[Benchmark] Add benchmark scripts for block sparse attention

benchmark/blocksparse_attention/benchmark_configs.py

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+# BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK
+configs = [[4, 2, 256, 64, 2, 64]]

benchmark/blocksparse_attention/benchmark_library_dense_fmha.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ruff: noqa
+import torch
+from tilelang.profiler import do_bench
+
+
+def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
+    bsz, num_head, downsample_len, _ = x.shape
+    # N_CTX = downsample_len * BLOCK
+    sparse_index = torch.topk(x, topk, dim=-1).indices
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len],
+                            False,
+                            dtype=torch.bool,
+                            device=x.device)
+    dense_mask.scatter_(-1, sparse_index, True)
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
+    dense_mask = x > threshold
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def benchmark_topk_sparse_attention():
+    from benchmark_configs import configs
+    torch.manual_seed(0)
+
+    # Config
+    for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs:
+
+        # Create inputs
+        q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+
+        import flash_attn
+
+        def benchmark_fn():
+            flash_attn.flash_attn_func(q, k, v, causal=True)
+
+        ref_latency = do_bench(
+            benchmark_fn,
+            warmup=10,
+            rep=100,
+        )
+        print(
+            f"BATCH: {BATCH}, N_HEADS: {N_HEADS}, SEQ_LEN: {SEQ_LEN}, D_HEAD: {D_HEAD}, TOPK: {TOPK}, BLOCK: {BLOCK}, ref_latency: {ref_latency}"
+        )
+
+
+if __name__ == "__main__":
+    benchmark_topk_sparse_attention()

benchmark/blocksparse_attention/benchmark_tilelang_block_sparse_fmha.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ruff: noqa
+import math
+import torch
+
+import tilelang
+from tilelang import language as T
+from tilelang.profiler import do_bench
+
+
+def is_hip():
+    return False
+
+
+def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
+    bsz, num_head, downsample_len, _ = x.shape
+    # N_CTX = downsample_len * BLOCK
+    sparse_index = torch.topk(x, topk, dim=-1).indices
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len],
+                            False,
+                            dtype=torch.bool,
+                            device=x.device)
+    dense_mask.scatter_(-1, sparse_index, True)
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
+    dense_mask = x > threshold
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal):
+    block_M = 64
+    block_N = 64
+    num_stages = 0
+    threads = 128
+    scale = (1.0 / dim)**0.5 * 1.44269504  # log2(e)
+    shape = [batch, heads, seq_len, dim]
+    block_mask_shape = [batch, heads, downsample_len, downsample_len]
+
+    dtype = "float16"
+    accum_dtype = "float"
+    block_mask_dtype = "int8"
+
+    def kernel_func(block_M, block_N, num_stages, threads):
+
+        @T.macro
+        def MMA0(
+            K: T.Buffer(shape, dtype),
+            Q_shared: T.Buffer([block_M, dim], dtype),
+            K_shared: T.Buffer([block_N, dim], dtype),
+            acc_s: T.Buffer([block_M, block_N], accum_dtype),
+            k: T.int32,
+            bx: T.int32,
+            by: T.int32,
+            bz: T.int32,
+        ):
+            T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared)
+            if is_causal:
+                for i, j in T.Parallel(block_M, block_N):
+                    acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0,
+                                                 -T.infinity(acc_s.dtype))
+            else:
+                T.clear(acc_s)
+            T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+
+        @T.macro
+        def MMA1(
+                V: T.Buffer(shape, dtype),
+                V_shared: T.Buffer([block_M, dim], dtype),
+                acc_s_cast: T.Buffer([block_M, block_N], dtype),
+                acc_o: T.Buffer([block_M, dim], accum_dtype),
+                k: T.int32,
+                by: T.int32,
+                bz: T.int32,
+        ):
+            T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared)
+            T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+        @T.macro
+        def Softmax(
+                acc_s: T.Buffer([block_M, block_N], accum_dtype),
+                acc_s_cast: T.Buffer([block_M, block_N], dtype),
+                scores_max: T.Buffer([block_M], accum_dtype),
+                scores_max_prev: T.Buffer([block_M], accum_dtype),
+                scores_scale: T.Buffer([block_M], accum_dtype),
+                scores_sum: T.Buffer([block_M], accum_dtype),
+                logsum: T.Buffer([block_M], accum_dtype),
+        ):
+            T.copy(scores_max, scores_max_prev)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+            T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+            # To do causal softmax, we need to set the scores_max to 0 if it is -inf
+            # This process is called Check_inf in FlashAttention3 code, and it only need to be done
+            # in the first ceil_div(kBlockM, kBlockN) steps.
+            # for i in T.Parallel(block_M):
+            #     scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i])
+            for i in T.Parallel(block_M):
+                scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+            for i, j in T.Parallel(block_M, block_N):
+                # Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
+                # max * log_2(e)) This allows the compiler to use the ffma
+                # instruction instead of fadd and fmul separately.
+                acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+            T.reduce_sum(acc_s, scores_sum, dim=1)
+            for i in T.Parallel(block_M):
+                logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+            T.copy(acc_s, acc_s_cast)
+
+        @T.macro
+        def Rescale(
+                acc_o: T.Buffer([block_M, dim], accum_dtype),
+                scores_scale: T.Buffer([block_M], accum_dtype),
+        ):
+            for i, j in T.Parallel(block_M, dim):
+                acc_o[i, j] *= scores_scale[i]
+
+        @T.prim_func
+        def main(
+                Q: T.Buffer(shape, dtype),
+                K: T.Buffer(shape, dtype),
+                V: T.Buffer(shape, dtype),
+                BlockSparseMask: T.Buffer(block_mask_shape, block_mask_dtype),
+                Output: T.Buffer(shape, dtype),
+        ):
+            with T.Kernel(
+                    T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz):
+                Q_shared = T.alloc_shared([block_M, dim], dtype)
+                K_shared = T.alloc_shared([block_N, dim], dtype)
+                V_shared = T.alloc_shared([block_N, dim], dtype)
+                O_shared = T.alloc_shared([block_M, dim], dtype)
+                acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+                acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
+                acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
+                scores_max = T.alloc_fragment([block_M], accum_dtype)
+                scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
+                scores_scale = T.alloc_fragment([block_M], accum_dtype)
+                scores_sum = T.alloc_fragment([block_M], accum_dtype)
+                logsum = T.alloc_fragment([block_M], accum_dtype)
+                block_mask = T.alloc_local([downsample_len], block_mask_dtype)
+
+                T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+
+                for vj in T.serial(downsample_len):
+                    block_mask[vj] = BlockSparseMask[bz, by, bx, vj]
+
+                loop_range = (
+                    T.min(T.ceildiv(seq_len, block_N), T.ceildiv(
+                        (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N))
+
+                for k in T.Pipelined(loop_range, num_stages=num_stages):
+                    if block_mask[k] != 0:
+                        MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz)
+                        Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale,
+                                scores_sum, logsum)
+                        Rescale(acc_o, scores_scale)
+                        MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz)
+                for i, j in T.Parallel(block_M, dim):
+                    acc_o[i, j] /= logsum[i]
+                T.copy(acc_o, O_shared)
+                T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :])
+
+        return main
+
+    return kernel_func(block_M, block_N, num_stages, threads)
+
+
+def benchmark_topk_sparse_attention():
+    from benchmark_configs import configs
+    torch.manual_seed(0)
+
+    # Config
+    for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs:
+
+        # Create inputs
+        q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+
+        sm_scale = 1.0 / (D_HEAD**0.5)
+
+        # Create sparse mask (downsampled to block level)
+        downsample_factor = BLOCK
+        downsample_len = math.ceil(SEQ_LEN / downsample_factor)
+        x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len],
+                           device='cuda',
+                           dtype=torch.bfloat16)
+        x_ds[:, :, :, 0] = 100
+        block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
+        program = blocksparse_flashattn(
+            BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True)
+        kernel = tilelang.compile(program, out_idx=4)
+
+        def benchmark_fn():
+            # Compute reference
+            # Expand block mask to full attention matrix
+            kernel(q, k, v, block_mask)
+
+        ref_latency = do_bench(
+            benchmark_fn,
+            warmup=10,
+            rep=100,
+        )
+        print(
+            f"BATCH: {BATCH}, N_HEADS: {N_HEADS}, SEQ_LEN: {SEQ_LEN}, D_HEAD: {D_HEAD}, TOPK: {TOPK}, BLOCK: {BLOCK}, ref_latency: {ref_latency}"
+        )
+
+
+if __name__ == "__main__":
+    benchmark_topk_sparse_attention()

benchmark/blocksparse_attention/benchmark_torch_block_sparse_fmha.py

@@ -0,0 +1,82 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ruff: noqa
+import math
+import torch
+
+import torch.nn.functional as F
+from tilelang.profiler import do_bench
+
+
+def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
+    bsz, num_head, downsample_len, _ = x.shape
+    # N_CTX = downsample_len * BLOCK
+    sparse_index = torch.topk(x, topk, dim=-1).indices
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len],
+                            False,
+                            dtype=torch.bool,
+                            device=x.device)
+    dense_mask.scatter_(-1, sparse_index, True)
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
+    dense_mask = x > threshold
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def benchmark_topk_sparse_attention():
+    from benchmark_configs import configs
+    torch.manual_seed(0)
+
+    # Config
+    for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs:
+
+        # Create inputs
+        q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+        v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
+
+        sm_scale = 1.0 / (D_HEAD**0.5)
+
+        # Create sparse mask (downsampled to block level)
+        downsample_factor = BLOCK
+        downsample_len = math.ceil(SEQ_LEN / downsample_factor)
+        x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len],
+                           device='cuda',
+                           dtype=torch.bfloat16)
+        x_ds[:, :, :, 0] = 100
+        block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
+
+        def benchmark_fn():
+            # Compute reference
+            # Expand block mask to full attention matrix
+            full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda'))
+            full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool()
+            full_mask = full_mask & torch.tril(torch.ones_like(full_mask))  # Apply causal
+
+            # PyTorch reference implementation
+            attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale
+            attn = attn.masked_fill(~full_mask, float('-inf'))
+            attn = F.softmax(attn, dim=-1)
+            ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v)
+            return ref_output
+
+        ref_latency = do_bench(
+            benchmark_fn,
+            warmup=10,
+            rep=100,
+        )
+        print(
+            f"BATCH: {BATCH}, N_HEADS: {N_HEADS}, SEQ_LEN: {SEQ_LEN}, D_HEAD: {D_HEAD}, TOPK: {TOPK}, BLOCK: {BLOCK}, ref_latency: {ref_latency}"
+        )
+
+
+if __name__ == "__main__":
+    benchmark_topk_sparse_attention()