xc-llm-ascend/vllm_ascend/quantization/methods/w4a16.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# 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.
#

from collections.abc import Callable
from typing import Any

import torch
import torch_npu
from vllm.config import get_current_vllm_config

from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import _EXTRA_CTX
from vllm_ascend.ops.fused_moe.experts_selector import select_experts
from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input

from .base import AscendMoEScheme, QuantType
from .registry import register_scheme


def unpack_from_int32(
    weight: torch.Tensor,
    shape: torch.Size,
    num_bits: int,
    packed_dim: int = 1,
) -> torch.Tensor:
    """Unpacks quantized weights from int32 format back to original bits.

    :param weight: The packed int32 tensor containing quantized weights
    :param shape: Original shape to restore, defaults to None
    :param num_bits: The number of bits used for quantization (<= 8)
    :param packed_dim: Dimension along which weights are packed (0 or 1), defaults to 1
    :return: Unpacked tensor with int8 dtype after applying offset correction
    """
    assert weight.dtype == torch.int32, f"Expecting `weight.dtype` is torch.int32 but got {weight.dtype}."
    assert num_bits <= 8, f"Expecting `num_bits` should not be larger than 8 but got {num_bits}."

    pack_factor = 32 // num_bits
    mask = (1 << num_bits) - 1

    if packed_dim == 1:
        unpacked_weight = torch.zeros(
            (weight.shape[0], weight.shape[1] * pack_factor),
            device=weight.device,
            dtype=torch.int32,
        )
        for i in range(pack_factor):
            unpacked_weight[:, i::pack_factor] = (weight >> (num_bits * i)) & mask
        original_row_size = int(shape[1])
        unpacked_weight = unpacked_weight[:, :original_row_size]
    else:
        unpacked_weight = torch.zeros(
            (weight.shape[0] * pack_factor, weight.shape[1]),
            device=weight.device,
            dtype=torch.int32,
        )
        for i in range(pack_factor):
            unpacked_weight[i::pack_factor, :] = (weight >> (num_bits * i)) & mask
        original_row_size = int(shape[0])
        unpacked_weight = unpacked_weight[:original_row_size, :]

    offset = pow(2, num_bits) // 2
    unpacked_weight = (unpacked_weight - offset).to(torch.int8)

    return unpacked_weight


def pack_to_int32(weight: torch.Tensor) -> torch.Tensor:
    """Packs quantized weights into int32 format for storage.

    :param weight: The 3D tensor to pack, must be int8 or int32 dtype
    :return: Packed tensor with int32 dtype optimized for storage
    """
    assert weight.dim() == 3, f"Expecting `weight.dim()` is 3 ([e, n, k] or [e, k, n]) but got {weight.dim()}."
    assert weight.dtype in [torch.int8, torch.int32], (
        f"Expecting `weight.dtype` is torch.int8 or torch.int32 bug got {weight.dtype}."
    )

    if weight.dtype == torch.int32:
        assert weight.shape[-1] % 8 == 0, "the last dim of weight needs to be divided by 8."
        packed_weight = torch_npu.npu_convert_weight_to_int4pack(weight.flatten(0, 1))
        packed_weight = packed_weight.view(weight.shape[0], weight.shape[1], -1)
    else:
        assert weight.shape[-1] % 4 == 0, "the last dim of weight needs to be divided by 4."
        packed_weight = weight.view(torch.int32).contiguous()

    return packed_weight


@register_scheme("W4A16", "moe")
class AscendW4A16FusedMoEMethod(AscendMoEScheme):
    """FusedMoE method for Ascend W4A16."""

    quant_type: QuantType = QuantType.W4A16

    def __init__(self) -> None:
        self.transpose_weight = True
        self.num_bits = 4  # dtype = torch.int4
        self.pack_factor = 8  # pack 8 of torch.int4 tensors to torch.int32

        vllm_config = get_current_vllm_config()
        self.group_size = vllm_config.quant_config.quant_description.get("group_size", 32)
        self.dynamic_eplb = get_ascend_config().eplb_config.dynamic_eplb

    def get_weight(
        self,
        num_experts: int,
        intermediate_size_per_partition: int,
        hidden_sizes: int,
        params_dtype: torch.dtype,
    ) -> dict[str, Any]:
        assert intermediate_size_per_partition % self.pack_factor == 0, (
            f"Expecting `intermediate_size_per_partition` {intermediate_size_per_partition} "
            f"can be divided by `pack_factor` {self.pack_factor}"
        )
        assert hidden_sizes % self.pack_factor == 0, (
            f"Expecting `hidden_sizes` {hidden_sizes} can be divided by `pack_factor` {self.pack_factor}"
        )

        param_dict = {}

        param_dict["w13_weight_packed"] = torch.empty(
            num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.pack_factor, dtype=torch.int32
        )
        param_dict["w2_weight_packed"] = torch.empty(
            num_experts, hidden_sizes, intermediate_size_per_partition // self.pack_factor, dtype=torch.int32
        )

        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]:
        assert intermediate_size_per_partition % self.group_size == 0, (
            f"Expecting `intermediate_size_per_partition` {intermediate_size_per_partition} "
            f"can be divided by `group_size` {self.group_size}"
        )
        assert hidden_sizes % self.group_size == 0, (
            f"Expecting `hidden_sizes` {hidden_sizes} can be divided by `group_size` {self.group_size}"
        )

        param_dict = {}

        param_dict["w13_weight_scale"] = torch.empty(
            num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.group_size, dtype=torch.bfloat16
        )
        param_dict["w2_weight_scale"] = torch.empty(
            num_experts, hidden_sizes, intermediate_size_per_partition // self.group_size, dtype=torch.bfloat16
        )
        param_dict["w13_weight_shape"] = torch.empty(num_experts, 2, dtype=torch.int32)
        param_dict["w2_weight_shape"] = torch.empty(num_experts, 2, dtype=torch.int32)
        param_dict["w13_weight_offset"] = torch.zeros(
            num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.group_size, dtype=torch.bfloat16
        )
        param_dict["w2_weight_offset"] = torch.zeros(
            num_experts, hidden_sizes, intermediate_size_per_partition // self.group_size, dtype=torch.bfloat16
        )

        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 = True,
        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:
        assert router_logits.shape[1] == global_num_experts - global_redundant_expert_num, (
            "Number of global experts mismatch (excluding redundancy)"
        )

        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,
        )

        topk_ids = topk_ids.to(torch.int32)
        topk_weights = topk_weights.to(x.dtype)

        moe_comm_method = _EXTRA_CTX.moe_comm_method
        return 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_packed,
                w2=layer.w2_weight_packed,
                quant_type=self.quant_type,
                dynamic_eplb=self.dynamic_eplb,
                expert_map=expert_map,
                global_redundant_expert_num=global_redundant_expert_num,
                mc2_mask=mc2_mask,
                apply_router_weight_on_input=apply_router_weight_on_input,
                log2phy=log2phy,
                pertoken_scale=pertoken_scale,
                activation=activation,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
                w1_offset=layer.w13_weight_offset,
                w2_offset=layer.w2_weight_offset,
            )
        )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        if self.transpose_weight:
            w13_shape = layer.w13_weight_packed.data.shape
            w2_shape = layer.w2_weight_packed.data.shape
            unpacked_w13_weight = (
                unpack_from_int32(
                    layer.w13_weight_packed.data.flatten(0, 1),
                    torch.Size([w13_shape[0] * w13_shape[1], w13_shape[2] * self.pack_factor]),
                    self.num_bits,
                )
                .view(w13_shape[0], w13_shape[1], -1)
                .transpose(1, 2)
                .contiguous()
                .int()
            )
            unpacked_w2_weight = (
                unpack_from_int32(
                    layer.w2_weight_packed.data.flatten(0, 1),
                    torch.Size([w2_shape[0] * w2_shape[1], w2_shape[2] * self.pack_factor]),
                    self.num_bits,
                )
                .view(w2_shape[0], w2_shape[1], -1)
                .transpose(1, 2)
                .contiguous()
                .int()
            )
            layer.w13_weight_packed.data = pack_to_int32(unpacked_w13_weight)
            layer.w2_weight_packed.data = pack_to_int32(unpacked_w2_weight)

            layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(1, 2).contiguous()
            layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(1, 2).contiguous()

            layer.w13_weight_offset.data = layer.w13_weight_offset.data.transpose(1, 2).contiguous()
            layer.w2_weight_offset.data = layer.w2_weight_offset.data.transpose(1, 2).contiguous()