181 lines
7.5 KiB
Python
181 lines
7.5 KiB
Python
# Copyright 2023-2024 SGLang Team
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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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# Integrates "S-LoRA: Serving Thousands of Concurrent LoRA Adapters"
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# and "Punica: Multi-Tenant LoRA Serving"
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# LoRA layers class inheritance adapted from:
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# https://github.com/vllm-project/vllm/blob/4abf6336ec65c270343eb895e7b18786e9274176/vllm/lora/layers.py
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import logging
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import re
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from typing import Dict, List
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import torch
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from torch import nn
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from sglang.srt.configs.load_config import LoadConfig
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from sglang.srt.hf_transformers_utils import AutoConfig
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from sglang.srt.lora.backend.base_backend import BaseLoRABackend
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from sglang.srt.lora.backend.chunked_backend import ChunkedSgmvLoRABackend
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from sglang.srt.lora.backend.triton_backend import TritonLoRABackend
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from sglang.srt.lora.lora_config import LoRAConfig
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from sglang.srt.model_loader.loader import DefaultModelLoader
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logger = logging.getLogger(__name__)
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SUPPORTED_BACKENDS = (TritonLoRABackend, ChunkedSgmvLoRABackend)
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class LoRALayer(nn.Module):
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def __init__(self, config: LoRAConfig, base_hf_config: AutoConfig):
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super().__init__()
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self.config: LoRAConfig = config
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self.base_hf_config: AutoConfig = base_hf_config
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# lora weights in cpu. The weights are loaded from checkpoint.
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self.weights: Dict[str, torch.Tensor] = {}
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class LoRAAdapter(nn.Module):
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def __init__(
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self,
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uid: str,
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config: LoRAConfig,
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base_hf_config: AutoConfig,
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load_config: LoadConfig,
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lora_backend: BaseLoRABackend,
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):
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super().__init__()
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self.uid: str = uid
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self.config: LoRAConfig = config
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assert self.config.hf_config["peft_type"].lower() == "lora"
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self.base_hf_config: AutoConfig = base_hf_config
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self.load_config: LoadConfig = load_config
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self.lora_backend: BaseLoRABackend = lora_backend
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self.scaling: float = self.config.lora_alpha / self.config.r
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self.layers: List[LoRALayer] = nn.ModuleList(
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[
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LoRALayer(config, base_hf_config)
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for _ in range(base_hf_config.num_hidden_layers)
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]
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)
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self.weights: Dict[str, torch.Tensor] = {}
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# initialize the LoRA weights to cpu
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def initialize_weights(self):
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model_path = self.config.path
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loader = DefaultModelLoader(self.load_config)
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revision = getattr(self.config.hf_config, "revision", None)
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for name, loaded_weight in loader._get_weights_iterator(
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DefaultModelLoader.Source(
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model_path, revision=revision, fall_back_to_pt=True
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)
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):
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match = re.search(r"layers\.(\d+)\.", name)
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if match is not None:
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layer_id = int(match.group(1))
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self.layers[layer_id].weights[name] = loaded_weight.cpu()
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else:
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self.weights[name] = loaded_weight.cpu()
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# normalize kv_proj and gate_up_proj
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for layer in self.layers:
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weight_names = list(layer.weights.keys())
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self.normalize_qkv_proj(weight_names, layer.weights)
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self.normalize_gate_up_proj(weight_names, layer.weights)
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def normalize_qkv_proj(
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self, weight_names: List[str], weights: Dict[str, torch.Tensor]
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):
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# Collect target q/k/v modules. This process is necessary since there might be no lora attached to k_proj
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target_module = set()
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for weight_name in weight_names:
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if "k_proj" in weight_name:
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target_module.add("k_proj")
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if "q_proj" in weight_name:
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target_module.add("q_proj")
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if "v_proj" in weight_name:
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target_module.add("v_proj")
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if "qkv_proj" in weight_name:
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target_module.add("qkv_proj")
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if len(target_module) == 0:
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return
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for weight_name in weight_names:
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# We assume every lora adaptor should contain lora modules for q_proj
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if "q_proj" in weight_name:
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q_name = weight_name
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k_name = weight_name.replace("q_proj", "k_proj")
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v_name = weight_name.replace("q_proj", "v_proj")
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qkv_name = weight_name.replace("q_proj", "qkv_proj")
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# If k_proj doesn't have lora, initialize it to zero
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k_proj_weight = (
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weights[k_name]
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if "k_proj" in target_module
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else torch.zeros_like(weights[v_name])
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)
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weights[qkv_name] = torch.cat(
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(
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weights[q_name],
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k_proj_weight,
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weights[v_name],
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),
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0,
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)
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weights.pop(q_name)
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if "k_proj" in target_module:
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weights.pop(k_name)
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weights.pop(v_name)
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elif "qkv_proj" in weight_name:
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# If qkv_proj is already stacked, we normalize it following the SGL convention.
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qkv_name = weight_name
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q_name = weight_name.replace("qkv_proj", "q_proj")
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k_name = weight_name.replace("qkv_proj", "k_proj")
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v_name = weight_name.replace("qkv_proj", "v_proj")
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if "lora_A" in weight_name:
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weights[qkv_name] = weights[qkv_name].repeat(3, 1)
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# else: no-op as LoRA B weight is already stacked.
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def normalize_gate_up_proj(
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self, weight_names: List[str], weights: Dict[str, torch.Tensor]
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):
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for weight_name in weight_names:
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if "gate_proj" in weight_name:
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up_name = weight_name.replace("gate_proj", "up_proj")
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gate_up_name = weight_name.replace("gate_proj", "gate_up_proj")
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if up_name not in weights:
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weights[up_name] = torch.zeros_like(weights[weight_name])
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assert isinstance(self.lora_backend, SUPPORTED_BACKENDS), (
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f"LoRA weight initialization currently only supported for LoRA backends: {', '.join(b.name for b in SUPPORTED_BACKENDS)}"
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f"Received backend: {self.lora_backend.name}. Please verify your backend configuration "
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f"or consider implementing custom initialization logic for other backends."
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)
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weights[gate_up_name] = torch.cat(
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(weights[weight_name], weights[up_name]), 0
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)
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weights.pop(weight_name)
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if up_name in weights:
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weights.pop(up_name)
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elif "gate_up_proj" in weight_name:
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# If gate_up_proj is already stacked, we normalize it following the SGL convention
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gate_up_name = weight_name
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if "lora_A" in weight_name:
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weights[gate_up_name] = weights[gate_up_name].repeat(2, 1)
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# else: no-op as LoRA B weight is already stacked.
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