88d03a783f
### What this PR does / why we need it? Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business `**kwargs` with typed request objects and explicit stage boundaries. - Prepare, dispatch, MLP, and quant stages now have clearer ownership. - Main MoE path no longer depends on business `kwargs.get(...)` lookups. - Comm and dispatcher interfaces are request-only on the main path. - UTs can assert stage-level fields directly instead of inferring behavior indirectly. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? CI passed. --------- Signed-off-by: linfeng-yuan <1102311262@qq.com>
282 lines
11 KiB
Python
282 lines
11 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections.abc import Callable
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from typing import Any
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import torch
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import torch_npu
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from vllm.config import get_current_vllm_config
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ascend_forward_context import _EXTRA_CTX
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from vllm_ascend.ops.fused_moe.experts_selector import select_experts
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from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
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from .base import AscendMoEScheme, QuantType
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from .registry import register_scheme
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def unpack_from_int32(
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weight: torch.Tensor,
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shape: torch.Size,
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num_bits: int,
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packed_dim: int = 1,
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) -> torch.Tensor:
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"""Unpacks quantized weights from int32 format back to original bits.
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:param weight: The packed int32 tensor containing quantized weights
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:param shape: Original shape to restore, defaults to None
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:param num_bits: The number of bits used for quantization (<= 8)
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:param packed_dim: Dimension along which weights are packed (0 or 1), defaults to 1
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:return: Unpacked tensor with int8 dtype after applying offset correction
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"""
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assert weight.dtype == torch.int32, f"Expecting `weight.dtype` is torch.int32 but got {weight.dtype}."
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assert num_bits <= 8, f"Expecting `num_bits` should not be larger than 8 but got {num_bits}."
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pack_factor = 32 // num_bits
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mask = (1 << num_bits) - 1
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if packed_dim == 1:
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unpacked_weight = torch.zeros(
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(weight.shape[0], weight.shape[1] * pack_factor),
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device=weight.device,
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dtype=torch.int32,
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)
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for i in range(pack_factor):
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unpacked_weight[:, i::pack_factor] = (weight >> (num_bits * i)) & mask
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original_row_size = int(shape[1])
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unpacked_weight = unpacked_weight[:, :original_row_size]
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else:
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unpacked_weight = torch.zeros(
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(weight.shape[0] * pack_factor, weight.shape[1]),
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device=weight.device,
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dtype=torch.int32,
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)
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for i in range(pack_factor):
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unpacked_weight[i::pack_factor, :] = (weight >> (num_bits * i)) & mask
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original_row_size = int(shape[0])
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unpacked_weight = unpacked_weight[:original_row_size, :]
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offset = pow(2, num_bits) // 2
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unpacked_weight = (unpacked_weight - offset).to(torch.int8)
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return unpacked_weight
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def pack_to_int32(weight: torch.Tensor) -> torch.Tensor:
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"""Packs quantized weights into int32 format for storage.
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:param weight: The 3D tensor to pack, must be int8 or int32 dtype
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:return: Packed tensor with int32 dtype optimized for storage
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"""
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assert weight.dim() == 3, f"Expecting `weight.dim()` is 3 ([e, n, k] or [e, k, n]) but got {weight.dim()}."
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assert weight.dtype in [torch.int8, torch.int32], (
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f"Expecting `weight.dtype` is torch.int8 or torch.int32 bug got {weight.dtype}."
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)
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if weight.dtype == torch.int32:
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assert weight.shape[-1] % 8 == 0, "the last dim of weight needs to be divided by 8."
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packed_weight = torch_npu.npu_convert_weight_to_int4pack(weight.flatten(0, 1))
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packed_weight = packed_weight.view(weight.shape[0], weight.shape[1], -1)
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else:
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assert weight.shape[-1] % 4 == 0, "the last dim of weight needs to be divided by 4."
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packed_weight = weight.view(torch.int32).contiguous()
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return packed_weight
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@register_scheme("W4A16", "moe")
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class AscendW4A16FusedMoEMethod(AscendMoEScheme):
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"""FusedMoE method for Ascend W4A16."""
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quant_type: QuantType = QuantType.W4A16
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def __init__(self) -> None:
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self.transpose_weight = True
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self.num_bits = 4 # dtype = torch.int4
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self.pack_factor = 8 # pack 8 of torch.int4 tensors to torch.int32
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vllm_config = get_current_vllm_config()
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self.group_size = vllm_config.quant_config.quant_description.get("group_size", 32)
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self.dynamic_eplb = get_ascend_config().eplb_config.dynamic_eplb
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def get_weight(
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self,
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num_experts: int,
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intermediate_size_per_partition: int,
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hidden_sizes: int,
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params_dtype: torch.dtype,
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) -> dict[str, Any]:
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assert intermediate_size_per_partition % self.pack_factor == 0, (
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f"Expecting `intermediate_size_per_partition` {intermediate_size_per_partition} "
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f"can be divided by `pack_factor` {self.pack_factor}"
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)
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assert hidden_sizes % self.pack_factor == 0, (
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f"Expecting `hidden_sizes` {hidden_sizes} can be divided by `pack_factor` {self.pack_factor}"
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)
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param_dict = {}
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param_dict["w13_weight_packed"] = torch.empty(
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num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.pack_factor, dtype=torch.int32
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)
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param_dict["w2_weight_packed"] = torch.empty(
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num_experts, hidden_sizes, intermediate_size_per_partition // self.pack_factor, dtype=torch.int32
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)
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return param_dict
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def get_dynamic_quant_param(
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self,
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num_experts: int,
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intermediate_size_per_partition: int,
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hidden_sizes: int,
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params_dtype: torch.dtype,
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) -> dict[str, Any]:
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assert intermediate_size_per_partition % self.group_size == 0, (
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f"Expecting `intermediate_size_per_partition` {intermediate_size_per_partition} "
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f"can be divided by `group_size` {self.group_size}"
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)
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assert hidden_sizes % self.group_size == 0, (
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f"Expecting `hidden_sizes` {hidden_sizes} can be divided by `group_size` {self.group_size}"
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)
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param_dict = {}
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param_dict["w13_weight_scale"] = torch.empty(
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num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.group_size, dtype=torch.bfloat16
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)
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param_dict["w2_weight_scale"] = torch.empty(
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num_experts, hidden_sizes, intermediate_size_per_partition // self.group_size, dtype=torch.bfloat16
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)
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param_dict["w13_weight_shape"] = torch.empty(num_experts, 2, dtype=torch.int32)
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param_dict["w2_weight_shape"] = torch.empty(num_experts, 2, dtype=torch.int32)
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param_dict["w13_weight_offset"] = torch.zeros(
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num_experts, 2 * intermediate_size_per_partition, hidden_sizes // self.group_size, dtype=torch.bfloat16
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)
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param_dict["w2_weight_offset"] = torch.zeros(
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num_experts, hidden_sizes, intermediate_size_per_partition // self.group_size, dtype=torch.bfloat16
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)
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return param_dict
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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global_num_experts: int = -1,
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expert_map: torch.Tensor | None = None,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: torch.Tensor | None = None,
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is_prefill: bool = True,
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enable_force_load_balance: bool = True,
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log2phy: torch.Tensor | None = None,
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global_redundant_expert_num: int = 0,
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pertoken_scale: Any | None = None,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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mc2_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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assert router_logits.shape[1] == global_num_experts - global_redundant_expert_num, (
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"Number of global experts mismatch (excluding redundancy)"
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)
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts,
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)
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = topk_weights.to(x.dtype)
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moe_comm_method = _EXTRA_CTX.moe_comm_method
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return moe_comm_method.fused_experts(
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fused_experts_input=build_fused_experts_input(
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hidden_states=x,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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w1=layer.w13_weight_packed,
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w2=layer.w2_weight_packed,
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quant_type=self.quant_type,
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dynamic_eplb=self.dynamic_eplb,
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expert_map=expert_map,
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global_redundant_expert_num=global_redundant_expert_num,
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mc2_mask=mc2_mask,
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apply_router_weight_on_input=apply_router_weight_on_input,
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log2phy=log2phy,
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pertoken_scale=pertoken_scale,
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activation=activation,
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w1_scale=layer.w13_weight_scale,
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w2_scale=layer.w2_weight_scale,
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w1_offset=layer.w13_weight_offset,
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w2_offset=layer.w2_weight_offset,
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)
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)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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if self.transpose_weight:
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w13_shape = layer.w13_weight_packed.data.shape
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w2_shape = layer.w2_weight_packed.data.shape
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unpacked_w13_weight = (
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unpack_from_int32(
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layer.w13_weight_packed.data.flatten(0, 1),
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torch.Size([w13_shape[0] * w13_shape[1], w13_shape[2] * self.pack_factor]),
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self.num_bits,
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)
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.view(w13_shape[0], w13_shape[1], -1)
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.transpose(1, 2)
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.contiguous()
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.int()
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)
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unpacked_w2_weight = (
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unpack_from_int32(
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layer.w2_weight_packed.data.flatten(0, 1),
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torch.Size([w2_shape[0] * w2_shape[1], w2_shape[2] * self.pack_factor]),
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self.num_bits,
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)
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.view(w2_shape[0], w2_shape[1], -1)
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.transpose(1, 2)
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.contiguous()
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.int()
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)
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layer.w13_weight_packed.data = pack_to_int32(unpacked_w13_weight)
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layer.w2_weight_packed.data = pack_to_int32(unpacked_w2_weight)
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layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(1, 2).contiguous()
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layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(1, 2).contiguous()
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layer.w13_weight_offset.data = layer.w13_weight_offset.data.transpose(1, 2).contiguous()
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layer.w2_weight_offset.data = layer.w2_weight_offset.data.transpose(1, 2).contiguous()
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