diff --git a/README.md b/README.md index a29d575..867b29e 100644 --- a/README.md +++ b/README.md @@ -6,69 +6,7 @@ πŸ€— Models & Datasets | πŸ“ƒ Technical Report

--> -# The Alignment Handbook +# Reinforcement Learning with Active Queries -## Installation instructions +The website of Reinforcement Learning with Active Queries, See [here](https://jkx19.github.io/ActiveQuery/) -To run the code in this project, first, create a Python virtual environment using e.g. Conda: - -```shell -conda create -n handbook python=3.10 && conda activate handbook -``` - -Next, install PyTorch `v2.1.0` - the precise version is important for reproducibility! Since this is hardware-dependent, we -direct you to the [PyTorch Installation Page](https://pytorch.org/get-started/locally/). - -You can then install the remaining package dependencies as follows: - -```shell -git clone https://github.com/huggingface/alignment-handbook.git -cd ./alignment-handbook/ -python -m pip install . -``` - -You will also need Flash Attention 2 installed, which can be done by running: - -> **Note** -> If your machine has less than 96GB of RAM and many CPU cores, reduce the MAX_JOBS., e.g. `MAX_JOBS=4 pip install flash-attn --no-build-isolation` - -```shell -python -m pip install flash-attn --no-build-isolation -``` - -Next, log into your Hugging Face account as follows: - -```shell -huggingface-cli login -``` - -Finally, install Git LFS so that you can push models to the Hugging Face Hub: - -```shell -sudo apt-get install git-lfs -``` - -You can now check out the `scripts` and `recipes` directories for instructions on how to train some models πŸͺ! - -## Project structure - -``` -β”œβ”€β”€ LICENSE -β”œβ”€β”€ Makefile <- Makefile with commands like `make style` -β”œβ”€β”€ README.md <- The top-level README for developers using this project -β”œβ”€β”€ chapters <- Educational content to render on hf.co/learn -β”œβ”€β”€ recipes <- Recipe configs, accelerate configs, slurm scripts -β”œβ”€β”€ scripts <- Scripts to train and evaluate chat models -β”œβ”€β”€ setup.cfg <- Installation config (mostly used for configuring code quality & tests) -β”œβ”€β”€ setup.py <- Makes project pip installable (pip install -e .) so `alignment` can be imported -β”œβ”€β”€ src <- Source code for use in this project -└── tests <- Unit tests -``` - -## Running - -First checkout `recipes/zephyr-7b-beta/dpo/config_lora.yaml` and set the following arguments. gradient_accumulation_steps, loss_type (choose from "corr", "sigmoid", "hinge") and per_device_train_batch_size. Then edit --num_processes in `pipeline.sh`. Make sure gradient_accumulation_steps\*per_device_train_batch_size\*num_processes=true_batch_size. Then run the following command in shell: - -``` -bash pipeline.sh -``` diff --git a/assets/handbook.png b/assets/handbook.png deleted file mode 100644 index a1146bf..0000000 Binary files a/assets/handbook.png and /dev/null differ diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml deleted file mode 100644 index e8fc7c0..0000000 --- a/chapters/en/_toctree.yml +++ /dev/null @@ -1,4 +0,0 @@ -- title: Unit 0. Welcome to the RLHF Handbook! - sections: - - local: chapter0/introduction - title: What is this about? \ No newline at end of file diff --git a/chapters/en/chapter0/introduction.mdx b/chapters/en/chapter0/introduction.mdx deleted file mode 100644 index 26f500f..0000000 --- a/chapters/en/chapter0/introduction.mdx +++ /dev/null @@ -1,3 +0,0 @@ -# Welcome to the RLHF Handbook! - -Stay tuned for more details πŸ€— \ No newline at end of file diff --git a/images/algo.png b/images/algo.png new file mode 100644 index 0000000..4af409e Binary files /dev/null and b/images/algo.png differ diff --git a/images/chart.png b/images/chart.png new file mode 100644 index 0000000..3e37c01 Binary files /dev/null and b/images/chart.png differ diff --git a/images/table.png b/images/table.png new file mode 100644 index 0000000..98b862a Binary files /dev/null and b/images/table.png differ diff --git a/images/them.png b/images/them.png new file mode 100644 index 0000000..b106f5a Binary files /dev/null and b/images/them.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..71bae19 --- /dev/null +++ b/index.html @@ -0,0 +1,234 @@ + + + + + + + + + + + + + + + + + + + + Reinforcement Learning from Human Feedback with Active Queries + + + + + + + + + + + + + + + + + + + + + + +
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Reinforcement Learning from Human Feedback with Active Queries

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+ + + Yihe Deng, + + Weitong Zhang, + + Zixiang Chen, + + Quanquan Gu + +
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+ University of California, Los Angeles +
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Abstract

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+ Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement learning from human feedback (RLHF). Despite their superior performance, current RLHF approaches often require a large amount of human-labelled preference data, which is expensive to collect. In this paper, inspired by the success of active learning, we address this problem by proposing query-efficient RLHF methods. We first formalize the alignment problem as a contextual dueling bandit problem and design an active-query-based proximal policy optimization APPO algorithm with a constant regret bound and a constant query complexity. We then propose ADPO, a practical version of our algorithm based on direct preference optimization (DPO) and apply it to fine-tuning LLMs. Our experiments show that ADPO, while only making about half of queries for human preference, matches the performance of the state-of-the-art DPO method. +

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Active Proximal Policy Optimization

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+ The details of our algorithm Active Proximal Policy Optimization +

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+ We propose Active Proximal Policy Optimization (APPO) for learning linear contextual bandits with global sub-optimal gap. In each round, the algorithm first use the following MLE estimator to estimate the parameter: + $$ \lambda\kappa_{\sigma} \mathbf{\theta} + \sum_{\tau \in \mathcal{C}_{t-1}}\Big(o_{\tau} - \mu\big(\langle \mathbf{\theta}, \mathbf{\phi}^1_{\tau}-\mathbf{\phi}^2_{\tau} \rangle\big)\Big) (\mathbf{\phi}^1_{\tau}-\mathbf{\phi}^2_{\tau}) = \mathbf{0} $$. + With the estimated parameter, APPO then compute the estimated reward and choose the best arm. APPO will not query for the label if the uncertainty is low. Our theoretical analysis shows the following guarantees regarding regret upper bound and query complexity. +

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Active Direct Preference Optimization

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Method

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+ We further proposed Active Direct Preference Optimization (ADPO) as a label-efficient alternative to Direct Preference Optimization. For each piece of prompt and the two answers, DPO treat the LLM as a reward model and assign rewards to the answers. Therefore, the difference of the two scores indicates the model's confidence of its prediction of the label. In ADPO, LLM will only queries the laebls of those answer pairs with high uncertainty. For those with low uncertainty, LLM will use its predicted label as its training target. +

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Experiment Results

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+ We trained zephry-7b-sft-full on the 62k Ultrachat-Feedback dataset using both DPO and our method ADPO. We evaluate the trained models on Open LLM LeaderBoard dataset. We also tested the performance of our method but without training on the pseudo-labels (denoted as ADPO w/o PL). Here are the key points of our results: +

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  • while only quering about half (32k in 62k) of human preference labels than DPO, ADPO outperforms DPO on Arc Challenge, TruthfulQA, HellaSwag dataset and get a higher average score than DPO;
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  • without the pseudo-labels predicted by the LLM, the performance of ADPO w/o PL can no longer out perform DPO, so the pseudo-labels play a key role in ADPO;
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+ The results of DPO and ADPO. ADPO outperforms DPO with only half queries +

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+ The training curves of DPO, ADPO w/o PL and ADPO on different datasets. ADPO enjoys a faster improvement than DPO and ADPO w/o PL. +

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BibTeX

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@misc{deng2023rephrase,
+        title={Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves}, 
+        author={Yihe Deng and Weitong Zhang and Zixiang Chen and Quanquan Gu},
+        year={2023},
+        eprint={2311.04205},
+        archivePrefix={arXiv},
+        primaryClass={cs.CL}
+      }
+
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+ + + + + + + + + + + + + \ No newline at end of file diff --git a/recipes/accelerate_configs/deepspeed_zero3.yaml b/recipes/accelerate_configs/deepspeed_zero3.yaml deleted file mode 100644 index b5a1201..0000000 --- a/recipes/accelerate_configs/deepspeed_zero3.yaml +++ /dev/null @@ -1,22 +0,0 @@ -compute_environment: LOCAL_MACHINE -debug: false -deepspeed_config: - deepspeed_multinode_launcher: standard - offload_optimizer_device: none - offload_param_device: none - zero3_init_flag: true - zero3_save_16bit_model: true - zero_stage: 3 -distributed_type: DEEPSPEED -downcast_bf16: 'no' -machine_rank: 0 -main_training_function: main -mixed_precision: bf16 -num_machines: 1 -num_processes: 8 -rdzv_backend: static -same_network: true -tpu_env: [] -tpu_use_cluster: false -tpu_use_sudo: false -use_cpu: false diff --git a/recipes/accelerate_configs/multi_gpu.yaml b/recipes/accelerate_configs/multi_gpu.yaml deleted file mode 100644 index 4f05571..0000000 --- a/recipes/accelerate_configs/multi_gpu.yaml +++ /dev/null @@ -1,16 +0,0 @@ -compute_environment: LOCAL_MACHINE -debug: false -distributed_type: MULTI_GPU -downcast_bf16: 'no' -gpu_ids: all -machine_rank: 0 -main_training_function: main -mixed_precision: bf16 -num_machines: 1 -num_processes: 8 -rdzv_backend: static -same_network: true -tpu_env: [] -tpu_use_cluster: false -tpu_use_sudo: false -use_cpu: false diff --git a/recipes/launch.slurm b/recipes/launch.slurm deleted file mode 100644 index a5f4359..0000000 --- a/recipes/launch.slurm +++ /dev/null @@ -1,93 +0,0 @@ -#!/bin/bash -#SBATCH --ntasks-per-node=1 -#SBATCH --exclusive -#SBATCH --gres=gpu:8 -#SBATCH --partition=production-cluster # Adjust this for your cluster -#SBATCH --output=/fsx/h4/logs/%x-%j.out # Adjust this for your cluster -#SBATCH --err=/fsx/h4/logs/%x-%j.err # Adjust this for your cluster - -set -x -e - -source ~/.bashrc -conda activate handbook -echo "START TIME: $(date)" - -MODEL=$1 -TASK=$2 -PRECISION=$3 -ACCELERATOR=$4 -OPTIONAL_ARGS=$5 - -# Training setup -NUM_NODES=$SLURM_NNODES -GPUS_PER_NODE=8 -WORLD_SIZE=$(($NUM_NODES*$GPUS_PER_NODE)) -# Due to conflicts between Accelerate's DeepSpeed configs and Transformers' TrainingArguments, we need to parse the gradient accumulation steps from the config file to ensure they match -CONFIG_FILE=recipes/$MODEL/$TASK/config_$PRECISION.yaml -GRAD_ACC_STEPS=$(grep 'gradient_accumulation_steps' $CONFIG_FILE | awk '{print $2}') - -# Split the string into individual arguments -IFS=' ' read -ra ARGS <<< "$OPTIONAL_ARGS" - -# Loop through the arguments and find the one with "--gradient_accumulation_steps" -for arg in "${ARGS[@]}"; do - if [[ "$arg" == "--gradient_accumulation_steps="* ]]; then - # Extract the value after the equals sign - GRAD_ACC_STEPS="${arg#*=}" - break # Exit the loop once we find the desired argument - fi -done - -echo "Gradient accumulation steps: $GRAD_ACC_STEPS" -# so processes know who to talk to -MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1) -MASTER_PORT=6000 - -export CMD=" \ - scripts/run_$TASK.py $CONFIG_FILE $OPTIONAL_ARGS - " - -export LAUNCHER="ACCELERATE_LOG_LEVEL=info accelerate launch \ - --config_file recipes/accelerate_configs/$ACCELERATOR.yaml \ - --gradient_accumulation_steps $GRAD_ACC_STEPS \ - --num_machines $NUM_NODES \ - --num_processes $WORLD_SIZE \ - --main_process_ip $MASTER_ADDR \ - --main_process_port $MASTER_PORT \ - --machine_rank \$SLURM_PROCID \ - --rdzv_conf "rdzv_backend=c10d,rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT" \ - --max_restarts 1 \ - --role \$(hostname -s): \ - --tee 3 \ - " - -# force crashing on nccl issues like hanging broadcast -export NCCL_ASYNC_ERROR_HANDLING=1 -# export NCCL_DEBUG=INFO -# export NCCL_DEBUG_SUBSYS=COLL -# export NCCL_SOCKET_NTHREADS=1 -# export NCCL_NSOCKS_PERTHREAD=1 -# export CUDA_LAUNCH_BLOCKING=1 - -# Specific configuration optimized for the Hugging Face Compute Cluster -# Be ye warned this may not work on other clusters! -export NCCL_PROTO=simple -export RDMAV_FORK_SAFE=1 -export FI_EFA_FORK_SAFE=1 -export FI_EFA_USE_DEVICE_RDMA=1 -export FI_PROVIDER=efa -export FI_LOG_LEVEL=1 -export NCCL_IB_DISABLE=1 -export NCCL_SOCKET_IFNAME=ens - -# srun error handling: -# --wait=60: wait 60 sec after the first task terminates before terminating all remaining tasks -# --kill-on-bad-exit=1: terminate a step if any task exits with a non-zero exit code -SRUN_ARGS=" \ - --wait=60 \ - --kill-on-bad-exit=1 \ - " - -clear; srun $SRUN_ARGS --jobid $SLURM_JOB_ID bash -c "$LAUNCHER --role \$SLURMD_NODENAME: $CMD" 2>&1 - -echo "END TIME: $(date)" \ No newline at end of file diff --git a/recipes/zephyr-7b-beta/README.md b/recipes/zephyr-7b-beta/README.md deleted file mode 100644 index 836bcc3..0000000 --- a/recipes/zephyr-7b-beta/README.md +++ /dev/null @@ -1,29 +0,0 @@ - -# Instructions to Replicate Zephyr-7b-Ξ² - -As described in the Zephyr [technical report](https://huggingface.co/papers/2310.16944), training this model proceeds in two steps: - -1. Apply SFT to fine-tune Mistral 7B on a filtered version of the UltraChat dataset ([link](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)). The result is an SFT model like [`zephyr-7b-sft-full`](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) or [`zephyr-7b-sft-lora`](https://huggingface.co/alignment-handbook/zephyr-7b-sft-lora). -2. Align the SFT model to AI feedback via DPO on a preprocessed version of the UltraFeedback dataset ([link](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)). The result is an DPO model like [`zephyr-7b-dpo-full`](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-full) or [`zephyr-7b-dpo-lora`](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-lora). - -See below for commands to train these models using either DeepSpeed ZeRO-3 or LoRA. - -## Full training examples -You will require 8 GPUs (80GB of VRAM) to train the full model. -```shell -# Step 1 - SFT -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/zephyr-7b-beta/sft/config_full.yaml - -# Step 2 - DPO -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py recipes/zephyr-7b-beta/dpo/config_full.yaml -``` - -## LoRA training examples - -```shell -# Step 1 - SFT -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_sft.py recipes/zephyr-7b-beta/sft/config_lora.yaml - -# Step 2 - DPO -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_dpo.py recipes/zephyr-7b-beta/dpo/config_lora.yaml -``` \ No newline at end of file diff --git a/recipes/zephyr-7b-beta/dpo/config_full.yaml b/recipes/zephyr-7b-beta/dpo/config_full.yaml deleted file mode 100644 index 27e9f64..0000000 --- a/recipes/zephyr-7b-beta/dpo/config_full.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# Model arguments -model_name_or_path: alignment-handbook/zephyr-7b-sft-full - -# Data training arguments -# For definitions, see: src/h4/training/config.py -dataset_mixer: - HuggingFaceH4/ultrafeedback_binarized: 1.0 -dataset_splits: -- train_prefs -- test_prefs -preprocessing_num_workers: 12 - -# DPOTrainer arguments -bf16: true -beta: 0.1 -do_eval: true -evaluation_strategy: steps -eval_steps: 100 -gradient_accumulation_steps: 1 -gradient_checkpointing: true -hub_model_id: zephyr-7b-dpo-full -learning_rate: 5.0e-7 -log_level: info -logging_steps: 10 -lr_scheduler_type: linear -max_length: 1024 -max_prompt_length: 512 -num_train_epochs: 3 -optim: rmsprop -output_dir: data/zephyr-7b-dpo-full -per_device_train_batch_size: 8 -per_device_eval_batch_size: 4 -push_to_hub: true -save_strategy: "epoch" -save_total_limit: null -seed: 42 -warmup_ratio: 0.1 \ No newline at end of file diff --git a/recipes/zephyr-7b-beta/dpo/config_lora.yaml b/recipes/zephyr-7b-beta/dpo/config_lora.yaml deleted file mode 100644 index 9ee59c8..0000000 --- a/recipes/zephyr-7b-beta/dpo/config_lora.yaml +++ /dev/null @@ -1,55 +0,0 @@ -# Model arguments -model_name_or_path: alignment-handbook/zephyr-7b-sft-full -torch_dtype: auto - -# LoRA arguments -use_peft: true -lora_r: 64 -lora_alpha: 16 -lora_dropout: 0.1 -lora_target_modules: -- q_proj -- k_proj -- v_proj -- o_proj - -# Data training arguments - -dataset_mixer: - HuggingFaceH4/ultrafeedback_binarized: 1.0 -dataset_splits: -- train_prefs -- test_prefs -preprocessing_num_workers: 12 - -# DPOTrainer arguments -bf16: true -beta: 0.1 -do_eval: true -evaluation_strategy: epoch -eval_steps: 100 -gradient_accumulation_steps: 8 -gradient_checkpointing: true -gradient_checkpointing_kwargs: - use_reentrant: false -hub_model_id: zephyr-ds -learning_rate: 1.0e-5 -log_level: info -logging_steps: 10 -lr_scheduler_type: linear -max_length: 1024 -max_prompt_length: 512 -num_train_epochs: 1 -optim: rmsprop -loss_type: sigmoid -output_dir: data/zephyr-full-1e-5-psd # It is handy to append `hub_model_revision` to keep track of your local experiments -per_device_train_batch_size: 4 -per_device_eval_batch_size: 4 -push_to_hub: true -save_strategy: "steps" -save_steps: 100 -save_total_limit: null -seed: 42 -warmup_ratio: 0.1 -data_selection: true -Gamma: 1.3 \ No newline at end of file diff --git a/recipes/zephyr-7b-beta/sft/config_full.yaml b/recipes/zephyr-7b-beta/sft/config_full.yaml deleted file mode 100644 index 4d8d2d1..0000000 --- a/recipes/zephyr-7b-beta/sft/config_full.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# Model arguments -model_name_or_path: mistralai/Mistral-7B-v0.1 -model_revision: main -torch_dtype: bfloat16 -use_flash_attention_2: true - -# Data training arguments -dataset_mixer: - HuggingFaceH4/ultrachat_200k: 1.0 -dataset_splits: -- train_sft -- test_sft -preprocessing_num_workers: 12 - -# SFT trainer config -bf16: true -do_eval: true -evaluation_strategy: epoch -gradient_accumulation_steps: 2 -gradient_checkpointing: true -hub_model_id: zephyr-7b-sft-full -hub_strategy: every_save -learning_rate: 2.0e-05 -log_level: info -logging_steps: 5 -logging_strategy: steps -lr_scheduler_type: cosine -max_seq_length: 2048 -max_steps: -1 -num_train_epochs: 1 -output_dir: data/zephyr-7b-sft-full -overwrite_output_dir: true -per_device_eval_batch_size: 16 -per_device_train_batch_size: 32 -push_to_hub: true -remove_unused_columns: true -report_to: -- tensorboard -save_strategy: "no" -save_total_limit: null -seed: 42 -tf32: true \ No newline at end of file diff --git a/recipes/zephyr-7b-beta/sft/config_lora.yaml b/recipes/zephyr-7b-beta/sft/config_lora.yaml deleted file mode 100644 index 286166a..0000000 --- a/recipes/zephyr-7b-beta/sft/config_lora.yaml +++ /dev/null @@ -1,52 +0,0 @@ -# Model arguments -model_name_or_path: mistralai/Mistral-7B-v0.1 -torch_dtype: auto -use_flash_attention_2: true - -# LoRA arguments -use_peft: true -lora_r: 64 -lora_alpha: 16 -lora_dropout: 0.1 -lora_target_modules: -- q_proj -- k_proj -- v_proj -- o_proj - -# Data training arguments -dataset_mixer: - HuggingFaceH4/ultrachat_200k: 1.0 -dataset_splits: -- train_sft -- test_sft -preprocessing_num_workers: 12 - -# SFT trainer config -bf16: true -do_eval: true -evaluation_strategy: epoch -gradient_accumulation_steps: 128 -gradient_checkpointing: true -gradient_checkpointing_kwargs: - use_reentrant: false -hub_model_id: zephyr-7b-sft-lora -hub_strategy: every_save -learning_rate: 2.0e-05 -log_level: info -logging_steps: 5 -logging_strategy: steps -lr_scheduler_type: cosine -max_seq_length: 2048 -max_steps: -1 -num_train_epochs: 1 -output_dir: data/zephyr-7b-sft-lora -overwrite_output_dir: true -per_device_eval_batch_size: 8 -per_device_train_batch_size: 4 -push_to_hub: true -report_to: -- tensorboard -save_strategy: "no" -save_total_limit: null -seed: 42 \ No newline at end of file diff --git a/scripts/README.md b/scripts/README.md deleted file mode 100644 index 07e2976..0000000 --- a/scripts/README.md +++ /dev/null @@ -1,131 +0,0 @@ - -# Scripts to Train and Evaluate Chat Models - -## Fine-tuning - -In the handbook, we provide three main ways to align LLMs for chat: - -- Full fine-tuning on a multi-GPU machine with DeepSpeed ZeRO-3 (tested on an 8 x A100 (80GB) node). -- LoRA or QLoRA fine-tuning on a single consumer 24GB GPU (tested on an RTX 4090). -- LoRA fine-tuning on a multi-GPU machine with DeepSpeed ZeRO-3 (tested on a 2 x A100s (80GB)). - -In practice, we find comparable performance for both full and LoRA fine-tuning, with the latter having the advantage of producing small adapter weights that are fast to upload and download from the Hugging Face Hub. Here are the general commands to fine-tune your models: - -```shell -# Full training with ZeRO-3 on 8 GPUs -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml - -# LoRA training on a single GPU -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_{task}.py recipes/{model_name}/{task}/config_lora.yaml - -# QLoRA 4-bit training on a single GPU -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_{task}.py recipes/{model_name}/{task}/config_lora.yaml --load_in_4bit=true - -# LoRA training with ZeRO-3 on two or more GPUs -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml --num_processes={num_gpus} scripts/run_{task}.py recipes/{model_name}/{task}/config_lora.yaml -``` - -Here `{task}` refers to the type of training you wish to run (SFT, DPO, etc), while `{model_name}` refers to the choice of a recipe in the `recipes` directory. For example, to replicate Zephyr-7B-Ξ² you can run: - -```shell -# Step 1 - train SFT policy -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/zephyr-7b-beta/sft/config_full.yaml - -# Step 2 - align with DPO -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py recipes/zephyr-7b-beta/dpo/config_full.yaml -``` - -**πŸ’‘ Tip:** If you scale up/down the number of GPUs, we recommend also scaling up the per-device batch size or number of gradient accumulation steps to keep the global batch size constant (and thus replicate our results). - -By default, these scripts will push each model to your Hugging Face Hub username, i.e. `{username}/{model_name}-{task}`. You can override the parameters in each YAML config by appending them to the command as follows: - -```shell -# Change batch size, number of epochs etc -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml --per_device_train_batch_size=42 --num_train_epochs=5 -``` - -By default all training metrics are logged with TensorBoard. If you have a [Weights and Biases](https://wandb.ai/site) account and are logged in, you can view the training metrics by appending `--report_to=wandb`, e.g. - -```shell -ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml --report_to=wandb -``` - -## Launching jobs on a Slurm cluster - -If you have access to a Slurm cluster, we provide a `recipes/launch.slurm` script that will automatically queue training jobs for you. Here's how you can use it: - -```shell -sbatch --job-name=handbook_{task} --nodes=1 recipes/launch.slurm {model_name} {task} {precision} {accelerator} -``` - -Here `{model_name}` and `{task}` are defined as above, while `{precision}` refers to the type of training (`full` vs `lora`) and `{accelerator}` refers to the choice of πŸ€— Accelerate config in `recipes/accelerate_configs`. If you wish to override the default config parameters, you can provide them by appending a space-separated string like `'--arg1=value1 --arg2=value2'. Here's a concrete example to run SFT on 1 node of 8 GPUs: - -```shell -# Launch on Slurm and override default hyperparameters -sbatch --job-name=handbook_sft --nodes=1 recipes/launch.slurm zephyr-7b-beta sft full deepspeed_zero3 '--per_device_train_batch_size=42 --num_train_epochs=5' -``` - -You can scale the number of nodes by increasing the `--nodes` flag. - -**⚠️ Note:** the configuration in `recipes/launch.slurm` is optimised for the Hugging Face Compute Cluster and may require tweaking to be adapted to your own compute nodes. - -## Fine-tuning on your datasets - -Under the hood, each training script uses the `get_datasets()` function which allows one to easily combine multiple datasets with varying proportions. For instance, this is how one can specify multiple datasets and which splits to combine in one of the YAML configs: - -```yaml -datasets_mixer: - dataset_1: 0.5 # Use 50% of the training examples - dataset_2: 0.66 # Use 66% of the training examples - dataset_3: 0.10 # Use 10% of the training examples -dataset_splits: -- train_xxx # The training splits to mix -- test_xxx # The test splits to mix -``` - -If you want to fine-tune on your datasets, the main thing to keep in mind is how the chat templates are applied to the dataset blend. Since each task (SFT, DPO, etc), requires a different format, we assume the datasets have the following columns: - -**SFT** - -* `messages`: A list of `dicts` in the form `{"role": "{role}", "content": {content}}`. -* See [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) for an example. - -**DPO** - -* `chosen`: A list of `dicts` in the form `{"role": "{role}", "content": {content}}` corresponding to the preferred dialogue. -* `rejected`: A list of `dicts` in the form `{"role": "{role}", "content": {content}}` corresponding to the dispreferred dialogue. -* See [ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) for an example. - -We also find it useful to include dedicated splits per task in our datasets, so e.g. we have: - -* `{train,test}_sft`: Splits for SFT training. -* `{train,test}_gen`: Splits for generation ranking like rejection sampling or PPO. -* `{train,test}_prefs`: Splits for preference modelling, like reward modelling or DPO. - -If you format your dataset in the same way, our training scripts should work out of the box! - -## Evaluating chat models - -We recommend benchmarking chat models on: - -* [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench): a multi-turn benchmark spanning 80 dialogues and 10 domains. -* [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): a single-turn benchmark which evaluates the helpfulness of chat and instruct models against `text-davinci-003`. - -For both benchmarks, we have added support for the [Zephyr chat template](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full/blob/ac6e600eefcce74f5e8bae1035d4f66019e93190/tokenizer_config.json#L30) (which is the default produced by our scripts), so you can evaluate models produced by our scripts as follows: - -**MT-Bench** - -* Follow the installation instructions [here](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) -* Make sure the word `zephyr` exists in the `--model-path` argument when generating the model responses [here](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge#step-1-generate-model-answers-to-mt-bench-questions). This will ensure the correct chat template is loaded. For example, the following model name is valid: `--model-path {hub_username}/my-baby-zephyr` -* Generate the model responses and GPT-4 rankings. - -**AlpacaEval** - -* Follow the installation instructions [here](https://github.com/tatsu-lab/alpaca_eval#quick-start) -* Copy-paste the [config](https://github.com/tatsu-lab/alpaca_eval/blob/main/src/alpaca_eval/models_configs/zephyr-7b-beta/configs.yaml) for `zephyr-7b-beta` and place it in the `model_configs` directory under `{your_zephyr_model}`. - * Next, update the [config name](https://github.com/tatsu-lab/alpaca_eval/blob/2daa6e11b194653043ca74f735728dc068e04aae/src/alpaca_eval/models_configs/zephyr-7b-beta/configs.yaml#L1) and [Hub model ID](https://github.com/tatsu-lab/alpaca_eval/blob/2daa6e11b194653043ca74f735728dc068e04aae/src/alpaca_eval/models_configs/zephyr-7b-beta/configs.yaml#L5) to match your model name. -* Follow the steps to evaluate your model [here](https://github.com/tatsu-lab/alpaca_eval/tree/main#evaluating-a-model). - -Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. For that reason, we also recommend submitting your best models for human evaluation in: - -* [Chatbot Arena](https://chat.lmsys.org): a live, human evaluation of chat models in head-to-head comparisons. \ No newline at end of file diff --git a/scripts/run_dpo.py b/scripts/run_dpo.py deleted file mode 100644 index 504442e..0000000 --- a/scripts/run_dpo.py +++ /dev/null @@ -1,234 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import logging -import sys - -import torch -import transformers -from transformers import AutoModelForCausalLM, set_seed - -from accelerate import Accelerator -from alignment import ( - DataArguments, - DPOConfig, - H4ArgumentParser, - ModelArguments, - apply_chat_template, - get_datasets, - get_kbit_device_map, - get_peft_config, - get_quantization_config, - get_tokenizer, - is_adapter_model, -) -from peft import PeftConfig, PeftModel -from trainer import DPOTrainer -# from trl import DPOTrainer - - -logger = logging.getLogger(__name__) - - -def main(): - parser = H4ArgumentParser((ModelArguments, DataArguments, DPOConfig)) - model_args, data_args, training_args = parser.parse() - training_args.save_steps = 50 - - ####### - # Setup - ####### - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - handlers=[logging.StreamHandler(sys.stdout)], - ) - log_level = training_args.get_process_log_level() - logger.setLevel(log_level) - transformers.utils.logging.set_verbosity(log_level) - transformers.utils.logging.enable_default_handler() - transformers.utils.logging.enable_explicit_format() - - # Log on each process the small summary: - logger.info(f"Model parameters {model_args}") - logger.info(f"Data parameters {data_args}") - logger.info(f"Training/evaluation parameters {training_args}") - - # Set seed for reproducibility - set_seed(training_args.seed) - - # Increase distributed timeout to 3h to enable push to Hub to complete - accelerator = Accelerator() - - ############### - # Load datasets - ############### - raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits) - logger.info( - f"Training on the following splits: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}" - ) - column_names = list(raw_datasets["train"].features) - - ##################################### - # Load tokenizer and process datasets - ##################################### - data_args.truncation_side = "left" # Truncate from left to ensure we don't lose labels in final turn - tokenizer = get_tokenizer(model_args, data_args) - - ##################### - # Apply chat template - ##################### - raw_datasets = raw_datasets.map( - apply_chat_template, - fn_kwargs={"tokenizer": tokenizer, "task": "dpo"}, - num_proc=data_args.preprocessing_num_workers, - remove_columns=column_names, - desc="Formatting comparisons with prompt template", - ) - - # Replace column names with what TRL needs, text_chosen -> chosen and text_rejected -> rejected - for split in ["train", "test"]: - raw_datasets[split] = raw_datasets[split].rename_columns( - {"text_prompt": "prompt", "text_chosen": "chosen", "text_rejected": "rejected"} - ) - - torch_dtype = ( - model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype) - ) - quantization_config = get_quantization_config(model_args) - - model_kwargs = dict( - revision=model_args.model_revision, - trust_remote_code=model_args.trust_remote_code, - use_flash_attention_2=model_args.use_flash_attention_2, - torch_dtype=torch_dtype, - use_cache=False if training_args.gradient_checkpointing else True, - device_map=get_kbit_device_map() if quantization_config is not None else None, - quantization_config=quantization_config, - ) - - model = model_args.model_name_or_path - if is_adapter_model(model, model_args.model_revision): - # load the model, merge the adapter weights and unload the adapter - # Note: to run QLora, you will need to merge the based model separately as the merged model in 16bit - logger.info(f"Merging peft adapters for {model_args.model_name_or_path=}") - - peft_config = PeftConfig.from_pretrained(model_args.model_name_or_path, revision=model_args.model_revision) - - model_kwargs = dict( - revision=model_args.base_model_revision, - trust_remote_code=model_args.trust_remote_code, - use_flash_attention_2=model_args.use_flash_attention_2, - torch_dtype=torch_dtype, - use_cache=False if training_args.gradient_checkpointing else True, - ) - base_model = AutoModelForCausalLM.from_pretrained( - peft_config.base_model_name_or_path, - **model_kwargs, - ) - model = PeftModel.from_pretrained( - base_model, model_args.model_name_or_path, revision=model_args.model_revision - ) - model.eval() - model = model.merge_and_unload() - model_kwargs = None - - ref_model = model - ref_model_kwargs = model_kwargs - - if model_args.use_peft is True: - ref_model = None - ref_model_kwargs = None - - if training_args.data_selection: - training_args.output_dir = training_args.output_dir + f"-ds-{training_args.Gamma}" - - ######################### - # Instantiate DPO trainer - ######################### - dpo_trainer = DPOTrainer( - model, - ref_model, - model_init_kwargs=model_kwargs, - ref_model_init_kwargs=ref_model_kwargs, - args=training_args, - beta=training_args.beta, - loss_type=training_args.loss_type, - train_dataset=raw_datasets["train"], - eval_dataset=raw_datasets["test"], - tokenizer=tokenizer, - max_length=training_args.max_length, - max_prompt_length=training_args.max_prompt_length, - peft_config=get_peft_config(model_args), - data_selection=training_args.data_selection, - Gamma=training_args.Gamma, - ) - - ############### - # Training loop - ############### - train_result = dpo_trainer.train() - metrics = train_result.metrics - max_train_samples = ( - data_args.max_train_samples if data_args.max_train_samples is not None else len(raw_datasets["train"]) - ) - metrics["train_samples"] = min(max_train_samples, len(raw_datasets["train"])) - dpo_trainer.log_metrics("train", metrics) - dpo_trainer.save_metrics("train", metrics) - dpo_trainer.save_state() - - logger.info("*** Training complete ***") - - ########## - # Evaluate - ########## - if training_args.do_eval: - logger.info("*** Evaluate ***") - metrics = dpo_trainer.evaluate() - max_eval_samples = ( - data_args.max_eval_samples if data_args.max_eval_samples is not None else len(raw_datasets["test"]) - ) - metrics["eval_samples"] = min(max_eval_samples, len(raw_datasets["test"])) - dpo_trainer.log_metrics("eval", metrics) - dpo_trainer.save_metrics("eval", metrics) - - ################################## - # Save model and create model card - ################################## - dpo_trainer.save_model(training_args.output_dir) - # Save everything else on main process - if accelerator.is_main_process: - kwargs = { - "finetuned_from": model_args.model_name_or_path, - "dataset": list(data_args.dataset_mixer.keys()), - "dataset_tags": list(data_args.dataset_mixer.keys()), - "tags": ["alignment-handbook"], - } - dpo_trainer.create_model_card(**kwargs) - # Restore k,v cache for fast inference - dpo_trainer.model.config.use_cache = True - dpo_trainer.model.config.save_pretrained(training_args.output_dir) - if training_args.push_to_hub is True: - dpo_trainer.push_to_hub() - - # Ensure we don't timeout on model save / push to Hub - logger.info("*** Waiting for all processes to finish ***") - accelerator.wait_for_everyone() - - logger.info("*** Run complete! ***") - - -if __name__ == "__main__": - main() diff --git a/scripts/run_sft.py b/scripts/run_sft.py deleted file mode 100644 index 748dd71..0000000 --- a/scripts/run_sft.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Supervised fine-tuning script for decoder language models. -""" - -import logging -import random -import sys - -import datasets -import torch -import transformers -from transformers import set_seed - -from accelerate import Accelerator -from alignment import ( - DataArguments, - H4ArgumentParser, - ModelArguments, - SFTConfig, - apply_chat_template, - get_datasets, - get_kbit_device_map, - get_peft_config, - get_quantization_config, - get_tokenizer, -) -from trl import SFTTrainer - - -logger = logging.getLogger(__name__) - - -def main(): - parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig)) - model_args, data_args, training_args = parser.parse() - - # Set seed for reproducibility - set_seed(training_args.seed) - - accelerator = Accelerator() - - ############### - # Setup logging - ############### - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - handlers=[logging.StreamHandler(sys.stdout)], - ) - log_level = training_args.get_process_log_level() - logger.setLevel(log_level) - datasets.utils.logging.set_verbosity(log_level) - transformers.utils.logging.set_verbosity(log_level) - transformers.utils.logging.enable_default_handler() - transformers.utils.logging.enable_explicit_format() - - # Log on each process a small summary - logger.warning( - f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" - + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" - ) - logger.info(f"Model parameters {model_args}") - logger.info(f"Data parameters {data_args}") - logger.info(f"Training/evaluation parameters {training_args}") - - ############### - # Load datasets - ############### - raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits) - logger.info( - f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}" - ) - - ################ - # Load tokenizer - ################ - tokenizer = get_tokenizer(model_args, data_args) - - ##################### - # Apply chat template - ##################### - raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"}) - train_dataset = raw_datasets["train"] - eval_dataset = raw_datasets["test"] - - with training_args.main_process_first(desc="Log a few random samples from the processed training set"): - for index in random.sample(range(len(raw_datasets["train"])), 3): - logger.info(f"Sample {index} of the processed training set:\n\n{raw_datasets['train'][index]['text']}") - - ####################### - # Load pretrained model - ####################### - logger.info("*** Load pretrained model ***") - torch_dtype = ( - model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype) - ) - quantization_config = get_quantization_config(model_args) - - model_kwargs = dict( - revision=model_args.model_revision, - trust_remote_code=model_args.trust_remote_code, - use_flash_attention_2=model_args.use_flash_attention_2, - torch_dtype=torch_dtype, - use_cache=False if training_args.gradient_checkpointing else True, - device_map=get_kbit_device_map() if quantization_config is not None else None, - quantization_config=quantization_config, - ) - logger.info("*** Model loaded! ***") - - ######################## - # Initialize the Trainer - ######################## - trainer = SFTTrainer( - model=model_args.model_name_or_path, - model_init_kwargs=model_kwargs, - args=training_args, - train_dataset=train_dataset, - eval_dataset=eval_dataset, - dataset_text_field="text", - max_seq_length=training_args.max_seq_length, - tokenizer=tokenizer, - packing=True, - peft_config=get_peft_config(model_args), - ) - - ############### - # Training loop - ############### - logger.info("*** Train ***") - train_result = trainer.train() - metrics = train_result.metrics - max_train_samples = data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) - metrics["train_samples"] = min(max_train_samples, len(train_dataset)) - trainer.log_metrics("train", metrics) - trainer.save_metrics("train", metrics) - trainer.save_state() - - ########## - # Evaluate - ########## - if training_args.do_eval: - logger.info("*** Evaluate ***") - metrics = trainer.evaluate() - max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) - metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) - trainer.log_metrics("eval", metrics) - trainer.save_metrics("eval", metrics) - - ################################## - # Save model and create model card - ################################## - logger.info("*** Save model ***") - trainer.save_model(training_args.output_dir) - logger.info(f"Model saved to {training_args.output_dir}") - - # Save everything else on main process - if accelerator.is_main_process: - kwargs = { - "finetuned_from": model_args.model_name_or_path, - "dataset": list(data_args.dataset_mixer.keys()), - "dataset_tags": list(data_args.dataset_mixer.keys()), - "tags": ["alignment-handbook"], - } - trainer.create_model_card(**kwargs) - # Restore k,v cache for fast inference - trainer.model.config.use_cache = True - trainer.model.config.save_pretrained(training_args.output_dir) - - if training_args.push_to_hub is True: - logger.info("Pushing to hub...") - trainer.push_to_hub() - - accelerator.wait_for_everyone() - - -if __name__ == "__main__": - main() diff --git a/scripts/trainer.py b/scripts/trainer.py deleted file mode 100644 index 42e463b..0000000 --- a/scripts/trainer.py +++ /dev/null @@ -1,782 +0,0 @@ -# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import inspect -import random -import warnings -from collections import defaultdict -from copy import deepcopy -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F -from accelerate.utils import is_deepspeed_available -from datasets import Dataset -from torch.utils.data import DataLoader -from transformers import ( - AutoModelForCausalLM, - DataCollator, - PreTrainedModel, - PreTrainedTokenizerBase, - Trainer, - TrainingArguments, -) -from transformers.trainer_callback import TrainerCallback -from transformers.trainer_utils import EvalLoopOutput - -from trl.import_utils import is_peft_available, is_wandb_available -from trl.models import PreTrainedModelWrapper, create_reference_model -from trl.trainer.utils import DPODataCollatorWithPadding, disable_dropout_in_model, pad_to_length - - -if is_peft_available(): - from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training - - -if is_wandb_available(): - import wandb - -if is_deepspeed_available(): - import deepspeed - - -class DPOTrainer(Trainer): - r""" - Initialize DPOTrainer. - - Args: - model (`transformers.PreTrainedModel`): - The model to train, preferably an `AutoModelForSequenceClassification`. - ref_model (`PreTrainedModelWrapper`): - Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no - reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized. - beta (`float`, defaults to 0.1): - The beta factor in DPO loss. Higher beta means less divergence from the initial policy. - loss_type (`str`, defaults to `"sigmoid"`): - The type of DPO loss to use. Either `"sigmoid"` the default DPO loss or `"hinge"` loss from SLiC paper. - args (`transformers.TrainingArguments`): - The arguments to use for training. - data_collator (`transformers.DataCollator`): - The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used - which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. - label_pad_token_id (`int`, defaults to `-100`): - The label pad token id. This argument is required if you want to use the default data collator. - padding_value (`int`, defaults to `0`): - The padding value. This argument is required if you want to use the default data collator. - truncation_mode (`str`, defaults to `keep_end`): - The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator. - train_dataset (`datasets.Dataset`): - The dataset to use for training. - eval_dataset (`datasets.Dataset`): - The dataset to use for evaluation. - tokenizer (`transformers.PreTrainedTokenizerBase`): - The tokenizer to use for training. This argument is required if you want to use the default data collator. - model_init (`Callable[[], transformers.PreTrainedModel]`): - The model initializer to use for training. If None is specified, the default model initializer will be used. - callbacks (`List[transformers.TrainerCallback]`): - The callbacks to use for training. - optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): - The optimizer and scheduler to use for training. - preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): - The function to use to preprocess the logits before computing the metrics. - max_length (`int`, defaults to `None`): - The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator. - max_prompt_length (`int`, defaults to `None`): - The maximum length of the prompt. This argument is required if you want to use the default data collator. - max_target_length (`int`, defaults to `None`): - The maximum length of the target. This argument is required if you want to use the default data collator and your model is an encoder-decoder. - peft_config (`Dict`, defaults to `None`): - The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model. - is_encoder_decoder (`Optional[bool]`, `optional`, defaults to `None`): - If no model is provided, we need to know if the model_init returns an encoder-decoder. - disable_dropout (`bool`, defaults to `True`): - Whether or not to disable dropouts in `model` and `ref_model`. - generate_during_eval (`bool`, defaults to `False`): - Whether to sample and log generations during evaluation step. - compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*): - The function to use to compute the metrics. Must take a `EvalPrediction` and return - a dictionary string to metric values. - model_init_kwargs: (`Optional[Dict]`, *optional*): - Dict of Optional kwargs to pass when instantiating the model from a string - ref_model_init_kwargs: (`Optional[Dict]`, *optional*): - Dict of Optional kwargs to pass when instantiating the ref model from a string - - """ - - def __init__( - self, - model: Union[PreTrainedModel, nn.Module, str] = None, - ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, - beta: float = 0.1, - loss_type: Literal["sigmoid", "hinge", "corr"] = "sigmoid", - args: TrainingArguments = None, - data_collator: Optional[DataCollator] = None, - label_pad_token_id: int = -100, - padding_value: int = 0, - truncation_mode: str = "keep_end", - train_dataset: Optional[Dataset] = None, - eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None, - tokenizer: Optional[PreTrainedTokenizerBase] = None, - model_init: Optional[Callable[[], PreTrainedModel]] = None, - callbacks: Optional[List[TrainerCallback]] = None, - optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = ( - None, - None, - ), - preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, - max_length: Optional[int] = None, - max_prompt_length: Optional[int] = None, - max_target_length: Optional[int] = None, - peft_config: Optional[Dict] = None, - is_encoder_decoder: Optional[bool] = None, - disable_dropout: bool = True, - generate_during_eval: bool = False, - compute_metrics: Optional[Callable[[EvalLoopOutput], Dict]] = None, - model_init_kwargs: Optional[Dict] = None, - ref_model_init_kwargs: Optional[Dict] = None, - data_selection: bool = False, - Gamma: float = 0, - ): - if model_init_kwargs is None: - model_init_kwargs = {} - elif not isinstance(model, str): - raise ValueError("You passed model_kwargs to the DPOTrainer. But your model is already instantiated.") - - if ref_model_init_kwargs is None: - ref_model_init_kwargs = {} - elif not isinstance(ref_model, str): - raise ValueError( - "You passed ref_model_kwargs to the DPOTrainer. But your ref_model is already instantiated." - ) - - if isinstance(model, str): - warnings.warn( - "You passed a model_id to the DPOTrainer. This will automatically create an " - "`AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you." - ) - model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) - - if isinstance(ref_model, str): - warnings.warn( - "You passed a ref model_id to the DPOTrainer. This will automatically create an " - "`AutoModelForCausalLM`" - ) - ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs) - - if not is_peft_available() and peft_config is not None: - raise ValueError( - "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models" - ) - elif is_peft_available() and peft_config is not None: - # if model is a peft model and we have a peft_config, we merge and unload it first - if isinstance(model, PeftModel): - model = model.merge_and_unload() - - if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): - _support_gc_kwargs = hasattr( - args, "gradient_checkpointing_kwargs" - ) and "gradient_checkpointing_kwargs" in list( - inspect.signature(prepare_model_for_kbit_training).parameters - ) - - preprare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} - - if _support_gc_kwargs: - preprare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs - - model = prepare_model_for_kbit_training(model, **preprare_model_kwargs) - elif getattr(args, "gradient_checkpointing", False): - # For backward compatibility with older versions of transformers - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - # get peft model with the given config - model = get_peft_model(model, peft_config) - - # For models that use gradient_checkpoiting, we need to attach a hook that enables input - # to explicitly have `requires_grad=True`, otherwise training will either silently - # fail or completely fail. - elif getattr(args, "gradient_checkpointing", False): - # For backward compatibility with older versions of transformers - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - if generate_during_eval and not is_wandb_available(): - raise ValueError( - "`generate_during_eval=True` requires Weights and Biases to be installed." - " Please install `wandb` to resolve." - ) - - if model is not None: - self.is_encoder_decoder = model.config.is_encoder_decoder - elif is_encoder_decoder is None: - raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") - else: - self.is_encoder_decoder = is_encoder_decoder - - self.is_peft_model = is_peft_available() and isinstance(model, PeftModel) - - if ref_model: - self.ref_model = ref_model - elif self.is_peft_model: - # The `model` with adapters turned off will be used as the reference model - self.ref_model = None - else: - self.ref_model = create_reference_model(model) - - if data_collator is None: - if tokenizer is None: - raise ValueError( - "max_length or a tokenizer must be specified when using the default DPODataCollatorWithPadding" - ) - if max_length is None: - warnings.warn( - "When using DPODataCollatorWithPadding, you should set `max_length` in the DPOTrainer's init" - " it will be set to `512` by default, but you should do it yourself in the future.", - UserWarning, - ) - max_length = 512 - if max_prompt_length is None: - warnings.warn( - "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the DPOTrainer's init" - " it will be set to `128` by default, but you should do it yourself in the future.", - UserWarning, - ) - max_prompt_length = 128 - - if max_target_length is None and self.is_encoder_decoder: - warnings.warn( - "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_target_length` in the DPOTrainer's init" - " it will be set to `128` by default, but you should do it yourself in the future.", - UserWarning, - ) - max_target_length = 128 - - data_collator = DPODataCollatorWithPadding( - tokenizer, - max_length=max_length, - max_prompt_length=max_prompt_length, - label_pad_token_id=label_pad_token_id, - padding_value=padding_value, - truncation_mode=truncation_mode, - is_encoder_decoder=self.is_encoder_decoder, - max_target_length=max_target_length, - ) - - if args.remove_unused_columns: - args.remove_unused_columns = False - # warn users - warnings.warn( - "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments" - " we have set it for you, but you should do it yourself in the future.", - UserWarning, - ) - - self.use_dpo_data_collator = True - else: - self.use_dpo_data_collator = False - - if disable_dropout: - disable_dropout_in_model(model) - if self.ref_model is not None: - disable_dropout_in_model(self.ref_model) - - self.max_length = max_length - self.generate_during_eval = generate_during_eval - self.label_pad_token_id = label_pad_token_id - self.padding_value = padding_value - - self.beta = beta - self.loss_type = loss_type - self.data_selection = data_selection - self.Gamma = Gamma - self.use_labeled = 0 - self.predicted_label = 0 - self.counter = 0 - - self._stored_metrics = defaultdict(lambda: defaultdict(list)) - - super().__init__( - model=model, - args=args, - data_collator=data_collator, - train_dataset=train_dataset, - eval_dataset=eval_dataset, - tokenizer=tokenizer, - model_init=model_init, - compute_metrics=compute_metrics, - callbacks=callbacks, - optimizers=optimizers, - preprocess_logits_for_metrics=preprocess_logits_for_metrics, - ) - - if not hasattr(self, "accelerator"): - raise AttributeError( - "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." - ) - - if self.ref_model is None: - if not hasattr(self.accelerator.unwrap_model(self.model), "disable_adapter"): - raise ValueError( - "You are using a `peft` version that does not support `disable_adapter`. Please update your `peft` version to the latest version." - ) - else: - if self.is_deepspeed_enabled: - self.ref_model = self._prepare_deepspeed(self.ref_model) - else: - self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) - - def _prepare_deepspeed(self, model: PreTrainedModelWrapper): - # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473 - deepspeed_plugin = self.accelerator.state.deepspeed_plugin - config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config) - - if model is not None: - if hasattr(model, "config"): - hidden_size = ( - max(model.config.hidden_sizes) - if getattr(model.config, "hidden_sizes", None) - else getattr(model.config, "hidden_size", None) - ) - if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3: - # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0` - # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081 - config_kwargs.update( - { - "zero_optimization.reduce_bucket_size": hidden_size * hidden_size, - "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size, - "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size, - } - ) - - # If ZeRO-3 is used, we shard both the active and reference model. - # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0) - if config_kwargs["zero_optimization"]["stage"] != 3: - config_kwargs["zero_optimization"]["stage"] = 0 - model, *_ = deepspeed.initialize(model=model, config=config_kwargs) - model.eval() - return model - - def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]: - """Concatenate the chosen and rejected inputs into a single tensor. - - Args: - batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length). - - Returns: - A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'. - """ - concatenated_batch = {} - - # print(batch.keys()) - # exit() - - if self.is_encoder_decoder: - max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1]) - else: - max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1]) - - for k in batch: - if k.startswith("chosen") and isinstance(batch[k], torch.Tensor): - pad_value = self.label_pad_token_id if "labels" in k or self.is_encoder_decoder else self.padding_value - concatenated_key = k.replace("chosen", "concatenated") - concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value) - for k in batch: - if k.startswith("rejected") and isinstance(batch[k], torch.Tensor): - pad_value = self.label_pad_token_id if "labels" in k or self.is_encoder_decoder else self.padding_value - concatenated_key = k.replace("rejected", "concatenated") - concatenated_batch[concatenated_key] = torch.cat( - ( - concatenated_batch[concatenated_key], - pad_to_length(batch[k], max_length, pad_value=pad_value), - ), - dim=0, - ).to(self.accelerator.device) - - if self.is_encoder_decoder: - concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1) - concatenated_batch["concatenated_attention_mask"] = batch["prompt_attention_mask"].repeat(2, 1) - - return concatenated_batch - - def dpo_loss( - self, - policy_chosen_logps: torch.FloatTensor, - policy_rejected_logps: torch.FloatTensor, - reference_chosen_logps: torch.FloatTensor, - reference_rejected_logps: torch.FloatTensor, - reference_free: bool = False, - ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: - """Compute the DPO loss for a batch of policy and reference model log probabilities. - - Args: - policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,) - policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,) - reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,) - reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,) - beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0. - reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses. - - Returns: - A tuple of three tensors: (losses, chosen_rewards, rejected_rewards). - The losses tensor contains the DPO loss for each example in the batch. - The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively. - """ - pi_logratios = policy_chosen_logps - policy_rejected_logps - ref_logratios = reference_chosen_logps - reference_rejected_logps - - chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach() - rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach() - - if reference_free: - ref_logratios = 0 - - # print(chosen_rewards, rejected_rewards) # batch_size - # Gamma = -0.1 - # self.Gamma = 0.05 - - if self.data_selection: - flag = (chosen_rewards - rejected_rewards + self.Gamma).sign().to(chosen_rewards.device) - counter = torch.zeros_like(chosen_rewards).to(chosen_rewards.device) - counter[(chosen_rewards - rejected_rewards).abs() > self.Gamma] = 1 - neglected = torch.count_nonzero(counter).detach().cpu() - self.use_labeled += (counter.shape[0] - neglected) - self.predicted_label += neglected - # print(self.use_labeled, self.predicted_label) - # print(flag) - flag.requires_grad = False - - logits = (pi_logratios - ref_logratios)*flag - - else: - logits = (pi_logratios - ref_logratios) - - if self.loss_type == "sigmoid": - losses = -F.logsigmoid(self.beta * logits) - elif self.loss_type == "hinge": - losses = torch.relu(1 - self.beta * logits) - elif self.loss_type == "corr": - losses = 1 - self.beta *logits - else: - raise ValueError(f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge']") - - return losses, chosen_rewards, rejected_rewards - - def _get_batch_logps( - self, - logits: torch.FloatTensor, - labels: torch.LongTensor, - average_log_prob: bool = False, - ) -> torch.FloatTensor: - """Compute the log probabilities of the given labels under the given logits. - - Args: - logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) - labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length) - average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. - - Returns: - A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits. - """ - if logits.shape[:-1] != labels.shape: - raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") - - if not self.is_encoder_decoder: - labels = labels[:, 1:].clone() - logits = logits[:, :-1, :] - loss_mask = labels != self.label_pad_token_id - - # dummy token; we'll ignore the losses on these tokens later - labels[labels == self.label_pad_token_id] = 0 - - per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2) - - if average_log_prob: - return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) - else: - return (per_token_logps * loss_mask).sum(-1) - - def concatenated_forward( - self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]] - ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: - """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. - - We do this to avoid doing two forward passes, because it's faster for FSDP. - """ - concatenated_batch = self.concatenated_inputs(batch) - len_chosen = batch["chosen_labels"].shape[0] - - model_kwargs = ( - { - "labels": concatenated_batch["concatenated_labels"], - "decoder_input_ids": concatenated_batch.pop("concatenated_decoder_input_ids", None), - } - if self.is_encoder_decoder - else {} - ) - all_logits = model( - concatenated_batch["concatenated_input_ids"], - attention_mask=concatenated_batch["concatenated_attention_mask"], - **model_kwargs, - ).logits.to(torch.float32) - - all_logps = self._get_batch_logps( - all_logits, - concatenated_batch["concatenated_labels"], - average_log_prob=False, - ) - - chosen_logps = all_logps[:len_chosen] - rejected_logps = all_logps[len_chosen:] - - chosen_logits = all_logits[:len_chosen] - rejected_logits = all_logits[len_chosen:] - - return (chosen_logps, rejected_logps, chosen_logits, rejected_logits) - - def get_batch_metrics( - self, - model, - batch: Dict[str, Union[List, torch.LongTensor]], - train_eval: Literal["train", "eval"] = "train", - ): - """Compute the DPO loss and other metrics for the given batch of inputs for train or test.""" - metrics = {} - - ( - policy_chosen_logps, - policy_rejected_logps, - policy_chosen_logits, - policy_rejected_logits, - ) = self.concatenated_forward(model, batch) - with torch.no_grad(): - if self.ref_model is None: - with self.accelerator.unwrap_model(self.model).disable_adapter(): - ( - reference_chosen_logps, - reference_rejected_logps, - _, - _, - ) = self.concatenated_forward(self.model, batch) - else: - ( - reference_chosen_logps, - reference_rejected_logps, - _, - _, - ) = self.concatenated_forward(self.ref_model, batch) - - losses, chosen_rewards, rejected_rewards = self.dpo_loss( - policy_chosen_logps, - policy_rejected_logps, - reference_chosen_logps, - reference_rejected_logps, - ) - reward_accuracies = (chosen_rewards > rejected_rewards).float() - - prefix = "eval_" if train_eval == "eval" else "" - metrics[f"{prefix}rewards/chosen"] = chosen_rewards.cpu().mean() - metrics[f"{prefix}rewards/rejected"] = rejected_rewards.cpu().mean() - metrics[f"{prefix}rewards/accuracies"] = reward_accuracies.cpu().mean() - metrics[f"{prefix}rewards/margins"] = (chosen_rewards - rejected_rewards).cpu().mean() - metrics[f"{prefix}logps/rejected"] = policy_rejected_logps.detach().cpu().mean() - metrics[f"{prefix}logps/chosen"] = policy_chosen_logps.detach().cpu().mean() - metrics[f"{prefix}logits/rejected"] = policy_rejected_logits.detach().cpu().mean() - metrics[f"{prefix}logits/chosen"] = policy_chosen_logits.detach().cpu().mean() - metrics[f"{prefix}use_label"] = float(self.use_labeled) - metrics[f"{prefix}pred_label"] = float(self.predicted_label) - - return losses.mean(), metrics - - def compute_loss( - self, - model: Union[PreTrainedModel, nn.Module], - inputs: Dict[str, Union[torch.Tensor, Any]], - return_outputs=False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]: - if not self.use_dpo_data_collator: - warnings.warn( - "compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than " - "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator" - ) - loss, metrics = self.get_batch_metrics(model, inputs, train_eval="train") - - # force log the metrics - if self.accelerator.is_main_process: - self.store_metrics(metrics, train_eval="train") - - if return_outputs: - return (loss, metrics) - return loss - - def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]: - """Generate samples from the model and reference model for the given batch of inputs.""" - - policy_output = model.generate( - input_ids=batch["prompt_input_ids"], - attention_mask=batch["prompt_attention_mask"], - max_length=self.max_length, - do_sample=True, - pad_token_id=self.tokenizer.pad_token_id, - ) - - if self.ref_model is None: - with self.accelerator.unwrap_model(self.model).disable_adapter(): - reference_output = self.model.generate( - batch["prompt_input_ids"], - attention_mask=batch["prompt_attention_mask"], - max_length=self.max_length, - do_sample=True, - pad_token_id=self.tokenizer.pad_token_id, - ) - else: - reference_output = self.ref_model.generate( - batch["prompt_input_ids"], - attention_mask=batch["prompt_attention_mask"], - max_length=self.max_length, - do_sample=True, - pad_token_id=self.tokenizer.pad_token_id, - ) - - policy_output = pad_to_length(policy_output, self.max_length, self.tokenizer.pad_token_id) - policy_output_decoded = self.tokenizer.batch_decode(policy_output, skip_special_tokens=True) - - reference_output = pad_to_length(reference_output, self.max_length, self.tokenizer.pad_token_id) - reference_output_decoded = self.tokenizer.batch_decode(reference_output, skip_special_tokens=True) - - return policy_output_decoded, reference_output_decoded - - def prediction_step( - self, - model: Union[PreTrainedModel, nn.Module], - inputs: Dict[str, Union[torch.Tensor, Any]], - prediction_loss_only: bool, - ignore_keys: Optional[List[str]] = None, - ): - if not self.use_dpo_data_collator: - warnings.warn( - "prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than " - "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator" - ) - if ignore_keys is None: - if hasattr(model, "config"): - ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) - else: - ignore_keys = [] - - with torch.no_grad(): - loss, metrics = self.get_batch_metrics(model, inputs, train_eval="eval") - - # force log the metrics - if self.accelerator.is_main_process: - self.store_metrics(metrics, train_eval="eval") - - if prediction_loss_only: - return (loss.detach(), None, None) - - # logits for the chosen and rejected samples from model - logits_dict = { - "eval_logits/chosen": metrics["eval_logits/chosen"], - "eval_logits/rejected": metrics["eval_logits/rejected"], - } - logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys) - logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device) - labels = torch.zeros(logits.shape[0], device=self.accelerator.device) - - return (loss.detach(), logits, labels) - - def store_metrics(self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: - for key, value in metrics.items(): - self._stored_metrics[train_eval][key].append(value) - - def evaluation_loop( - self, - dataloader: DataLoader, - description: str, - prediction_loss_only: Optional[bool] = None, - ignore_keys: Optional[List[str]] = None, - metric_key_prefix: str = "eval", - ) -> EvalLoopOutput: - """ - Overriding built-in evaluation loop to store metrics for each batch. - Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`. - - Works both with or without labels. - """ - - # Sample and save to game log if requested (for one batch to save time) - print(self.counter) - self.counter = 0 - if self.generate_during_eval: - # Generate random indices within the range of the total number of samples - num_samples = len(dataloader.dataset) - random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) - - # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader - random_batch_dataset = dataloader.dataset.select(random_indices) - random_batch = self.data_collator(random_batch_dataset) - random_batch = self._prepare_inputs(random_batch) - - policy_output_decoded, ref_output_decoded = self.get_batch_samples(self.model, random_batch) - - self.log( - { - "game_log": wandb.Table( - columns=["Prompt", "Policy", "Ref Model"], - rows=[ - [prompt, pol[len(prompt) :], ref[len(prompt) :]] - for prompt, pol, ref in zip( - random_batch["prompt"], policy_output_decoded, ref_output_decoded - ) - ], - ) - } - ) - self.state.log_history.pop() - - # Base evaluation - initial_output = super().evaluation_loop( - dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix - ) - - return initial_output - - def log(self, logs: Dict[str, float]) -> None: - """ - Log `logs` on the various objects watching training, including stored metrics. - - Args: - logs (`Dict[str, float]`): - The values to log. - """ - # logs either has 'loss' or 'eval_loss' - train_eval = "train" if "loss" in logs else "eval" - # Add averaged stored metrics to logs - for key, metrics in self._stored_metrics[train_eval].items(): - logs[key] = torch.tensor(metrics).mean().item() - del self._stored_metrics[train_eval] - return super().log(logs) diff --git a/src/alignment/__init__.py b/src/alignment/__init__.py deleted file mode 100644 index 17f4767..0000000 --- a/src/alignment/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -__version__ = "0.2.0.dev0" - -from .configs import DataArguments, DPOConfig, H4ArgumentParser, ModelArguments, SFTConfig -from .data import apply_chat_template, get_datasets -from .model_utils import get_kbit_device_map, get_peft_config, get_quantization_config, get_tokenizer, is_adapter_model diff --git a/src/alignment/configs.py b/src/alignment/configs.py deleted file mode 100644 index 652bb7c..0000000 --- a/src/alignment/configs.py +++ /dev/null @@ -1,274 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import dataclasses -import os -import sys -from dataclasses import dataclass, field -from typing import Any, Dict, List, NewType, Optional, Tuple, Literal - -import transformers -from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, HfArgumentParser - - -MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys()) -MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) - - -DataClassType = NewType("DataClassType", Any) - - -class H4ArgumentParser(HfArgumentParser): - def parse_yaml_and_args(self, yaml_arg: str, other_args: Optional[List[str]] = None) -> List[dataclass]: - """ - Parse a YAML file and overwrite the default/loaded values with the values provided to the command line. - - Args: - yaml_arg (`str`): - The path to the config file used - other_args (`List[str]`, *optional`): - A list of strings to parse as command line arguments, e.g. ['--arg=val', '--arg2=val2']. - - Returns: - [`List[dataclass]`]: a list of dataclasses with the values from the YAML file and the command line - """ - arg_list = self.parse_yaml_file(os.path.abspath(yaml_arg)) - - outputs = [] - # strip other args list into dict of key-value pairs - other_args = {arg.split("=")[0].strip("-"): arg.split("=")[1] for arg in other_args} - used_args = {} - - # overwrite the default/loaded value with the value provided to the command line - # adapted from https://github.com/huggingface/transformers/blob/d0b5002378daabf62769159add3e7d66d3f83c3b/src/transformers/hf_argparser.py#L327 - for data_yaml, data_class in zip(arg_list, self.dataclass_types): - keys = {f.name for f in dataclasses.fields(data_yaml) if f.init} - inputs = {k: v for k, v in vars(data_yaml).items() if k in keys} - for arg, val in other_args.items(): - # add only if in keys - if arg in keys: - base_type = data_yaml.__dataclass_fields__[arg].type - inputs[arg] = val - - # cast type for ints, floats (default to strings) - if base_type in [int, float]: - inputs[arg] = base_type(val) - - if base_type == List[str]: - inputs[arg] = [str(v) for v in val.split(",")] - - # bool of a non-empty string is True, so we manually check for bools - if base_type == bool: - if val in ["true", "True"]: - inputs[arg] = True - else: - inputs[arg] = False - - # add to used-args so we can check if double add - if arg not in used_args: - used_args[arg] = val - else: - raise ValueError(f"Duplicate argument provided: {arg}, may cause unexpected behavior") - - obj = data_class(**inputs) - outputs.append(obj) - - return outputs - - def parse(self) -> DataClassType | Tuple[DataClassType]: - if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"): - # If we pass only one argument to the script and it's the path to a YAML file, - # let's parse it to get our arguments. - output = self.parse_yaml_file(os.path.abspath(sys.argv[1])) - # parse command line args and yaml file - elif len(sys.argv) > 2 and sys.argv[1].endswith(".yaml"): - output = self.parse_yaml_and_args(os.path.abspath(sys.argv[1]), sys.argv[2:]) - # parse command line args only - else: - output = self.parse_args_into_dataclasses() - - if len(output) == 1: - output = output[0] - return output - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune. - """ - - base_model_revision: Optional[str] = field( - default=None, - metadata={"help": ("The base model checkpoint for weights initialization with PEFT adatpers.")}, - ) - model_name_or_path: Optional[str] = field( - default=None, - metadata={ - "help": ( - "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch." - ) - }, - ) - model_revision: str = field( - default="main", - metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, - ) - model_code_revision: str = field(default=None, metadata={"help": "The branch of the IFT model"}) - torch_dtype: Optional[str] = field( - default=None, - metadata={ - "help": ( - "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the " - "dtype will be automatically derived from the model's weights." - ), - "choices": ["auto", "bfloat16", "float16", "float32"], - }, - ) - trust_remote_code: bool = field(default=False, metadata={"help": "Trust remote code when loading a model."}) - use_flash_attention_2: bool = field( - default=False, - metadata={ - "help": ( - "Whether to use flash attention 2. You must install this manually by running `pip install flash-attn --no-build-isolation`" - ) - }, - ) - use_peft: bool = field( - default=False, - metadata={"help": ("Whether to use PEFT or not for training.")}, - ) - lora_r: Optional[int] = field( - default=16, - metadata={"help": ("LoRA R value.")}, - ) - lora_alpha: Optional[int] = field( - default=32, - metadata={"help": ("LoRA alpha.")}, - ) - lora_dropout: Optional[float] = field( - default=0.05, - metadata={"help": ("LoRA dropout.")}, - ) - lora_target_modules: Optional[List[str]] = field( - default=None, - metadata={"help": ("LoRA target modules.")}, - ) - lora_modules_to_save: Optional[List[str]] = field( - default=None, - metadata={"help": ("Model layers to unfreeze & train")}, - ) - load_in_8bit: bool = field(default=False, metadata={"help": "use 8 bit precision"}) - load_in_4bit: bool = field(default=False, metadata={"help": "use 4 bit precision"}) - - bnb_4bit_quant_type: Optional[str] = field( - default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"} - ) - use_bnb_nested_quant: bool = field(default=False, metadata={"help": "use nested quantization"}) - - def __post_init__(self): - if self.load_in_8bit and self.load_in_4bit: - raise ValueError("You can't use 8 bit and 4 bit precision at the same time") - - -@dataclass -class DataArguments: - """ - Arguments pertaining to what data we are going to input our model for training and eval. - """ - - chat_template: Optional[str] = field(default=None, metadata={"help": "The chat template to use."}) - dataset_mixer: Optional[Dict[str, float]] = field( - default=None, - metadata={"help": ("Datasets and their proportions to be used for training ift/rl.")}, - ) - dataset_splits: Optional[List[str]] = field( - default_factory=lambda: ["train", "test"], - metadata={"help": ("List of train test splits to use in the dataset")}, - ) - max_train_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." - ) - }, - ) - max_eval_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of evaluation examples to this " - "value if set." - ) - }, - ) - preprocessing_num_workers: Optional[int] = field( - default=None, - metadata={"help": "The number of processes to use for the preprocessing."}, - ) - truncation_side: Optional[str] = field( - default=None, metadata={"help": "Truncation side to use for the tokenizer."} - ) - - -@dataclass -class SFTConfig(transformers.TrainingArguments): - """ - Arguments related to the training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments - """ - - max_seq_length: Optional[int] = field( - default=None, - metadata={"help": ("Used by TRL for reward model training, which tries to read this parameter in init.")}, - ) - logging_first_step: bool = field( - default=True, - metadata={"help": ("Whether to log and evaluate the first global_step or not.")}, - ) - optim: Optional[str] = field(default="adamw_torch") - - -@dataclass -class DPOConfig(transformers.TrainingArguments): - """ - Arguments related to the DPO training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments - """ - - beta: Optional[float] = field( - default=0.1, - metadata={"help": "The beta factor in DPO loss. Higher beta means less divergence from the initial policy."}, - ) - hub_model_revision: Optional[str] = field( - default="main", - metadata={"help": ("The Hub model branch to push the model to.")}, - ) - logging_first_step: bool = field( - default=True, - metadata={"help": ("Whether to log and evaluate the first global_step or not.")}, - ) - max_prompt_length: Optional[int] = field( - default=None, - metadata={"help": ("For DPO, the maximum length of the prompt to use for conditioning the model.")}, - ) - max_length: Optional[int] = field( - default=None, - metadata={"help": ("Used by TRL for reward model training, which tries to read this parameter in init.")}, - ) - optim: Optional[str] = field(default="rmsprop") - remove_unused_columns: bool = field(default=False) - loss_type: Literal["sigmoid", "hinge", "corr"]=field(default="sigmoid") - data_selection: bool=field(default=False) - Gamma: float=field(default=0.02) diff --git a/src/alignment/data.py b/src/alignment/data.py deleted file mode 100644 index 532bddc..0000000 --- a/src/alignment/data.py +++ /dev/null @@ -1,187 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import os -import re -from typing import List, Literal, Optional - -from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk -from datasets.builder import DatasetGenerationError - -from .configs import DataArguments - - -DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}" - - -def apply_chat_template( - example, tokenizer, task: Literal["sft", "generation", "rm", "dpo"] = "sft", assistant_prefix="<|assistant|>\n" -): - def _strip_prefix(s, pattern): - # Use re.escape to escape any special characters in the pattern - return re.sub(f"^{re.escape(pattern)}", "", s) - - if task in ["sft", "generation"]: - messages = example["messages"] - # We add an empty system message if there is none - if messages[0]["role"] != "system": - messages.insert(0, {"role": "system", "content": ""}) - example["text"] = tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True if task == "generation" else False - ) - elif task == "rm": - if all(k in example.keys() for k in ("chosen", "rejected")): - chosen_messages = example["chosen"] - rejected_messages = example["rejected"] - # We add an empty system message if there is none - if chosen_messages[0]["role"] != "system": - chosen_messages.insert(0, {"role": "system", "content": ""}) - if rejected_messages[0]["role"] != "system": - rejected_messages.insert(0, {"role": "system", "content": ""}) - example["text_chosen"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False) - example["text_rejected"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False) - else: - raise ValueError( - f"Could not format example as dialogue for `rm` task! Require `[chosen, rejected]` keys but found {list(example.keys())}" - ) - elif task == "dpo": - if all(k in example.keys() for k in ("chosen", "rejected")): - # Compared to reward modeling, we filter out the prompt, so the text is everything after the last assistant token - prompt_messages = [[msg for msg in example["chosen"] if msg["role"] == "user"][0]] - # Insert system message - if example["chosen"][0]["role"] != "system": - prompt_messages.insert(0, {"role": "system", "content": ""}) - else: - prompt_messages.insert(0, example["chosen"][0]) - # TODO: handle case where chosen/rejected also have system messages - chosen_messages = example["chosen"][1:] - rejected_messages = example["rejected"][1:] - example["text_chosen"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False) - example["text_rejected"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False) - example["text_prompt"] = tokenizer.apply_chat_template( - prompt_messages, tokenize=False, add_generation_prompt=True - ) - example["text_chosen"] = _strip_prefix(example["text_chosen"], assistant_prefix) - example["text_rejected"] = _strip_prefix(example["text_rejected"], assistant_prefix) - else: - raise ValueError( - f"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}" - ) - else: - raise ValueError( - f"Task {task} not supported, please ensure that the provided task is one of {['sft', 'generation', 'rm', 'dpo']}" - ) - return example - - -def get_datasets( - data_config: DataArguments | dict, - splits: List[str] = ["train", "test"], - shuffle: bool = True, -) -> DatasetDict: - """ - Loads one or more datasets with varying training set proportions. - - Args: - data_config (`DataArguments` or `dict`): - Dataset configuration and split proportions. - splits (`List[str]`, *optional*, defaults to `['train', 'test']`): - Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix. - shuffle (`bool`, *optional*, defaults to `True`): - Whether to shuffle the training and testing/validation data. - - Returns - [`DatasetDict`]: The dataset dictionary containing the loaded datasets. - """ - - if type(data_config) is DataArguments: - # Structure of the config to read the datasets and their mix - # datasets_mixer: - # - 'dataset1': 0.5 - # - 'dataset2': 0.3 - # - 'dataset3': 0.2 - dataset_mixer = data_config.dataset_mixer - elif type(data_config) is dict: - # Structure of the input is: - # dataset_mixer = { - # "dataset1": 0.5, - # "dataset1": 0.3, - # "dataset1": 0.2, - # } - dataset_mixer = data_config - else: - raise ValueError(f"Data config {data_config} not recognized.") - - raw_datasets = mix_datasets(dataset_mixer, splits=splits, shuffle=shuffle) - return raw_datasets - - -def mix_datasets(dataset_mixer: dict, splits: Optional[List[str]] = None, shuffle=True) -> DatasetDict: - """ - Loads and mixes datasets according to proportions specified in `dataset_mixer`. - - Args: - dataset_mixer (`dict`): - Dictionary containing the dataset names and their training proportions. By default, all test proportions are 1. - splits (Optional[List[str]], *optional*, defaults to `None`): - Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix. - shuffle (`bool`, *optional*, defaults to `True`): - Whether to shuffle the training and testing/validation data. - """ - raw_datasets = DatasetDict() - raw_train_datasets = [] - raw_val_datasets = [] - fracs = [] - for ds, frac in dataset_mixer.items(): - fracs.append(frac) - for split in splits: - try: - # Try first if dataset on a Hub repo - dataset = load_dataset(ds, split=split) - except DatasetGenerationError: - # If not, check local dataset - dataset = load_from_disk(os.path.join(ds, split)) - - if "train" in split: - raw_train_datasets.append(dataset) - elif "test" in split: - raw_val_datasets.append(dataset) - else: - raise ValueError(f"Split type {split} not recognized as one of test or train.") - - if any(frac < 0 for frac in fracs): - raise ValueError("Dataset fractions cannot be negative.") - - if len(raw_train_datasets) > 0: - train_subsets = [] - for dataset, frac in zip(raw_train_datasets, fracs): - train_subset = dataset.select(range(int(frac * len(dataset)))) - train_subsets.append(train_subset) - if shuffle: - raw_datasets["train"] = concatenate_datasets(train_subsets).shuffle(seed=42) - else: - raw_datasets["train"] = concatenate_datasets(train_subsets) - # No subsampling for test datasets to enable fair comparison across models - if len(raw_val_datasets) > 0: - if shuffle: - raw_datasets["test"] = concatenate_datasets(raw_val_datasets).shuffle(seed=42) - else: - raw_datasets["test"] = concatenate_datasets(raw_val_datasets) - - if len(raw_datasets) == 0: - raise ValueError( - f"Dataset {dataset_mixer} not recognized with split {split}. Check the dataset has been correctly formatted." - ) - - return raw_datasets diff --git a/src/alignment/model_utils.py b/src/alignment/model_utils.py deleted file mode 100644 index ab2025f..0000000 --- a/src/alignment/model_utils.py +++ /dev/null @@ -1,106 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import os -from typing import Dict - -import torch -from transformers import AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizer - -from accelerate import Accelerator -from huggingface_hub import list_repo_files -from huggingface_hub.utils._validators import HFValidationError -from peft import LoraConfig, PeftConfig - -from .configs import DataArguments, ModelArguments -from .data import DEFAULT_CHAT_TEMPLATE - - -def get_current_device() -> int: - """Get the current device. For GPU we return the local process index to enable multiple GPU training.""" - return Accelerator().local_process_index if torch.cuda.is_available() else "cpu" - - -def get_kbit_device_map() -> Dict[str, int] | None: - """Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`""" - return {"": get_current_device()} if torch.cuda.is_available() else None - - -def get_quantization_config(model_args) -> BitsAndBytesConfig | None: - if model_args.load_in_4bit: - quantization_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, # For consistency with model weights, we use the same value as `torch_dtype` which is float16 for PEFT models - bnb_4bit_quant_type=model_args.bnb_4bit_quant_type, - bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant, - ) - elif model_args.load_in_8bit: - quantization_config = BitsAndBytesConfig( - load_in_8bit=True, - ) - else: - quantization_config = None - - return quantization_config - - -def get_tokenizer(model_args: ModelArguments, data_args: DataArguments) -> PreTrainedTokenizer: - """Get the tokenizer for the model.""" - tokenizer = AutoTokenizer.from_pretrained( - model_args.model_name_or_path, - revision=model_args.model_revision, - ) - if tokenizer.pad_token_id is None: - tokenizer.pad_token_id = tokenizer.eos_token_id - - if data_args.truncation_side is not None: - tokenizer.truncation_side = data_args.truncation_side - - # Set reasonable default for models without max length - if tokenizer.model_max_length > 100_000: - tokenizer.model_max_length = 2048 - - if data_args.chat_template is not None: - tokenizer.chat_template = data_args.chat_template - elif tokenizer.chat_template is None: - tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE - - return tokenizer - - -def get_peft_config(model_args: ModelArguments) -> PeftConfig | None: - if model_args.use_peft is False: - return None - - peft_config = LoraConfig( - r=model_args.lora_r, - lora_alpha=model_args.lora_alpha, - lora_dropout=model_args.lora_dropout, - bias="none", - task_type="CAUSAL_LM", - target_modules=model_args.lora_target_modules, - modules_to_save=model_args.lora_modules_to_save, - ) - - return peft_config - - -def is_adapter_model(model_name_or_path: str, revision: str = "main") -> bool: - try: - # Try first if model on a Hub repo - repo_files = list_repo_files(model_name_or_path, revision=revision) - except HFValidationError: - # If not, check local repo - repo_files = os.listdir(model_name_or_path) - return "adapter_model.safetensors" in repo_files or "adapter_model.bin" in repo_files diff --git a/src/alignment/release.py b/src/alignment/release.py deleted file mode 100644 index 7f3f767..0000000 --- a/src/alignment/release.py +++ /dev/null @@ -1,106 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import re - -import packaging.version - - -REPLACE_PATTERNS = { - "init": (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'), - "setup": (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'), -} -REPLACE_FILES = { - "init": "src/alignment/__init__.py", - "setup": "setup.py", -} -README_FILE = "README.md" - - -def update_version_in_file(fname, version, pattern): - """Update the version in one file using a specific pattern.""" - with open(fname, "r", encoding="utf-8", newline="\n") as f: - code = f.read() - re_pattern, replace = REPLACE_PATTERNS[pattern] - replace = replace.replace("VERSION", version) - code = re_pattern.sub(replace, code) - with open(fname, "w", encoding="utf-8", newline="\n") as f: - f.write(code) - - -def global_version_update(version, patch=False): - """Update the version in all needed files.""" - for pattern, fname in REPLACE_FILES.items(): - update_version_in_file(fname, version, pattern) - - -def get_version(): - """Reads the current version in the __init__.""" - with open(REPLACE_FILES["init"], "r") as f: - code = f.read() - default_version = REPLACE_PATTERNS["init"][0].search(code).groups()[0] - return packaging.version.parse(default_version) - - -def pre_release_work(patch=False): - """Do all the necessary pre-release steps.""" - # First let's get the default version: base version if we are in dev, bump minor otherwise. - default_version = get_version() - if patch and default_version.is_devrelease: - raise ValueError("Can't create a patch version from the dev branch, checkout a released version!") - if default_version.is_devrelease: - default_version = default_version.base_version - elif patch: - default_version = f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}" - else: - default_version = f"{default_version.major}.{default_version.minor + 1}.0" - - # Now let's ask nicely if that's the right one. - version = input(f"Which version are you releasing? [{default_version}]") - if len(version) == 0: - version = default_version - - print(f"Updating version to {version}.") - global_version_update(version, patch=patch) - - -def post_release_work(): - """Do all the necessary post-release steps.""" - # First let's get the current version - current_version = get_version() - dev_version = f"{current_version.major}.{current_version.minor + 1}.0.dev0" - current_version = current_version.base_version - - # Check with the user we got that right. - version = input(f"Which version are we developing now? 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function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 5); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export addClasses */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; }); +/* unused harmony export show */ +/* unused harmony export hide */ +/* unused harmony export offset */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; }); +/* unused harmony export outerWidth */ +/* unused harmony export position */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2); + + +var addClasses = function addClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.add(cls); + }); +}; + +var removeClasses = function removeClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.remove(cls); + }); +}; + +var show = function show(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = ''; + }); +}; + +var hide = function hide(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = 'none'; + }); +}; + +var offset = function offset(element) { + var rect = element.getBoundingClientRect(); + return { + top: rect.top + document.body.scrollTop, + left: rect.left + document.body.scrollLeft + }; +}; + +// returns an element's width +var width = function width(element) { + return element.getBoundingClientRect().width || element.offsetWidth; +}; +// returns an element's height +var height = function height(element) { + return element.getBoundingClientRect().height || element.offsetHeight; +}; + +var outerHeight = function outerHeight(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "
" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 2, + slidesToScroll: 2 + }, { + changePoint: 768, + slidesToShow: 3, + slidesToScroll: 3 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/static/js/bulma-carousel.min.js b/static/js/bulma-carousel.min.js new file mode 100644 index 0000000..5fff069 --- /dev/null +++ b/static/js/bulma-carousel.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return 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"symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); + // Check and execute Middleware + if (Array.isArray(middlewares)) { + middlewares.forEach(function (middleware) { + middleware(data, function () { + var newData = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (newData !== null) { + data = newData; + } + doneCount++; + }, name); + }); + + if (doneCount >= middlewares.length) { + execute = true; + } + } else { + execute = true; + } + } + + // If Middleware checks have been passed, execute + if (execute) { + if (listener.once) { + listeners[index] = null; + } + listener.callback(data); + } + }); + + // Dirty way of removing used Events + while (listeners.indexOf(null) !== -1) { + listeners.splice(listeners.indexOf(null), 1); + } + } + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/static/js/bulma-slider.min.js b/static/js/bulma-slider.min.js new file mode 100644 index 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self.dataset.map(lambda x: {"messages": x["messages"][:-1]}) - dataset = dataset.map( - apply_chat_template, - fn_kwargs={"tokenizer": self.tokenizer, "task": "generation"}, - remove_columns=self.dataset.column_names, - ) - self.assertDictEqual( - dataset[0], - {"text": "<|system|>\n\n<|user|>\nHello!\n<|assistant|>\n"}, - ) - - def test_rm(self): - dataset = self.dataset.map( - apply_chat_template, - fn_kwargs={"tokenizer": self.tokenizer, "task": "rm"}, - remove_columns=self.dataset.column_names, - ) - self.assertDictEqual( - dataset[0], - { - "text_chosen": "<|system|>\n\n<|user|>\nHello!\n<|assistant|>\nBonjour!\n", - "text_rejected": "<|system|>\n\n<|user|>\nHello!\n<|assistant|>\nHola!\n", - }, - ) - - def test_dpo(self): - dataset = self.dataset.map( - apply_chat_template, - fn_kwargs={"tokenizer": self.tokenizer, "task": "dpo"}, - remove_columns=self.dataset.column_names, - ) - self.assertDictEqual( - dataset[0], - { - "text_prompt": "<|system|>\n\n<|user|>\nHello!\n<|assistant|>\n", - "text_chosen": "Bonjour!\n", - "text_rejected": "Hola!\n", - }, - ) diff --git a/tests/test_model_utils.py b/tests/test_model_utils.py deleted file mode 100644 index d20afec..0000000 --- a/tests/test_model_utils.py +++ /dev/null @@ -1,76 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import unittest - -import torch - -from alignment import DataArguments, ModelArguments, get_peft_config, get_quantization_config, get_tokenizer -from alignment.data import DEFAULT_CHAT_TEMPLATE - - -class GetQuantizationConfigTest(unittest.TestCase): - def test_4bit(self): - model_args = ModelArguments(load_in_4bit=True) - quantization_config = get_quantization_config(model_args) - self.assertTrue(quantization_config.load_in_4bit) - self.assertEqual(quantization_config.bnb_4bit_compute_dtype, torch.float16) - self.assertEqual(quantization_config.bnb_4bit_quant_type, "nf4") - self.assertFalse(quantization_config.bnb_4bit_use_double_quant) - - def test_8bit(self): - model_args = ModelArguments(load_in_8bit=True) - quantization_config = get_quantization_config(model_args) - self.assertTrue(quantization_config.load_in_8bit) - - def test_no_quantization(self): - model_args = ModelArguments() - quantization_config = get_quantization_config(model_args) - self.assertIsNone(quantization_config) - - -class GetTokenizerTest(unittest.TestCase): - def setUp(self) -> None: - self.model_args = ModelArguments(model_name_or_path="HuggingFaceH4/zephyr-7b-alpha") - - def test_right_truncation_side(self): - tokenizer = get_tokenizer(self.model_args, DataArguments(truncation_side="right")) - self.assertEqual(tokenizer.truncation_side, "right") - - def test_left_truncation_side(self): - tokenizer = get_tokenizer(self.model_args, DataArguments(truncation_side="left")) - self.assertEqual(tokenizer.truncation_side, "left") - - def test_default_chat_template(self): - tokenizer = get_tokenizer(self.model_args, DataArguments()) - self.assertEqual(tokenizer.chat_template, DEFAULT_CHAT_TEMPLATE) - - def test_chatml_chat_template(self): - chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" - tokenizer = get_tokenizer(self.model_args, DataArguments(chat_template=chat_template)) - self.assertEqual(tokenizer.chat_template, chat_template) - - -class GetPeftConfigTest(unittest.TestCase): - def test_peft_config(self): - model_args = ModelArguments(use_peft=True, lora_r=42, lora_alpha=0.66, lora_dropout=0.99) - peft_config = get_peft_config(model_args) - self.assertEqual(peft_config.r, 42) - self.assertEqual(peft_config.lora_alpha, 0.66) - self.assertEqual(peft_config.lora_dropout, 0.99) - - def test_no_peft_config(self): - model_args = ModelArguments(use_peft=False) - peft_config = get_peft_config(model_args) - self.assertIsNone(peft_config)