mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
07e39620ea
### What this PR does / why we need it? Currently, when executing to the Linear layer of models in vLLM-Ascend, the weights format is ND in unquantized case and skipped ascend case. This PR supplements the execution logic for Linear layer. We use a new global variable: VLLM_ASCEND_ENABLE_NZ. When VLLM_ASCEND_ENABLE_NZ=1 and CANN version is 8.3, the weights of the Linear layer will be converted to FRACTAL_NZ, in both unquantized case and skipped ascend case. We also use VLLM_ASCEND_ENABLE_NZ to control the existing NZ conversion, such as w8a8-quantized case. ### Does this PR introduce _any_ user-facing change? Add a new global variable VLLM_ASCEND_ENABLE_NZ. If you want to use NZ format, you should set VLLM_ASCEND_ENABLE_NZ=1. ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
403 lines
18 KiB
Python
403 lines
18 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 typing import Any, Callable, Dict, Optional
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import numpy as np
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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.distributed import get_ep_group
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from vllm.forward_context import get_forward_context
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.ops.moe.experts_selector import select_experts
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from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_enable_nz
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class AscendW4A8DynamicLinearMethod:
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"""Linear method for Ascend W4A8_DYNAMIC
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"""
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def __init__(self):
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self.transpose_weight = True
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try:
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self.group_size = get_current_vllm_config(
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).quant_config.quant_description.get("group_size", 256)
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except AttributeError:
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self.group_size = 256
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@staticmethod
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def get_weight(input_size: int, output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {
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"weight": torch.empty(output_size, input_size, dtype=torch.int8)
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}
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return params_dict
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@staticmethod
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def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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@staticmethod
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def get_perchannel_param(output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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def get_pergroup_param(self, input_size: int, output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {}
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params_dict["weight_scale"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_offset"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_scale_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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params_dict["weight_offset_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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return params_dict
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@staticmethod
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def process_scale_second(weight: torch.Tensor, scale: torch.Tensor,
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per_group_scale: torch.Tensor):
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k, n = weight.shape
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group_num, n = per_group_scale.shape
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weight_high = weight.to(torch.float32).reshape(
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group_num, -1, n) * per_group_scale.reshape(group_num, 1, n)
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weight_high = weight_high.reshape(k, n)
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bias = 8 * (weight_high.to(torch.float32) * scale).sum(dim=0)
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antiquant_scale = (scale * per_group_scale).reshape(group_num, n)
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return antiquant_scale.npu(), bias
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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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bias: Optional[torch.Tensor] = None,
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tp_rank: Optional[int] = None,
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) -> torch.Tensor:
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return torch_npu.npu_weight_quant_batchmatmul(
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x,
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layer.weight,
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antiquant_scale=layer.weight_scale_second.to(x.dtype),
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antiquant_group_size=self.group_size,
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)
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def process_weights_after_loading(self, layer: torch.nn.Module):
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if self.transpose_weight:
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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layer.weight_scale.data = layer.weight_scale.data.flatten().to(
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torch.float32)
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layer.weight_offset.data = layer.weight_offset.data.flatten()
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layer.weight_scale_second.data, scale_bias = self.process_scale_second(
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layer.weight.data,
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layer.weight_scale.data,
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layer.weight_scale_second.data.transpose(0, 1).contiguous(),
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)
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param = torch.nn.Parameter(scale_bias, requires_grad=False)
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layer.register_parameter("weight_scale_bias", param)
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layer.weight.data = torch_npu.npu_convert_weight_to_int4pack(
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layer.weight.data.to(torch.int32))
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class AscendW4A8DynamicFusedMoEMethod:
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"""FusedMoe method for Ascend W4A8_DYNAMIC.
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"""
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def __init__(self):
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self.transpose_weight = True
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self.ep_group = get_ep_group()
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vllm_config = get_current_vllm_config()
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self.group_size = vllm_config.quant_config.quant_description.get(
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"group_size", 256)
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# NOTE: the weights are quantized from bf16 to int4 through a per-channel quantization process
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self.is_per_channel_weight = self.group_size == 0
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quant_version = vllm_config.quant_config.quant_description.get(
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"version", "0")
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# NOTE: new quantize weights: 2 int4 pack into int8
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self.new_quant_version = quant_version == "1.0.0"
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self.tp_size = 1 if vllm_config.parallel_config.enable_expert_parallel else self.ep_group.world_size
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self.dynamic_eplb = get_ascend_config().dynamic_eplb
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if self.new_quant_version and self.tp_size > 16:
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raise ValueError(
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"The current weight does not support moe part tp>16.")
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try:
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device_group = get_mc2_group().device_group
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# TODO: Try local_rank = ep_group.rank_in_group
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local_rank = torch.distributed.get_rank(group=device_group)
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backend = device_group._get_backend(torch.device("npu"))
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self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
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local_rank)
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except AttributeError:
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self.moe_all_to_all_group_name = ""
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def get_weight(self, num_experts: int,
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intermediate_size_per_partition: int, hidden_sizes: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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param_dict = {}
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if self.new_quant_version:
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w13_output_size = intermediate_size_per_partition
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w2_output_size = hidden_sizes // 2
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else:
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w13_output_size = 2 * intermediate_size_per_partition
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w2_output_size = hidden_sizes
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param_dict["w13_weight"] = torch.empty(num_experts,
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w13_output_size,
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hidden_sizes,
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dtype=torch.int8)
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param_dict["w2_weight"] = torch.empty(num_experts,
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w2_output_size,
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intermediate_size_per_partition,
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dtype=torch.int8)
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return param_dict
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def get_dynamic_quant_param(self, 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) -> Dict[str, Any]:
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param_dict = {}
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param_dict["w13_weight_scale"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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1,
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dtype=torch.float32)
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param_dict["w13_weight_offset"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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1,
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dtype=torch.float32)
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param_dict["w2_weight_scale"] = torch.empty(num_experts,
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hidden_sizes,
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1,
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dtype=torch.float32)
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param_dict["w2_weight_offset"] = torch.empty(num_experts,
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hidden_sizes,
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1,
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dtype=torch.float32)
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if not self.is_per_channel_weight:
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param_dict["w13_weight_scale_second"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_sizes // self.group_size,
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dtype=torch.float32)
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param_dict["w13_weight_offset_second"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_sizes // self.group_size,
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dtype=torch.float32)
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param_dict["w2_weight_scale_second"] = torch.empty(
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num_experts,
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hidden_sizes,
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intermediate_size_per_partition // self.group_size,
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dtype=torch.float32)
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param_dict["w2_weight_offset_second"] = torch.empty(
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num_experts,
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hidden_sizes,
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intermediate_size_per_partition // self.group_size,
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dtype=torch.float32)
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if self.new_quant_version:
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param_dict["w13_scale_bias"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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1,
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dtype=torch.float32)
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param_dict["w2_scale_bias"] = torch.empty(num_experts,
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hidden_sizes,
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16 // self.tp_size,
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dtype=torch.float32)
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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: Optional[torch.Tensor] = None,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = 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,
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global_redundant_expert_num: int = 0,
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shared_experts: Optional[Any] = None,
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quantized_x_for_share: Optional[Any] = None,
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dynamic_scale_for_share: Optional[Any] = None,
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**kwargs,
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) -> torch.Tensor:
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assert router_logits.shape[
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1] == global_num_experts, "Number of global experts mismatch"
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# NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
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topk_weights, topk_ids, row_idx = 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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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance:
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topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
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topk_weights = topk_weights.to(x.dtype)
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moe_comm_method = get_forward_context().moe_comm_method
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return moe_comm_method.fused_experts(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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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_scale_bias=layer.w13_scale_bias,
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w2_scale_bias=layer.w2_scale_bias,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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row_idx=row_idx,
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use_int4_w4a8=True,
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expert_map=expert_map,
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log2phy=log2phy,
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global_redundant_expert_num=global_redundant_expert_num,
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shared_experts=shared_experts,
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quantized_x_for_share=quantized_x_for_share,
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dynamic_scale_for_share=dynamic_scale_for_share,
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dynamic_eplb=self.dynamic_eplb)
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def process_scale(self, weight: torch.Tensor, scale, per_group_scale):
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scale = scale.transpose(1, 2).contiguous()
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if self.is_per_channel_weight:
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scale_np = scale.cpu().numpy()
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scale_np.dtype = np.uint32
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scale_uint64_tensor = torch.from_numpy(scale_np.astype(
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np.int64)).npu()
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return scale_uint64_tensor, None
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per_group_scale = per_group_scale.transpose(1, 2).contiguous()
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group_num, k, n = weight.shape
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# the weight of the new version is reduced by half by pack n, so it needs to be restored
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if self.new_quant_version:
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n = n * 2
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per_group_scale = per_group_scale.reshape(group_num, -1, n)
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group_num, quantgroup_num, n = per_group_scale.shape
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bias = None
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if not self.new_quant_version:
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weight_high = weight.to(torch.float32).reshape([group_num, quantgroup_num, -1, n]) * \
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per_group_scale.reshape([group_num, quantgroup_num, 1, n])
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weight_high = weight_high.reshape([group_num, k, n])
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bias = 8 * (weight_high.to(torch.float32) * scale).sum(axis=1)
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scale_fp32 = (scale * per_group_scale).to(torch.float16).to(
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torch.float32)
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scale_fp32_np = scale_fp32.cpu().numpy()
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scale_fp32_np.dtype = np.uint32
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sscale_uint64 = np.zeros((group_num, quantgroup_num, n * 2),
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dtype=np.uint32)
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sscale_uint64[..., ::2] = scale_fp32_np
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sscale_uint64_buffer = np.frombuffer(sscale_uint64.tobytes(),
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dtype=np.int64).copy()
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sscale_uint64_tensor = torch.from_numpy(sscale_uint64_buffer).reshape(
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group_num, quantgroup_num, n)
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sscale_uint64_tensor = sscale_uint64_tensor.npu()
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return sscale_uint64_tensor, bias
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def update_bias(self, layer, w13_bias, w2_bias):
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if self.new_quant_version:
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layer.w13_scale_bias.data = layer.w13_scale_bias.data.transpose(
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1, 2).contiguous().sum(axis=1)
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layer.w2_scale_bias.data = layer.w2_scale_bias.data.transpose(
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1, 2).contiguous().sum(axis=1)
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else:
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w13_scale_bias = torch.nn.Parameter(w13_bias, requires_grad=False)
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layer.register_parameter("w13_scale_bias", w13_scale_bias)
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w2_scale_bias = torch.nn.Parameter(w2_bias, requires_grad=False)
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layer.register_parameter("w2_scale_bias", w2_scale_bias)
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def pack_to_int32(self, weight: torch.Tensor):
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if self.new_quant_version:
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# pack 4 int8(int4*2) to int32, because in pytorch, we need to use int32 to represent int4
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assert weight.shape[
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-1] % 4 == 0, "the last dim of weight needs to be divided by 4"
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return weight.view(torch.int32).contiguous()
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else:
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return torch_npu.npu_quantize(weight.to(torch.float32),
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torch.tensor([1.]).npu(), None,
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torch.quint4x2, -1, False)
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def process_weights_after_loading(self, layer):
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if self.transpose_weight:
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layer.w13_weight.data = layer.w13_weight.data.transpose(
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1, 2).contiguous()
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layer.w2_weight.data = layer.w2_weight.data.transpose(
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1, 2).contiguous()
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w13_weight_scale_second = layer.w13_weight_scale_second.data if hasattr(
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layer, "w13_weight_scale_second") else None
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w2_weight_scale_second = layer.w2_weight_scale_second.data if hasattr(
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layer, "w2_weight_scale_second") else None
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layer.w13_weight_scale.data, w13_bias = self.process_scale(
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layer.w13_weight, layer.w13_weight_scale.data,
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w13_weight_scale_second)
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layer.w2_weight_scale.data, w2_bias = self.process_scale(
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layer.w2_weight, layer.w2_weight_scale.data,
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w2_weight_scale_second)
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if hasattr(layer, "w13_weight_scale_second"):
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# scale_second is no longer used, release this part of the memory
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del layer.w13_weight_scale_second
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del layer.w2_weight_scale_second
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del layer.w13_weight_offset_second
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del layer.w2_weight_offset_second
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self.update_bias(layer, w13_bias, w2_bias)
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if is_enable_nz():
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layer.w13_weight.data = torch_npu.npu_format_cast(
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layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
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layer.w2_weight.data = torch_npu.npu_format_cast(
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layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
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layer.w13_weight.data = self.pack_to_int32(layer.w13_weight.data)
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layer.w2_weight.data = self.pack_to_int32(layer.w2_weight.data)
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