199 lines
7.8 KiB
Python
199 lines
7.8 KiB
Python
# Copyright 2024 the LlamaFactory team.
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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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import os
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from typing import Dict, Sequence
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import pytest
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import torch
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from peft import LoraModel, PeftModel
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from transformers import AutoModelForCausalLM
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from trl import AutoModelForCausalLMWithValueHead
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from llamafactory.extras.misc import get_current_device
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from llamafactory.hparams import get_infer_args, get_train_args
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from llamafactory.model import load_model, load_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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TINY_LLAMA_ADAPTER = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")
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TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "lora",
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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"overwrite_cache": True,
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"output_dir": "dummy_dir",
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"overwrite_output_dir": True,
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"fp16": True,
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}
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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"adapter_name_or_path": TINY_LLAMA_ADAPTER,
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"finetuning_type": "lora",
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"template": "llama3",
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"infer_dtype": "float16",
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}
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def load_reference_model(is_trainable: bool = False) -> "LoraModel":
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model = AutoModelForCausalLM.from_pretrained(
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TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
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)
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lora_model = PeftModel.from_pretrained(model, TINY_LLAMA_ADAPTER, is_trainable=is_trainable)
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for param in filter(lambda p: p.requires_grad, lora_model.parameters()):
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param.data = param.data.to(torch.float32)
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return lora_model
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def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: Sequence[str] = []):
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state_dict_a = model_a.state_dict()
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state_dict_b = model_b.state_dict()
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assert set(state_dict_a.keys()) == set(state_dict_b.keys())
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for name in state_dict_a.keys():
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if any(key in name for key in diff_keys):
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assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
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else:
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assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
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@pytest.fixture
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def fix_valuehead_cpu_loading():
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def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
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state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
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self.v_head.load_state_dict(state_dict, strict=False)
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del state_dict
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AutoModelForCausalLMWithValueHead.post_init = post_init
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def test_lora_train_qv_modules():
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model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "q_proj,v_proj", **TRAIN_ARGS})
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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linear_modules = set()
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for name, param in model.named_parameters():
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if any(module in name for module in ["lora_A", "lora_B"]):
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linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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else:
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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assert linear_modules == {"q_proj", "v_proj"}
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def test_lora_train_all_modules():
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model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "all", **TRAIN_ARGS})
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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linear_modules = set()
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for name, param in model.named_parameters():
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if any(module in name for module in ["lora_A", "lora_B"]):
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linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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else:
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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assert linear_modules == {"q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"}
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def test_lora_train_extra_modules():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{"lora_target": "all", "additional_target": "embed_tokens,lm_head", **TRAIN_ARGS}
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)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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extra_modules = set()
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for name, param in model.named_parameters():
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if any(module in name for module in ["lora_A", "lora_B"]):
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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elif "modules_to_save" in name:
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extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-1])
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assert param.requires_grad is True
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assert param.dtype == torch.float32
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else:
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assert param.requires_grad is False
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assert param.dtype == torch.float16
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assert extra_modules == {"embed_tokens", "lm_head"}
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def test_lora_train_old_adapters():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{"adapter_name_or_path": TINY_LLAMA_ADAPTER, "create_new_adapter": False, **TRAIN_ARGS}
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)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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ref_model = load_reference_model(is_trainable=True)
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compare_model(model, ref_model)
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def test_lora_train_new_adapters():
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model_args, _, _, finetuning_args, _ = get_train_args(
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{"adapter_name_or_path": TINY_LLAMA_ADAPTER, "create_new_adapter": True, **TRAIN_ARGS}
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)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
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ref_model = load_reference_model(is_trainable=True)
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compare_model(
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model, ref_model, diff_keys=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"]
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)
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@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
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def test_lora_train_valuehead():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(
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tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True, add_valuehead=True
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)
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ref_model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
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TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
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)
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state_dict = model.state_dict()
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ref_state_dict = ref_model.state_dict()
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assert torch.allclose(state_dict["v_head.summary.weight"], ref_state_dict["v_head.summary.weight"])
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assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])
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def test_lora_inference():
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model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
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ref_model = load_reference_model().merge_and_unload()
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compare_model(model, ref_model)
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