test_flash_mla_fp8.py

import argparse
import math
import random

import torch
import triton

from flash_mla import flash_mla_with_kvcache_fp8, get_mla_decoding_metadata_dense_fp8
torch.set_printoptions(precision=4, profile="default", sci_mode=False)

def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False, k_scale=1.0):
    query = query.float()
    key = key.float() * k_scale
    value = value.float() * k_scale
    key = key.repeat_interleave(h_q // h_kv, dim=0)
    value = value.repeat_interleave(h_q // h_kv, dim=0)
    attn_weight = query @ key.transpose(-2, -1) / math.sqrt(query.size(-1))
    if is_causal:
        s_q = query.shape[-2]
        s_k = key.shape[-2]
        attn_bias = torch.zeros(s_q, s_k, dtype=query.dtype)
        temp_mask = torch.ones(s_q, s_k, dtype=torch.bool).tril(diagonal=s_k - s_q)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)
        attn_weight += attn_bias
    lse = attn_weight.logsumexp(dim=-1)
    attn_weight = torch.softmax(attn_weight, dim=-1, dtype=torch.float32)
    return attn_weight @ value, lse


def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str) -> None:
    torch_dtype = x.dtype
    x, y = x.double(), y.double()
    RMSE = ((x - y) * (x - y)).mean().sqrt().item()
    cos_diff = 1 - 2 * (x * y).sum().item() / max((x * x + y * y).sum().item(), 1e-12)
    amax_diff = (x - y).abs().max().item()
    print(f"{name}: {cos_diff=}, {RMSE=}, {amax_diff=}")
    assert cos_diff < (1e-4 if torch_dtype == torch.bfloat16 else 1e-5)

@torch.inference_mode()
def test_flash_mla_fp8_e5m2(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, is_prof=False):
    print(
        f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {varlen=}"
    )

    cache_seqlens = torch.full((b,), mean_sk, dtype=torch.int32)
    if varlen:
        for i in range(b):
            cache_seqlens[i] = max(random.normalvariate(mean_sk, mean_sk / 2), s_q)
    total_seqlens = cache_seqlens.sum().item()
    mean_seqlens = cache_seqlens.float().mean().int().item()
    max_seqlen = cache_seqlens.max().item()
    max_seqlen_pad = triton.cdiv(max_seqlen, 256) * 256
    print(f"{total_seqlens=}, {mean_seqlens=}, {max_seqlen=}, {max_seqlen_pad=}")

    q = torch.randn(b, s_q, h_q, d)
    # q = torch.ones(b, s_q, h_q, d)
    block_size = 64
    block_table = torch.arange(
        b * max_seqlen_pad // block_size, dtype=torch.int32
    ).view(b, max_seqlen_pad // block_size)
    
    # blocked_k = torch.randint(low=0, high=4, size = (block_table.numel(), block_size, h_kv, d), dtype = torch.int8)
    # blocked_k = torch.ones(size = (block_table.numel(), block_size, h_kv, d), dtype = torch.int8)
    blocked_k = (torch.randn(block_table.numel(), block_size, h_kv, d)).to(torch.float8_e5m2)
    # blocked_k[0, 0, 0, 56] = 1
    # blocked_k[0, 1, 0, 8] = 2
    # blocked_k[0, 2, 0, 8] = 5
    # blocked_k[0, 3, 0, 8] = 4
    # for i in range(64):
    #     for j in range(64):
    #         blocked_k[0, i, 0, j] = j
            # blocked_k[0, i, 0, j] = (i * 50 + j) % 128
    # print("blocked_k  ", blocked_k[0, 0, 0, 0:10])
    # for i in range(b):
    #     blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = (
    #         -128
    #     )
    blocked_v = blocked_k[..., :dv]

    tile_scheduler_metadata, num_splits = get_mla_decoding_metadata_dense_fp8(
        cache_seqlens, s_q * h_q // h_kv, h_kv
    )

    # print("q:", q.shape, q.dtype, q)
    # print("cache_seqlens:", cache_seqlens.shape, cache_seqlens)
    # print("block_table:", block_table.shape, block_table)
    # print("blocked_k:", blocked_k.shape, blocked_k[0])
    # print("blocked_v:", blocked_v.shape)
    # torch.set_printoptions(precision=4, profile="full", sci_mode=False)
    # print("tile_scheduler_metadata:", tile_scheduler_metadata.shape, tile_scheduler_metadata)
    # torch.set_printoptions(precision=4, profile="default", sci_mode=False)
    # print("num_splits:", num_splits.shape, num_splits)
    # k_scale = torch.tensor(1.0).to(torch.float32).to("cuda:0")  
    # k_scale = torch.tensor(2.1).to(torch.float32).to("cuda:0")  
    descale_q = torch.ones((1), dtype=torch.float32)
    descale_k = torch.ones((1), dtype=torch.float32)
    def flash_mla():
        return flash_mla_with_kvcache_fp8(
            q,
            blocked_k,
            block_table,
            cache_seqlens,
            dv,
            tile_scheduler_metadata,
            num_splits,
            causal=causal,
            descale_q = descale_q,
            descale_k = descale_k,
        )

    def ref_mla():
        out = torch.empty(b, s_q, h_q, dv, dtype=torch.float32)
        lse = torch.empty(b, h_q, s_q, dtype=torch.float32)
        for i in range(b):
            begin = i * max_seqlen_pad
            end = begin + cache_seqlens[i]
            O, LSE = scaled_dot_product_attention(
                q[i].transpose(0, 1),
                blocked_k.view(-1, h_kv, d)[begin:end].transpose(0, 1),
                blocked_v.view(-1, h_kv, dv)[begin:end].transpose(0, 1),
                h_q=h_q,
                h_kv=h_kv,
                is_causal=causal,
                k_scale = k_scale
            )
            out[i] = O.transpose(0, 1)
            lse[i] = LSE
        return out, lse
    out_flash, lse_flash = flash_mla()
    out_torch, lse_torch = ref_mla()

    # print("out_flash ", out_flash[0, 0, 0, 0:14])
    # print("out_torch ", out_torch[0, 0, 0, 0:14])
    # print("lse_flash ", lse_flash[0, 0, 0:10])
    # print("lse_torch ", lse_torch[0, 0, 0:10])

    # print("out max_diff ", (out_flash - out_torch).abs().max())
    # print("lse max_diff ", (lse_flash - lse_torch).abs().max())
    # print(" out ", torch.nonzero((out_flash - out_torch).abs()))
    # print(" out_torch", out_torch)
    cal_diff(lse_flash, lse_torch, "lse")
    cal_diff(out_flash, out_torch, "out")
   
    t = triton.testing.do_bench(flash_mla)
    FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2
    bytes = ( b * s_q * h_q * d + b * s_q * h_q * dv) * (
        torch.finfo(q.dtype).bits // 8
    ) + total_seqlens * h_kv * d
    print(
        f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS, {bytes / 10 ** 6 / t:.0f} GB/s"
    )

def main(torch_dtype, is_prof=False):
    device = torch.device("cuda:0")
    torch.set_default_dtype(torch_dtype)
    torch.set_default_device(device)
    torch.cuda.set_device(device)
    torch.manual_seed(0)
    random.seed(0)
    '''
    h_kv = 1
    d, dv = 576, 512
    causal = True

    for b in [128]:
        for s in [4096, 8192]:
            for h_q in [16, 32, 64, 128]:  # TP = 8, 4, 2, 1
                for s_q in [1, 2]:  # MTP = 1, 2
                    for varlen in [False, True]:
                        test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
    #                b, s_q,    s,   h_q, h_kv,   d,  dv, causal, varlen'''
    # test_flash_mla(  1,   1,  64,    16,    1, 576, 512,   True,  False, is_prof=is_prof)
    # test_flash_mla_fp8( 1,   1, 1000,     1,    1, 576, 512,   True,  False, is_prof=is_prof)
    # test_flash_mla_fp8( 1,   1, 4096,     8,    1, 576, 512,   True,  False, is_prof=is_prof)
    # test_flash_mla_fp8(32,   1, 4096,     16,    1, 576, 512,   False,  False, is_prof=is_prof)
    # '''
    h_kv = 1
    d, dv = 576, 512
    causal = True

    # for b in [1, 32]:
    #     for s in [200, 1002, 2002, 1024, 2000, 4000, 32768, 65536]:
    #         for h_q in [4, 16, 32, 64]:
    #             for s_q in [1, 2]:  # MTP = 1, 2
    #                 for varlen in [True]:
    #                     test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
    # for b in [32]:
    #     for s in [16384, 32768, 65536*2]:
    #         for h_q in [16]:
    #             for s_q in [1]:  # MTP = 1, 2
    #                 for varlen in [False]:
    #                 # for varlen in [True]:
    #                     test_flash_mla_fp8_e5m2(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
                        # test_flash_mla_fp8_e4m3(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
                        
    # '''
    for b in [3, 6, 9, 12, 15, 18, 21, 24]:
        for s in [111, 112, 123, 1234, 432, 4325, 4000, 8192, 11111]:
            for h_q in [16, 128]:
                for s_q in [1, 2, 3]:  # MTP = 1, 2
                    for varlen in [False, True]:
                        test_flash_mla_fp8_e5m2(b, s_q, s, h_q, h_kv, d, dv, causal, varlen,True)
    for b in [3, 6, 9, 12, 15, 18, 21, 24, 32, 64, 128, 256]:
        for s in [4000]:
            for h_q in [16, 128]:
                for s_q in [1]:  # MTP = 1, 2
                    for varlen in [False]:
                        test_flash_mla_fp8_e5m2(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
    for b in [3, 6, 9, 12, 15, 18, 21, 24, 32, 64, 128, 256]:
        for s in [4000]:
            for h_q in [16]:
                for s_q in [1, 2, 3, 4]:  # MTP = 1, 2
                    for varlen in [False]:
                        test_flash_mla_fp8_e5m2(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--dtype",
        type=str,
        choices=["bf16", "fp16"],
        default="bf16",
        help="Data type to use for testing (bf16 or fp16)",
    )
    parser.add_argument('--prof', default=False, action='store_true', help='prof or not')

    args = parser.parse_args()

    torch_dtype = torch.bfloat16
    if args.dtype == "fp16":
        torch_dtype = torch.float16

    main(torch_dtype, args.prof)