xc-llm-ascend/vllm_ascend/_310p/quantization/methods/w8a8_dynamic.py

#
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#

from collections.abc import Callable
from typing import Any

import torch
import torch_npu
from vllm.config import get_current_vllm_config
from vllm.distributed import get_ep_group

from vllm_ascend._310p.fused_moe.experts_selector import select_experts
from vllm_ascend.ascend_forward_context import _EXTRA_CTX
from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
from vllm_ascend.quantization.methods.base import AscendLinearScheme, AscendMoEScheme, QuantType
from vllm_ascend.utils import maybe_trans_nz

from .registry import register_scheme


@register_scheme("W8A8_DYNAMIC", "moe")
class AscendW8A8DynamicFusedMoEMethod310(AscendMoEScheme):
    """310P-only FusedMoE method for Ascend W8A8_DYNAMIC.

    Notes:
      - This scheme is discovered via 310P local registry.
    """

    # Declare the quantization type for this scheme
    quant_type: QuantType = QuantType.W8A8

    def __init__(self):
        self.ep_group = get_ep_group()
        vllm_config = get_current_vllm_config()
        self.in_dtype = vllm_config.model_config.dtype

    def get_weight(
        self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
    ) -> dict[str, Any]:
        param_dict = {}
        # Fused gate_up_proj (column parallel)
        param_dict["w13_weight"] = torch.empty(
            num_experts, 2 * intermediate_size_per_partition, hidden_sizes, dtype=torch.int8
        )
        # down_proj (row parallel)
        param_dict["w2_weight"] = torch.empty(
            num_experts, hidden_sizes, intermediate_size_per_partition, dtype=torch.int8
        )
        return param_dict

    def get_dynamic_quant_param(
        self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
    ) -> dict[str, Any]:
        param_dict = {}
        param_dict["w13_weight_scale"] = torch.empty(
            num_experts, 2 * intermediate_size_per_partition, 1, dtype=torch.float32
        )
        param_dict["w13_weight_offset"] = torch.empty(
            num_experts, 2 * intermediate_size_per_partition, 1, dtype=params_dtype
        )
        param_dict["w2_weight_scale"] = torch.empty(num_experts, hidden_sizes, 1, dtype=torch.float32)
        param_dict["w2_weight_offset"] = torch.empty(num_experts, hidden_sizes, 1, dtype=params_dtype)
        return param_dict

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        global_num_experts: int = -1,
        expert_map: torch.Tensor | None = None,
        topk_group: int | None = None,
        num_expert_group: int | None = None,
        custom_routing_function: Callable | None = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: torch.Tensor | None = None,
        is_prefill: bool = True,
        enable_force_load_balance: bool = False,
        log2phy: torch.Tensor | None = None,
        global_redundant_expert_num: int = 0,
        pertoken_scale: Any | None = None,
        activation: str = "silu",
        apply_router_weight_on_input: bool = False,
        mc2_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        zero_expert_num = getattr(layer, "zero_expert_num", 0)
        zero_expert_type = getattr(layer, "zero_expert_type", None)

        topk_weights, topk_ids = select_experts(
            hidden_states=x,
            router_logits=router_logits,
            top_k=top_k,
            use_grouped_topk=use_grouped_topk,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            global_num_experts=global_num_experts,
        )

        if zero_expert_num > 0 and zero_expert_type is not None:
            topk_ids, topk_weights, zero_expert_result = zero_experts_compute(
                expert_indices=topk_ids,
                expert_scales=topk_weights,
                num_experts=global_num_experts,
                zero_expert_type=zero_expert_type,
                hidden_states=x,
            )

        topk_weights = topk_weights.to(self.in_dtype)

        moe_comm_method = _EXTRA_CTX.moe_comm_method

        final_hidden_states = moe_comm_method.fused_experts(
            fused_experts_input=build_fused_experts_input(
                hidden_states=x,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                quant_type=self.quant_type,
                dynamic_eplb=False,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
            ),
        )
        if zero_expert_num > 0 and zero_expert_type is not None:
            final_hidden_states += zero_expert_result
        return final_hidden_states

    def process_weights_after_loading(self, layer):
        layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(layer.w13_weight_scale.data.shape[0], -1)
        layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(layer.w13_weight_offset.data.shape[0], -1)
        layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(layer.w2_weight_scale.data.shape[0], -1)
        layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(layer.w2_weight_offset.data.shape[0], -1)


@register_scheme("W8A8_DYNAMIC", "linear")
class AscendW8A8DynamicLinearMethod310(AscendLinearScheme):
    """310P-only W8A8 dynamic linear scheme.

    Notes:
      - This scheme is discovered via 310P local registry.
    """

    def get_weight(
        self,
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype = torch.float16,
    ) -> dict[str, Any]:
        return {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}

    def get_perchannel_param(
        self,
        output_size: int,
        params_dtype: torch.dtype,
    ) -> dict[str, Any]:
        params: dict[str, Any] = {}
        params["weight_scale"] = torch.empty(output_size, 1, dtype=torch.float32)
        params["weight_offset"] = torch.empty(output_size, 1, dtype=torch.float32)
        return params

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
        tp_rank: int | None = 0,
    ) -> torch.Tensor:
        # NOTE(310P):
        # - There is an accuracy issue currently, which is expected to be fixed in the next version.
        quantized_x, pertoken_scale = torch_npu.npu_dynamic_quant(x)
        need_unsqz = False
        if pertoken_scale.dim() == 2:
            need_unsqz = True
            quantized_x = quantized_x.squeeze(dim=1)
            pertoken_scale = pertoken_scale.squeeze(dim=1)

        # NOTE(310P):
        # - Currently, W8A8 dynamic quantization supports only symmetric quantization.
        output = torch_npu.npu_quant_matmul(
            quantized_x,
            layer.weight.data,
            layer.weight_scale,
            pertoken_scale=pertoken_scale,
            bias=bias,
            output_dtype=x.dtype,
        )
        if need_unsqz:
            output = output.unsqueeze(dim=1)
        return output

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # cast quantized weight tensors in NZ format for higher inference speed
        layer.weight.data = maybe_trans_nz(layer.weight.data).transpose(0, 1)
        layer.weight_scale.data = layer.weight_scale.data.flatten()
        layer.weight_offset.data = layer.weight_offset.data.flatten()