Revert "[Enhancement] Add flash attn example for AMD MI300 series(#671)" (#672)

This reverts commit e8cc372f.

examples/amd/example_amd_flash_attn_fwd.py

-# 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)