Add triton_tutorial_flash_v2_on_host_tma_ws_oss_blackwell

Summary: Works with hacky compiler implementation of autoWS

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Author: manman-ren

tritonbench/kernels/triton_fused_attention.py

@@ -1952,6 +1952,7 @@ def _attn_fwd_tma_ws_persistent(  # Q, V, desc_k, desc_v, sm_scale, M, Out,  #
 ]
 
 
+# on-device TMA
 @triton.autotune(list(filter(keep, configsCutlassBlackwell)), key=["N_CTX"])
 @triton.jit
 def _attn_fwd_tma_ws_persistent_with_dp(  # Q, V, desc_k, desc_v, sm_scale, M, Out,  #
@@ -2110,6 +2111,146 @@ def _attn_fwd_tma_ws_persistent_with_dp(  # Q, V, desc_k, desc_v, sm_scale, M, O
         tile_idx += num_progs
 
 
+@triton.jit
+def _attn_fwd_subtile(q, k, offs_m, start_n, offs_n, qk_scale, l_i, m_i, acc, v, dtype: tl.constexpr, STAGE: tl.constexpr):
+    qk = tl.dot(q, k)
+    if STAGE == 2:
+        mask = offs_m[:, None] >= (start_n + offs_n[None, :])
+        qk = qk * qk_scale + tl.where(mask, 0, -1.0e6)
+        m_ij = tl.maximum(m_i, tl.max(qk, 1))
+        qk -= m_ij[:, None]
+    else:
+        m_ij = tl.maximum(m_i, tl.max(qk, 1) * qk_scale)
+        qk = qk * qk_scale - m_ij[:, None]
+    p = tl.math.exp2(qk)
+    # -- compute correction factor
+    alpha = tl.math.exp2(m_i - m_ij)
+    l_ij = tl.sum(p, 1)
+
+    # -- update output accumulator --
+    BM: tl.constexpr = acc.shape[0]
+    BN: tl.constexpr = acc.shape[1]
+
+    acc0, acc1 = acc.reshape([BM, 2, BN//2]).permute(0, 2, 1).split()
+    acc0 = acc0 * alpha[:, None]
+    acc1 = acc1 * alpha[:, None]
+    acc = tl.join(acc0, acc1).permute(0, 2, 1).reshape([BM, BN])
+
+    # prepare p and v for the dot
+    p = p.to(dtype)
+    # note that this non transposed v for FP8 is only supported on Blackwell
+    acc = tl.dot(p, v, acc)
+    # update m_i and l_i
+    # place this at the end of the loop to reduce register pressure
+    l_i = l_i * alpha + l_ij
+    m_i = m_ij
+
+    return l_i, m_i, acc
+
+
+@triton.jit
+def _attn_fwd_inner_oss_dp(acc0, acc1, l_i0, l_i1, m_i0, m_i1, q0, q1,  #
+                           desc_k, desc_v,  #
+                           offset_y, dtype: tl.constexpr, start_m, qk_scale,  #
+                           BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,  #
+                           STAGE: tl.constexpr, offs_m0: tl.constexpr, offs_m1: tl.constexpr,  #
+                           offs_n: tl.constexpr,  #
+                           N_CTX: tl.constexpr, warp_specialize: tl.constexpr):
+    # range of values handled by this stage
+    if STAGE == 1:
+        lo, hi = 0, start_m * BLOCK_M
+    elif STAGE == 2:
+        lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M
+        lo = tl.multiple_of(lo, BLOCK_M)
+    # causal = False
+    else:
+        lo, hi = 0, N_CTX
+    offsetkv_y = offset_y + lo
+
+    # loop over k, v and update accumulator
+    for start_n in tl.range(lo, hi, BLOCK_N, warp_specialize=warp_specialize, disallow_acc_multi_buffer=True):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+
+        k = desc_k.load([offsetkv_y, 0]).T
+        v = desc_v.load([offsetkv_y, 0])
+
+        l_i0, m_i0, acc0 = _attn_fwd_subtile(q0, k, offs_m0, start_n, offs_n, qk_scale, l_i0, m_i0, acc0, v, dtype, STAGE)
+        l_i1, m_i1, acc1 = _attn_fwd_subtile(q1, k, offs_m1, start_n, offs_n, qk_scale, l_i1, m_i1, acc1, v, dtype, STAGE)
+
+        offsetkv_y += BLOCK_N
+
+    return acc0, acc1, l_i0, l_i1, m_i0, m_i1
+
+
+#@triton.autotune(configs=list(filter(keep_tma, configs_tma_dp)),
+#                 key=["N_CTX", "HEAD_DIM", "FP8_OUTPUT", "warp_specialize"])
+@triton.jit
+def _attn_fwd_tma_oss_dp(sm_scale, M,  #
+                     Z, H, desc_q, desc_k, desc_v, desc_o, N_CTX,  #
+                     HEAD_DIM: tl.constexpr,  #
+                     BLOCK_M: tl.constexpr,  #
+                     BLOCK_N: tl.constexpr,  #
+                     FP8_OUTPUT: tl.constexpr,  #
+                     STAGE: tl.constexpr,  #
+                     warp_specialize: tl.constexpr,  #
+                     ENABLE_TMA: tl.constexpr,
+                     ):
+    dtype = tl.float8e5 if FP8_OUTPUT else tl.bfloat16
+    tl.static_assert(BLOCK_N <= HEAD_DIM)
+    start_m = tl.program_id(0)
+    off_hz = tl.program_id(1)
+    off_z = off_hz // H
+    off_h = off_hz % H
+
+    offset_y = off_z + off_h * N_CTX
+    qo_offset_y = offset_y + start_m * BLOCK_M
+    # initialize offsets
+    offs_m0 = start_m * BLOCK_M + tl.arange(0, BLOCK_M//2)
+    offs_m1 = start_m * BLOCK_M + tl.arange(BLOCK_M//2, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+
+    m_i0 = tl.zeros([BLOCK_M//2], dtype=tl.float32) - float("inf")
+    l_i0 = tl.zeros([BLOCK_M//2], dtype=tl.float32) + 1.0
+    acc0 = tl.zeros([BLOCK_M//2, HEAD_DIM], dtype=tl.float32)
+
+    m_i1 = tl.zeros([BLOCK_M//2], dtype=tl.float32) - float("inf")
+    l_i1 = tl.zeros([BLOCK_M//2], dtype=tl.float32) + 1.0
+    acc1 = tl.zeros([BLOCK_M//2, HEAD_DIM], dtype=tl.float32)
+
+    qk_scale = sm_scale
+    qk_scale *= 1.44269504  # 1/log(2)
+
+    q0 = desc_q.load([qo_offset_y, 0])
+    q1 = desc_q.load([qo_offset_y + BLOCK_M//2, 0])
+
+    if STAGE & 1:
+        acc0, acc1, l_i0, l_i1, m_i0, m_i1 = _attn_fwd_inner_oss_dp(acc0, acc1, l_i0, l_i1, m_i0, m_i1, q0, q1,  #
+                                            desc_k, desc_v,  #
+                                            offset_y, dtype, start_m, qk_scale,  #
+                                            BLOCK_M, HEAD_DIM, BLOCK_N,  #
+                                            4 - STAGE, offs_m0, offs_m1, offs_n, N_CTX,  #
+                                            warp_specialize)
+    if STAGE & 2:
+        acc0, acc1, l_i0, l_i1, m_i0, m_i1 = _attn_fwd_inner_oss_dp(acc0, acc1, l_i0, l_i1, m_i0, m_i1, q0, q1,  #
+                                            desc_k, desc_v,  #
+                                            offset_y, dtype, start_m, qk_scale,  #
+                                            BLOCK_M, HEAD_DIM, BLOCK_N,  #
+                                            2, offs_m0, offs_m1, offs_n, N_CTX,  #
+                                            warp_specialize)
+
+    m_i0 += tl.math.log2(l_i0)
+    acc0 = acc0 / l_i0[:, None]
+    m_ptrs0 = M + off_hz * N_CTX + offs_m0
+    tl.store(m_ptrs0, m_i0)
+    desc_o.store([qo_offset_y, 0], acc0.to(dtype))
+
+    m_i1 += tl.math.log2(l_i1)
+    acc1 = acc1 / l_i1[:, None]
+    m_ptrs1 = M + off_hz * N_CTX + offs_m1
+    tl.store(m_ptrs1, m_i1)
+    desc_o.store([qo_offset_y+BLOCK_M//2, 0], acc1.to(dtype))
+
+
 @triton.jit
 def _attn_bwd_preprocess(
     O,
@@ -2472,6 +2613,7 @@ def forward(ctx, q, k, v, causal, sm_scale, baseVariant):
 
         # no autotune with fixed BLOCK_N
         if HAS_TMA_DESC is True and torch.version.hip is None:
+            # Legacy on-host grid constant TMA
             desc_helper = TmaAutoTuneHelper()
             desc_helper.init_tma_descriptor("k")
             desc_helper.init_tma_descriptor("v")
@@ -2628,6 +2770,17 @@ def grid_tma_persistent(META):
             desc_v = desc_helper.get_tma_descriptor_kernel_param("v")
             desc_o = desc_helper.get_tma_descriptor_kernel_param("o")
 
+        # For variants using new on-host TMA
+        if baseVariant == "on_host_tma_ws_oss":
+            from triton.tools.tensor_descriptor import TensorDescriptor
+            y_dim = q.shape[0] * q.shape[1] * q.shape[2]
+            BLOCK_M = 256
+            BLOCK_N = 128
+            desc_q = TensorDescriptor(q, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=[BLOCK_M//2, HEAD_DIM_K])
+            desc_v = TensorDescriptor(v, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=[BLOCK_N, HEAD_DIM_K])
+            desc_k = TensorDescriptor(k, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=[BLOCK_N, HEAD_DIM_K])
+            desc_o = TensorDescriptor(o, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=[BLOCK_M//2, HEAD_DIM_K])
+
         M = torch.empty(
             (q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32
         )
@@ -2810,6 +2963,24 @@ def alloc_fn(size: int, alignment: int, stream: Optional[int]):
                 ENABLE_WS=True,
                 **extra_kern_args,
             )
+        elif baseVariant == "on_host_tma_ws_oss":
+            BLOCK_M = 256
+            BLOCK_N = 128
+            _attn_fwd_tma_oss_dp[grid_tma](
+                sm_scale, M,  #
+                q.shape[0], q.shape[1],  #
+                desc_q, desc_k, desc_v, desc_o,  #
+                N_CTX=q.shape[2],  #
+                HEAD_DIM=HEAD_DIM_K,  #
+                FP8_OUTPUT=q.dtype == torch.float8_e5m2,  #
+                STAGE=stage,  #
+                warp_specialize=True,  #
+                ENABLE_TMA=True,
+                BLOCK_N=BLOCK_N, BLOCK_M=BLOCK_M,  #
+                num_warps=4,
+                num_stages=2,
+                #maxnreg=64,
+                **extra_kern_args)
 
         ctx.save_for_backward(q, k, v, o, M)
         ctx.grid = grid_tma

tritonbench/operators/blackwell_attentions/operator.py

@@ -282,6 +282,7 @@ def causal_mask(b, h, q_idx, kv_idx):
 
         return lambda: flex_attention(q, k, v, block_mask=block_mask)
 
+    # use Meta's warpspec + on device TMA + persistent
     @register_benchmark(enabled=False)
     def triton_tutorial_flash_v2_tma_ws_persistent_blackwell(
         self,
@@ -293,6 +294,29 @@ def triton_tutorial_flash_v2_tma_ws_persistent_blackwell(
             q, k, v, self.causal, self.sm_scale, "tma_ws_persistent_blackwell"
         )
 
+    @register_benchmark(enabled=False)
+    def triton_tutorial_flash_v2_blackwell(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> Callable:
+        return lambda: triton_tutorial_FA2_opt(
+            q, k, v, self.causal, self.sm_scale, "base_opt"
+        )
+
+    # use OSS warpspec + on host TMA
+    @register_benchmark(enabled=False)
+    def triton_tutorial_flash_v2_on_host_tma_ws_oss_blackwell(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> Callable:
+        return lambda: triton_tutorial_FA2_opt(
+            q, k, v, self.causal, self.sm_scale, "on_host_tma_ws_oss"
+        )
+
     # Only works with triton main, forward only.
     @register_benchmark(enabled=False)
     def gluon_blackwell_tutorial_fwd(