[Enhancement] Add flash attn example for AMD MI300 series(#671)

Alex4210987 · xinxyxiao · web-flow · commit e8cc372f911d · 2025-07-29T12:05:40.000+08:00
* [Enhancement] Refactor buffer index handling for improved precision and clarity (#668) - Enhanced buffer index handling to address precision issues by removing redundant operations. - Streamlined the logic for determining buffer overlaps, ensuring more accurate conflict detection. - Updated related documentation to reflect changes in buffer management practices. * Remove obsolete test script for AMD example, streamlining the examples directory. * Remove unused dtype_size variable in AMD example script to streamline code. --------- Co-authored-by: xinxyxiao <xinyxiao@amd.com>
diff --git a/examples/amd/example_amd_flash_attn_fwd.py b/examples/amd/example_amd_flash_attn_fwd.py
@@ -0,0 +1,270 @@
+# Copyright (c) Tile-AI Corporation.
+# Licensed under the MIT License.
+#
+# Modified to implement FlashAttention-2 forward pass principles.
+# Corrected loop implementation using T.while_loop.
+
+import torch
+import torch.nn.functional as F
+import tilelang
+import tilelang.language as T
+import itertools
+import argparse
+from functools import partial
+
+
+# PyTorch 参考实现保持不变
+def ref_program(Q, K, V, is_causal, groups=1):
+    assert Q.size(
+        2) == K.size(2) * groups, f"Q heads {Q.size(2)} K heads {K.size(2)} groups {groups}"
+    assert Q.size(
+        2) == V.size(2) * groups, f"Q heads {Q.size(2)} V heads {V.size(2)} groups {groups}"
+    dim = Q.size(-1)
+    K = K.repeat_interleave(groups, dim=2)
+    V = V.repeat_interleave(groups, dim=2)
+    scores = torch.einsum('bqhd,bkhd->bhqk', Q, K)
+    scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype))
+    if is_causal:
+        seq_len = Q.size(1)
+        mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device))
+        mask = mask.unsqueeze(0).unsqueeze(0)
+        scores = scores.masked_fill(mask == 0, float('-inf'))
+    attention_weights = F.softmax(scores, dim=-1)
+    output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V)
+    return output
+
+
+def get_v2_configs():
+    """Generates configurations for the autotuner, tailored for FA-2 style parallelism."""
+    block_M = [64, 128, 256]
+    block_N = [32, 64, 128]
+    threads = [128, 256, 512]
+    num_split_q = [32, 64, 128]
+    num_stages = [1, 2, 3]
+    enable_rasterization = [True]
+    k_pack = [2]
+
+    valid_configs = []
+
+    for m, n, s, t, stages, r, k in itertools.product(block_M, block_N, num_split_q, threads,
+                                                      num_stages, enable_rasterization, k_pack):
+        valid_configs.append({
+            "block_M": m,
+            "block_N": n,
+            "num_split_q": s,
+            "threads": t,
+            "num_stages": stages,
+            "enable_rasterization": r,
+            "k_pack": k
+        })
+    if not valid_configs:
+        valid_configs.append({
+            'block_M': 64,
+            'block_N': 64,
+            'num_split_q': 64,
+            'threads': 256,
+            'num_stages': 1,
+            'enable_rasterization': True,
+            'k_pack': 2
+        })
+    return valid_configs
+
+
+@tilelang.autotune(configs=get_v2_configs(), cache_input_tensors=True)
+@tilelang.jit(out_idx=[3])
+def fast_flashattn_v2(
+    batch,
+    heads,
+    seq_len,
+    dim,
+    is_causal,
+    groups,
+    block_M: int,
+    block_N: int,
+    num_split_q: int,
+    threads: int,
+    num_stages: int,
+    enable_rasterization: bool,
+    k_pack: int,
+):
+    scale = (1.0 / dim)**0.5 * 1.44269504
+    head_kv = heads // groups
+    q_shape = [batch, seq_len, heads, dim]
+    kv_shape = [batch, seq_len, head_kv, dim]
+    dtype = "float16"
+    accum_dtype = "float"
+
+    v_vec_size = 4
+
+    vec_size = 4 * k_pack
+
+    @T.macro
+    def compute_block(
+            bz,
+            by,
+            bx,
+            Q: T.Tensor(q_shape, dtype),
+            K: T.Tensor(kv_shape, dtype),
+            V: T.Tensor(kv_shape, dtype),
+            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+            m_i: T.FragmentBuffer([block_M], accum_dtype),
+            l_i: T.FragmentBuffer([block_M], accum_dtype),
+    ):
+        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)
+        P_shared = T.alloc_shared([block_M, block_N], dtype)
+
+        acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+        m_prev = T.alloc_fragment([block_M], accum_dtype)
+        scale_factor = T.alloc_fragment([block_M], accum_dtype)
+
+        q_block_offset = bx * block_M
+        T.copy(
+            Q[bz, q_block_offset:q_block_offset + block_M, by, :],
+            Q_shared,
+            coalesced_width=vec_size)
+
+        loop_end_k = T.ceildiv(q_block_offset +
+                               block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)
+        for k in T.Pipelined(loop_end_k, num_stages=num_stages):
+            kv_idx = k * block_N
+            T.copy(
+                K[bz, kv_idx:kv_idx + block_N, by // groups, :], K_shared, coalesced_width=vec_size)
+            T.copy(
+                V[bz, kv_idx:kv_idx + block_N, by // groups, :],
+                V_shared,
+                coalesced_width=v_vec_size)
+
+            T.clear(acc_s)
+            T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, k_pack=k_pack)
+
+            if is_causal:
+                for i, j in T.Parallel(block_M, block_N):
+                    acc_s[i, j] = T.if_then_else(q_block_offset + i >= kv_idx + j, acc_s[i, j],
+                                                 -T.infinity(acc_s.dtype))
+
+            T.copy(m_i, m_prev)
+            T.reduce_max(acc_s, m_i, dim=1, clear=False)
+
+            for i in T.Parallel(block_M):
+                sf = T.exp2(m_prev[i] * scale - m_i[i] * scale)
+                l_i[i] *= sf
+                scale_factor[i] = sf
+
+            for i, j in T.Parallel(block_M, dim):
+                acc_o[i, j] *= scale_factor[i]
+
+            for i, j in T.Parallel(block_M, block_N):
+                acc_s[i, j] = T.exp2(acc_s[i, j] * scale - m_i[i] * scale)
+
+            row_sum = T.alloc_fragment([block_M], accum_dtype)
+            T.reduce_sum(acc_s, row_sum, dim=1)
+            for i in T.Parallel(block_M):
+                l_i[i] += row_sum[i]
+
+            T.copy(acc_s, P_shared)
+            T.sync_threads()
+
+            T.gemm(P_shared, V_shared, acc_o)
+
+    # 修复：将宏移至内核外部，以实现清晰的代码结构。
+    @T.macro
+    def scale_and_write_back(src_buffer, scale_vector, dest_tensor, bz, by, q_block_offset):
+        # 此宏执行融合的缩放和写回操作，这对性能至关重要。
+        for i, j in T.Parallel(block_M, dim):
+            dest_tensor[bz, q_block_offset + i, by, j] = src_buffer[i, j] * scale_vector[i]
+
+    @T.macro
+    def flash_attn_forward_kernel(Q: T.Tensor(q_shape, dtype), K: T.Tensor(kv_shape, dtype),
+                                  V: T.Tensor(kv_shape, dtype), Output: T.Tensor(q_shape, dtype)):
+        with T.Kernel(num_split_q, batch * heads, threads=threads) as (b_split, byz_combined):
+            T.use_swizzle(10, enable=enable_rasterization)
+
+            bz = byz_combined // heads
+            by = byz_combined % heads
+
+            num_q_blocks = T.ceildiv(seq_len, block_M)
+
+            bx = T.alloc_var("int32")
+            bx[0] = b_split
+
+            with T.While(bx[0] < num_q_blocks):
+                acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
+                m_i = T.alloc_fragment([block_M], accum_dtype)
+                l_i = T.alloc_fragment([block_M], accum_dtype)
+                T.fill(acc_o, 0)
+                T.fill(m_i, -T.infinity(accum_dtype))
+                T.fill(l_i, 0)
+
+                current_bx = bx[0]
+
+                compute_block(bz, by, current_bx, Q, K, V, acc_o, m_i, l_i)
+
+                l_inv = T.alloc_fragment([block_M], accum_dtype)
+                for i in T.Parallel(block_M):
+                    safe_l = T.if_then_else(l_i[i] > 1e-6, l_i[i], 1.0)
+                    l_inv[i] = 1.0 / safe_l
+
+                # 修复：现在对宏的调用对编译器来说更清晰。
+                q_block_offset = current_bx * block_M
+                scale_and_write_back(acc_o, l_inv, Output, bz, by, q_block_offset)
+
+                bx[0] = current_bx + num_split_q
+
+    @T.prim_func
+    def main(
+            Q: T.Tensor(q_shape, dtype),
+            K: T.Tensor(kv_shape, dtype),
+            V: T.Tensor(kv_shape, dtype),
+            Output: T.Tensor(q_shape, dtype),
+    ):
+        flash_attn_forward_kernel(Q, K, V, Output)
+
+    return main
+
+
+# main 函数保持不变
+def main_v2(batch: int = 1,
+            heads: int = 8,
+            seq_len: int = 4096,
+            dim: int = 128,
+            is_causal: bool = False,
+            groups: int = 1):
+
+    flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim
+    total_flops = 2 * flops_per_matmul
+    if is_causal:
+        total_flops *= 0.5
+
+    print("Starting autotuning for FlashAttention-V2...")
+    kernel = fast_flashattn_v2(batch, heads, seq_len, dim, is_causal, groups=groups)
+    print(f"Autotuning finished. Best Configuration: {kernel.config}")
+
+    ref_program_processed = partial(ref_program, is_causal=is_causal, groups=groups)
+
+    profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
+
+    print("Verifying correctness...")
+    profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01)
+    print("All checks pass.")
+
+    latency = profiler.do_bench(ref_program_processed, warmup=100)
+    print(f"Reference (PyTorch): {latency:.2f} ms | {total_flops / latency * 1e-9:.2f} TFlops")
+
+    latency = profiler.do_bench(warmup=100)
+    print(
+        f"Fast Flash Attention V2 (Tile-lang): {latency:.2f} ms | {total_flops / latency * 1e-9:.2f} TFlops"
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=1, help='batch size')
+    parser.add_argument('--heads', type=int, default=8, help='heads')
+    parser.add_argument('--seq_len', type=int, default=4096, help='sequence length')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument('--is_causal', action='store_true', help='causal')
+    parser.add_argument('--groups', type=int, default=1, help='groups')
+    args = parser.parse_args()
+    main_v2(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups)
