example_mamba_chunk_scan.py

import argparse
import torch
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
from einops import rearrange, repeat
import itertools


def chunk_scan_triton(cb, x, dt, dA_cumsum, C, states, D):
    from mamba_ssm.ops.triton.ssd_chunk_scan import _chunk_scan_fwd
    out, _ = _chunk_scan_fwd(cb, x, dt, dA_cumsum, C, states, D)
    return out


def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D):
    """
    Argument:
        cb: (batch, nchunks, ngroups, chunk_size, chunk_size)
        x: (batch, seqlen, nheads, headdim)
        dt: (batch, nheads, nchunks, chunk_size)
        dA_cumsum: (batch, nheads, nchunks, chunk_size)
        C: (batch, seqlen, ngroups, dstate)
        prev_states: (batch, nchunks, nheads, headdim, dstate)
        D: (nheads, headdim) or (nheads,)
        z: (batch, seqlen, nheads, headdim)
    Return:
        out: (batch, seqlen, nheads, headdim)
    """
    _, _, ngroups, _, _ = cb.shape
    batch, seqlen, nheads, headdim = x.shape
    # _, _, ngroups, dstate = B.shape
    # assert B.shape == (batch, seqlen, ngroups, dstate)
    _, _, nchunks, chunk_size = dt.shape
    assert seqlen == nchunks * chunk_size
    # assert C.shape == B.shape
    # B = repeat(B, "b l g d -> b l (g h) d", h=nheads // ngroups)
    C = repeat(C, "b l g d -> b l (g h) d", h=nheads // ngroups)
    cb = repeat(cb, "b c g l s -> b c (g h) l s", h=nheads // ngroups)
    # CB = torch.einsum("bclhn,bcshn->bchls", rearrange(C, "b (c l) h n -> b c l h n", c=nchunks),
    #                   rearrange(B, "b (c s) h n -> b c s h n", c=nchunks))
    # (batch, nheads, nchunks, chunksize, chunksize)
    dt_segment_sum = dA_cumsum[:, :, :, :, None] - dA_cumsum[:, :, :, None, :]
    decay = torch.exp(dt_segment_sum)
    scores_decay = cb * rearrange(decay, "b h c l s -> b c h l s")
    causal_mask = torch.tril(
        torch.ones(chunk_size, chunk_size, device=x.device, dtype=bool), diagonal=0)
    scores_decay = scores_decay.masked_fill(~causal_mask, 0)
    out = torch.einsum('bchls,bhcs,bcshp->bclhp', scores_decay.to(x.dtype), dt.to(x.dtype),
                       rearrange(x, "b (c s) h p -> b c s h p", c=nchunks))
    state_decay_out = torch.exp(rearrange(dA_cumsum, "b h c l -> b c l h 1"))
    out_prev = torch.einsum('bclhn,bchpn->bclhp', rearrange(
        C, "b (c l) h n -> b c l h n", c=nchunks), prev_states.to(C.dtype)) * state_decay_out
    out = out + out_prev
    out = rearrange(out, "b c l h p -> b (c l) h p")
    if D is not None:
        if D.dim() == 1:
            D = rearrange(D, "h -> h 1")
        out = out + x * D
    return out


def get_configs():
    iter_params = dict(
        block_M=[64, 128, 256],
        block_N=[32, 64],
        block_K=[64, 128, 256],
        block_Dstate=[128],
        num_stages=[1, 2, 3, 4, 5])
    return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]


@autotune(configs=get_configs(), warmup=10, rep=10)
@tilelang.jit(out_idx=[7])
def chunk_scan_fwd(batch,
                   seqlen,
                   chunk_size,
                   ngroups,
                   nheads,
                   headdim,
                   dstate,
                   block_M=64,
                   block_N=64,
                   block_K=64,
                   block_Dstate=128,
                   num_stages=2,
                   threads=128):
    dtype = "float16"
    accum_dtype = "float"
    nchunks = T.ceildiv(seqlen, chunk_size)
    p = 1.44269504

    @T.prim_func
    def main(cb: T.Tensor((batch, nchunks, ngroups, chunk_size, chunk_size), dtype), x: T.Tensor(
        (batch, seqlen, nheads, headdim), dtype), dt: T.Tensor(
            (batch, nheads, nchunks, chunk_size), dtype), dA_cumsum: T.Tensor(
                (batch, nheads, nchunks, chunk_size), dtype),
             C: T.Tensor((batch, seqlen, ngroups, dstate), dtype), prev_states: T.Tensor(
                 (batch, nchunks, nheads, headdim, dstate), dtype), D: T.Tensor(
                     (nheads), dtype), Output: T.Tensor((batch, seqlen, nheads, headdim), dtype)):
        with T.Kernel(
                nheads,
                T.ceildiv(chunk_size, block_M) * T.ceildiv(headdim, block_N),
                batch * nchunks,
                threads=threads) as (bz, bx, by):
            acc_o = T.alloc_fragment((block_M, block_N), accum_dtype)
            acc_o_shared = T.alloc_shared((block_M, block_N), dtype)
            cb_shared = T.alloc_shared((block_M, block_K), dtype, scope="shared.dyn")
            cb_local = T.alloc_fragment((block_M, block_K), dtype)
            dA_cs_k_shared = T.alloc_shared((block_K), dtype, scope="shared")
            dA_cs_k_local = T.alloc_fragment((block_K), accum_dtype)
            dA_cs_m_local = T.alloc_fragment((block_M), accum_dtype)
            dt_shared = T.alloc_shared((block_K), dtype, scope="shared")
            dt_local = T.alloc_fragment((block_K), accum_dtype)
            x_shared = T.alloc_shared((block_K, block_N), dtype, scope="shared.dyn")
            dA_cs_m_shared = T.alloc_shared((block_M), dtype, scope="shared")
            scale_m_local = T.alloc_fragment((block_M), accum_dtype)
            C_shared = T.alloc_shared((block_M, block_Dstate), dtype)
            prev_state_shared = T.alloc_shared((block_N, block_Dstate), dtype)
            D_local = T.alloc_fragment((1), accum_dtype)
            x_residual_shared = T.alloc_shared((block_M, block_N), dtype, scope="shared.dyn")
            x_residual_local = T.alloc_fragment((block_M, block_N), accum_dtype)

            batch_idx = by % batch
            chunk_idx = by // batch
            # m: chunk_size
            # n : headdim
            m_idx = bx // T.ceildiv(headdim, block_N)
            n_idx = bx % T.ceildiv(headdim, block_N)

            T.annotate_layout({
                acc_o_shared: tilelang.layout.make_swizzled_layout(acc_o_shared),
                cb_shared: tilelang.layout.make_swizzled_layout(cb_shared),
                x_residual_shared: tilelang.layout.make_swizzled_layout(x_residual_shared)
            })

            T.copy(dA_cumsum[batch_idx, bz, chunk_idx, m_idx * block_M:(m_idx + 1) * block_M],
                   dA_cs_m_shared)
            T.copy(dA_cs_m_shared, dA_cs_m_local)
            T.clear(acc_o)

            for i in T.Parallel(block_M):
                scale_m_local[i] = T.exp2(dA_cs_m_local[i] * p)
            T.copy(
                C[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size +
                  (m_idx + 1) * block_M, bz // (nheads // ngroups), 0:block_Dstate], C_shared)
            T.copy(
                prev_states[batch_idx, chunk_idx, bz, n_idx * block_N:(n_idx + 1) * block_N,
                            0:block_Dstate], prev_state_shared)
            T.gemm(C_shared, prev_state_shared, acc_o, transpose_B=True)
            for i, j in T.Parallel(block_M, block_N):
                acc_o[i, j] *= scale_m_local[i]

            loop_range = T.ceildiv((m_idx + 1) * block_M, block_K)

            for k in T.Pipelined(loop_range, num_stages=num_stages):
                T.copy(
                    cb[batch_idx, chunk_idx, bz // (nheads // ngroups),
                       m_idx * block_M:(m_idx + 1) * block_M, k * block_K:(k + 1) * block_K],
                    cb_shared)
                T.copy(cb_shared, cb_local)
                T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K],
                       dA_cs_k_shared)
                T.copy(dA_cs_k_shared, dA_cs_k_local)
                for i, j in T.Parallel(block_M, block_K):
                    cb_local[i,
                             j] = cb_local[i,
                                           j] * T.exp2(dA_cs_m_local[i] * p - dA_cs_k_local[j] * p)
                T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared)
                T.copy(dt_shared, dt_local)
                for i, j in T.Parallel(block_M, block_K):
                    cb_local[i, j] *= dt_local[j]
                for i, j in T.Parallel(block_M, block_K):
                    cb_local[i, j] = T.if_then_else(m_idx * block_M + i >= k * block_K + j,
                                                    cb_local[i, j], 0)
                T.copy(
                    x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size +
                      (k + 1) * block_K, bz, n_idx * block_N:(n_idx + 1) * block_N], x_shared)
                T.gemm(cb_local, x_shared, acc_o)

            D_local[0] = D[bz]
            T.copy(
                x[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size +
                  (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N],
                x_residual_shared)
            T.copy(x_residual_shared, x_residual_local)
            for i, j in T.Parallel(block_M, block_N):
                acc_o[i, j] += x_residual_local[i, j] * D_local[0]

            T.copy(acc_o, acc_o_shared)
            T.copy(
                acc_o_shared,
                Output[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size +
                       (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N])

    return main


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch', type=int, default=8, help='batch size')
    parser.add_argument('--heads', type=int, default=80, help='heads')
    parser.add_argument('--groups', type=int, default=1, help='groups')
    parser.add_argument('--seq_len', type=int, default=4096, help='sequence length')
    parser.add_argument('--chunk_size', type=int, default=256, help='chunk size')
    parser.add_argument('--dim', type=int, default=64, help='dim')
    parser.add_argument('--dstate', type=int, default=128, help='dstate')
    parser.add_argument('--tune', action='store_true', help='tune configs')
    args = parser.parse_args()
    batch, heads, groups, seq_len, chunk_size, dim, dstate = args.batch, args.heads, args.groups, args.seq_len, args.chunk_size, args.dim, args.dstate
    total_flops = 2 * batch * seq_len * chunk_size * heads * dim * 0.5 + 2 * batch * seq_len * heads * dim * dstate

    if (not args.tune):
        kernel = chunk_scan_fwd(
            batch,
            seq_len,
            chunk_size,
            groups,
            heads,
            dim,
            dstate,
            block_M=64,
            block_N=64,
            block_K=64,
            block_Dstate=128,
            num_stages=2,
            threads=128)
        profiler = kernel.get_profiler(tilelang.TensorSupplyType.Normal)
        profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)
        print("All checks pass.")
        latency = profiler.do_bench(ref_program, warmup=500)
        print("Ref: {:.2f} ms".format(latency))
        print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
        latency = profiler.do_bench(warmup=500)
        print("Tile-lang: {:.2f} ms".format(latency))
        print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
    else:
        kernel = chunk_scan_fwd(batch, seq_len, chunk_size, groups, heads, dim, dstate)
        best_latency = kernel.latency
        best_config = kernel.config
        ref_latency = kernel.ref_latency
        print(f"Best latency: {best_latency}")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
        print(f"Best config: {best_config}")