[Feature] Initial support for multi-LoRA serving (#1307)
This commit is contained in:
Ying Sheng
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"""
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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import re
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from dataclasses import dataclass
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import torch
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from flashinfer import SegmentGEMMWrapper
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from sglang.srt.lora.lora import LoRAAdapter, get_lora_layer
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from sglang.srt.lora.lora_config import LoRAConfig
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.utils import replace_submodule
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def get_stacked_name(name):
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# origin name -> (name for A, name for B)
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params_mapping = {
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"q_proj": ("qkv_proj", "q_proj"),
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"k_proj": ("qkv_proj", "kv_proj"),
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"v_proj": ("qkv_proj", "kv_proj"),
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"gate_proj": ("gate_up_proj", "gate_up_proj"),
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"up_proj": ("gate_up_proj", "gate_up_proj"),
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}
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return params_mapping.get(name, (name, name))
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def get_layer_id(name):
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match = re.search(r"layers\.(\d+)\.", name)
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if match is None:
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return None
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return int(match.group(1))
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class LoRAManager:
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def __init__(
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self,
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base_model,
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lora_paths,
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base_hf_config,
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max_loras_per_batch,
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load_config,
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dtype,
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):
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self.base_model = base_model
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self.lora_paths = lora_paths
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self.base_hf_config = base_hf_config
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self.max_loras_per_batch = max_loras_per_batch
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self.load_config = load_config
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self.dtype = dtype
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workspace_buffer = torch.empty(1 * 1024 * 1024, dtype=torch.int8, device="cuda")
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self.segment_gemm = SegmentGEMMWrapper(workspace_buffer)
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self.init_loras()
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self.init_lora_memory_pool()
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self.init_lora_batch()
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def match_target_modules(self, module_name):
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for target_module in self.target_modules:
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if module_name.split(".")[-1] == target_module:
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return True
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return False
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def get_target_modules(self):
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modules = []
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for module_name, module in self.base_model.named_modules():
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if self.match_target_modules(module_name):
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modules.append((module_name, module))
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return modules
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def set_lora_module(self, module_name, module):
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lora_module = get_lora_layer(
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module, self.segment_gemm, self.max_lora_dim, self.scaling
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)
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replace_submodule(self.base_model, module_name, lora_module)
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return lora_module
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def init_loras(self):
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# get configs and target modules
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self.configs = {}
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self.origin_target_modules = set()
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for path in self.lora_paths:
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self.configs[path] = LoRAConfig(path)
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self.origin_target_modules = set(self.origin_target_modules) | set(
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self.configs[path].target_modules
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)
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self.target_modules = set(
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[
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self.base_model.get_module_name(module)
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for module in self.origin_target_modules
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]
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)
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self.target_weights = set(
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[get_stacked_name(module) for module in self.origin_target_modules]
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)
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# load all weights to cpu
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self.loras = []
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self.lora_id = {}
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for path in self.lora_paths:
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self.lora_id[path] = len(self.loras)
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self.loras.append(
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LoRAAdapter(
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path, self.configs[path], self.base_hf_config, self.load_config
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)
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)
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self.loras[-1].initialize_weights()
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# misc lora configs
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self.max_lora_dim = max([x.hf_config["r"] for x in self.configs.values()])
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self.scaling = self.loras[0].scaling
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# FIXME remove the restrictions
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assert all(x.hf_config["r"] == self.max_lora_dim for x in self.configs.values())
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assert all(x.scaling == self.scaling for x in self.loras)
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# monkey patch to use the LoRA version
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self.lora_modules = []
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for module_name, module in self.get_target_modules():
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self.lora_modules.append(
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(module_name, self.set_lora_module(module_name, module))
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)
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def init_lora_memory_pool(self):
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# preallocate lora memory pool
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self.A_buffer = {}
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self.B_buffer = {}
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num_layer = self.base_hf_config.num_hidden_layers
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for module_A, module_B in self.target_weights:
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# init A tensor, column_major=True
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hidden_dim_A, _ = self.base_model.get_hidden_dim(module_A)
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c = self.loras[-1].get_stacked_multiply(module_A)
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if module_A not in self.A_buffer:
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self.A_buffer[module_A] = [
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torch.empty(
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(
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self.max_loras_per_batch,
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self.max_lora_dim * c,
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hidden_dim_A,
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),
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dtype=self.dtype,
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device="cuda",
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)
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for i in range(num_layer)
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]
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# init B tensor, column_major=True
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_, hidden_dim_B = self.base_model.get_hidden_dim(module_B)
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c = self.loras[-1].get_stacked_multiply(module_B)
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if module_B not in self.B_buffer:
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self.B_buffer[module_B] = [
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torch.empty(
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(
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self.max_loras_per_batch,
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hidden_dim_B * c,
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self.max_lora_dim,
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),
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dtype=self.dtype,
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device="cuda",
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)
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for i in range(num_layer)
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]
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def init_lora_batch(self):
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self.active_uids = set() # set of active loras
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self.buffer_id = {} # lora uid -> idx in memory pool
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def get_weight_name(self, name, idx):
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for target_weight_name in self.target_weights:
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if target_weight_name[idx] in name:
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return target_weight_name[idx]
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def load_lora(self, uid, buffer_id):
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num_layer = self.base_hf_config.num_hidden_layers
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if uid is None:
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for i in range(num_layer):
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for k in self.A_buffer.keys():
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self.A_buffer[k][i][buffer_id] *= 0
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return
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for i in range(num_layer):
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layer_weights = self.loras[self.lora_id[uid]].layers[i].weights
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for name, weights in layer_weights.items():
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if "lora_A" in name:
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lora_weight_name = self.get_weight_name(name, 0)
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if lora_weight_name:
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self.A_buffer[lora_weight_name][i][buffer_id].copy_(weights)
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else:
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lora_weight_name = self.get_weight_name(name, 1)
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if lora_weight_name:
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self.B_buffer[lora_weight_name][i][buffer_id].copy_(weights)
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def prepare_lora_batch(self, batch, extend_seq_lens=None):
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# load active loras into lora memory pool
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cur_uids = set([req.lora_path for req in batch.reqs])
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assert len(cur_uids) <= self.max_loras_per_batch
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i = 0
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evictable_uids = list(self.active_uids)
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for uid in cur_uids:
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if uid not in self.active_uids:
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while i < len(evictable_uids) and evictable_uids[i] in cur_uids:
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i += 1
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if i < len(evictable_uids):
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self.active_uids.remove(evictable_uids[i])
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self.buffer_id.pop(evictable_uids[i])
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self.load_lora(uid, i)
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self.active_uids.add(uid)
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self.buffer_id[uid] = i
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i += 1
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if cur_uids == set([None]):
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return
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# setup lora in forward modules
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bs = len(batch.reqs)
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seg_lens = extend_seq_lens if batch.forward_mode.is_extend() else torch.ones(bs)
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weight_indices = torch.empty((bs,), dtype=torch.int64, device="cuda")
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for i, req in enumerate(batch.reqs):
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weight_indices[i] = self.buffer_id[req.lora_path]
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for module_name, module in self.lora_modules:
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layer_id = get_layer_id(module_name)
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if "qkv_proj" not in module_name:
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weight_name = self.get_weight_name(module_name, 0)
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module.set_lora_info(
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self.A_buffer[weight_name][layer_id],
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self.B_buffer[weight_name][layer_id],
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bs,
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seg_lens,
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weight_indices,
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)
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else:
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module.set_lora_info(
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self.A_buffer["qkv_proj"][layer_id],
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self.B_buffer["q_proj"][layer_id],
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self.B_buffer["kv_proj"][layer_id],
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bs,
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seg_lens,
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weight_indices,
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)
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