vllm.model_executor.warmup.deep_gemm_warmup ¶

def _deepgemm_fp8_gemm_nt_warmup(w: torch.Tensor, ws: torch.Tensor, max_tokens: int):
    if w.size() in FP8_GEMM_NT_WARMUP_CACHE:
        return

    n, k = w.size()
    block_m = deep_gemm_block_shape()[0]

    device = w.device
    a1q = torch.empty((max_tokens, k), device=device, dtype=torch.float8_e4m3fn)
    a1q_scales = torch.empty(
        (max_tokens, k // block_m), device=device, dtype=torch.float32
    )
    out = torch.empty((max_tokens, n), device=device, dtype=torch.bfloat16)

    # Use optimal M values only if VLLM_DEEP_GEMM_WARMUP is set to "relax".
    # Otherwise warmup all token sizes to avoid JIT compilation in hotpath
    if envs.VLLM_DEEP_GEMM_WARMUP == "relax":
        m_values = _generate_optimal_warmup_m_values(max_tokens, n, device)
        desc = f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()}) [relaxed]"
    else:
        assert envs.VLLM_DEEP_GEMM_WARMUP == "full", (
            "Expected "
            'VLLM_DEEP_GEMM_WARMUP env to be set to "full" but got '
            f"{envs.VLLM_DEEP_GEMM_WARMUP}"
        )
        m_values = list(range(1, max_tokens + 1))
        desc = f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()}) [all tokens]"

    pbar = tqdm(total=len(m_values), desc=desc)

    for num_tokens in m_values:
        fp8_gemm_nt(
            (a1q[:num_tokens], a1q_scales[:num_tokens]), (w, ws), out[:num_tokens]
        )
        pbar.update(1)

    FP8_GEMM_NT_WARMUP_CACHE.add(w.size())

def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
    w1: torch.Tensor,
    w2: torch.Tensor,
    w1_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    num_topk: int,
    max_tokens: int,
):
    if (
        w1.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE
        and w2.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE
    ):
        return

    assert w1.size(0) == w2.size(0), "w1 and w2 must have the same number of experts"

    block_m = deep_gemm_block_shape()[0]
    num_experts = w1.size(0)
    device = w1.device

    # Assumes all ranks have the same max_num_batched_tokens
    max_tokens_across_dp = get_dp_group().world_size * max_tokens
    max_tokens = min(max_tokens_across_dp, envs.VLLM_FUSED_MOE_CHUNK_SIZE)

    # This is the maximum GroupedGemm M size that we expect to run
    # the grouped_gemm with.
    MAX_M = compute_aligned_M(
        max_tokens, num_topk, num_experts, block_m, expert_tokens_meta=None
    )
    # Distribute expert-ids evenly.
    MAX_BLOCKS = MAX_M // block_m
    expert_ids_block = torch.randint(
        low=0, high=num_experts, size=(MAX_BLOCKS,), device=device, dtype=torch.int32
    )
    expert_ids = torch.repeat_interleave(expert_ids_block, block_m, dim=0)

    def _warmup(w: torch.Tensor, w_scale: torch.Tensor):
        _, n, k = w.size()
        a1q = torch.empty((MAX_M, k), device=device, dtype=torch.float8_e4m3fn)
        a1q_scales = torch.empty(
            (MAX_M, k // block_m), device=device, dtype=torch.float32
        )
        out = torch.empty((MAX_M, n), device=device, dtype=torch.bfloat16)

        # Generate M values in block_m increments (already optimized for MoE)
        m_values = list(range(block_m, MAX_M + 1, block_m))

        pbar = tqdm(
            total=len(m_values),
            desc=f"DeepGemm(m_grouped_fp8_gemm_nt_contiguous) warmup (W={w.size()}) "
            f"[{len(m_values)} values, block_m={block_m}]",
        )

        for num_tokens in m_values:
            m_grouped_fp8_gemm_nt_contiguous(
                (a1q[:num_tokens], a1q_scales[:num_tokens]),
                (w, w_scale),
                out[:num_tokens],
                expert_ids[:num_tokens],
            )
            pbar.update(1)

    for w, ws in [(w1, w1_scale), (w2, w2_scale)]:
        if w.size() not in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE:
            _warmup(w, ws)
            GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE.add(w.size())

def _extract_data_from_fused_moe_module(
    m: torch.nn.Module,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
    """
    Extract weights, weight scales and num_topk from FusedMoE module.
    """
    assert isinstance(m, FusedMoE)
    w13 = m.w13_weight
    w13_s = (
        m.w13_weight_scale_inv
        if hasattr(m, "w13_weight_scale_inv")
        else m.w13_weight_scale
    )
    w2 = m.w2_weight
    w2_s = (
        m.w2_weight_scale_inv
        if hasattr(m, "w2_weight_scale_inv")
        else m.w2_weight_scale
    )
    num_topk = m.top_k

    assert isinstance(w13, torch.Tensor)
    assert isinstance(w13_s, torch.Tensor)
    assert isinstance(w2, torch.Tensor)
    assert isinstance(w2_s, torch.Tensor)
    return w13, w13_s, w2, w2_s, num_topk

def _extract_data_from_linear_base_module(
    m: torch.nn.Module,
) -> tuple[torch.Tensor, torch.Tensor, list[int]]:
    """
    Extract weights, weight scales and quantization block sizes from the given
    LinearBase module.
    """
    assert isinstance(m, LinearBase)
    assert isinstance(m.quant_method, Fp8LinearMethod)
    assert m.quant_method.block_quant
    assert m.quant_method.quant_config is not None

    w = m.weight
    ws = m.weight_scale
    quant_block_size = m.quant_method.quant_config.weight_block_size

    assert isinstance(w, torch.Tensor)
    assert isinstance(ws, torch.Tensor)
    assert quant_block_size is not None
    return (w, ws, quant_block_size)

def _fp8_linear_may_use_deep_gemm(module: torch.nn.Module) -> bool:
    """
    Return True if the input module/layer could be processed with DeepGEMM.
    """
    block_size = deep_gemm_block_shape()[0]
    if not (
        isinstance(module, LinearBase)
        and isinstance(module.quant_method, Fp8LinearMethod)
        and module.quant_method.block_quant
    ):
        return False

    w, _, block_sizes = _extract_data_from_linear_base_module(module)
    return (
        block_sizes == deep_gemm_block_shape()
        and w.ndim == 2
        and w.shape[0] % block_size == 0
        and w.shape[1] % block_size == 0
    )

def _fused_moe_grouped_gemm_may_use_deep_gemm(module: torch.nn.Module) -> bool:
    if not isinstance(module, FusedMoE):
        return False

    moe_quant_config = module.quant_method.get_fused_moe_quant_config(module)

    if (
        moe_quant_config is None
        or moe_quant_config.quant_dtype != torch.float8_e4m3fn
        or moe_quant_config.block_shape != deep_gemm_block_shape()
    ):
        return False

    if not isinstance(module.quant_method.fused_experts, FusedMoEModularKernel):
        # fused_experts could invoke deep_gemm_moe_fp8
        return True

    mk: FusedMoEModularKernel = module.quant_method.fused_experts
    # Further check if the ModularKernel implementation uses the DeepGemmExperts
    return isinstance(mk.fused_experts, (DeepGemmExperts, TritonOrDeepGemmExperts))

def _generate_optimal_warmup_m_values(
    max_tokens: int, n: int, device: torch.device
) -> list[int]:
    """
    Generate M values that cover all possible DeepGEMM kernel configurations.
    Reference: https://github.com/deepseek-ai/DeepGEMM/blob/79f48ee15a82dd5fad5cd9beaa393c1f755e6b55/csrc/jit_kernels/heuristics/common.hpp

    Args:
        max_tokens: Maximum number of tokens to warmup for
        n: The actual N dimension from the weight tensor
        device: The torch device to get properties from.
    """

    def ceil_div(a: int, b: int) -> int:
        return (a + b - 1) // b

    # DeepGEMM's possible block sizes
    block_ms = [64, 128, 256]
    block_ns = list(range(16, min(257, n + 1), 16))
    num_sms = torch.cuda.get_device_properties(device).multi_processor_count

    m_values = set()

    # Always include small cases
    m_values.update([1, 2, 4] + [i for i in range(8, 65, 8)])

    # Collect M values where different wave patterns occur
    for block_m in block_ms:
        for block_n in block_ns:
            if block_n > n:
                continue

            # Add key M boundaries for this block combination
            for wave in range(1, 11):  # Up to 10 waves
                # M where this block config transitions to next wave
                target_blocks = wave * num_sms
                m = target_blocks * block_m // ceil_div(n, block_n)
                if 1 <= m <= max_tokens:
                    m_values.add(m)

            # Add block_m boundaries
            for multiple in range(1, max_tokens // block_m + 1):
                m = multiple * block_m
                if m <= max_tokens:
                    m_values.add(m)

    return sorted(m_values)

def deep_gemm_warmup(model: torch.nn.Module, max_tokens: int):
    deepgemm_fp8_gemm_nt_warmup(model, max_tokens)
    deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model, max_tokens)

def deepgemm_fp8_gemm_nt_warmup(model: torch.nn.Module, max_tokens: int):
    dg_modules = [m for m in model.modules() if _fp8_linear_may_use_deep_gemm(m)]

    for dgm in dg_modules:
        w, ws, _ = _extract_data_from_linear_base_module(dgm)
        _deepgemm_fp8_gemm_nt_warmup(w=w, ws=ws, max_tokens=max_tokens)

def deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
    model: torch.nn.Module, max_tokens: int
):
    dg_modules = [
        m for m in model.modules() if _fused_moe_grouped_gemm_may_use_deep_gemm(m)
    ]

    for dgm in dg_modules:
        w13, w13_scale, w2, w2_scale, num_topk = _extract_data_from_fused_moe_module(
            dgm
        )
        _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
            w13, w2, w13_scale, w2_scale, num_topk, max_tokens
        )