example_mamba_chunk_state.py

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


def chunk_state_triton(B, x, dt, dA_cumsum):
    from mamba_ssm.ops.triton.ssd_chunk_state import _chunk_state_fwd
    return _chunk_state_fwd(B, x, dt, dA_cumsum, states_in_fp32=False)


def ref_program(B, x, dt, dA_cumsum):
    """
    Argument:
        B: (batch, seqlen, ngroups, headdim)
        x: (batch, seqlen, nheads, headdim)
        dt: (batch, nheads, nchunks, chunk_size)
        dA_cumsum: (batch, nheads, nchunks, chunk_size)
    Return:
        states: (batch, nchunks, nheads, headdim, dstate)
    """
    # Check constraints.
    batch, seqlen, nheads, headdim = x.shape
    dstate = B.shape[-1]
    _, _, nchunks, chunk_size = dt.shape
    assert seqlen <= nchunks * chunk_size
    assert x.shape == (batch, seqlen, nheads, headdim)
    assert dt.shape == (batch, nheads, nchunks, chunk_size)
    ngroups = B.shape[2]
    assert nheads % ngroups == 0
    assert B.shape == (batch, seqlen, ngroups, dstate)
    B = repeat(B, "b l g d -> b l (g h) d", h=nheads // ngroups)
    assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size)
    if seqlen < nchunks * chunk_size:
        x = F.pad(x, (0, 0, 0, 0, 0, nchunks * chunk_size - seqlen))
        B = F.pad(B, (0, 0, 0, 0, 0, nchunks * chunk_size - seqlen))
    x = rearrange(x, "b (c l) h p -> b c l h p", l=chunk_size)
    B = rearrange(B, "b (c l) ... -> b c l ...", l=chunk_size)
    decay_states = torch.exp((dA_cumsum[:, :, :, -1:] - dA_cumsum))
    return torch.einsum("bclhn,bhcl,bhcl,bclhp->bchpn", B.to(x.dtype), decay_states.to(x.dtype),
                        dt.to(x.dtype), x)


def get_configs():
    block_M = [64, 128]
    block_N = [32, 64, 128]
    block_K = [32, 64]
    num_stages = [1, 2, 3, 4, 5]
    _configs = list(itertools.product(block_M, block_N, block_K, num_stages))

    configs = [{
        'block_M': c[0],
        'block_N': c[1],
        'block_K': c[2],
        'num_stages': c[3],
        'threads': c[0] * 2
    } for c in _configs]
    return configs


def chunk_state_fwd(batch, seqlen, chunk_size, ngroups, nheads, headdim, dstate, tune=False):
    dtype = "float16"
    accum_dtype = "float"
    nchunks = T.ceildiv(seqlen, chunk_size)
    p = 1.44269504

    def kernel_func(block_M, block_N, block_K, num_stages, threads):

        @T.prim_func
        def main(B: T.Tensor((batch, seqlen, ngroups, dstate), 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), Output: T.Tensor(
                        (batch, nchunks, nheads, headdim, dstate), dtype)):
            with T.Kernel(
                    nheads,
                    T.ceildiv(headdim, block_M) * T.ceildiv(dstate, block_N),
                    batch * nchunks,
                    threads=threads) as (bz, bx, by):
                x_shared = T.alloc_shared((block_K, block_M), dtype)
                x_local = T.alloc_fragment((block_K, block_M), dtype)
                xt_local = T.alloc_fragment((block_M, block_K), dtype)
                B_shared = T.alloc_shared((block_K, block_N), dtype)
                dt_shared = T.alloc_shared((block_K), dtype)
                dA_cumsum_shared = T.alloc_shared((block_K), dtype)
                acc_o = T.alloc_fragment((block_M, block_N), accum_dtype)
                acc_o_shared = T.alloc_shared((block_M, block_N), dtype)
                scale = T.alloc_fragment((block_K), accum_dtype)
                dA_cs_last = T.alloc_fragment((1), accum_dtype)
                dA_cumsum_local = T.alloc_fragment((block_K), accum_dtype)
                dt_local = T.alloc_fragment((block_K), accum_dtype)

                loop_range = T.ceildiv(chunk_size, block_K)

                batch_idx = by % batch
                chunk_idx = by // batch
                m_idx = bx // T.ceildiv(dstate, block_N)
                n_idx = bx % T.ceildiv(dstate, block_N)

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

                dA_cs_last[0] = dA_cumsum[batch_idx, bz, chunk_idx, chunk_size - 1]
                T.clear(acc_o)
                for k in T.Pipelined(loop_range, num_stages=num_stages):
                    T.copy(
                        x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size +
                          (k + 1) * block_K, bz, m_idx * block_M:(m_idx + 1) * block_M], x_shared)
                    T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K],
                           dA_cumsum_shared)
                    T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared)
                    T.copy(dA_cumsum_shared, dA_cumsum_local)
                    T.copy(dt_shared, dt_local)
                    for i in T.Parallel(block_K):
                        scale[i] = T.exp2(dA_cs_last[0] * p - dA_cumsum_local[i] * p) * dt_local[i]
                    T.copy(x_shared, x_local)
                    for i, j in T.Parallel(block_M, block_K):
                        xt_local[i, j] = x_local[j, i] * scale[j]
                    T.copy(
                        B[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size +
                          (k + 1) * block_K, bz // (nheads // ngroups),
                          n_idx * block_N:(n_idx + 1) * block_N], B_shared)
                    T.gemm(xt_local, B_shared, acc_o)
                T.copy(acc_o, acc_o_shared)
                T.copy(
                    acc_o_shared,
                    Output[batch_idx, chunk_idx, bz, m_idx * block_M:(m_idx + 1) * block_M,
                           n_idx * block_N:(n_idx + 1) * block_N])

        return main

    if tune:

        @autotune(
            configs=get_configs(),
            keys=["block_M", "block_N", "block_K", "num_stages", "threads"],
            warmup=10,
            rep=10)
        @jit(out_idx=[4], supply_type=tilelang.TensorSupplyType.Normal, ref_prog=None)
        def kernel(block_M=None, block_N=None, block_K=None, num_stages=None, threads=None):
            return kernel_func(block_M, block_N, block_K, num_stages, threads)

        return kernel()
    else:

        def kernel(block_M, block_N, block_K, num_stages, threads):
            return kernel_func(block_M, block_N, block_K, num_stages, threads)

        return kernel


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 * heads * dim * dstate

    if (not args.tune):
        program = chunk_state_fwd(
            batch, seq_len, chunk_size, groups, heads, dim, dstate, tune=args.tune)(
                block_M=64, block_N=128, block_K=64, num_stages=4, threads=128)
        kernel = tilelang.compile(program, out_idx=[4])
        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:
        best_latency, best_config, _ = chunk_state_fwd(
            batch, seq_len, chunk_size, groups, heads, dim, dstate, tune=args.tune)
        print(f"Best latency: {best_latency}")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
        print(f"Best config: {best_config}")