[kernel] Add triton kernel for context attention (FAv2) without padding (#5192)

* add context attn unpadded triton kernel

* test compatibility

* kv cache copy (testing)

* fix k/v cache copy

* fix kv cache copy and test

* fix boundary of block ptrs

* add support for GQA/MQA and testing

* fix import statement

---------

Co-authored-by: Round Heng <yuanhengzhao@Rounds-MacBook-Pro.local>

colossalai/kernel/triton/__init__.py

@@ -8,11 +8,13 @@ except ImportError:
 
 # There may exist import error even if we have triton installed.
 if HAS_TRITON:
+    from .context_attn_unpad import context_attention_unpadded
     from .fused_layernorm import layer_norm
     from .gptq_triton import gptq_fused_linear_triton
     from .softmax import softmax
 
     __all__ = [
+        "context_attention_unpadded",
         "softmax",
         "layer_norm",
         "gptq_fused_linear_triton",

colossalai/kernel/triton/context_attn_unpad.py

@@ -0,0 +1,262 @@
+# Applying the FlashAttention V2 as described in:
+# "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning"
+# by Tri Dao, 2023
+# https://github.com/Dao-AILab/flash-attention
+#
+# Inspired and modified from Triton Tutorial - Fused Attention
+# https://triton-lang.org/main/getting-started/tutorials/06-fused-attention.html
+import torch
+import triton
+import triton.language as tl
+
+
+# Triton 2.1.0
+@triton.jit
+def _fwd_context_paged_attention_kernel(
+    Q,
+    K,
+    V,
+    O,
+    KCache,
+    VCache,
+    BLOCK_TABLES,  # [num_seqs, max_blocks_per_sequence]
+    stride_qt,
+    stride_qh,
+    stride_qd,
+    stride_kt,
+    stride_kh,
+    stride_kd,
+    stride_vt,
+    stride_vh,
+    stride_vd,
+    stride_ot,
+    stride_oh,
+    stride_od,
+    stride_cacheb,
+    stride_cacheh,
+    stride_cached,
+    stride_cachebs,
+    stride_bts,
+    stride_btb,
+    context_lengths,
+    sm_scale,
+    KV_GROUPS: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    cur_seq_idx = tl.program_id(0)
+    cur_head_idx = tl.program_id(1)
+    block_start_m = tl.program_id(2)  # Br, max_input_len // Block_M
+    cur_kv_head_idx = cur_head_idx // KV_GROUPS
+
+    # NOTE It requires BLOCK_M, BLOCK_N, and BLOCK_SIZE to be the same
+    tl.static_assert(BLOCK_M == BLOCK_N)
+    tl.static_assert(BLOCK_N == BLOCK_SIZE)
+
+    # get the current sequence length from provided context lengths tensor
+    cur_seq_len = tl.load(context_lengths + cur_seq_idx)
+    # NOTE when talking to fused QKV and a nopadding context attention,
+    # we assume that the input Q/K/V is contiguous, and thus here `prev_seq_len_sum`
+    # could be considered as the start index of the current sequence.
+    # FIXME might want to explore better way to get the summation of prev seq lengths.
+    # `tl.sum(tensor[:end])` is invalid as tensor slice is not supported in triton.
+    prev_seq_len_sum = 0
+    for i in range(0, cur_seq_idx):
+        prev_seq_len_sum += tl.load(context_lengths + i)
+
+    q_offset = prev_seq_len_sum * stride_qt + cur_head_idx * stride_qh
+    kv_offset = prev_seq_len_sum * stride_kt + cur_kv_head_idx * stride_kh
+    Q_block_ptr = tl.make_block_ptr(
+        base=Q + q_offset,
+        shape=(cur_seq_len, BLOCK_DMODEL),
+        strides=(stride_qt, stride_qd),
+        offsets=(block_start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    K_block_ptr = tl.make_block_ptr(
+        base=K + kv_offset,
+        shape=(BLOCK_DMODEL, cur_seq_len),
+        strides=(stride_kd, stride_kt),
+        offsets=(0, 0),
+        block_shape=(BLOCK_DMODEL, BLOCK_N),
+        order=(0, 1),
+    )
+    V_block_ptr = tl.make_block_ptr(
+        base=V + kv_offset,
+        shape=(cur_seq_len, BLOCK_DMODEL),
+        strides=(stride_vt, stride_vd),
+        offsets=(0, 0),
+        block_shape=(BLOCK_N, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    O_block_ptr = tl.make_block_ptr(
+        base=O + q_offset,
+        shape=(cur_seq_len, BLOCK_DMODEL),
+        strides=(stride_ot, stride_od),
+        offsets=(block_start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+
+    # block table for the current sequence
+    block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts
+    # block indexes on block table (i.e. 0, 1, 2, ..., max_blocks_per_seq)
+    # Consider `block_start_m` as the logical block idx in the current block table,
+    # as we have BLOCK_M the same size as the block size.
+    cur_block_table_idx = block_start_m
+    cur_block_id = tl.load(block_table_ptr + cur_block_table_idx * stride_btb)
+    kvcache_offset = cur_block_id * stride_cacheb + cur_kv_head_idx * stride_cacheh
+
+    offsets_m = block_start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offsets_n = tl.arange(0, BLOCK_N)
+    m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+
+    if block_start_m * BLOCK_M >= cur_seq_len:
+        return
+
+    Q_i = tl.load(Q_block_ptr, boundary_check=(1, 0))
+
+    for block_start_n in range(0, (block_start_m + 1) * BLOCK_M, BLOCK_N):
+        block_start_n = tl.multiple_of(block_start_n, BLOCK_N)
+
+        k = tl.load(K_block_ptr, boundary_check=(0, 1))
+        S_ij = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        S_ij += tl.dot(Q_i, k)
+        S_ij *= sm_scale
+        S_ij += tl.where(offsets_m[:, None] >= (block_start_n + offsets_n[None, :]), 0, float("-inf"))
+
+        m_ij = tl.max(S_ij, 1)  # rowmax(Sij)
+        m_ij = tl.maximum(m_i, m_ij)  # m_ij
+        S_ij -= m_ij[:, None]
+        p_ij_hat = tl.exp(S_ij)
+        scale = tl.exp(m_i - m_ij)
+        l_ij = scale * l_i + tl.sum(p_ij_hat, 1)
+        acc = acc * scale[:, None]
+
+        v = tl.load(V_block_ptr, boundary_check=(1, 0))
+        p_ij_hat = p_ij_hat.to(v.type.element_ty)
+
+        acc += tl.dot(p_ij_hat, v)
+        l_i = l_ij
+        m_i = m_ij
+        K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
+        V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
+
+    acc = acc / l_i[:, None]
+    tl.store(O_block_ptr, acc.to(O.type.element_ty), boundary_check=(1, 0))
+
+    if cur_head_idx % KV_GROUPS == 0:
+        # Copy k to corresponding cache block
+        kd_offsets = tl.arange(0, BLOCK_DMODEL)
+        kt_offsets = block_start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+        k_offsets = K + kv_offset + kd_offsets[:, None] * stride_kd + kt_offsets[None, :] * stride_kt
+        k = tl.load(k_offsets, mask=kt_offsets[None, :] < cur_seq_len, other=0.0)
+        kcached_offsets = tl.arange(0, BLOCK_DMODEL)
+        kcachebs_offsets = tl.arange(0, BLOCK_SIZE)
+        kcache_offsets = (
+            KCache
+            + kvcache_offset
+            + kcached_offsets[:, None] * stride_cached
+            + kcachebs_offsets[None, :] * stride_cachebs
+        )
+        tl.store(kcache_offsets, k, mask=kcachebs_offsets[None, :] < cur_seq_len - block_start_m * BLOCK_SIZE)
+        # Copy v to corresponding cache block
+        vd_offsets = kd_offsets
+        vt_offsets = block_start_m * BLOCK_N + tl.arange(0, BLOCK_N)
+        v_offsets = V + kv_offset + vt_offsets[:, None] * stride_vt + vd_offsets[None, :] * stride_vd
+        v = tl.load(v_offsets, mask=vt_offsets[:, None] < cur_seq_len, other=0.0)
+        vcached_offsets = kcached_offsets
+        vcachebs_offsets = kcachebs_offsets
+        vcache_offsets = (
+            VCache
+            + kvcache_offset
+            + vcachebs_offsets[:, None] * stride_cachebs
+            + vcached_offsets[None, :] * stride_cached
+        )
+        tl.store(vcache_offsets, v, mask=vcachebs_offsets[:, None] < cur_seq_len - block_start_m * BLOCK_SIZE)
+
+    return
+
+
+def context_attention_unpadded(
+    q: torch.Tensor,  # [num_tokens, num_heads, head_size]
+    k: torch.Tensor,  # [num_tokens, num_kv_heads, head_size]
+    v: torch.Tensor,  # [num_tokens, num_kv_heads, head_size]
+    k_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_size, block_size]
+    v_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_size, block_size]
+    context_lengths: torch.Tensor,  # [num_seqs]
+    block_tables: torch.Tensor,  # [num_seqs, max_blocks_per_sequence],
+    block_size: int,
+):
+    # q/k in context stage are supposed to be put into k_cache and v_cache.
+    # This step can be optimized in future.
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+
+    Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
+    assert Lq == Lk == Lv
+    assert Lk in {32, 64, 128, 256}
+    assert q.shape[0] == k.shape[0] == v.shape[0]
+    assert k_cache.shape == v_cache.shape
+    assert context_lengths.shape[0] == block_tables.shape[0]
+
+    num_tokens, num_heads, _ = q.shape
+    num_kv_heads = k.shape[-2]
+    assert num_kv_heads > 0 and num_heads % num_kv_heads == 0
+    num_kv_group = num_heads // num_kv_heads
+
+    num_seqs, max_blocks_per_seq = block_tables.shape
+    max_seq_len = context_lengths.max().item()
+    sm_scale = 1.0 / (Lq**0.5)
+
+    output = torch.zeros_like(q)
+
+    # NOTE For now, BLOCK_M and BLOCK_N are supposed to be equivalent with
+    # the size of physical cache block (i.e. `block_size`)
+    assert block_size in {16, 32, 64, 128}
+    BLOCK_M = BLOCK_N = block_size
+
+    grid = (num_seqs, num_heads, triton.cdiv(max_seq_len, BLOCK_M))
+
+    _fwd_context_paged_attention_kernel[grid](
+        q,
+        k,
+        v,
+        output,
+        k_cache,
+        v_cache,
+        block_tables,
+        q.stride(0),
+        q.stride(1),
+        q.stride(2),
+        k.stride(0),
+        k.stride(1),
+        k.stride(2),
+        v.stride(0),
+        v.stride(1),
+        v.stride(2),
+        output.stride(0),
+        output.stride(1),
+        output.stride(2),
+        k_cache.stride(0),
+        k_cache.stride(1),
+        k_cache.stride(2),
+        k_cache.stride(3),
+        block_tables.stride(0),
+        block_tables.stride(1),
+        context_lengths,
+        sm_scale,
+        num_kv_group,
+        block_size,
+        BLOCK_DMODEL=Lk,
+        BLOCK_M=BLOCK_M,
+        BLOCK_N=BLOCK_N,
+    )
+
+    return output