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vllm.model_executor.layers.fused_moe.fused_marlin_moe

Fused MoE utilities for GPTQ.

fused_marlin_moe 

fused_marlin_moe(
    hidden_states: Tensor,
    w1: Tensor,
    w2: Tensor,
    w1_scale: Tensor,
    w2_scale: Tensor,
    gating_output: Tensor,
    topk_weights: Tensor,
    topk_ids: Tensor,
    quant_type_id: int,
    apply_router_weight_on_input: bool = False,
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    global_scale1: Optional[Tensor] = None,
    global_scale2: Optional[Tensor] = None,
    g_idx1: Optional[Tensor] = None,
    g_idx2: Optional[Tensor] = None,
    sort_indices1: Optional[Tensor] = None,
    sort_indices2: Optional[Tensor] = None,
    w1_zeros: Optional[Tensor] = None,
    w2_zeros: Optional[Tensor] = None,
    workspace: Optional[Tensor] = None,
    is_k_full: bool = True,
    inplace: bool = False,
) -> Tensor

This function computes a Mixture of Experts (MoE) layer using two sets of weights, w1 and w2, and top-k gating mechanism.

Parameters: - hidden_states (torch.Tensor): The input tensor to the MoE layer. - w1 (torch.Tensor): The first set of expert weights. - w2 (torch.Tensor): The second set of expert weights. - w1_scale (torch.Tensor): Scale to be used for w1. - w2_scale (torch.Tensor): Scale to be used for w2. - gating_output (torch.Tensor): The output of the gating operation (before softmax). - g_idx1 (Optional[torch.Tensor]): The first set of act_order indices. - g_idx2 (Optional[torch.Tensor]): The second set of act_order indices. - sort_indices1 (Optional[torch.Tensor]): The first act_order input permutation. - sort_indices2 (Optional[torch.Tensor]): The second act_order input permutation. - topk_weights (torch.Tensor): Top-k weights. - topk_ids (torch.Tensor): Indices of topk-k elements. - w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1. - w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2. - num_bits (bool): The number of bits in expert weights quantization.

Returns: - torch.Tensor: The output tensor after applying the MoE layer.

Source code in vllm/model_executor/layers/fused_moe/fused_marlin_moe.py 
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def fused_marlin_moe(hidden_states: torch.Tensor,
                     w1: torch.Tensor,
                     w2: torch.Tensor,
                     w1_scale: torch.Tensor,
                     w2_scale: torch.Tensor,
                     gating_output: torch.Tensor,
                     topk_weights: torch.Tensor,
                     topk_ids: torch.Tensor,
                     quant_type_id: int,
                     apply_router_weight_on_input: bool = False,
                     global_num_experts: int = -1,
                     expert_map: Optional[torch.Tensor] = None,
                     global_scale1: Optional[torch.Tensor] = None,
                     global_scale2: Optional[torch.Tensor] = None,
                     g_idx1: Optional[torch.Tensor] = None,
                     g_idx2: Optional[torch.Tensor] = None,
                     sort_indices1: Optional[torch.Tensor] = None,
                     sort_indices2: Optional[torch.Tensor] = None,
                     w1_zeros: Optional[torch.Tensor] = None,
                     w2_zeros: Optional[torch.Tensor] = None,
                     workspace: Optional[torch.Tensor] = None,
                     is_k_full: bool = True,
                     inplace: bool = False) -> torch.Tensor:
    """
    This function computes a Mixture of Experts (MoE) layer using two sets of
    weights, w1 and w2, and top-k gating mechanism.

    Parameters:
    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
    - w1 (torch.Tensor): The first set of expert weights.
    - w2 (torch.Tensor): The second set of expert weights.
    - w1_scale (torch.Tensor): Scale to be used for w1.
    - w2_scale (torch.Tensor): Scale to be used for w2.
    - gating_output (torch.Tensor): The output of the gating operation
        (before softmax).
    - g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.
    - g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.
    - sort_indices1 (Optional[torch.Tensor]): The first act_order input
        permutation.
    - sort_indices2 (Optional[torch.Tensor]): The second act_order input
        permutation.
    - topk_weights (torch.Tensor): Top-k weights.
    - topk_ids (torch.Tensor): Indices of topk-k elements.
    - w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.
    - w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.
    - num_bits (bool): The number of bits in expert weights quantization.

    Returns:
    - torch.Tensor: The output tensor after applying the MoE layer.
    """
    quant_type = ScalarType.from_id(quant_type_id)
    assert quant_type in [
        scalar_types.uint4, scalar_types.uint8b128, scalar_types.uint4b8,
        scalar_types.float8_e4m3fn, scalar_types.float4_e2m1f
    ]

    bit4_scalar_types = [
        scalar_types.uint4, scalar_types.uint4b8, scalar_types.float4_e2m1f
    ]
    num_bits = 4 if quant_type in bit4_scalar_types else 8

    # Check constraints.
    assert hidden_states.shape[0] == gating_output.shape[
        0], "Number of tokens mismatch"
    assert hidden_states.shape[
        1] == w1.shape[1] * 16, "Hidden size mismatch w1"
    assert hidden_states.shape[1] == w2.shape[2] // (
        num_bits // 2), "Hidden size mismatch w2"
    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
    assert hidden_states.dtype in [torch.float16, torch.bfloat16]
    assert num_bits in [4, 8]

    M, K = hidden_states.shape
    E = w1.shape[0]
    N = w2.shape[1] * 16
    topk = topk_ids.shape[1]

    get_config_func = functools.partial(
        try_get_optimal_moe_config,
        w1.shape,
        w2.shape,
        topk_ids.shape[1],
        None,
        is_marlin=True,
    )
    config = get_config_func(M)

    block_size_m = config["BLOCK_SIZE_M"]

    if global_num_experts == -1:
        global_num_experts = E
    sorted_token_ids, expert_ids, num_tokens_post_padded = \
        moe_align_block_size(topk_ids, block_size_m, global_num_experts,
                             expert_map)

    if workspace is None:
        workspace = marlin_make_workspace_new(hidden_states.device, 4)

    intermediate_cache2 = torch.empty(
        (M * topk_ids.shape[1], N),
        device=hidden_states.device,
        dtype=hidden_states.dtype,
    )
    intermediate_cache13 = torch.empty(
        (M * topk_ids.shape[1] * max(2 * N, K), ),
        device=hidden_states.device,
        dtype=hidden_states.dtype,
    )
    intermediate_cache1 = intermediate_cache13[:M * topk_ids.shape[1] * 2 * N]
    intermediate_cache1 = intermediate_cache1.view(-1, 2 * N)
    intermediate_cache3 = intermediate_cache13[:M * topk_ids.shape[1] * K]
    intermediate_cache3 = intermediate_cache3.view(-1, K)

    maybe_warn_marlin_atomic_add(hidden_states.device, hidden_states.dtype)
    use_atomic_add = hidden_states.dtype == torch.half or \
        torch.cuda.get_device_capability(hidden_states.device)[0] >= 9

    intermediate_cache1 = ops.moe_wna16_marlin_gemm(
        hidden_states,
        intermediate_cache1,
        w1,
        w1_scale,
        global_scale1,
        w1_zeros,
        g_idx1,
        sort_indices1,
        workspace,
        sorted_token_ids,
        expert_ids,
        num_tokens_post_padded,
        topk_weights,
        moe_block_size=block_size_m,
        top_k=topk,
        mul_topk_weights=apply_router_weight_on_input,
        is_ep=expert_map is not None,
        b_q_type=quant_type,
        size_m=M,
        size_n=2 * N,
        size_k=K,
        is_k_full=is_k_full,
        use_atomic_add=use_atomic_add,
        use_fp32_reduce=True,
        is_zp_float=False)

    torch.ops._C.silu_and_mul(intermediate_cache2,
                              intermediate_cache1.view(-1, 2 * N))

    if expert_map is not None:
        intermediate_cache3.zero_()

    intermediate_cache3 = ops.moe_wna16_marlin_gemm(
        intermediate_cache2,
        intermediate_cache3,
        w2,
        w2_scale,
        global_scale2,
        w2_zeros,
        g_idx2,
        sort_indices2,
        workspace,
        sorted_token_ids,
        expert_ids,
        num_tokens_post_padded,
        topk_weights,
        moe_block_size=block_size_m,
        top_k=1,
        mul_topk_weights=not apply_router_weight_on_input,
        is_ep=expert_map is not None,
        b_q_type=quant_type,
        size_m=M * topk,
        size_n=K,
        size_k=N,
        is_k_full=is_k_full,
        use_atomic_add=use_atomic_add,
        use_fp32_reduce=True,
        is_zp_float=False).view(-1, topk, K)

    output = hidden_states if inplace else torch.empty_like(hidden_states)
    return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape),
                     dim=1,
                     out=output)

fused_marlin_moe_fake 

fused_marlin_moe_fake(
    hidden_states: Tensor,
    w1: Tensor,
    w2: Tensor,
    w1_scale: Tensor,
    w2_scale: Tensor,
    gating_output: Tensor,
    topk_weights: Tensor,
    topk_ids: Tensor,
    quant_type_id: int,
    apply_router_weight_on_input: bool = False,
    global_num_experts: int = -1,
    global_scale1: Optional[Tensor] = None,
    global_scale2: Optional[Tensor] = None,
    expert_map: Optional[Tensor] = None,
    g_idx1: Optional[Tensor] = None,
    g_idx2: Optional[Tensor] = None,
    sort_indices1: Optional[Tensor] = None,
    sort_indices2: Optional[Tensor] = None,
    w1_zeros: Optional[Tensor] = None,
    w2_zeros: Optional[Tensor] = None,
    workspace: Optional[Tensor] = None,
    is_k_full: bool = True,
    inplace: bool = False,
) -> Tensor
Source code in vllm/model_executor/layers/fused_moe/fused_marlin_moe.py 
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def fused_marlin_moe_fake(hidden_states: torch.Tensor,
                          w1: torch.Tensor,
                          w2: torch.Tensor,
                          w1_scale: torch.Tensor,
                          w2_scale: torch.Tensor,
                          gating_output: torch.Tensor,
                          topk_weights: torch.Tensor,
                          topk_ids: torch.Tensor,
                          quant_type_id: int,
                          apply_router_weight_on_input: bool = False,
                          global_num_experts: int = -1,
                          global_scale1: Optional[torch.Tensor] = None,
                          global_scale2: Optional[torch.Tensor] = None,
                          expert_map: Optional[torch.Tensor] = None,
                          g_idx1: Optional[torch.Tensor] = None,
                          g_idx2: Optional[torch.Tensor] = None,
                          sort_indices1: Optional[torch.Tensor] = None,
                          sort_indices2: Optional[torch.Tensor] = None,
                          w1_zeros: Optional[torch.Tensor] = None,
                          w2_zeros: Optional[torch.Tensor] = None,
                          workspace: Optional[torch.Tensor] = None,
                          is_k_full: bool = True,
                          inplace: bool = False) -> torch.Tensor:
    return torch.empty_like(hidden_states)