset_1[:, None] * query_stride_1 + offs_d[None, :])
+
+    dim_mask = tl.where(offs_d < HEAD_SIZE, 1, 0).to(tl.int1)
+    query_mask_0 = tl.where(query_pos < cur_batch_query_len, 1, 0).to(tl.int1)
+    query_mask_1 = tl.where(query_offset_1 < num_query_heads, 1, 0).to(tl.int1)
+
+    # Q : (BLOCK_M, HEAD_SIZE_PADDED)
+    Q = tl.load(
+        query_ptr + query_offset,
+        mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
+        other=0.0,
+    )
+
+    block_table_offset = seq_idx * block_table_stride
+
+    M = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    L = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, HEAD_SIZE_PADDED], dtype=tl.float32)
+
+    # context length for this particular sequences
+    context_len = seq_len - cur_batch_query_len
+
+    # alibi slope for this head
+    if USE_ALIBI_SLOPES:
+        alibi_slope = tl.load(alibi_slopes_ptr + query_offset_1,
+                              mask=query_mask_1,
+                              other=0.0)
+
+    num_blocks = cdiv_fn(seq_len, BLOCK_SIZE)
+
+    # iterate through tiles within current segment
+    for j in range(
+            segm_idx * blocks_per_segment,
+            min((segm_idx + 1) * blocks_per_segment, num_blocks),
+    ):
+        physical_block_idx = tl.load(block_tables_ptr + block_table_offset + j)
+
+        offs_n = tl.arange(0, BLOCK_SIZE)
+
+        v_offset = (physical_block_idx * stride_v_cache_0 +
+                    kv_head_idx * stride_v_cache_2 +
+                    offs_d[None, :] * stride_v_cache_3 +
+                    offs_n[:, None] * stride_v_cache_1)
+
+        k_offset = (physical_block_idx * stride_k_cache_0 +
+                    kv_head_idx * stride_k_cache_2 +
+                    offs_d[:, None] * stride_k_cache_3 +
+                    offs_n[None, :] * stride_k_cache_1)
+
+        # K : (HEAD_SIZE, BLOCK_SIZE)
+        K_load = tl.load(key_cache_ptr + k_offset,
+                         mask=dim_mask[:, None],
+                         other=0.0)
+
+        if K_load.dtype.is_fp8():
+            if Q.dtype.is_fp8():
+                K = K_load
+            else:
+                K = (K_load.to(tl.float32) * tl.load(k_scale)).to(Q.dtype)
+        else:
+            K = K_load
+
+        # V : (BLOCK_SIZE, HEAD_SIZE)
+        V_load = tl.load(value_cache_ptr + v_offset,
+                         mask=dim_mask[None, :],
+                         other=0.0)
+
+        if V_load.dtype.is_fp8():
+            if Q.dtype.is_fp8():
+                V = V_load
+            else:
+                V = (V_load.to(tl.float32) * tl.load(v_scale)).to(Q.dtype)
+        else:
+            V = V_load
+
+        seq_offset = j * BLOCK_SIZE + offs_n
+
+        seq_mask = seq_offset[None, :] < context_len + query_pos[:, None] + 1
+
+        # S : (BLOCK_M, BLOCK_SIZE)
+        S = tl.zeros(shape=(BLOCK_M, BLOCK_SIZE), dtype=tl.float32)
+
+        S += scale * tl.dot(Q, K)
+
+        if USE_SOFTCAP:
+            S = apply_softcap(S, softcap)
+
+        S = tl.where(query_mask_1[:, None] & query_mask_0[:, None] & seq_mask,
+                     S, float("-inf"))
+
+        if SLIDING_WINDOW > 0:
+            S = tl.where((context_len + query_pos[:, None] - seq_offset)
+                         < SLIDING_WINDOW, S, float("-inf"))
+
+        if USE_ALIBI_SLOPES:
+            S += alibi_slope[:, None] * (seq_offset - context_len)
+
+        # compute running maximum
+        # m_j : (BLOCK_M,)
+        m_j = tl.maximum(M, tl.max(S, axis=1))
+        # For sliding window there's a chance the max is -inf due to masking of
+        # the entire row. In this case we need to set m_j 0 to avoid NaN
+        m_j = tl.where(m_j > float("-inf"), m_j, 0.0)
+
+        # P : (BLOCK_M, BLOCK_SIZE,)
+        P = tl.exp(S - m_j[:, None])
+
+        # l_j : (BLOCK_M,)
+        l_j = tl.sum(P, axis=1)
+
+        # alpha : (BLOCK_M, )
+        alpha = tl.exp(M - m_j)
+
+        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
+        acc = acc * alpha[:, None]
+
+        # update constants
+        L = L * alpha + l_j
+        M = m_j
+
+        # acc : (BLOCK_M, HEAD_SIZE_PADDED)
+        acc += tl.dot(P.to(V.dtype), V)
+
+    segm_output_offset = (
+        query_offset_0[:, None].to(tl.int64) *
+        (num_query_heads * NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED) +
+        query_offset_1[:, None] * (NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED) +
+        segm_idx * HEAD_SIZE_PADDED + tl.arange(0, HEAD_SIZE_PADDED)[None, :])
+    tl.store(
+        segm_output_ptr + segm_output_offset,
+        acc,
+        mask=dim_mask[None, :] & query_mask_0[:, None] & query_mask_1[:, None],
+    )
+    segm_offset = (query_offset_0.to(tl.int64) *
+                   (num_query_heads * NUM_SEGMENTS_PER_SEQ) +
+                   query_offset_1 * NUM_SEGMENTS_PER_SEQ + segm_idx)
+    tl.store(segm_max_ptr + segm_offset, M, mask=query_mask_0 & query_mask_1)
+    tl.store(segm_expsum_ptr + segm_offset,
+             L,
+             mask=query_mask_0 & query_mask_1)
+
+
+@triton.jit
+def reduce_segments(
+        output_ptr,  # [num_tokens, num_query_heads, head_size]
+        segm_output_ptr,
+        #[num_tokens, num_query_heads, max_num_segments, head_size]
+        segm_max_ptr,  # [num_tokens, num_query_heads, max_num_segments]
+        segm_expsum_ptr,  # [num_tokens, num_query_heads, max_num_segments]
+        seq_lens_ptr,  # [num_seqs]
+        num_seqs,  # int
+        num_query_heads: tl.constexpr,  # int
+        output_stride_0: tl.int64,  # int
+        output_stride_1: tl.int64,  # int, should be equal to head_size
+        block_table_stride: tl.int64,  # int
+        BLOCK_SIZE: tl.constexpr,  # int
+        HEAD_SIZE: tl.constexpr,  # int, must be power of 2
+        HEAD_SIZE_PADDED: tl.constexpr,  # int, must be power of 2
+        query_start_len_ptr,  # [num_seqs+1]
+        BLOCK_Q: tl.constexpr,  # int
+        NUM_SEGMENTS_PER_SEQ: tl.constexpr,  # int
+):
+    query_token_idx = tl.program_id(0)
+    query_head_idx = tl.program_id(1)
+
+    seq_idx = find_seq_idx(query_start_len_ptr, query_token_idx, num_seqs,
+                           BLOCK_Q, False)
+
+    # sequence len for this particular sequence
+    seq_len = tl.load(seq_lens_ptr + seq_idx)
+
+    # number of segments for this particular sequence
+    num_segments = NUM_SEGMENTS_PER_SEQ
+    blocks_per_segment = cdiv_fn(seq_len, num_segments * BLOCK_SIZE)
+
+    # create masks for subsequent loads
+    act_num_segments = cdiv_fn(seq_len, blocks_per_segment * BLOCK_SIZE)
+    segm_mask = tl.arange(0, NUM_SEGMENTS_PER_SEQ) < tl.full(
+        [NUM_SEGMENTS_PER_SEQ], act_num_segments, dtype=tl.int32)
+    dim_mask = tl.where(tl.arange(0, HEAD_SIZE_PADDED) < HEAD_SIZE, 1,
+                        0).to(tl.int1)
+
+    # load segment maxima
+    segm_offset = (query_token_idx.to(tl.int64) *
+                   (num_query_heads * NUM_SEGMENTS_PER_SEQ) +
+                   query_head_idx * NUM_SEGMENTS_PER_SEQ +
+                   tl.arange(0, NUM_SEGMENTS_PER_SEQ))
+    segm_max = tl.load(segm_max_ptr + segm_offset,
+                       mask=segm_mask,
+                       other=float("-inf"))
+    overall_max = tl.max(segm_max)
+
+    # load and rescale segment exp sums
+    segm_expsum = tl.load(segm_expsum_ptr + segm_offset,
+                          mask=segm_mask,
+                          other=0.0)
+    segm_expsum = segm_expsum * tl.exp(segm_max - overall_max)
+    overall_expsum = tl.sum(segm_expsum)
+
+    # load, rescale, and add segment attention outputs
+    segm_output_offset = (
+        query_token_idx.to(tl.int64) *
+        (num_query_heads * NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED) +
+        query_head_idx * (NUM_SEGMENTS_PER_SEQ * HEAD_SIZE_PADDED) +
+        tl.arange(0, NUM_SEGMENTS_PER_SEQ)[:, None] * HEAD_SIZE_PADDED +
+        tl.arange(0, HEAD_SIZE_PADDED)[None, :])
+    segm_output = tl.load(
+        segm_output_ptr + segm_output_offset,
+        mask=segm_mask[:, None] & dim_mask[None, :],
+        other=0.0,
+    )
+    segm_output *= tl.exp(segm_max - overall_max)[:, None]
+    acc_sum = tl.sum(segm_output, axis=0)
+    # safely divide by overall_expsum, returning 0.0 if overall_expsum is 0
+    acc = tl.where(overall_expsum == 0.0, 0.0, acc_sum / overall_expsum)
+
+    # write result
+    output_offset = (query_token_idx * output_stride_0 +
+                     query_head_idx * output_stride_1 +
+                     tl.arange(0, HEAD_SIZE_PADDED))
+    tl.store(output_ptr + output_offset, acc, mask=dim_mask)
+
+
 def unified_attention(
     q,
     k,
@@ -291,6 +606,8 @@ def unified_attention(
     #    = floor(q.shape[0] / BLOCK_Q) + num_seqs
     total_num_q_blocks = q.shape[0] // BLOCK_Q + num_seqs
 
+    # if batch contains a prefill
+    if max_seqlen_q > 1 or total_num_q_blocks * num_kv_heads > 128:
         kernel_unified_attention_2d[(
             total_num_q_blocks,
             num_kv_heads,
@@ -332,3 +649,90 @@ def unified_attention(
             num_seqs=num_seqs,
             BLOCK_M=BLOCK_M,
         )
+    else:
+        # for initial version, NUM_SEGMENTS = 16 is chosen as a default
+        # value that showed good performance in tests
+        NUM_SEGMENTS = 16
+
+        segm_output = torch.empty(
+            q.shape[0],
+            num_query_heads,
+            NUM_SEGMENTS,
+            triton.next_power_of_2(head_size),
+            dtype=torch.float32,
+            device=q.device,
+        )
+        segm_max = torch.empty(
+            q.shape[0],
+            num_query_heads,
+            NUM_SEGMENTS,
+            dtype=torch.float32,
+            device=q.device,
+        )
+        segm_expsum = torch.empty(
+            q.shape[0],
+            num_query_heads,
+            NUM_SEGMENTS,
+            dtype=torch.float32,
+            device=q.device,
+        )
+
+        kernel_unified_attention_3d[(
+            total_num_q_blocks, num_kv_heads, NUM_SEGMENTS)](
+                segm_output_ptr=segm_output,
+                segm_max_ptr=segm_max,
+                segm_expsum_ptr=segm_expsum,
+                query_ptr=q,
+                key_cache_ptr=k,
+                value_cache_ptr=v,
+                block_tables_ptr=block_table,
+                seq_lens_ptr=seqused_k,
+                alibi_slopes_ptr=alibi_slopes,
+                scale=softmax_scale,
+                k_scale=k_descale,
+                v_scale=v_descale,
+                softcap=softcap,
+                num_query_heads=num_query_heads,
+                num_queries_per_kv=num_queries_per_kv,
+                block_table_stride=block_table.stride(0),
+                query_stride_0=q.stride(0),
+                query_stride_1=q.stride(1),
+                BLOCK_SIZE=block_size,
+                HEAD_SIZE=head_size,
+                HEAD_SIZE_PADDED=triton.next_power_of_2(head_size),
+                USE_ALIBI_SLOPES=use_alibi_slopes,
+                USE_SOFTCAP=(softcap > 0),
+                SLIDING_WINDOW=(1 + window_size[0]),
+                stride_k_cache_0=k.stride(0),
+                stride_k_cache_1=k.stride(1),
+                stride_k_cache_2=k.stride(2),
+                stride_k_cache_3=k.stride(3),
+                stride_v_cache_0=v.stride(0),
+                stride_v_cache_1=v.stride(1),
+                stride_v_cache_2=v.stride(2),
+                stride_v_cache_3=v.stride(3),
+                query_start_len_ptr=cu_seqlens_q,
+                BLOCK_Q=BLOCK_Q,
+                num_seqs=num_seqs,
+                BLOCK_M=BLOCK_M,
+                NUM_SEGMENTS_PER_SEQ=NUM_SEGMENTS,
+            )
+
+        reduce_segments[(q.shape[0], num_query_heads)](
+            output_ptr=out,
+            segm_output_ptr=segm_output,
+            segm_max_ptr=segm_max,
+            segm_expsum_ptr=segm_expsum,
+            seq_lens_ptr=seqused_k,
+            num_seqs=num_seqs,
+            num_query_heads=num_query_heads,
+            output_stride_0=out.stride(0),
+            output_stride_1=out.stride(1),
+            block_table_stride=block_table.stride(0),
+            BLOCK_SIZE=block_size,
+            HEAD_SIZE=head_size,
+            HEAD_SIZE_PADDED=triton.next_power_of_2(head_size),
+            query_start_len_ptr=cu_seqlens_q,
+            BLOCK_Q=BLOCK_Q,
+            NUM_SEGMENTS_PER_SEQ=NUM_SEGMENTS,
+        )