mirror of
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104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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from dataclasses import dataclass, field
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from typing import Dict, List, Optional
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from ._utils import DictConversion
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def get_missing_qkv_modules_from_lora_modules(
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lora_target_modules: List[str]) -> List[str]:
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"""Get missing QKV modules from LoRA target modules.
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In current design, q_lora_params, k_lora_params and v_lora_params should be all enabled or
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all disabled at the same time. However, some lora checkpoints (e.g. BART) only contain two of them,
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so we use zero tensor to fill the missing ones.
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"""
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missing_qkv_modules = []
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if any(x in lora_target_modules for x in ["attn_q", "attn_k", "attn_v"]):
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for lora_module in ["attn_q", "attn_k", "attn_v"]:
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if lora_module not in lora_target_modules:
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missing_qkv_modules.append(lora_module)
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if any(x in lora_target_modules
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for x in ["cross_attn_q", "cross_attn_k", "cross_attn_v"]):
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for lora_module in ["cross_attn_q", "cross_attn_k", "cross_attn_v"]:
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if lora_module not in lora_target_modules:
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missing_qkv_modules.append(lora_module)
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return missing_qkv_modules
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def get_default_trtllm_modules_to_hf_modules():
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"""Get default mapping from TensorRT-LLM module names to HuggingFace module names."""
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return {
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"attn_q": "q_proj",
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"attn_k": "k_proj",
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"attn_v": "v_proj",
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"attn_qkv": "qkv_proj",
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"attn_dense": "o_proj",
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"mlp_h_to_4h": "gate_proj",
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"mlp_4h_to_h": "down_proj",
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"mlp_gate": "up_proj",
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"mlp_gate_up": "gate_up_proj",
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"moe_h_to_4h": "w1",
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"moe_4h_to_h": "w2",
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"moe_gate": "w3",
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"moe_router": "gate",
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}
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def use_lora(
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model,
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lora_config: "LoraConfig",
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trtllm_modules_to_hf_modules: Optional[Dict[str, str]] = None,
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):
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"""Use LoRA with the given model and configuration.
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This function is a wrapper that delegates to the appropriate loading function
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based on the LoRA checkpoint source.
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"""
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if lora_config.lora_ckpt_source == "nemo":
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from .lora_manager import load_nemo_lora
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load_nemo_lora(model, lora_config)
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elif lora_config.lora_ckpt_source == "hf":
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from .lora_manager import load_hf_lora
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load_hf_lora(model, lora_config, trtllm_modules_to_hf_modules)
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else:
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raise ValueError(
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f"Unsupported lora_ckpt_source: {lora_config.lora_ckpt_source}")
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@dataclass
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class LoraConfig(DictConversion):
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lora_dir: List[str] = field(default_factory=list)
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lora_ckpt_source: str = "hf"
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max_lora_rank: int = 64
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lora_target_modules: List[str] = field(default_factory=list)
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trtllm_modules_to_hf_modules: Dict[str, str] = field(default_factory=dict)
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max_loras: Optional[int] = None
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max_cpu_loras: Optional[int] = None
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swap_gate_up_proj_lora_b_weight: bool = True
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def __post_init__(self):
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assert self.lora_ckpt_source in [
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"hf", "nemo"
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], (f"lora_ckpt_source must be one of 'hf' or 'nemo', got {self.lora_ckpt_source}"
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)
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@property
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def missing_qkv_modules(self) -> List[str]:
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return get_missing_qkv_modules_from_lora_modules(
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self.lora_target_modules)
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