vllm.model_executor.layers.fused_moe.modular_kernel

FusedMoEModularKernel

Bases: Module

This class combines a FusedMoEPrepareAndFinalize instance and a FusedMoEPermuteExpertsUnpermute to provide an interface that is compatible with the fused_experts function in fused_moe.py.

It takes care of managing any required scratch space.

Note: Instances of this class should only be used for a single model layer due to any layer specific state that may be used by the component objects.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

class FusedMoEModularKernel(torch.nn.Module):
    """
    This class combines a FusedMoEPrepareAndFinalize instance and
    a FusedMoEPermuteExpertsUnpermute to provide an interface that
    is compatible with the `fused_experts` function in fused_moe.py.

    It takes care of managing any required scratch space.

    Note: Instances of this class should only be used for a single model
    layer due to any layer specific state that may be used by the component
    objects.
    """

    def __init__(
        self,
        prepare_finalize: FusedMoEPrepareAndFinalize,
        fused_experts: FusedMoEPermuteExpertsUnpermute,
    ):
        super().__init__()
        self.prepare_finalize = prepare_finalize
        self.fused_experts = fused_experts

    def forward(
        self,
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        inplace: bool = False,
        activation: str = "silu",
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        w1_zp: Optional[torch.Tensor] = None,
        w2_zp: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: 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.
        - topk_weights (torch.Tensor): The topk weights applied at the end of
          the layer.
        - topk_ids (torch.Tensor): A map of row to expert id.
        - inplace (bool): If True, perform the operation in-place.
          Defaults to False.
        - activation (str): The activation function to apply after the first
          MoE layer.
        - global_num_experts (int): The total number of experts in the global
          expert space.
        - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
          from the global expert space to the local expert space of the expert
          parallel shard.
        - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1.
        - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2.
        - w1_zp (Optional[torch.Tensor]): Optional zero points to be used for
          w1.
        - w2_zp (Optional[torch.Tensor]): Optional zero points to be used for
          w2.
        - a1_scale (Optional[torch.Tensor]): Optional scale to be used for a1.
        - a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2.
        - apply_router_weight_on_input (bool): When true, the topk weights are
          applied directly on the inputs. This is only applicable when topk is
          1.

        Returns:
        - torch.Tensor: The output tensor after applying the MoE layer.
        """
        a1 = hidden_states
        E, M, N, K, top_k = _moe_problem_size(a1, w1, w2, topk_ids)

        if global_num_experts == -1:
            global_num_experts = E

        output = a1 if inplace else torch.zeros_like(a1)

        workspace13_shape, workspace2_shape, workspace_dtype = (
            self.fused_experts.workspace_shapes(a1, M, N, K, top_k,
                                                global_num_experts))

        # We can reuse the memory between cache1 and cache3 because by the time
        # we need cache3, we're done with cache1
        workspace13 = torch.zeros(workspace13_shape,
                                  device=a1.device,
                                  dtype=workspace_dtype)
        workspace2 = torch.zeros(workspace2_shape,
                                 device=a1.device,
                                 dtype=workspace_dtype)

        a1q, a1q_scale, expert_num_tokens = self.prepare_finalize.prepare(
            a1, a1_scale, a2_scale, topk_weights, topk_ids, global_num_experts,
            expert_map, apply_router_weight_on_input)

        fused_out = self.fused_experts.apply(
            a1q,
            w1,
            w2,
            topk_ids,
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=w1_scale,
            w2_scale=w2_scale,
            w1_zp=w1_zp,
            w2_zp=w2_zp,
            a1q_scale=a1q_scale,
            a2_scale=a2_scale,
            workspace13=workspace13,
            workspace2=workspace2,
            expert_num_tokens=expert_num_tokens,
        )

        self.prepare_finalize.finalize(output, fused_out, topk_weights,
                                       topk_ids, apply_router_weight_on_input)

        return output

fused_experts instance-attribute

fused_experts = fused_experts

prepare_finalize instance-attribute

prepare_finalize = prepare_finalize

__init__

__init__(
    prepare_finalize: FusedMoEPrepareAndFinalize,
    fused_experts: FusedMoEPermuteExpertsUnpermute,
)

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

def __init__(
    self,
    prepare_finalize: FusedMoEPrepareAndFinalize,
    fused_experts: FusedMoEPermuteExpertsUnpermute,
):
    super().__init__()
    self.prepare_finalize = prepare_finalize
    self.fused_experts = fused_experts

forward

forward(
    hidden_states: Tensor,
    w1: Tensor,
    w2: Tensor,
    topk_weights: Tensor,
    topk_ids: Tensor,
    inplace: bool = False,
    activation: str = "silu",
    global_num_experts: int = -1,
    expert_map: Optional[Tensor] = None,
    w1_scale: Optional[Tensor] = None,
    w2_scale: Optional[Tensor] = None,
    w1_zp: Optional[Tensor] = None,
    w2_zp: Optional[Tensor] = None,
    a1_scale: Optional[Tensor] = None,
    a2_scale: Optional[Tensor] = None,
    apply_router_weight_on_input: 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. - topk_weights (torch.Tensor): The topk weights applied at the end of the layer. - topk_ids (torch.Tensor): A map of row to expert id. - inplace (bool): If True, perform the operation in-place. Defaults to False. - activation (str): The activation function to apply after the first MoE layer. - global_num_experts (int): The total number of experts in the global expert space. - expert_map (Optional[torch.Tensor]): A tensor mapping expert indices from the global expert space to the local expert space of the expert parallel shard. - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1. - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2. - w1_zp (Optional[torch.Tensor]): Optional zero points to be used for w1. - w2_zp (Optional[torch.Tensor]): Optional zero points to be used for w2. - a1_scale (Optional[torch.Tensor]): Optional scale to be used for a1. - a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2. - apply_router_weight_on_input (bool): When true, the topk weights are applied directly on the inputs. This is only applicable when topk is 1.

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

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

def forward(
    self,
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    inplace: bool = False,
    activation: str = "silu",
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    w1_scale: Optional[torch.Tensor] = None,
    w2_scale: Optional[torch.Tensor] = None,
    w1_zp: Optional[torch.Tensor] = None,
    w2_zp: Optional[torch.Tensor] = None,
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    apply_router_weight_on_input: 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.
    - topk_weights (torch.Tensor): The topk weights applied at the end of
      the layer.
    - topk_ids (torch.Tensor): A map of row to expert id.
    - inplace (bool): If True, perform the operation in-place.
      Defaults to False.
    - activation (str): The activation function to apply after the first
      MoE layer.
    - global_num_experts (int): The total number of experts in the global
      expert space.
    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
      from the global expert space to the local expert space of the expert
      parallel shard.
    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1.
    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2.
    - w1_zp (Optional[torch.Tensor]): Optional zero points to be used for
      w1.
    - w2_zp (Optional[torch.Tensor]): Optional zero points to be used for
      w2.
    - a1_scale (Optional[torch.Tensor]): Optional scale to be used for a1.
    - a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2.
    - apply_router_weight_on_input (bool): When true, the topk weights are
      applied directly on the inputs. This is only applicable when topk is
      1.

    Returns:
    - torch.Tensor: The output tensor after applying the MoE layer.
    """
    a1 = hidden_states
    E, M, N, K, top_k = _moe_problem_size(a1, w1, w2, topk_ids)

    if global_num_experts == -1:
        global_num_experts = E

    output = a1 if inplace else torch.zeros_like(a1)

    workspace13_shape, workspace2_shape, workspace_dtype = (
        self.fused_experts.workspace_shapes(a1, M, N, K, top_k,
                                            global_num_experts))

    # We can reuse the memory between cache1 and cache3 because by the time
    # we need cache3, we're done with cache1
    workspace13 = torch.zeros(workspace13_shape,
                              device=a1.device,
                              dtype=workspace_dtype)
    workspace2 = torch.zeros(workspace2_shape,
                             device=a1.device,
                             dtype=workspace_dtype)

    a1q, a1q_scale, expert_num_tokens = self.prepare_finalize.prepare(
        a1, a1_scale, a2_scale, topk_weights, topk_ids, global_num_experts,
        expert_map, apply_router_weight_on_input)

    fused_out = self.fused_experts.apply(
        a1q,
        w1,
        w2,
        topk_ids,
        activation=activation,
        global_num_experts=global_num_experts,
        expert_map=expert_map,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        w1_zp=w1_zp,
        w2_zp=w2_zp,
        a1q_scale=a1q_scale,
        a2_scale=a2_scale,
        workspace13=workspace13,
        workspace2=workspace2,
        expert_num_tokens=expert_num_tokens,
    )

    self.prepare_finalize.finalize(output, fused_out, topk_weights,
                                   topk_ids, apply_router_weight_on_input)

    return output

FusedMoEPermuteExpertsUnpermute

Bases: ABC

An abstract base class for the [Permute-Experts-Unpermute] step described above.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

class FusedMoEPermuteExpertsUnpermute(ABC):
    """
    An abstract base class for the [Permute-Experts-Unpermute] step described
    above.
    """

    @abstractmethod
    def workspace_shapes(
        self,
        a: torch.Tensor,
        M: int,
        N: int,
        K: int,
        topk: int,
        num_experts: int,
    ) -> tuple[int, int, torch.dtype]:
        """
        Compute the number of elements for the temporary outputs of the two
        gemms and activation in the fused expert function.  Since the
        gemms are independent, the workspace for the first gemm can be shared
        with the workspace for the last gemm.

        Returns a tuple of:
        - Number of workspace13 elements: must be large enough to hold the
          result of either expert gemm.
        - Number of workspace2 elements: must be large enough to hold the
          result of the activation function.
        - Workspace type: The dtype to use for the workspace tensors.
        """
        raise NotImplementedError

    def activation(self, activation: str, output: torch.Tensor,
                   input: torch.Tensor) -> None:
        assert output.size(-1) * 2 == input.size(-1)
        if activation == "silu":
            torch.ops._C.silu_and_mul(output, input)
        elif activation == "gelu":
            torch.ops._C.gelu_and_mul(output, input)
        else:
            raise ValueError(f"Unsupported FusedMoe activation: {activation}")

    @abstractmethod
    def apply(
        self,
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_ids: torch.Tensor,
        activation: str,
        global_num_experts: int,
        expert_map: Optional[torch.Tensor],
        w1_scale: Optional[torch.Tensor],
        w2_scale: Optional[torch.Tensor],
        w1_zp: Optional[torch.Tensor],
        w2_zp: Optional[torch.Tensor],
        a1q_scale: Optional[torch.Tensor],
        a2_scale: Optional[torch.Tensor],
        workspace13: torch.Tensor,
        workspace2: torch.Tensor,
        expert_num_tokens: Optional[torch.Tensor],
    ) -> torch.Tensor:
        """
        This function computes the intermediate result of a Mixture of Experts
        (MoE) layer using two sets of weights, w1 and w2.

        Parameters:
        - hidden_states: (torch.Tensor): The (quantized) input tensor to the MoE
          layer.
        - w1 (torch.Tensor): The first set of expert weights.
        - w2 (torch.Tensor): The second set of expert weights.
        - topk_ids (torch.Tensor): A map of row to expert id.
        - activation (str): The activation function to apply after the first
          MoE layer.
        - global_num_experts (int): The total number of experts in the global
          expert space.
        - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
          from the global expert space to the local expert space of the expert
          parallel shard.
        - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1.
        - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2.
        - w1_zp (Optional[torch.Tensor]): Optional zero points to be used for
          w1.
        - w2_zp (Optional[torch.Tensor]): Optional zero points to be used for
          w2.
        - a1q_scale (Optional[torch.Tensor]): Optional quantized scale to be
          used for a1.
        - a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2.
        - workspace13 (torch.Tensor): A scratch tensor used for gemm outputs
          must be large enough to hold output of either MoE gemm.
        - workspace2 (torch.Tensor): A scratch tensor used for the activation
          function.
        - expert_num_tokens: An optional tensor containing the number of tokens
          assigned to each expert when using batched experts format input.

        Returns:
        - torch.Tensor: The unweighted, unreduced output tensor
        """
        raise NotImplementedError

activation

activation(
    activation: str, output: Tensor, input: Tensor
) -> None

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

def activation(self, activation: str, output: torch.Tensor,
               input: torch.Tensor) -> None:
    assert output.size(-1) * 2 == input.size(-1)
    if activation == "silu":
        torch.ops._C.silu_and_mul(output, input)
    elif activation == "gelu":
        torch.ops._C.gelu_and_mul(output, input)
    else:
        raise ValueError(f"Unsupported FusedMoe activation: {activation}")

apply abstractmethod

apply(
    hidden_states: Tensor,
    w1: Tensor,
    w2: Tensor,
    topk_ids: Tensor,
    activation: str,
    global_num_experts: int,
    expert_map: Optional[Tensor],
    w1_scale: Optional[Tensor],
    w2_scale: Optional[Tensor],
    w1_zp: Optional[Tensor],
    w2_zp: Optional[Tensor],
    a1q_scale: Optional[Tensor],
    a2_scale: Optional[Tensor],
    workspace13: Tensor,
    workspace2: Tensor,
    expert_num_tokens: Optional[Tensor],
) -> Tensor

This function computes the intermediate result of a Mixture of Experts (MoE) layer using two sets of weights, w1 and w2.

• hidden_states: (torch.Tensor): The (quantized) input tensor to the MoE layer.
• w1 (torch.Tensor): The first set of expert weights.
• w2 (torch.Tensor): The second set of expert weights.
• topk_ids (torch.Tensor): A map of row to expert id.
• activation (str): The activation function to apply after the first MoE layer.
• global_num_experts (int): The total number of experts in the global expert space.
• expert_map (Optional[torch.Tensor]): A tensor mapping expert indices from the global expert space to the local expert space of the expert parallel shard.
• w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1.
• w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2.
• w1_zp (Optional[torch.Tensor]): Optional zero points to be used for w1.
• w2_zp (Optional[torch.Tensor]): Optional zero points to be used for w2.
• a1q_scale (Optional[torch.Tensor]): Optional quantized scale to be used for a1.
• a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2.
• workspace13 (torch.Tensor): A scratch tensor used for gemm outputs must be large enough to hold output of either MoE gemm.
• workspace2 (torch.Tensor): A scratch tensor used for the activation function.
• expert_num_tokens: An optional tensor containing the number of tokens assigned to each expert when using batched experts format input.

Returns: - torch.Tensor: The unweighted, unreduced output tensor

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

@abstractmethod
def apply(
    self,
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_ids: torch.Tensor,
    activation: str,
    global_num_experts: int,
    expert_map: Optional[torch.Tensor],
    w1_scale: Optional[torch.Tensor],
    w2_scale: Optional[torch.Tensor],
    w1_zp: Optional[torch.Tensor],
    w2_zp: Optional[torch.Tensor],
    a1q_scale: Optional[torch.Tensor],
    a2_scale: Optional[torch.Tensor],
    workspace13: torch.Tensor,
    workspace2: torch.Tensor,
    expert_num_tokens: Optional[torch.Tensor],
) -> torch.Tensor:
    """
    This function computes the intermediate result of a Mixture of Experts
    (MoE) layer using two sets of weights, w1 and w2.

    Parameters:
    - hidden_states: (torch.Tensor): The (quantized) input tensor to the MoE
      layer.
    - w1 (torch.Tensor): The first set of expert weights.
    - w2 (torch.Tensor): The second set of expert weights.
    - topk_ids (torch.Tensor): A map of row to expert id.
    - activation (str): The activation function to apply after the first
      MoE layer.
    - global_num_experts (int): The total number of experts in the global
      expert space.
    - expert_map (Optional[torch.Tensor]):  A tensor mapping expert indices
      from the global expert space to the local expert space of the expert
      parallel shard.
    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for w1.
    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for w2.
    - w1_zp (Optional[torch.Tensor]): Optional zero points to be used for
      w1.
    - w2_zp (Optional[torch.Tensor]): Optional zero points to be used for
      w2.
    - a1q_scale (Optional[torch.Tensor]): Optional quantized scale to be
      used for a1.
    - a2_scale (Optional[torch.Tensor]): Optional scale to be used for a2.
    - workspace13 (torch.Tensor): A scratch tensor used for gemm outputs
      must be large enough to hold output of either MoE gemm.
    - workspace2 (torch.Tensor): A scratch tensor used for the activation
      function.
    - expert_num_tokens: An optional tensor containing the number of tokens
      assigned to each expert when using batched experts format input.

    Returns:
    - torch.Tensor: The unweighted, unreduced output tensor
    """
    raise NotImplementedError

workspace_shapes abstractmethod

workspace_shapes(
    a: Tensor,
    M: int,
    N: int,
    K: int,
    topk: int,
    num_experts: int,
) -> tuple[int, int, dtype]

Compute the number of elements for the temporary outputs of the two gemms and activation in the fused expert function. Since the gemms are independent, the workspace for the first gemm can be shared with the workspace for the last gemm.

Returns a tuple of: - Number of workspace13 elements: must be large enough to hold the result of either expert gemm. - Number of workspace2 elements: must be large enough to hold the result of the activation function. - Workspace type: The dtype to use for the workspace tensors.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

@abstractmethod
def workspace_shapes(
    self,
    a: torch.Tensor,
    M: int,
    N: int,
    K: int,
    topk: int,
    num_experts: int,
) -> tuple[int, int, torch.dtype]:
    """
    Compute the number of elements for the temporary outputs of the two
    gemms and activation in the fused expert function.  Since the
    gemms are independent, the workspace for the first gemm can be shared
    with the workspace for the last gemm.

    Returns a tuple of:
    - Number of workspace13 elements: must be large enough to hold the
      result of either expert gemm.
    - Number of workspace2 elements: must be large enough to hold the
      result of the activation function.
    - Workspace type: The dtype to use for the workspace tensors.
    """
    raise NotImplementedError

FusedMoEPrepareAndFinalize

Bases: ABC

An abstract base class for the [Quantize-Prepare] and [Finalize] steps described above.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

class FusedMoEPrepareAndFinalize(ABC):
    """
    An abstract base class for the [Quantize-Prepare] and [Finalize] steps
    described above.
    """

    @abstractmethod
    def prepare(
        self,
        a1: torch.Tensor,
        a1_scale: Optional[torch.Tensor],
        a2_scale: Optional[torch.Tensor],
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        num_experts: int,
        expert_map: Optional[torch.Tensor],
        apply_router_weight_on_input: bool,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform any quantization (and/or) dispatching needed
        for this kernel.
        - a1: The (unquantized) input to the MoE layer.
        - a1_scale: Optional scales for a1
        - a2_scale: Optional scales for the second MoE gemm.  Required to make
          sure the quantization is consistent for both gemms.
        - topk_ids: The topk ids.
        - topk_weights: The topk weights.
        - num_experts: The total number of experts in the global expert space.
        - expert_map: A tensor mapping expert indices from the global expert
          space to the local expert space of the expert parallel shard.
        - apply_router_weight_on_input: When True, apply the weights to the
          activations, before quantization + dispatching.

        Returns a tuple of:
        - quantized + dispatched a.
        - quantized + dispatched a1_scales.
        """
        raise NotImplementedError

    @abstractmethod
    def finalize(
        self,
        output: torch.Tensor,
        fused_expert_output: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        apply_router_weight_on_input: bool,
    ) -> None:
        """
        Perform any combine plus apply weights and perform a reduction on the
        fused experts output.
        - output: The output tensor, written in place.  Must be (M, K) shape.
        - fused_expert_output: The unweighted, unreduced output of the fused
          experts, it will have (M, topk, K) shape.
        - topk_weights: The weights to be applied to the fused_experts_output.
        - topk_ids: The topk_ids.
        - apply_router_weight_on_input: When False, apply the weights to
          fused_expert_output.
        """
        raise NotImplementedError

finalize abstractmethod

finalize(
    output: Tensor,
    fused_expert_output: Tensor,
    topk_weights: Tensor,
    topk_ids: Tensor,
    apply_router_weight_on_input: bool,
) -> None

Perform any combine plus apply weights and perform a reduction on the fused experts output. - output: The output tensor, written in place. Must be (M, K) shape. - fused_expert_output: The unweighted, unreduced output of the fused experts, it will have (M, topk, K) shape. - topk_weights: The weights to be applied to the fused_experts_output. - topk_ids: The topk_ids. - apply_router_weight_on_input: When False, apply the weights to fused_expert_output.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

@abstractmethod
def finalize(
    self,
    output: torch.Tensor,
    fused_expert_output: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    apply_router_weight_on_input: bool,
) -> None:
    """
    Perform any combine plus apply weights and perform a reduction on the
    fused experts output.
    - output: The output tensor, written in place.  Must be (M, K) shape.
    - fused_expert_output: The unweighted, unreduced output of the fused
      experts, it will have (M, topk, K) shape.
    - topk_weights: The weights to be applied to the fused_experts_output.
    - topk_ids: The topk_ids.
    - apply_router_weight_on_input: When False, apply the weights to
      fused_expert_output.
    """
    raise NotImplementedError

prepare abstractmethod

prepare(
    a1: Tensor,
    a1_scale: Optional[Tensor],
    a2_scale: Optional[Tensor],
    topk_weights: Tensor,
    topk_ids: Tensor,
    num_experts: int,
    expert_map: Optional[Tensor],
    apply_router_weight_on_input: bool,
) -> tuple[Tensor, Optional[Tensor], Optional[Tensor]]

Perform any quantization (and/or) dispatching needed for this kernel. - a1: The (unquantized) input to the MoE layer. - a1_scale: Optional scales for a1 - a2_scale: Optional scales for the second MoE gemm. Required to make sure the quantization is consistent for both gemms. - topk_ids: The topk ids. - topk_weights: The topk weights. - num_experts: The total number of experts in the global expert space. - expert_map: A tensor mapping expert indices from the global expert space to the local expert space of the expert parallel shard. - apply_router_weight_on_input: When True, apply the weights to the activations, before quantization + dispatching.

Returns a tuple of: - quantized + dispatched a. - quantized + dispatched a1_scales.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

@abstractmethod
def prepare(
    self,
    a1: torch.Tensor,
    a1_scale: Optional[torch.Tensor],
    a2_scale: Optional[torch.Tensor],
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    num_experts: int,
    expert_map: Optional[torch.Tensor],
    apply_router_weight_on_input: bool,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
    """
    Perform any quantization (and/or) dispatching needed
    for this kernel.
    - a1: The (unquantized) input to the MoE layer.
    - a1_scale: Optional scales for a1
    - a2_scale: Optional scales for the second MoE gemm.  Required to make
      sure the quantization is consistent for both gemms.
    - topk_ids: The topk ids.
    - topk_weights: The topk weights.
    - num_experts: The total number of experts in the global expert space.
    - expert_map: A tensor mapping expert indices from the global expert
      space to the local expert space of the expert parallel shard.
    - apply_router_weight_on_input: When True, apply the weights to the
      activations, before quantization + dispatching.

    Returns a tuple of:
    - quantized + dispatched a.
    - quantized + dispatched a1_scales.
    """
    raise NotImplementedError

_moe_problem_size

_moe_problem_size(
    a1: Tensor, w1: Tensor, w2: Tensor, topk_ids: Tensor
) -> tuple[int, int, int, int, int]

Extract the MoE problem size from the given tensor arguments: - a: The hidden states, input to the MoE layer. - w1: The first set of expert weights. - w2: The second set of expert weights. - topk_ids: The topk ids.

Note: extracting the problem shape from the weight and activation tensors is not obvious. It needs to be done this way specifically due to subtle issues with particular kernels, e.g. the int4 kernels divide the trailing dimension by two, so it's not "correct" to extract N or K from the trailing dimension of w1 or w2. Similarly, some kernels transpose the weights, so this needs to be kept in mind.

Source code in vllm/model_executor/layers/fused_moe/modular_kernel.py

def _moe_problem_size(
    a1: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_ids: torch.Tensor,
) -> tuple[int, int, int, int, int]:
    """
    Extract the MoE problem size from the given tensor arguments:
    - a: The hidden states, input to the MoE layer.
    - w1: The first set of expert weights.
    - w2: The second set of expert weights.
    - topk_ids: The topk ids.

    Note: extracting the problem shape from the weight and activation tensors is
    not obvious.  It needs to be done this way specifically due to subtle issues
    with particular kernels, e.g. the int4 kernels divide the trailing dimension
    by two, so it's not "correct" to extract N or K from the trailing dimension
    of w1 or w2.  Similarly, some kernels transpose the weights, so this needs
    to be kept in mind.
    """
    assert w1.dim() == 3 and w2.dim() == 3
    E, N, _ = w1.size()
    K = w2.size(1)

    if a1.dim() == 2:
        # Make sure we are using the correct a1 (pre-permute).
        assert topk_ids.size(0) == a1.size(0), \
            f"{topk_ids.size(0)} != {a1.size(0)}"
        M = a1.size(0)
    else:
        assert a1.dim() == 3
        assert a1.size(0) == E, f"{a1.size(0)} == {E}"
        M = a1.size(1)  # This is max_num_tokens

    assert topk_ids.dim() == 2
    topk = topk_ids.size(1)

    return E, M, N, K, topk