[Feature] totaly support multi-lora support,latest xspeedgate needed (#133)
Co-authored-by: wanghao <wanghao@example.com>
This commit is contained in:
WANG HAO
0
vllm_kunlun/lora/__init__.py
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0
vllm_kunlun/lora/__init__.py
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0
vllm_kunlun/lora/ops/__init__.py
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0
vllm_kunlun/lora/ops/__init__.py
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16
vllm_kunlun/lora/ops/kunlun_ops/__init__.py
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16
vllm_kunlun/lora/ops/kunlun_ops/__init__.py
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"""# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project"""
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from vllm_kunlun.lora.ops.kunlun_ops.lora_ops import (bgmv_expand,bgmv_expand_slice, bgmv_shrink,
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sgmv_expand, sgmv_expand_slice,
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sgmv_shrink)
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__all__ = [
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"bgmv_expand",
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"bgmv_expand_slice",
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"bgmv_shrink",
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"sgmv_expand",
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"sgmv_expand_slice",
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"sgmv_shrink"
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]
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133
vllm_kunlun/lora/ops/kunlun_ops/lora_ops.py
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133
vllm_kunlun/lora/ops/kunlun_ops/lora_ops.py
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"""kunlun_ops for lora"""
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import torch
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import xspeedgate_ops
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import time
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from torch._C import dtype
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import os
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from torch._dynamo import disable
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def sgmv_shrink(
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inputs: torch.Tensor,
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lora_a_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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expert_m: torch.Tensor,
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b_seq_start_loc: torch.Tensor,
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seq_len_tensor: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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batches: int,
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max_seq_length: int,
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token_nums: int,
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scaling: float,
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):
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"""
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sgmv_shrink
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"""
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return torch.ops.xspeedgate_ops.sgmv_shrink_cluster(inputs, lora_a_weights, seq_len_tensor, lora_indices_tensor, output_tensor, scaling)
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def sgmv_expand(inputs: torch.Tensor,
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lora_b_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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b_seq_start_loc: torch.Tensor,
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seq_len_tensor: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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batches: int,
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max_seq_length: int,
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token_nums: int,
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add_inputs: bool = False):
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"""
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sgmv_expand
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"""
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return torch.ops.xspeedgate_ops.sgmv_expand_cluster(inputs, lora_b_weights, seq_len_tensor, lora_indices_tensor, output_tensor, 0)
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def sgmv_expand_slice(inputs: torch.Tensor,
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lora_b_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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normed_scale: torch.Tensor,
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b_seq_start_loc: torch.Tensor,
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seq_len_tensor: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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batches: int,
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max_seq_length: int,
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token_nums: int,
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slice_offset: int,
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slice_size: int,
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add_inputs: bool = False):
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"""
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sgmv_expand_slice
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"""
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return torch.ops.xspeedgate_ops.sgmv_expand_cluster(inputs, lora_b_weights, seq_len_tensor, lora_indices_tensor, output_tensor, slice_offset)
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def bgmv_shrink(
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inputs: torch.Tensor, # [m, hidden_dim]
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lora_a_weights: torch.Tensor, # [n, 1, r, hidden_dim]
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output_tensor: torch.Tensor, # [m, r]
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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expert_m: torch.Tensor,
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lora_indices_tensor: torch.Tensor, # [m]
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scaling: float = 1.0
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) -> torch.Tensor:
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"""
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bgmv_shrink
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"""
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return torch.ops.xspeedgate_ops.bgmv_shrink_cluster(inputs, lora_a_weights, lora_indices_tensor, output_tensor, scaling)
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def bgmv_expand(inputs: torch.Tensor,
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lora_b_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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add_inputs: bool = True):
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""""
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bgmv_expand
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"""
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return torch.ops.xspeedgate_ops.bgmv_expand_cluster(inputs, lora_b_weights, lora_indices_tensor, output_tensor, 0)
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# @my_wrapper
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def bgmv_expand_slice(
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inputs: torch.Tensor,
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lora_b_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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normed_scale: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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slice_offset: int,
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slice_size: int,
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add_inputs: bool = True
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):
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"""
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bgmv_expand_slice
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"""
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return torch.ops.xspeedgate_ops.bgmv_expand_cluster(inputs, lora_b_weights, lora_indices_tensor, output_tensor, slice_offset)
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0
vllm_kunlun/lora/punica_wrapper/__init__.py
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0
vllm_kunlun/lora/punica_wrapper/__init__.py
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548
vllm_kunlun/lora/punica_wrapper/punica_kunlun.py
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548
vllm_kunlun/lora/punica_wrapper/punica_kunlun.py
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@@ -0,0 +1,548 @@
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#
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# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
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# Author: Wang Hao
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# Email: wanghao129@baidu.com
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# This file is a part of the vllm-kunlun 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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Based on:
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Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
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Punica: Multi-Tenant LoRA Serving.
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https://arxiv.org/abs/2310.18547
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"""
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from typing import TYPE_CHECKING, Optional, Union, final
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import torch
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# Disable torchdynamo for all functions in this file
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torch._dynamo.config.disable = True
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# SPDX-License-Identifier: Apache-2.0
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from typing import Callable, Optional, Tuple, Union
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from vllm_kunlun.lora.ops.kunlun_ops import (
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bgmv_expand,
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bgmv_expand_slice,
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bgmv_shrink,
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sgmv_expand,
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sgmv_expand_slice,
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sgmv_shrink,
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)
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from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
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# The platforms that are compatible with the PyTorch-native implementation can
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# inherit this class
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class PunicaWrapperKunlun(PunicaWrapperBase):
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"""
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PunicaWrapperKunlun with moe_fc
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"""
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def __init__(
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self,
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max_num_batched_tokens: int,
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max_batches: int,
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device: Union[torch.device, str],
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**kwargs,
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):
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PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device)
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def _shrink_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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scale: float,
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):
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expert_m = torch.zeros(9, dtype=torch.int32, device=x.device)
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sgmv_shrink(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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expert_m,
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*self.prefill_metadata,
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scale,
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)
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def _shrink_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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scale: float,
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):
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expert_m = torch.zeros(9, dtype=torch.int32, device=x.device)
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bgmv_shrink(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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expert_m,
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self.token_lora_indices,
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scale,
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)
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def _expand_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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add_inputs: bool,
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):
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sgmv_expand(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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*self.prefill_metadata,
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add_inputs,
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)
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def _expand_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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add_inputs: bool,
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):
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bgmv_expand(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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self.token_lora_indices,
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add_inputs,
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)
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def _expand_slice_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic,
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sorted_tokens_num_lod: torch.Tensor,
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moe_index: torch.Tensor,
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y_offset: int,
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y_slice_size: int,
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add_inputs: bool,
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):
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normed_scale = torch.ones([y.size(0), 1], dtype=torch.float32, device=x.device)
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sgmv_expand_slice(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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normed_scale,
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*self.prefill_metadata,
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y_offset,
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y_slice_size,
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add_inputs,
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)
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def _expand_slice_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic: torch.Tensor,
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sorted_tokens_num_lod: torch.Tensor,
|
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moe_index: torch.Tensor,
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y_offset: int,
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y_slice_size: int,
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add_inputs: bool,
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):
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normed_scale = torch.ones([y.size(0), 1], dtype=torch.float32, device=x.device)
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bgmv_expand_slice(
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x,
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w_t_all,
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y,
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block_statistic,
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sorted_tokens_num_lod,
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moe_index,
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normed_scale,
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self.token_lora_indices,
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y_offset,
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y_slice_size,
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add_inputs,
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)
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def _apply_expand(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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block_statistic,
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sorted_tokens_num_lod: torch.Tensor,
|
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moe_index: torch.Tensor,
|
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y_offset: int,
|
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y_slice_size: int,
|
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add_inputs: bool = True,
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):
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"""
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Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all`
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computation, which is suitable for the
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GEMM of lora'b.
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"""
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expand_slice_fun: Callable = (
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self._expand_slice_prefill if self.is_prefill else self._expand_slice_decode
|
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)
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expand_slice_fun(
|
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y,
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x,
|
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w_t_all,
|
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block_statistic,
|
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sorted_tokens_num_lod,
|
||||
moe_index,
|
||||
y_offset,
|
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y_slice_size,
|
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add_inputs,
|
||||
)
|
||||
|
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def _apply_shrink(
|
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self,
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y: torch.Tensor,
|
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x: torch.Tensor,
|
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w_t_all: torch.Tensor,
|
||||
block_statistic: torch.Tensor,
|
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sorted_tokens_num_lod: torch.Tensor,
|
||||
moe_index: torch.Tensor,
|
||||
scale: float,
|
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):
|
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"""
|
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Perform the ` y+=x@w_t_all` computation, which is suitable for the
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GEMM of lora'a.
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When `is_prefill is` true, it indicates that it is currently the
|
||||
prefill stage, and the `_shrink_prefill` function should be called.
|
||||
Otherwise, it is the decode stage, and the _shrink_decode function
|
||||
should be called.
|
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"""
|
||||
y_org = y
|
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y = y.view(-1, y.shape[-1])
|
||||
|
||||
shrink_fun: Callable = (
|
||||
self._shrink_prefill if self.is_prefill else self._shrink_decode
|
||||
)
|
||||
|
||||
shrink_fun(
|
||||
y, x, w_t_all, block_statistic, sorted_tokens_num_lod, moe_index, scale
|
||||
)
|
||||
|
||||
y = y.view_as(y_org)
|
||||
|
||||
def add_shrink(
|
||||
self,
|
||||
y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
|
||||
x: torch.Tensor,
|
||||
lora_a_stacked: Tuple[torch.Tensor, ...],
|
||||
block_statistic: torch.Tensor,
|
||||
sorted_tokens_num_lod: torch.Tensor,
|
||||
moe_index: torch.Tensor,
|
||||
scale: float,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
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Performs GEMM for multiple slices of lora_a.
|
||||
When `is_prefill is` true, it indicates that it is currently the
|
||||
prefill stage, and the `_shrink_prefill` function should be called.
|
||||
Otherwise, it is the decode stage, and the _shrink_decode function
|
||||
should be called.
|
||||
|
||||
Semantics:
|
||||
for i in range(len(lora_a_stacked)):
|
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y[i] += (x @ lora_a_stacked[i]) * scale
|
||||
|
||||
Args:
|
||||
y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
|
||||
x (torch.Tensor): Input tensor
|
||||
lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights
|
||||
scale (float): Scaling factor for the operation
|
||||
"""
|
||||
|
||||
x = x.view(-1, x.shape[-1])
|
||||
|
||||
for slice_idx in range(len(lora_a_stacked)): # Each slice represents a layer
|
||||
|
||||
self._apply_shrink(
|
||||
y[slice_idx],
|
||||
x,
|
||||
lora_a_stacked[slice_idx],
|
||||
block_statistic,
|
||||
sorted_tokens_num_lod,
|
||||
moe_index,
|
||||
scale,
|
||||
)
|
||||
|
||||
def add_expand(
|
||||
self,
|
||||
y: torch.Tensor,
|
||||
x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
|
||||
lora_b_stacked: Tuple[torch.Tensor, ...],
|
||||
block_statistic: torch.Tensor,
|
||||
sorted_tokens_num_lod: torch.Tensor,
|
||||
moe_index: torch.Tensor,
|
||||
lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
|
||||
output_slices: Tuple[int, ...],
|
||||
offset_start: int = 0,
|
||||
add_inputs=True,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""
|
||||
Performs GEMM and bias addition for multiple slices of lora_b.
|
||||
|
||||
Semantics:
|
||||
for i in range(len(lora_b_stacked)):
|
||||
slice = output_slices[i]
|
||||
y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] +
|
||||
lora_bias_stacked[i]
|
||||
offset += slice
|
||||
|
||||
Args:
|
||||
y (torch.Tensor): Output tensor.
|
||||
x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
|
||||
lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight
|
||||
lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]):
|
||||
bias's weight
|
||||
output_slices (Tuple[int, ...]): Every slice's size
|
||||
add_inputs (bool): Defaults to True.
|
||||
"""
|
||||
|
||||
y_org = y
|
||||
y = y.view(-1, y.shape[-1])
|
||||
offset_left = offset_start
|
||||
|
||||
if lora_bias_stacked is not None:
|
||||
self._apply_bias(
|
||||
self.token_lora_indices, y, output_slices, lora_bias_stacked
|
||||
)
|
||||
|
||||
for slice_idx in range(len(lora_b_stacked)):
|
||||
self._apply_expand(
|
||||
y,
|
||||
x[slice_idx],
|
||||
lora_b_stacked[slice_idx],
|
||||
block_statistic,
|
||||
sorted_tokens_num_lod,
|
||||
moe_index,
|
||||
offset_left,
|
||||
output_slices[slice_idx],
|
||||
add_inputs=add_inputs,
|
||||
)
|
||||
offset_left += output_slices[slice_idx]
|
||||
|
||||
y = y.view_as(y_org)
|
||||
|
||||
def add_lora_embedding(
|
||||
self,
|
||||
y: torch.Tensor,
|
||||
x: torch.Tensor,
|
||||
lora_b_stacked: torch.Tensor,
|
||||
add_inputs: bool = True,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""
|
||||
Applies lora specifically for VocabParallelEmbeddingWithLoRA.
|
||||
|
||||
Semantics:
|
||||
y += x @ lora_b_stacked
|
||||
|
||||
Args:
|
||||
y (torch.Tensor): Output tensor.
|
||||
x (torch.Tensor): Input tensor.
|
||||
lora_b_stacked (torch.Tensor): lora_b's weights.
|
||||
add_inputs (bool): Default to True.
|
||||
"""
|
||||
|
||||
expand_fun: Callable = (
|
||||
self._expand_prefill if self.is_prefill else self._expand_decode
|
||||
)
|
||||
expand_fun(y, x, lora_b_stacked, add_inputs)
|
||||
|
||||
def add_lora_linear(
|
||||
self,
|
||||
y: torch.Tensor,
|
||||
x: torch.Tensor,
|
||||
lora_a_stacked: Tuple[torch.Tensor, ...],
|
||||
lora_b_stacked: Tuple[torch.Tensor, ...],
|
||||
lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
|
||||
scale: float,
|
||||
output_slices: Tuple[int, ...],
|
||||
*,
|
||||
buffer: Optional[Tuple[torch.Tensor, ...]] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""
|
||||
Applicable to linear-related lora.
|
||||
|
||||
Semantics:
|
||||
for i in range(len(lora_a_stacked)):
|
||||
y[i] += (
|
||||
x[i].unsqueeze(0)
|
||||
@ lora_a_stacked[indices[i], layer_idx, :, :]
|
||||
@ lora_b_stacked[indices[i], layer_idx, :, :]
|
||||
* scale
|
||||
).squeeze(0)+lora_bias_stacked[i]
|
||||
|
||||
Args:
|
||||
y (torch.Tensor): Output tensor. Will be changed in-place.
|
||||
x (torch.Tensor): Input tensor
|
||||
lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight.
|
||||
lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight.
|
||||
lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
|
||||
scale (float): Scaling factor.
|
||||
output_slices (Tuple[int, ...]): Every slice's size.
|
||||
buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
|
||||
"""
|
||||
|
||||
if self.no_lora:
|
||||
return
|
||||
|
||||
expert_num = 9
|
||||
block_statistic = torch.zeros(
|
||||
[12, expert_num], dtype=torch.int32, device=x.device
|
||||
)
|
||||
sorted_tokens_num_lod = torch.zeros(
|
||||
expert_num + 1, dtype=torch.int32, device=x.device
|
||||
)
|
||||
token_nums = x.size(0)
|
||||
moe_index = torch.zeros(token_nums, dtype=torch.int32, device=x.device)
|
||||
|
||||
assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
|
||||
if lora_bias_stacked is not None:
|
||||
assert len(lora_bias_stacked) == len(output_slices)
|
||||
y = self._apply_bias(
|
||||
self.token_lora_indices, y, output_slices, lora_bias_stacked
|
||||
)
|
||||
|
||||
if buffer is None:
|
||||
r = lora_b_stacked[0].size(-1)
|
||||
buffer = tuple(
|
||||
torch.zeros((x.size(0), r), dtype=torch.float16, device=x.device)
|
||||
for _ in range(len(output_slices))
|
||||
)
|
||||
# [tensor.squeeze_(1) for tensor in lora_a_stacked]
|
||||
new_lora_a_stacked = tuple(lora_a.squeeze(1) for lora_a in lora_a_stacked)
|
||||
self.add_shrink(
|
||||
buffer,
|
||||
x,
|
||||
new_lora_a_stacked,
|
||||
block_statistic,
|
||||
sorted_tokens_num_lod,
|
||||
moe_index,
|
||||
scale,
|
||||
**kwargs,
|
||||
)
|
||||
# [tensor.unsqueeze_(1) for tensor in lora_a_stacked]
|
||||
|
||||
# [tensor.squeeze_(1) for tensor in lora_b_stacked]
|
||||
new_lora_b_stacked = tuple(lora_b.squeeze(1) for lora_b in lora_b_stacked)
|
||||
self.add_expand(
|
||||
y,
|
||||
buffer,
|
||||
new_lora_b_stacked,
|
||||
block_statistic,
|
||||
sorted_tokens_num_lod,
|
||||
moe_index,
|
||||
None,
|
||||
output_slices,
|
||||
add_inputs=True,
|
||||
**kwargs,
|
||||
)
|
||||
# [tensor.unsqueeze_(1) for tensor in lora_b_stacked]
|
||||
|
||||
def add_lora_logits(
|
||||
self,
|
||||
y: torch.Tensor,
|
||||
x: torch.Tensor,
|
||||
lora_a_stacked: torch.Tensor,
|
||||
lora_b_stacked: torch.Tensor,
|
||||
scale,
|
||||
*,
|
||||
buffer: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""
|
||||
Applies lora specifically for LogitsProcessorWithLoRA.
|
||||
|
||||
Semantics:
|
||||
buffer = (x @ lora_a_stacked) * scale
|
||||
y += buffer @ lora_b_stacked
|
||||
|
||||
Args:
|
||||
y (torch.Tensor): Output tensor.
|
||||
x (torch.Tensor): Input tensor.
|
||||
lora_a_stacked (torch.Tensor): lora_a's weights.
|
||||
lora_b_stacked (torch.Tensor):lora_b's weights.
|
||||
scale (float): Scaling factor.
|
||||
buffer (Optional[torch.Tensor]):Default to None.
|
||||
"""
|
||||
y_org = y
|
||||
y = y.view(-1, y.shape[-1])
|
||||
x = x.view(-1, x.shape[-1])
|
||||
|
||||
if lora_a_stacked.dim() == 2:
|
||||
lora_a_stacked = lora_a_stacked.unsqueeze(0)
|
||||
if lora_b_stacked.dim() == 2:
|
||||
lora_b_stacked = lora_b_stacked.unsqueeze(0)
|
||||
|
||||
r = lora_a_stacked.size(-1)
|
||||
|
||||
if buffer is None:
|
||||
buffer = torch.zeros((x.size(0), r), dtype=torch.float32, device=x.device)
|
||||
|
||||
indices = self.sampler_indices
|
||||
if indices.max() >= lora_a_stacked.size(0):
|
||||
indices = torch.clamp(indices, 0, lora_a_stacked.size(0) - 1)
|
||||
|
||||
lora_a_reshaped = lora_a_stacked.transpose(1, 2)
|
||||
lora_b_reshaped = lora_b_stacked.transpose(1, 2)
|
||||
|
||||
bgmv_shrink(x, lora_a_reshaped, buffer, indices, scale)
|
||||
bgmv_expand(buffer, lora_b_reshaped, y, indices, add_inputs=True)
|
||||
|
||||
y = y.view_as(y_org)
|
||||
@@ -311,7 +311,10 @@ class KunlunPlatform(Platform):
|
||||
|
||||
@classmethod
|
||||
def get_punica_wrapper(cls):
|
||||
return "vllm.lora.punica_wrapper.punica_cpu.PunicaWrapperCPU"
|
||||
'''
|
||||
kunlun wrapper
|
||||
'''
|
||||
return "vllm_kunlun.lora.punica_wrapper.punica_kunlun.PunicaWrapperKunlun"
|
||||
|
||||
@classmethod
|
||||
def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
|
||||
|
||||
Reference in New Issue
Block a user