[WIP][Feature] DPO

xtuner/configs/internlm/internlm2_chat_1_8b/internlm2_chat_1_8b_qlora_dpo_ultra_e3.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from datasets import load_dataset
+from mmengine.dataset import DefaultSampler
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
+from peft import LoraConfig
+from torch.optim import AdamW
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig)
+
+from xtuner.dataset import DPODataset
+from xtuner.dataset.collate_fns import default_collate_fn
+from xtuner.dataset.map_fns import ultra_map_fn, template_map_fn_factory
+from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
+                                 VarlenAttnArgsToMessageHubHook)
+from xtuner.engine.runner import TrainLoop
+from xtuner.model import DPO
+from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
+
+#######################################################################
+#                          PART 1  Settings                           #
+#######################################################################
+# Model
+pretrained_model_name_or_path = 'internlm/internlm2-chat-1_8b'
+use_varlen_attn = False
+
+# Data
+ultra_path = 'HuggingFaceH4/ultrachat_200k'
+prompt_template = PROMPT_TEMPLATE.internlm2_chat
+max_length = 2048
+pack_to_max_length = True
+
+# Scheduler & Optimizer
+batch_size = 1  # per_device
+accumulative_counts = 16
+dataloader_num_workers = 0
+max_epochs = 3
+optim_type = AdamW
+lr = 2e-4
+betas = (0.9, 0.999)
+weight_decay = 0
+max_norm = 1  # grad clip
+warmup_ratio = 0.03
+
+# Save
+save_steps = 500
+save_total_limit = 2  # Maximum checkpoints to keep (-1 means unlimited)
+
+# Evaluate the generation performance during the training
+evaluation_freq = 500
+SYSTEM = SYSTEM_TEMPLATE.alpaca
+evaluation_inputs = [
+    '请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai'
+]
+
+#######################################################################
+#                      PART 2  Model & Tokenizer                      #
+#######################################################################
+tokenizer = dict(
+    type=AutoTokenizer.from_pretrained,
+    pretrained_model_name_or_path=pretrained_model_name_or_path,
+    trust_remote_code=True,
+    padding_side='right')
+
+model = dict(
+    type=DPO, # TODO
+    use_varlen_attn=use_varlen_attn,
+    llm=dict(
+        type=AutoModelForCausalLM.from_pretrained,
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        trust_remote_code=True,
+        torch_dtype=torch.float16,
+        quantization_config=dict(
+            type=BitsAndBytesConfig,
+            load_in_4bit=True,
+            load_in_8bit=False,
+            llm_int8_threshold=6.0,
+            llm_int8_has_fp16_weight=False,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4')),
+    lora=dict(
+        type=LoraConfig,
+        r=64,
+        lora_alpha=16,
+        lora_dropout=0.1,
+        bias='none',
+        task_type='CAUSAL_LM'),
+    beta=0.1)
+
+#######################################################################
+#                      PART 3  Dataset & Dataloader                   #
+#######################################################################
+ultra = dict(
+    type=DPODataset, # TODO
+    data_path=ultra_path,
+    tokenizer=tokenizer,
+    max_length=max_length,
+    dataset_map_fn=ultra_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    remove_unused_columns=True,
+    shuffle_before_pack=True,
+    pack_to_max_length=pack_to_max_length,
+    use_varlen_attn=use_varlen_attn)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=dataloader_num_workers,
+    dataset=ultra,
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
+
+#######################################################################
+#                    PART 4  Scheduler & Optimizer                    #
+#######################################################################
+# optimizer
+optim_wrapper = dict(
+    type=AmpOptimWrapper,
+    optimizer=dict(
+        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
+    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
+    accumulative_counts=accumulative_counts,
+    loss_scale='dynamic',
+    dtype='float16')
+
+# learning policy
+# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
+param_scheduler = [
+    dict(
+        type=LinearLR,
+        start_factor=1e-5,
+        by_epoch=True,
+        begin=0,
+        end=warmup_ratio * max_epochs,
+        convert_to_iter_based=True),
+    dict(
+        type=CosineAnnealingLR,
+        eta_min=0.0,
+        by_epoch=True,
+        begin=warmup_ratio * max_epochs,
+        end=max_epochs,
+        convert_to_iter_based=True)
+]
+
+# train, val, test setting
+train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
+
+#######################################################################
+#                           PART 5  Runtime                           #
+#######################################################################
+# Log the dialogue periodically during the training process, optional
+custom_hooks = [
+    dict(type=DatasetInfoHook, tokenizer=tokenizer),
+    dict(
+        type=EvaluateChatHook,
+        tokenizer=tokenizer,
+        every_n_iters=evaluation_freq,
+        evaluation_inputs=evaluation_inputs,
+        system=SYSTEM,
+        prompt_template=prompt_template)
+]
+
+if use_varlen_attn:
+    custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
+
+# configure default hooks
+default_hooks = dict(
+    # record the time of every iteration.
+    timer=dict(type=IterTimerHook),
+    # print log every 10 iterations.
+    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
+    # enable the parameter scheduler.
+    param_scheduler=dict(type=ParamSchedulerHook),
+    # save checkpoint per `save_steps`.
+    checkpoint=dict(
+        type=CheckpointHook,
+        by_epoch=False,
+        interval=save_steps,
+        max_keep_ckpts=save_total_limit),
+    # set sampler seed in distributed evrionment.
+    sampler_seed=dict(type=DistSamplerSeedHook),
+)
+
+# configure environment
+env_cfg = dict(
+    # whether to enable cudnn benchmark
+    cudnn_benchmark=False,
+    # set multi process parameters
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    # set distributed parameters
+    dist_cfg=dict(backend='nccl'),
+)
+
+# set visualizer
+visualizer = None
+
+# set log level
+log_level = 'INFO'
+
+# load from which checkpoint
+load_from = None
+
+# whether to resume training from the loaded checkpoint
+resume = False
+
+# Defaults to use random seed and disable `deterministic`
+randomness = dict(seed=None, deterministic=False)
+
+# set log processor
+log_processor = dict(by_epoch=False)

xtuner/dataset/dpo.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+import os
+
+import torch
+from datasets import Dataset as HFDataset
+from datasets import DatasetDict
+from mmengine.config import Config, ConfigDict
+from PIL import Image
+from torch.utils.data import Dataset
+
+from xtuner.registry import BUILDER
+from .huggingface import process_hf_dataset
+from .utils import expand2square
+
+
+class DPODataset(Dataset):
+
+    def __init__(self,
+                 data_path,
+                 tokenizer,
+                 max_dataset_length=None,
+                 dataset_map_fn=None,
+                 template_map_fn=None,
+                 max_length=2048):
+        super().__init__()
+        # TODO
+        pass
+
+    def __len__(self):
+        # TODO
+        pass
+
+    def __getitem__(self, index):
+        # TODO
+        pass

xtuner/model/dpo.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from collections import OrderedDict
+
+from mmengine.config import Config, ConfigDict
+from mmengine.model import BaseModel
+from mmengine.runner import load_checkpoint
+from peft import get_peft_model, prepare_model_for_kbit_training
+from torch import nn
+
+from xtuner.registry import BUILDER
+from .modules import dispatch_modules
+from .utils import (LoadWoInit, find_all_linear_names,
+                    get_peft_model_state_dict, make_inputs_require_grad,
+                    traverse_dict)
+
+
+class DPO(BaseModel):
+
+    def __init__(self,
+                 llm,
+                 ref_llm=None,
+                 lora=None,
+                 peft_model=None,
+                 use_activation_checkpointing=True,
+                 use_varlen_attn=False):
+        super().__init__()
+        with LoadWoInit():
+            self.llm = self._build_from_cfg_or_module(llm)
+        self.llm.config.use_cache = False
+        dispatch_modules(self.llm, use_varlen_attn=use_varlen_attn)
+
+        if use_activation_checkpointing:
+            # For backward compatibility
+            if hasattr(self.llm, 'enable_input_require_grads'):
+                self.llm.enable_input_require_grads()
+            else:
+                self.llm.get_input_embeddings().register_forward_hook(
+                    make_inputs_require_grad)
+
+            # enable gradient checkpointing for memory efficiency
+            self.gradient_checkpointing_enable()
+
+        if isinstance(lora, dict) or isinstance(lora, Config) or isinstance(
+                lora, ConfigDict):
+            self.lora = BUILDER.build(lora)
+        else:
+            self.lora = lora
+        self.peft_model = peft_model
+        self.use_lora = lora is not None
+        if self.use_lora:
+            self._prepare_for_lora(peft_model, use_activation_checkpointing)
+
+        self._is_init = True
+        # Determines whether to calculate attention based on the
+        # seq_len dimension (use_varlen_attn = False) or the actual length of
+        # the sequence.
+        self.use_varlen_attn = use_varlen_attn
+
+        # TODO: Add ref model and ref model config
+        self.ref_llm = None
+
+    def gradient_checkpointing_enable(self):
+        self.activation_checkpointing_enable()
+
+    def activation_checkpointing_enable(self):
+        self.llm.gradient_checkpointing_enable()
+
+    def gradient_checkpointing_disable(self):
+        self.activation_checkpointing_disable()
+
+    def activation_checkpointing_disable(self):
+        self.llm.gradient_checkpointing_disable()
+
+    def _prepare_for_lora(self,
+                          peft_model=None,
+                          use_activation_checkpointing=True):
+        self.llm = prepare_model_for_kbit_training(
+            self.llm, use_activation_checkpointing)
+        if self.lora.target_modules is None:
+            modules = find_all_linear_names(self.llm)
+            self.lora.target_modules = modules
+
+        self.llm = get_peft_model(self.llm, self.lora)
+        if peft_model is not None:
+            _ = load_checkpoint(self, peft_model)
+
+    def init_weights(self):
+        pass
+
+    def _build_from_cfg_or_module(self, cfg_or_mod):
+        if isinstance(cfg_or_mod, nn.Module):
+            return cfg_or_mod
+        elif isinstance(cfg_or_mod, dict):
+            traverse_dict(cfg_or_mod)
+            return BUILDER.build(cfg_or_mod)
+        else:
+            raise NotImplementedError
+
+    def forward(self, data, data_samples=None, mode='loss'):
+
+        if mode == 'loss':
+            return self.compute_loss(data, data_samples)
+        elif mode == 'predict':
+            return self.predict(data, data_samples)
+        elif mode == 'tensor':
+            return self._forward(data, data_samples)
+        else:
+            raise NotImplementedError
+
+    def _forward(self, data, data_samples=None):
+
+        outputs = self.llm(**data)
+
+        return outputs
+
+    def predict(self, data, data_samples=None):
+        outputs = self.llm(**data)
+        logits_dict = [{'logits': logits} for logits in outputs.logits]
+        return logits_dict
+
+    def compute_loss(self, data, data_samples=None):
+        # TODO
+        pass
+
+    def state_dict(self, *args, **kwargs):
+        state_dict = super().state_dict(*args, **kwargs)
+        if not self.use_lora:
+            return state_dict
+        to_return = get_peft_model_state_dict(self.llm, state_dict=state_dict)
+        return OrderedDict(to_return)
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.llm, name)