diff --git a/.gitignore b/.gitignore
index 9c8f254..fdcea04 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,15 @@ results/
datasets/
llm-comparator/
trained_d2l/
+checkpoints/
+runs/
+outputs/
+data/video2lora/
+hf_key*
+wandb_key*
+pat_token*
+cookie.txt
+www.youtube.com_cookies.txt
# Ignore data files but not scripts
/data/processed_datasets/
/data/raw_datasets/
diff --git a/LICENSE b/LICENSE
index 0d3bd47..ed7df49 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,7 @@
MIT License
Copyright (c) 2026 Sakana AI
+Copyright (c) 2026 Video2LoRA
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index ac7e27e..56c0b34 100644
--- a/README.md
+++ b/README.md
@@ -1,108 +1,161 @@
-
-
Doc-to-LoRA (D2L): Learning to Instantly Internalize Contexts
- :sparkles:
Interactive Web |
- :newspaper:
X |
- :scroll:
Paper |
- :hugs:
Hugging Face |
- :octocat:
GitHub
-
A reference implementation of Doc-to-LoRA (D2L).
-
-
-
-
+# Video2LoRA
----
+Video2LoRA trains a hypernetwork that converts a video into LoRA weights for a
+frozen vision-language model. The generated adapter lets the model answer later
+text prompts without feeding the video tokens again.
-## ๐ ๏ธ Installation
-```
-curl -LsSf https://astral.sh/uv/install.sh | sh
-./install.sh
-```
+
+
+
-## ๐ค Pre-Trained Models
-```
-uv run huggingface-cli login
-uv run huggingface-cli download SakanaAI/doc-to-lora --local-dir trained_d2l --include "*/"
-```
+## Install
-## ๐ Python API Usage
-```python
-# caveat: this interface only supports non-batched inputs
-# for batched inference please see `src/ctx_to_lora/modeling/hypernet.py`
-import torch
+Install `uv`.
-from ctx_to_lora.model_loading import get_tokenizer
-from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-
-# model loading
-checkpoint_path = "trained_d2l/gemma_demo/checkpoint-80000/pytorch_model.bin"
-state_dict = torch.load(checkpoint_path, weights_only=False)
-model = ModulatedPretrainedModel.from_state_dict(
- state_dict, train=False, use_sequence_packing=False
-)
-model.reset()
-tokenizer = get_tokenizer(model.base_model.name_or_path)
-
-# prepare data
-doc = open("data/sakana_wiki.txt", "r").read()
-chat = [{"role": "user", "content": "Tell me about Sakana AI."}]
-chat_ids = tokenizer.apply_chat_template(
- chat,
- add_special_tokens=False,
- return_attention_mask=False,
- add_generation_prompt=True,
- return_tensors="pt",
-).to(model.device)
-
-
-# calls after internalization will be influenced by internalized info
-model.internalize(doc)
-
-outputs = model.generate(input_ids=chat_ids, max_new_tokens=512)
-print(tokenizer.decode(outputs[0]))
-
-
-# remove internalized info
-# model.reset()
-
-# without internalized info, the model will halucinate
-# outputs = model.generate(input_ids=chat_ids, max_new_tokens=512)
-# print(tokenizer.decode(outputs[0]))
-```
-
-### ๐ฎ Interactive Demo
```bash
-uv run demo/app.py
+git clone https://github.com/MananSuri27/video2lora.git
+cd video2lora
+uv sync
```
-
-
Video Demo
-
-
-### ๐งช Experimental Scripts
-To run any of the following scripts, use `uv run $PATH_TO_SCRIPT` from the root of this project.
+For local video path resolution, set:
-
-| Experiment | Data prep | Training | Evaluation | Notes |
-| ------------------------------------ | ------------------------------------- | ----------------------------- | ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
-| [Main experiment](scripts/main_exp/) | `scripts/main_exp/0-download_data.sh` | `scripts/main_exp/1-train.sh` | `scripts/main_exp/eval/*.sh` | Downloading data is fastest; regenerate only if you need fresh synthetic data. Evaluation scripts reproduce the main paper metrics. |
-| [NIAH](scripts/niah/) | `scripts/niah/0-gen_data.sh` | `scripts/niah/1-train.sh` | `scripts/niah/2-eval.sh` | Run the scripts in order; data generation only needs to happen once |
-
-
-### ๐ฌ Self-Generated Data Viewer
-After downloading/generating the data, we can see samples of the data using this script.
```bash
-uv run webui/self_gen_viewer.py
+export VIDEO2LORA_DATA_ROOT=$PWD/data/video2lora
```
-See more info at [webui/SELF_GEN_VIEWER.md](webui/SELF_GEN_VIEWER.md).
-### ๐ Citation
-```bibtex
-@inproceedings{charakorn2026doctolora,
- title ={Doc-to-Lo{RA}: Learning to Instantly Internalize Contexts},
- author ={Rujikorn Charakorn and Edoardo Cetin and Shinnosuke Uesaka and Robert Tjarko Lange},
- booktitle ={Forty-third International Conference on Machine Learning},
- year ={2026},
- url ={https://openreview.net/forum?id=iW1oBBO72S}
+If unset, `VIDEO2LORA_DATA_ROOT` defaults to `data/video2lora`.
+
+## Checkpoints
+
+Download the released checkpoints from Hugging Face:
+
+```bash
+uv run huggingface-cli download MananSuri27/Video2LoRA-SmolVLM-ckpts \
+ --local-dir checkpoints/Video2LoRA-SmolVLM-ckpts
+```
+
+The repo contains:
+
+```text
+checkpoints/Video2LoRA-SmolVLM-ckpts/
+ video2lora-smolvlm2-500m-best-ce.pt
+ video2lora-smolvlm2-2.2b-best-ce.pt
+```
+
+
+## Inference
+
+Create a JSONL manifest with one row per video. For example:
+
+```json
+{"id":"sample-0001","video_path":"/path/to/video.mp4","prompt":"Describe what is happening in this video.","task_type":"caption"}
+```
+
+```bash
+uv run python -m scripts.video2lora.infer \
+ --checkpoint checkpoints/Video2LoRA-SmolVLM-ckpts/video2lora-smolvlm2-500m-best-ce.pt \
+ --manifest /path/to/manifest.jsonl \
+ --output outputs/tiny_generations.jsonl
+```
+
+For the 2.2B checkpoint, change `--checkpoint` to:
+
+```bash
+checkpoints/Video2LoRA-SmolVLM-ckpts/video2lora-smolvlm2-2.2b-best-ce.pt
+```
+
+The output JSONL includes the original row fields plus `prediction` and
+`checkpoint`.
+
+## Data Format
+
+Training and inference use JSONL manifests. Each line is one example:
+
+```json
+{
+ "id": "sample-0001",
+ "video_path": "raw/finevideo/sample.mp4",
+ "task_type": "caption",
+ "prompt": "Describe what is happening in this video.",
+ "target_text": "A person is cooking.",
+ "dataset": "finevideo",
+ "split": "train",
+ "metadata": {}
}
```
+
+Relative `video_path` values are resolved against `VIDEO2LORA_DATA_ROOT`.
+Absolute paths are used as-is.
+
+## Generate Teacher Data
+
+The final CE training recipe uses cached teacher-generated targets. Starting
+from readable manifests, generate teacher targets with:
+
+```bash
+export VIDEO2LORA_DATA_ROOT=$PWD/data/video2lora
+
+CUDA_VISIBLE_DEVICES=0 uv run python -m scripts.video2lora.generate_finevideo_teacher_targets \
+ --input-manifest $VIDEO2LORA_DATA_ROOT/processed/finevideo/train.jsonl \
+ --output-manifest $VIDEO2LORA_DATA_ROOT/processed/finevideo/train.teacher_visual_smolvlm.jsonl \
+ --smolvlm-name-or-path HuggingFaceTB/SmolVLM2-2.2B-Instruct \
+ --per-device-batch-size 8 \
+ --max-frames 12 \
+ --video-size-longest-edge 384
+```
+
+To shard generation manually, run the same command per GPU with
+`--num-shards N --shard-index R`, then merge with `--merge-only`.
+
+## Train
+
+The main CE training entrypoint is:
+
+```bash
+uv run accelerate launch \
+ --config_file accelerate_config.yaml \
+ --num_processes 4 \
+ -m scripts.video2lora.train_smolvlm_stage1 \
+ --smolvlm-name-or-path HuggingFaceTB/SmolVLM2-500M-Video-Instruct \
+ --train-manifest data/video2lora/processed/finevideo/train.teacher_visual_smolvlm.jsonl \
+ --val-manifest data/video2lora/processed/finevideo/val.teacher_visual_smolvlm.jsonl \
+ --val-core-manifest data/video2lora/processed/finevideo/val.teacher_visual_smolvlm.jsonl \
+ --output-dir runs/video2lora-smoke \
+ --per-device-batch-size 2 \
+ --gradient-accumulation-steps 8 \
+ --max-steps 1000 \
+ --target-modules down_proj \
+ --lora-r 16 \
+ --latent-size 512 \
+ --n-latent-queries 8 \
+ --num-blocks 9 \
+ --max-frames 12 \
+ --video-size-longest-edge 384 \
+ --kl-weight 0.0 \
+ --wandb-mode disabled
+```
+
+The self-distillation variant is also included:
+
+```bash
+uv run accelerate launch \
+ --config_file accelerate_config.yaml \
+ --num_processes 4 \
+ -m scripts.video2lora.train_smolvlm_stage1_selfdistill \
+ --smolvlm-name-or-path HuggingFaceTB/SmolVLM2-500M-Video-Instruct \
+ --train-manifest data/video2lora/processed/finevideo/train.jsonl \
+ --val-manifest data/video2lora/processed/finevideo/val.jsonl \
+ --val-gen-manifest data/video2lora/processed/finevideo/val_gen_100.jsonl \
+ --output-dir runs/video2lora-selfdistill \
+ --wandb-mode disabled
+```
+
+## Build FineVideo Manifests
+
+If you have raw FineVideo metadata/videos laid out under
+`$VIDEO2LORA_DATA_ROOT/raw/finevideo`, build Stage 1 manifests with:
+
+```bash
+uv run python -m scripts.video2lora.build_finevideo_stage1_manifest
+```
diff --git a/assets/cover.png b/assets/cover.png
deleted file mode 100644
index 17fe20f..0000000
Binary files a/assets/cover.png and /dev/null differ
diff --git a/assets/overview_animation.gif b/assets/overview_animation.gif
deleted file mode 100644
index 1b075ba..0000000
Binary files a/assets/overview_animation.gif and /dev/null differ
diff --git a/assets/video2lora-animated-diagram.svg b/assets/video2lora-animated-diagram.svg
new file mode 100644
index 0000000..37044c0
--- /dev/null
+++ b/assets/video2lora-animated-diagram.svg
@@ -0,0 +1,840 @@
+
+
\ No newline at end of file
diff --git a/chat_templates/Qwen/Qwen3-4B-Instruct-2507.jinja b/chat_templates/Qwen/Qwen3-4B-Instruct-2507.jinja
deleted file mode 100644
index 09575ea..0000000
--- a/chat_templates/Qwen/Qwen3-4B-Instruct-2507.jinja
+++ /dev/null
@@ -1,18 +0,0 @@
-{%- if messages[0].role == 'system' %}
- {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
-{%- endif %}
-{%- for message in messages %}
- {%- if message.content is string %}
- {%- set content = message.content %}
- {%- else %}
- {%- set content = '' %}
- {%- endif %}
- {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
- {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
- {%- elif message.role == "assistant" %}
- {{- '<|im_start|>' + message.role + '\n' }}{% generation %}{{ content + '<|im_end|>' }}{% endgeneration %}{{ '\n' }}
- {%- endif %}
-{%- endfor %}
-{%- if add_generation_prompt %}
- {{- '<|im_start|>assistant\n' }}
-{%- endif %}
\ No newline at end of file
diff --git a/chat_templates/google/gemma-2-2b-it.jinja b/chat_templates/google/gemma-2-2b-it.jinja
deleted file mode 100644
index 4b683e5..0000000
--- a/chat_templates/google/gemma-2-2b-it.jinja
+++ /dev/null
@@ -1,24 +0,0 @@
-{{- bos_token }}
-{%- if messages[0]['role'] == 'system' %}
- {%- set system_message = messages[0]['content'] %}
- {%- set loop_messages = messages[1:] %}
-{%- else %}
- {%- set loop_messages = messages %}
-{%- endif %}
-{% for message in loop_messages %}
- {% if (message['role'] == 'assistant') %}
- {% set role = 'model' %}
- {% else %}
- {% set role = message['role'] %}
- {% endif %}
- {%- if message['role'] == 'user' and loop.first and system_message is defined %}
- {{ '' + role + '\n' + system_message + '\n\n' + message['content'] | trim + '\n' }}
- {%- elif message['role'] == 'assistant' %}
- {{ '' + role + '\n' }}{% generation %}{{ message['content'] + '' }}{% endgeneration %}{{ '\n' }}
- {%- else %}
- {{ '' + role + '\n' + message['content'] | trim + '\n' }}
- {%- endif %}
-{% endfor %}
-{% if add_generation_prompt %}
- {{'model\n'}}
-{% endif %}
diff --git a/chat_templates/mistralai/Mistral-7B-Instruct-v0.2.jinja b/chat_templates/mistralai/Mistral-7B-Instruct-v0.2.jinja
deleted file mode 100644
index fa2ff94..0000000
--- a/chat_templates/mistralai/Mistral-7B-Instruct-v0.2.jinja
+++ /dev/null
@@ -1,24 +0,0 @@
-{%- if messages[0]['role'] == 'system' %}
- {%- set system_message = messages[0]['content'] %}
- {%- set loop_messages = messages[1:] %}
-{%- else %}
- {%- set loop_messages = messages %}
-{%- endif %}
-
-{{- bos_token }}
-{%- for message in loop_messages %}
- {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
- {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}
- {%- endif %}
- {%- if message['role'] == 'user' %}
- {%- if loop.first and system_message is defined %}
- {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST] ' }}
- {%- else %}
- {{- ' [INST] ' + message['content'] + ' [/INST] ' }}
- {%- endif %}
- {%- elif message['role'] == 'assistant' %}
- {% generation %}{{ message['content'] + eos_token }}{% endgeneration %}
- {%- else %}
- {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}
- {%- endif %}
-{%- endfor %}
\ No newline at end of file
diff --git a/configs/main_exp/mistral/self_gen_lv1_closed_qa_1_l2l.yaml b/configs/main_exp/mistral/self_gen_lv1_closed_qa_1_l2l.yaml
deleted file mode 100644
index a941048..0000000
--- a/configs/main_exp/mistral/self_gen_lv1_closed_qa_1_l2l.yaml
+++ /dev/null
@@ -1,31 +0,0 @@
-# LoRA
-lora_r: 8
-lora_dropout: 0.0
-target_modules:
- - down_proj
-
-use_kl_loss: true
-
-ctx_encoder_type: per_layer_activations
-n_latent_queries: 8
-num_blocks: 9
-num_self_attn_per_block: 0
-
-gradient_accumulation_steps: 11
-max_packed_inp_len: 6144
-max_packed_ctx_len: 6144
-
-# data
-train_ds_names:
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_1.0/fw_qa_v2/min_0_to_2000/train/*level_1*.parquet
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_0.0/pwc_compact
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_1.0/squad_compact
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_1.0/ropes_compact
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_1.0/drop_compact
-
-val_ds_names:
- - squad
- - pwc
- - drop
- - ropes
- - self_gen/mistralai/Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_0.0/fw_qa_v2/min_0_to_2000/train/*level_0_val*.parquet
diff --git a/configs/main_exp/qwen/self_gen_lv1_closed_qa_1_l2l.yaml b/configs/main_exp/qwen/self_gen_lv1_closed_qa_1_l2l.yaml
deleted file mode 100644
index 088d34d..0000000
--- a/configs/main_exp/qwen/self_gen_lv1_closed_qa_1_l2l.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-# LoRA
-lora_r: 8
-lora_dropout: 0.0
-target_modules:
- - down_proj
-
-use_kl_loss: true
-
-ctx_encoder_type: per_layer_activations
-n_latent_queries: 8
-num_blocks: 9
-num_self_attn_per_block: 0
-
-gradient_accumulation_steps: 11
-max_packed_inp_len: 6144
-max_packed_ctx_len: 6144
-
-# data
-train_ds_names:
- - self_gen/Qwen/Qwen3-4B-Instruct-2507_temp_0.0_closed_qa_prob_1.0/fw_qa_v2/min_0_to_2000/train/*level_1*.parquet
- - self_gen/Qwen/Qwen3-4B-Instruct-2507_temp_0.0_closed_qa_prob_0.0/pwc_compact
- - self_gen/Qwen/Qwen3-4B-Instruct-2507_temp_0.0_closed_qa_prob_1.0/squad_compact
- - self_gen/Qwen/Qwen3-4B-Instruct-2507_temp_0.0_closed_qa_prob_1.0/ropes_compact
- - self_gen/Qwen/Qwen3-4B-Instruct-2507_temp_0.0_closed_qa_prob_1.0/drop_compact
-
-val_ds_names:
- - squad
- - pwc
- - drop
- - ropes
diff --git a/configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml b/configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml
deleted file mode 100644
index 04db6b2..0000000
--- a/configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml
+++ /dev/null
@@ -1,31 +0,0 @@
-# LoRA
-lora_r: 8
-lora_dropout: 0.0
-target_modules:
- - down_proj
-
-use_kl_loss: true
-
-ctx_encoder_type: per_layer_activations
-n_latent_queries: 8
-num_blocks: 9
-num_self_attn_per_block: 0
-
-gradient_accumulation_steps: 11
-max_packed_inp_len: 6144
-max_packed_ctx_len: 6144
-
-# data
-train_ds_names:
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/fw_qa_v2/min_0_to_2000/train/*level_1*.parquet
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_0.0/pwc_compact
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/squad_compact
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/ropes_compact
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/drop_compact
-
-val_ds_names:
- - squad
- - pwc
- - drop
- - ropes
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_0.0/fw_qa_v2/min_0_to_2000/train/*level_0_val*.parquet
diff --git a/configs/main_exp/self_gen_lv1_closed_qa_1_no_qa_l2l.yaml b/configs/main_exp/self_gen_lv1_closed_qa_1_no_qa_l2l.yaml
deleted file mode 100644
index 2a8b6cc..0000000
--- a/configs/main_exp/self_gen_lv1_closed_qa_1_no_qa_l2l.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-# LoRA
-lora_r: 8
-lora_dropout: 0.0
-target_modules:
- - down_proj
-
-use_kl_loss: true
-
-ctx_encoder_type: per_layer_activations
-n_latent_queries: 8
-num_blocks: 9
-num_self_attn_per_block: 0
-
-gradient_accumulation_steps: 11
-max_packed_inp_len: 6144
-max_packed_ctx_len: 6144
-
-# data
-train_ds_names:
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/fw_qa_v2/min_0_to_2000/train/*level_1*.parquet
-
-val_ds_names:
- - squad
- - pwc
- - drop
- - ropes
- - self_gen/google/gemma-2-2b-it_temp_0.0_closed_qa_prob_0.0/fw_qa_v2/min_0_to_2000/train/*level_0_val*.parquet
diff --git a/configs/niah_exp/ctx_magic_number_32_256.yaml b/configs/niah_exp/ctx_magic_number_32_256.yaml
deleted file mode 100644
index 722f2c7..0000000
--- a/configs/niah_exp/ctx_magic_number_32_256.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-# LoRA
-lora_r: 8
-lora_dropout: 0.0
-target_modules:
- - down_proj
-
-# data
-train_ds_names:
-- ctx_magic_number_32_128
-- ctx_magic_number_128_256
-
-val_ds_names:
-- ctx_magic_number_32_128
-- ctx_magic_number_128_256
diff --git a/data/build_drop_compact.py b/data/build_drop_compact.py
deleted file mode 100644
index d9ce79e..0000000
--- a/data/build_drop_compact.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gc
-
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
-if __name__ == "__main__":
- ds_name = "ucinlp/drop"
-
- for split in ["train", "validation"]:
- ctx_qa_dict = dict()
- ds = load_dataset(ds_name, split=split)
- print(f"Original size: {len(ds)}")
- for i, sample in tqdm(enumerate(ds)):
- ctx = sample["passage"]
- if ctx not in ctx_qa_dict:
- ctx_qa_dict[ctx] = {"prompts": [], "responses": []}
- question = sample["question"]
- answer = sample["answers_spans"]["spans"][0]
- ctx_qa_dict[ctx]["prompts"].append(question)
- ctx_qa_dict[ctx]["responses"].append(answer)
-
- print(f"Unique contexts: {len(ctx_qa_dict)}")
- # convert ctx_qa_dict to a list of dictionaries
- samples = [
- {
- "context": ctx,
- "prompts": ctx_qa_dict[ctx]["prompts"],
- "responses": ctx_qa_dict[ctx]["responses"],
- }
- for ctx in ctx_qa_dict
- ]
- print(f"Sampled data: {samples[0]}")
- # breakpoint()
- # save to a new dataset
- ds = Dataset.from_list(samples)
-
- save_path = f"./data/raw_datasets/drop_compact/{split}/ds.parquet"
- print(f"Saving dataset to {save_path}")
- ds.to_parquet(save_path)
- print("=" * 80)
- del ds, samples, ctx_qa_dict
- gc.collect()
diff --git a/data/build_pwc_compact.py b/data/build_pwc_compact.py
deleted file mode 100644
index b2c3465..0000000
--- a/data/build_pwc_compact.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gc
-
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
-if __name__ == "__main__":
- ds_name = "sggetao/PwC"
-
- for split in ["train", "test"]:
- ctx_qa_dict = dict()
- ds = load_dataset(ds_name, split=split)
- print(f"Original size: {len(ds)}")
- for i, sample in tqdm(enumerate(ds)):
- ctx = sample["input"]
- if ctx not in ctx_qa_dict:
- ctx_qa_dict[ctx] = {"prompts": [], "responses": []}
- # question = closed_qa_prompting(sample["prompt"])
- question = sample["prompt"]
- answer = sample["answer"]
- ctx_qa_dict[ctx]["prompts"].append(question)
- ctx_qa_dict[ctx]["responses"].append(answer)
-
- print(f"Unique contexts: {len(ctx_qa_dict)}")
- # convert ctx_qa_dict to a list of dictionaries
- samples = [
- {
- "context": ctx,
- "prompts": ctx_qa_dict[ctx]["prompts"],
- "responses": ctx_qa_dict[ctx]["responses"],
- }
- for ctx in ctx_qa_dict
- ]
- print(f"Sampled data: {samples[0]}")
- # breakpoint()
- # save to a new dataset
- ds = Dataset.from_list(samples)
-
- save_path = f"./data/raw_datasets/pwc_compact/{split}/ds.parquet"
- print(f"Saving dataset to {save_path}")
- ds.to_parquet(save_path)
- print("=" * 80)
- del ds, samples, ctx_qa_dict
- gc.collect()
diff --git a/data/build_ropes_compact.py b/data/build_ropes_compact.py
deleted file mode 100644
index 8ea13a9..0000000
--- a/data/build_ropes_compact.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import gc
-
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
-if __name__ == "__main__":
- ds_name = "allenai/ropes"
-
- for split in ["train", "validation"]:
- ctx_qa_dict = dict()
- ds = load_dataset(ds_name, split=split)
- print(f"Original size: {len(ds)}")
- for i, sample in tqdm(enumerate(ds)):
- ctx_template = "{background}\n{situation}"
- response = sample["answers"]["text"][0]
- bg_txt = sample["background"]
- situation_txt = sample["situation"]
- ctx = ctx_template.format(background=bg_txt, situation=situation_txt)
- q = sample["question"]
- if ctx not in ctx_qa_dict:
- ctx_qa_dict[ctx] = {"prompts": [], "responses": []}
- ctx_qa_dict[ctx]["prompts"].append(q)
- ctx_qa_dict[ctx]["responses"].append(response)
-
- print(f"Unique contexts: {len(ctx_qa_dict)}")
- # convert ctx_qa_dict to a list of dictionaries
- samples = [
- {
- "context": ctx,
- "prompts": ctx_qa_dict[ctx]["prompts"],
- "responses": ctx_qa_dict[ctx]["responses"],
- }
- for ctx in ctx_qa_dict
- ]
- print(f"Sampled data: {samples[0]}")
- # breakpoint()
- # save to a new dataset
- ds = Dataset.from_list(samples)
-
- save_path = f"./data/raw_datasets/ropes_compact/{split}/ds.parquet"
- print(f"Saving dataset to {save_path}")
- ds.to_parquet(save_path)
- print("=" * 80)
- del ds, samples, ctx_qa_dict
- gc.collect()
diff --git a/data/build_squad_compact.py b/data/build_squad_compact.py
deleted file mode 100644
index b963180..0000000
--- a/data/build_squad_compact.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gc
-
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
-if __name__ == "__main__":
- ds_name = "data/raw_datasets/squad"
-
- for split in ["train", "validation"]:
- ctx_qa_dict = dict()
- ds = load_dataset(ds_name, split=split)
- print(f"Original size: {len(ds)}")
- for i, sample in tqdm(enumerate(ds)):
- ctx = sample["context"]
- if ctx not in ctx_qa_dict:
- ctx_qa_dict[ctx] = {"prompts": [], "responses": []}
- question = sample["question"]
- answer = sample["answers"]["text"][0]
- ctx_qa_dict[ctx]["prompts"].append(question)
- ctx_qa_dict[ctx]["responses"].append(answer)
-
- print(f"Unique contexts: {len(ctx_qa_dict)}")
- # convert ctx_qa_dict to a list of dictionaries
- samples = [
- {
- "context": ctx,
- "prompts": ctx_qa_dict[ctx]["prompts"],
- "responses": ctx_qa_dict[ctx]["responses"],
- }
- for ctx in ctx_qa_dict
- ]
- print(f"Sampled data: {samples[0]}")
- # breakpoint()
- # save to a new dataset
- ds = Dataset.from_list(samples)
-
- save_path = f"./data/raw_datasets/squad_compact/{split}/ds.parquet"
- print(f"Saving dataset to {save_path}")
- ds.to_parquet(save_path)
- print("=" * 80)
- del ds, samples, ctx_qa_dict
- gc.collect()
diff --git a/data/download_fineweb_edu.py b/data/download_fineweb_edu.py
deleted file mode 100644
index e0d740d..0000000
--- a/data/download_fineweb_edu.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from huggingface_hub import snapshot_download
-
-if __name__ == "__main__":
- fw_dir = "./data/raw_datasets/fineweb_edu/"
- snapshot_download(
- "HuggingFaceFW/fineweb-edu",
- repo_type="dataset",
- local_dir=fw_dir,
- allow_patterns="sample/100BT/*",
- )
diff --git a/data/generate_ctx_magic_number.py b/data/generate_ctx_magic_number.py
deleted file mode 100644
index 7ac07ee..0000000
--- a/data/generate_ctx_magic_number.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import argparse
-import json
-import math
-import os
-import random
-
-# -----------------------------
-# Config knobs (edit or use CLI)
-# -----------------------------
-TOKENS_PER_BLOCK = 40 # rough heuristic tokens per noise block
-BASE_SAMPLES_PER_BIN = (
- 320_000 # training samples budget scaler only (val/test fixed at 1000 each)
-)
-RNG_SEED = 42
-NOISE_BLOCK = "The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again."
-SPECIAL_TPL = "The special magic number is {magic_number}."
-SEP = "\n" # between blocks
-
-
-def save_jsonl(data: list[dict], filepath: str) -> None:
- parent_dir = os.path.dirname(filepath)
- if parent_dir:
- os.makedirs(parent_dir, exist_ok=True)
- with open(filepath, "w") as f:
- for entry in data:
- json.dump(entry, f)
- f.write("\n")
-
-
-essential_digits4 = lambda: f"{random.randint(0, 9_999):04d}"
-
-
-def _choose_position(total_blocks: int, depth_bin: int) -> int:
- """Choose an insertion index for the special sentence within [0, total_blocks-1]
- such that its relative depth falls within the depth bin [i/10, (i+1)/10).
- """
- if total_blocks <= 0:
- return 0
- # Use floor for start and ceil for end to cover boundaries evenly
- start = math.floor(total_blocks * (depth_bin / 10))
- end = math.ceil(total_blocks * ((depth_bin + 1) / 10)) - 1
- # clamp
- start = max(0, min(start, total_blocks - 1))
- end = max(start, min(end, total_blocks - 1))
- return random.randint(start, end)
-
-
-def _build_example(total_blocks: int, depth_bin: int) -> dict:
- """Build one example with a special line inserted among noise blocks.
-
- total_blocks: total number of blocks in the final context (including the special one)
- depth_bin: integer in [0, 9]
- """
- total_blocks = max(1, total_blocks)
-
- # Prepare blocks
- magic = essential_digits4()
- special_line = SPECIAL_TPL.format(magic_number=magic)
-
- # We'll have (total_blocks - 1) noise blocks and 1 special line
- noise_count = max(0, total_blocks - 1)
- blocks: list[str] = [NOISE_BLOCK for _ in range(noise_count)]
-
- insert_at = _choose_position(total_blocks, depth_bin)
- # Insert special line at the desired position within the final sequence
- # If noise_count == 0, we just return special
- if noise_count == 0:
- final_blocks = [special_line]
- else:
- # Compose by interleaving noise and inserting special at index
- # Build a list of length `total_blocks` and fill
- final_blocks = []
- noise_idx = 0
- for idx in range(total_blocks):
- if idx == insert_at:
- final_blocks.append(special_line)
- else:
- final_blocks.append(blocks[noise_idx])
- noise_idx += 1
-
- context = SEP.join(final_blocks)
- prompt = "What is the special magic number? Reply with only the number."
- response = magic
- return {"context": context, "prompt": prompt, "response": response}
-
-
-def generate_examples(n: int, k: int) -> list[dict]:
- """Generate n examples (all for block length k) evenly across 10 depth bins."""
- if n <= 0:
- return []
- base = n // 10
- rem = n % 10
- counts = [base + (1 if i < rem else 0) for i in range(10)]
- out: list[dict] = []
- for depth_bin, c in enumerate(counts):
- for _ in range(c):
- out.append(_build_example(total_blocks=k, depth_bin=depth_bin))
- random.shuffle(out)
- return out
-
-
-def main():
- parser = argparse.ArgumentParser(
- description="Generate noise-wrapped special magic number dataset (similar structure to generate_ctx_kv.py)",
- )
- parser.add_argument("--seed", type=int, default=RNG_SEED, help="Random seed")
- parser.add_argument(
- "--tokenizer-name",
- type=str,
- default="google/gemma-2-2b-it",
- help=("Tokenizer name"),
- )
- parser.add_argument(
- "--base-samples-per-bin",
- type=int,
- default=BASE_SAMPLES_PER_BIN,
- help="Baseline number of TRAINING samples per token bin (scaled by bin width). Validation & test are always 1000 each.",
- )
- parser.add_argument(
- "--out-prefix",
- type=str,
- default="data/raw_datasets/ctx_magic_number",
- help="Output directory prefix (bin range will be appended)",
- )
- parser.add_argument(
- "--tokens-per-block",
- "--tokens-per-pair",
- dest="tokens_per_block",
- type=int,
- default=TOKENS_PER_BLOCK,
- help="Heuristic tokens per noise block for bucketing",
- )
- parser.add_argument(
- "--only-first-n-bins",
- type=int,
- default=None,
- help="For quick tests: only generate the first N token bins",
- )
- parser.add_argument(
- "--dry-run",
- action="store_true",
- help="Print a small sample and exit without writing files",
- )
-
- args = parser.parse_args()
-
- random.seed(args.seed)
-
- # ----------------------------------------------------
- # Optional: report tokenizer-based token length stats
- # ----------------------------------------------------
- if args.tokenizer_name:
- try:
- from transformers import AutoTokenizer # type: ignore
- except Exception as e: # pragma: no cover
- raise RuntimeError(
- "Failed to import transformers. Install it or omit --tokenizer-name."
- ) from e
-
- tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
- noise_token_count = len(tokenizer(NOISE_BLOCK).input_ids)
- special_example = SPECIAL_TPL.format(magic_number="0000")
- special_token_count = len(tokenizer(special_example).input_ids)
- print(
- f"[Tokenizer: {args.tokenizer_name}] Noise block tokens: {noise_token_count} | Special line tokens: {special_token_count}"
- )
-
- tok_bins = [(32, 128), (128, 256), (256, 512), (512, 1024), (32, 1024)] + [
- (1024 * i, 1024 * (i + 1)) for i in range(1, 16)
- ]
- tok_bins += [(2**14 + 2**12 * (i), 2**14 + 2**12 * (i + 1)) for i in range(4)]
- tok_bins += [(2**15 + 2**13 * (i), 2**15 + 2**13 * (i + 1)) for i in range(12)]
- if args.only_first_n_bins is not None:
- tok_bins = tok_bins[: args.only_first_n_bins]
-
- if args.tokenizer_name:
- max_hi = max(hi for _, hi in tok_bins)
-
- def measure_len(k: int) -> int:
- if k == 1:
- ctx = SPECIAL_TPL.format(magic_number="0000")
- else:
- blocks = [NOISE_BLOCK] * (k - 1) + [
- SPECIAL_TPL.format(magic_number="0000")
- ]
- ctx = SEP.join(blocks)
- return len(tokenizer(ctx).input_ids)
-
- lengths: list[int] = [0]
- k = 1
- while True:
- L = measure_len(k)
- lengths.append(L)
- if L >= max_hi:
- break
- k += 1
-
- len_bins = []
- for lo, hi in tok_bins:
- k_lo = None
- for kk in range(1, len(lengths)):
- if lengths[kk] >= lo:
- k_lo = kk
- break
- if k_lo is None or lengths[k_lo] >= hi:
- len_bins.append((0, 0))
- continue
- k_hi = len(lengths)
- for kk in range(k_lo, len(lengths)):
- if lengths[kk] >= hi:
- k_hi = kk
- break
- len_bins.append((k_lo, k_hi))
-
- base_tokens = lengths[1]
- delta = (lengths[2] - lengths[1]) if len(lengths) > 2 else 0
- print(
- f"Using tokenizer-measured block ranges. base_tokens={base_tokens} approx_delta={delta}"
- )
- else:
- len_bins = [
- (lo // args.tokens_per_block, hi // args.tokens_per_block)
- for (lo, hi) in tok_bins
- ]
-
- if args.dry_run:
- for lb in len_bins:
- if lb[1] > lb[0]:
- k = max(1, lb[0])
- sample = generate_examples(10, k)
- print("Sample entry:")
- print(json.dumps(sample[0], indent=2))
- break
- return
- # -----------------------------------------------
- # Main generation per token bin
- # -----------------------------------------------
- TARGET_VAL = 1000
- TARGET_TEST = 1000
- for len_bin, tok_bin in zip(len_bins, tok_bins):
- if len_bin[1] <= len_bin[0]:
- print(f"Skipping token bin {tok_bin} (no valid block counts)")
- continue
- k_start = max(1, len_bin[0])
- k_end = max(1, len_bin[1])
- k_values = list(range(k_start, k_end))
- bin_size = len(k_values)
- save_dir = f"{args.out_prefix}_{tok_bin[0]}_{tok_bin[1]}"
- training_enabled = tok_bin[1] <= 1024 # unchanged policy
- if training_enabled:
- train_data: list[dict] = []
- # Distribute training budget across k values.
- # Scale: per_k = base_samples_per_bin / bin_size
- per_k_train = max(1, args.base_samples_per_bin // max(1, bin_size))
- for k in k_values:
- train_data += generate_examples(per_k_train, k)
- val_data: list[dict] = []
- test_data: list[dict] = []
- base_val = TARGET_VAL // bin_size
- rem_val = TARGET_VAL % bin_size
- base_test = TARGET_TEST // bin_size
- rem_test = TARGET_TEST % bin_size
- for idx, k in enumerate(k_values):
- n_val_k = base_val + (1 if idx < rem_val else 0)
- n_test_k = base_test + (1 if idx < rem_test else 0)
- if n_val_k:
- val_data += generate_examples(n_val_k, k)
- if n_test_k:
- test_data += generate_examples(n_test_k, k)
- random.shuffle(val_data)
- random.shuffle(test_data)
- os.makedirs(save_dir, exist_ok=True)
- if training_enabled:
- save_jsonl(train_data, f"{save_dir}/train.jsonl")
- save_jsonl(val_data, f"{save_dir}/val.jsonl")
- save_jsonl(test_data, f"{save_dir}/test.jsonl")
- if training_enabled:
- print(
- f"Dataset generated at {save_dir} (train={len(train_data)} val={len(val_data)} test={len(test_data)})"
- )
- else:
- print(
- f"Dataset (val/test only) generated at {save_dir} (val={len(val_data)} test={len(test_data)})"
- )
-
-
-if __name__ == "__main__":
- main()
diff --git a/data/generate_fw_edu_qa_v2.py b/data/generate_fw_edu_qa_v2.py
deleted file mode 100644
index 3ece436..0000000
--- a/data/generate_fw_edu_qa_v2.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import argparse
-import os
-import re
-from glob import glob
-
-import pandas as pd
-from datasets import Dataset, load_dataset
-from vllm import LLM, SamplingParams
-
-STOP_STRINGS = {
- "google/gemma-3-12b-it": ["", ""],
-}
-
-SYSTEM_TEMPLATE = (
- "You are a creative and helpful assistant.\n"
- "You are tasked with generating questions for reading comprehension tests.\n"
- "You will be given a context and you need to generate questions and corresponding answers from the given context.\n"
- "The questions should be highly specific to the information provided in the context, not general questions that suit any context.\n"
- "**DO NOT** hallucinate or make up information."
-)
-
-# based on Make Your LLM Fully Utilize the Context (https://arxiv.org/pdf/2404.16811)
-PROMPT_TEMPLATE = (
- "### Instructions ###\n"
- "Generate questions and corresponding answers from the given context. The questions should be highly specific to the "
- "information provided in the context, not general questions that suit any context.\n\n"
- "### Context ###\n"
- "{context}\n\n\n"
- "### Rules ###\n"
- "Rules to follow when generating the questions:\n"
- "1. The questions must be specific to the given context and fully answerable from information present in the given context.\n"
- "2. Ask questions that are fact-seeking based on the information provided.\n"
- "3. Make sure the questions are clear and unambiguous.\n"
- "4. Phrases like 'based on the provided context', 'according to the context', 'in the context', etc., are **NOT ALLOWED** to appear in "
- "the questions.\n"
- "5. The questions should not overlap. They should be diverse, covering many aspects of the context.\n"
- "6. Do not give away too much information in the questions. For example, ask 'Who is X?' instead of 'Who is X that did Y?' when Y is clear from the context.\n"
- "7. Ignore the text formatting of the context, e.g., bold, italic, underline, etc.\n"
- "8. Ignore typos, spacing, and grammatical errors in the context.\n\n"
- "Rules to follow when generating the answers:\n"
- "1. The answers must use the (implied) information provided in the context.\n"
- "2. Phrases like 'based on the provided context', 'according to the context', 'in the context', etc., are **NOT ALLOWED** to appear in "
- "the answers.\n"
- "3. Do not just copy words from the context. Answer the question in your own words.\n"
- "4. The answers should be detailed and comprehensive. Please include additional specific details from the context.\n\n"
- "Respond with {n_qa_pairs} question-answer pairs.\n"
- "Always use proper grammar and punctuation.\n"
- "Try to use different question forms and styles.\n"
- "Use simple words and make sure that the answers are clear and comprehensive.\n\n"
- "The question-answer pairs should be in the following format:\n"
- "Question 1: {{question_1}}\n"
- "Answer 1: {{answer_1}}\n"
- "Question 2: {{question_2}}\n"
- "Answer 2: {{answer_2}}\n"
- "..."
-)
-
-
-def get_prompt(context, n_qa_pairs):
- prompt = PROMPT_TEMPLATE.format(context=context, n_qa_pairs=n_qa_pairs)
- return prompt
-
-
-def check_should_skip(txt: str, vllm_model: str) -> bool:
- """Check if the response should be skipped based on stop strings."""
- for stop in STOP_STRINGS[vllm_model]:
- if stop in txt[-len(stop) :]:
- return (txt.split(stop)[0], False) # Found a valid stop string
- return (txt, True) # No valid stop string found, skip this response
-
-
-def postprocess_qa_pairs(res_txt: str):
- """
- Postprocesses the QA pairs from the response text.
-
- Args:
- res_txt: The response text.
- n_qa_pairs: The number of QA pairs.
-
- Returns:
- A tuple of two lists, the first containing the questions and the second containing the answers.
- """
- # capture everything after each "Question {number}:" until "Answer"
- res_txt = remove_think(res_txt)
- q_pattern = r"Question \d+:(.*?)(?=Answer|$)" # thanks chatgpt
- questions = re.findall(q_pattern, res_txt, flags=re.S)
-
- a_pattern = r"Answer \d+:(.*?)(?=Question|$)" # thanks chatgpt
- answers = re.findall(a_pattern, res_txt, flags=re.S)
-
- if len(questions) != len(answers):
- print(f"Warning---number of questions and answers do not match")
- print(f"Number of questions: {len(questions)}")
- print(f"Number of answers: {len(answers)}")
-
- out_q = []
- out_a = []
- n_skips = 0
- if (len(questions) > 0) and (len(answers) > 0):
- n_gen_pairs = min(len(questions), len(answers))
- has_left_over = n_gen_pairs < len(questions) or n_gen_pairs < len(answers)
- for i in range(n_gen_pairs):
- response = answers[i].strip()
- question = questions[i].strip()
- if not response or not question:
- print(f"Skipping empty question or answer at index {i}")
- continue
- if (not has_left_over) and (i == n_gen_pairs - 1):
- response, skip = check_should_skip(response, vllm_model)
- if skip:
- print(f"Skipping due to missing stop string")
- n_skips += 1
- continue
- out_q.append(question.strip())
- out_a.append(response.strip())
- print(f"Skipped {n_skips} responses due to missing stop strings")
-
- return out_q, out_a
-
-
-def length_filter(sample, min_len, max_len):
- return min_len <= len(sample["text"]) <= max_len
-
-
-def remove_think(txt):
- return txt.split("")[-1]
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(
- description="Generate QA pairs from FineWeb Edu dataset"
- )
- parser.add_argument(
- "--vllm_model",
- type=str,
- default=os.environ.get("vllm_model", "google/gemma-2-27b-it"),
- help="VLLM model to use for generation",
- )
- parser.add_argument(
- "--shard_pattern",
- type=str,
- required=True,
- help="Pattern to match shard files (e.g., '000_0000*')",
- )
- parser.add_argument(
- "--n_qa_pairs",
- type=int,
- required=True,
- help="Number of question-answer pairs to generate per context",
- )
- parser.add_argument(
- "--min_length",
- type=int,
- default=0,
- help="Minimum length of the context to consider for generation",
- )
- parser.add_argument(
- "--max_length",
- type=int,
- default=2000,
- help="Maximum length of the context to consider for generation",
- )
- parser.add_argument(
- "--max_model_length",
- type=int,
- default=2**14,
- help="Maximum length of the model input (context + prompt + response) in tokens",
- )
- parser.add_argument(
- "--debug",
- action="store_true",
- help="Debug mode - process only first 100 samples",
- )
-
- args = parser.parse_args()
- vllm_model = args.vllm_model
- print(f"Using model: {vllm_model}")
- llm_kwargs = dict(
- model=vllm_model,
- dtype="bfloat16",
- enable_prefix_caching=True,
- enable_chunked_prefill=True,
- max_model_len=args.max_model_length,
- limit_mm_per_prompt={"image": 0},
- )
-
- llm = LLM(**llm_kwargs)
- tokenizer = llm.get_tokenizer()
- shard_pattern = args.shard_pattern
- n_qa_pairs = args.n_qa_pairs
-
- paths = glob(
- f"./data/raw_datasets/fineweb_edu/sample/100BT/{shard_pattern}.parquet"
- )
-
- split = "train[:100]" if args.debug else "train"
- for path in paths:
- ds = load_dataset(
- "parquet",
- data_files=path,
- split=split,
- )
- ds = ds.filter(
- length_filter,
- fn_kwargs={"min_len": args.min_length, "max_len": args.max_length},
- num_proc=8,
- )
-
- ctxs = [sample["text"] for sample in iter(ds)]
- messages = [
- [
- {"role": "system", "content": SYSTEM_TEMPLATE},
- {"role": "user", "content": get_prompt(ctx, n_qa_pairs)},
- ]
- for ctx in ctxs
- ]
-
- print(f"Generating from {len(messages)} contexts")
- completions = llm.chat(
- messages,
- sampling_params=SamplingParams(
- max_tokens=2048,
- temperature=0.0,
- # needed for checking if stop tokens are present
- skip_special_tokens=False,
- include_stop_str_in_output=True,
- ),
- )
- samples = []
- for ctx, completion in zip(ctxs, completions):
- questions, answers = postprocess_qa_pairs(completion.outputs[0].text)
- samples.append(
- {
- "context": ctx,
- "prompts_level_0": questions,
- "responses_level_0": answers,
- }
- )
- if args.debug:
- print(f"{ctx=}")
- print(f"{completion.outputs[0].text=}")
- for q, a in zip(questions, answers):
- print(f"{q=}")
- print(f"{a=}")
- print()
- print("=" * 80)
-
- print(f"Generated {len(samples)} samples")
- df = pd.DataFrame(samples)
- ds = Dataset.from_pandas(df)
- val_ds = ds.take(10)
- ds = ds.skip(10)
-
- shard_name = path.split("/")[-1].split(".")[0]
- shard_name += "_level_0"
- if args.debug:
- shard_name += "_debug"
- ds.to_parquet(
- f"data/raw_datasets/fw_qa_v2/min_{args.min_length}_to_{args.max_length}/{shard_name}.parquet"
- )
- val_ds.to_parquet(
- f"data/raw_datasets/fw_qa_v2/min_{args.min_length}_to_{args.max_length}/{shard_name}_val.parquet"
- )
- print(
- f"Saved to data/raw_datasets/fw_qa_v2/min_{args.min_length}_to_{args.max_length}/{shard_name}.parquet"
- )
- print(
- f"Saved to data/raw_datasets/fw_qa_v2/min_{args.min_length}_to_{args.max_length}/{shard_name}_val.parquet"
- )
diff --git a/data/generate_fw_edu_qa_v2_repeat.py b/data/generate_fw_edu_qa_v2_repeat.py
deleted file mode 100644
index 7a85fe5..0000000
--- a/data/generate_fw_edu_qa_v2_repeat.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import argparse
-import gc
-import os
-import re
-from glob import glob
-
-from datasets import load_dataset
-from vllm import LLM, SamplingParams
-
-STOP_STRINGS = {
- "google/gemma-3-12b-it": ["", ""],
-}
-
-SYSTEM_TEMPLATE = (
- "You are a creative and helpful assistant.\n"
- "You are tasked with generating questions for reading comprehension tests.\n"
- "You will be given a context and you need to generate questions and corresponding answers from the given context.\n"
- "The questions should be highly specific to the information provided in the context, not general questions that suit any context.\n"
- "**DO NOT** hallucinate or make up information."
-)
-
-# based on Make Your LLM Fully Utilize the Context (https://arxiv.org/pdf/2404.16811)
-PROMPT_TEMPLATE = (
- "### Instructions ###\n"
- "Generate questions and corresponding answers from the given context. The questions should be highly specific to the "
- "information provided in the context, not general questions that suit any context.\n\n"
- "### Context ###\n"
- "{context}\n\n\n"
- "### Example Question-Answer Pairs ###\n"
- "{qa_pairs}\n\n\n"
- "### Rules ###\n"
- "Rules to follow when generating the questions:\n"
- "1. The questions must be specific to the given context and fully answerable from information present in *or* implied from the given context.\n"
- "2. The questions must *not* be redundant with the example questions-answer pairs provided.\n"
- "3. You should prioritize fact-seeking questions. Consider reversal questions, e.g., asking 'What causes X to happen?' is valid when 'Y causes X' is presented in the context.\n"
- "4. If all the facts in the context are already covered by the provided examples, you must generate *more complicated* questions that require reasoning beyond simple information retrieval.\nThis includes asking about information that can be inferred, requiring synthesizing information from multiple parts of the text, or understanding relationships between concepts, events, or individuals mentioned in the context. For example, if the context says 'The Eiffel Tower was completed in 1889 after 2 years of construction', you can ask 'When did the construction of the Eiffel Tower begin?'. Here's another example: if the context says 'Alice is Bob's mother. Bob is Charlie's Dad', you can ask 'Who is Charlie's grandmother?'.\n"
- "5. Phrases like 'based on the provided context', 'according to the context', 'in the context', etc., are **NOT ALLOWED** to appear in "
- "the questions.\n"
- "6. The questions should not overlap. They should be diverse, covering many aspects of the context.\n"
- "7. Do not give away too much information in the questions. For example, ask 'Who is X?' instead of 'Who is X that did Y?' when Y is clear from the context.\n"
- "8. Ignore the text formatting of the context, e.g., bold, italic, underline, etc.\n"
- "9. Ignore typos, spacing, and grammatical errors in the context.\n\n"
- "Rules to follow when generating the answers:\n"
- "1. The answers must use the (implied) information provided in the context.\n"
- "2. Phrases like 'based on the provided context', 'according to the context', 'in the context', etc., are **NOT ALLOWED** to appear in "
- "the answers.\n"
- "3. Do not just copy words from the context. Answer the question in your own words.\n"
- "4. The answers should be detailed and comprehensive. Please include additional specific details from the context.\n\n"
- "Respond with {n_qa_pairs} question-answer pairs.\n"
- "Always use proper grammar and punctuation.\n"
- "Try to use different question forms and styles.\n"
- "Use simple words and make sure that the answers are clear and comprehensive.\n\n"
- "The question-answer pairs should be in the following format:\n"
- "Question 1: {{question_1}}\n"
- "Answer 1: {{answer_1}}\n"
- "Question 2: {{question_2}}\n"
- "Answer 2: {{answer_2}}\n"
- "..."
-)
-
-
-def get_prompt(context, example_qa_pairs, n_qa_pairs):
- prompt = PROMPT_TEMPLATE.format(
- context=context,
- qa_pairs=example_qa_pairs,
- n_qa_pairs=n_qa_pairs,
- )
- return prompt
-
-
-def check_should_skip(txt: str, vllm_model: str) -> bool:
- """Check if the response should be skipped based on stop strings."""
- for stop in STOP_STRINGS[vllm_model]:
- if stop in txt[-len(stop) :]:
- return (txt.split(stop)[0], False) # Found a valid stop string
- return (txt, True) # No valid stop string found, skip this response
-
-
-def postprocess_qa_pairs(res_txt: str):
- """
- Postprocesses the QA pairs from the response text.
-
- Args:
- res_txt: The response text.
- n_qa_pairs: The number of QA pairs.
-
- Returns:
- A tuple of two lists, the first containing the questions and the second containing the answers.
- """
- # capture everything after each "Question {number}:" until "Answer"
- res_txt = remove_think(res_txt)
- q_pattern = r"Question \d+:(.*?)(?=Answer|$)" # thanks chatgpt
- questions = re.findall(q_pattern, res_txt, flags=re.S)
-
- a_pattern = r"Answer \d+:(.*?)(?=Question|$)" # thanks chatgpt
- answers = re.findall(a_pattern, res_txt, flags=re.S)
-
- if len(questions) != len(answers):
- print(f"Warning---number of questions and answers do not match")
- print(f"Number of questions: {len(questions)}")
- print(f"Number of answers: {len(answers)}")
-
- out_q = []
- out_a = []
- n_skips = 0
- if (len(questions) > 0) and (len(answers) > 0):
- n_gen_pairs = min(len(questions), len(answers))
- has_left_over = n_gen_pairs < len(questions) or n_gen_pairs < len(answers)
- for i in range(n_gen_pairs):
- response = answers[i].strip()
- question = questions[i].strip()
- if not response or not question:
- print(f"Skipping empty question or answer at index {i}")
- continue
- if (not has_left_over) and (i == n_gen_pairs - 1):
- response, skip = check_should_skip(response, vllm_model)
- if skip:
- print(f"Skipping due to missing stop string")
- n_skips += 1
- continue
- out_q.append(question.strip())
- out_a.append(response.strip())
- print(f"Skipped {n_skips} responses due to missing stop strings")
-
- return out_q, out_a
-
-
-def flatten_list(l):
- out = []
- for x in l:
- out += x
- return out
-
-
-def remove_think(txt):
- return txt.split("")[-1]
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(
- description="Generate QA pairs from FineWeb Edu dataset"
- )
- parser.add_argument(
- "--vllm_model",
- type=str,
- default=os.environ.get("vllm_model", "google/gemma-2-27b-it"),
- help="VLLM model to use for generation",
- )
- parser.add_argument(
- "--shard_pattern",
- type=str,
- required=True,
- help="Pattern to match shard files (e.g., '000_0000*')",
- )
- parser.add_argument(
- "--n_qa_pairs",
- type=int,
- required=True,
- help="Number of question-answer pairs to generate per context",
- )
- parser.add_argument(
- "--max_model_length",
- type=int,
- default=2**12,
- help="Maximum length of the model input (context + prompt + response) in tokens",
- )
- parser.add_argument(
- "--debug",
- action="store_true",
- help="Debug mode - process only first 100 samples",
- )
-
- args = parser.parse_args()
- vllm_model = args.vllm_model
- print(f"Using model: {vllm_model}")
- llm_kwargs = dict(
- model=vllm_model,
- dtype="bfloat16",
- enable_prefix_caching=True,
- enable_chunked_prefill=True,
- max_model_len=2**14,
- limit_mm_per_prompt={"image": 0},
- )
-
- llm = LLM(**llm_kwargs)
- tokenizer = llm.get_tokenizer()
- shard_pattern = args.shard_pattern
- n_qa_pairs = args.n_qa_pairs
-
- paths = glob(f"./data/raw_datasets/fw_qa_v2/{shard_pattern}.parquet")
-
- split = "train[:100]" if args.debug else "train"
- for path in paths:
- assert "_level" in path, (
- "Path must contain '_level' to indicate the dataset level"
- )
- shard_name = path.split("/")[-1].split(".")[0].split("_debug")[0]
- if "/" in shard_pattern:
- shard_name = "/".join(shard_pattern.split("/")[:-1]) + "/" + shard_name
- cur_level = int(shard_name.split("_level_")[-1])
- next_level = cur_level + 1
- ds = load_dataset(
- "parquet",
- data_files=path,
- split=split,
- )
- prompt_cols = [col for col in ds.column_names if col.startswith("prompts")]
- response_cols = [col for col in ds.column_names if col.startswith("responses")]
- assert len(prompt_cols) > 0, "No prompt columns found in the dataset"
- if len(prompt_cols) != len(response_cols):
- raise ValueError(
- "Number of prompt columns does not match number of response columns"
- )
-
- samples_data = []
- for sample in iter(ds):
- # Format existing QA pairs as examples
- example_qa_pairs = ""
- questions = flatten_list([sample[col] for col in prompt_cols])
- answers = flatten_list([sample[col] for col in response_cols])
- for i, (q, a) in enumerate(zip(questions, answers), 1):
- example_qa_pairs += f"Question {i}: {q}\nAnswer {i}: {a}\n"
-
- samples_data.append(
- {"context": sample["context"], "example_qa_pairs": example_qa_pairs}
- )
- del ds
- gc.collect()
-
- messages = [
- [
- {"role": "system", "content": SYSTEM_TEMPLATE},
- {
- "role": "user",
- "content": get_prompt(
- sample["context"], sample["example_qa_pairs"], n_qa_pairs
- ),
- },
- ]
- for sample in samples_data
- ]
-
- print(f"Generating from {len(messages)} contexts")
- completions = llm.chat(
- messages,
- sampling_params=SamplingParams(
- temperature=0.0,
- # needed for checking if stop tokens are present
- skip_special_tokens=False,
- include_stop_str_in_output=True,
- ),
- )
- samples = []
- for sample_data, completion in zip(samples_data, completions):
- questions, answers = postprocess_qa_pairs(completion.outputs[0].text)
- samples.append(
- {
- "context": sample_data["context"],
- f"prompts_level_{next_level}": questions,
- f"responses_level_{next_level}": answers,
- }
- )
- if args.debug:
- print(f"context={sample_data['context']}")
- print(f"example_qa_pairs={sample_data['example_qa_pairs']}")
- print(f"{completion.outputs[0].text=}")
- for q, a in zip(questions, answers):
- print(f"{q=}")
- print(f"{a=}")
- print()
- print("=" * 80)
-
- del samples_data
- gc.collect()
-
- print(f"Generated {len(samples)} samples")
- ds = load_dataset(
- "parquet",
- data_files=path,
- split=split,
- )
- ds = ds.add_column(
- f"prompts_level_{next_level}",
- [sample[f"prompts_level_{next_level}"] for sample in samples],
- )
- ds = ds.add_column(
- f"responses_level_{next_level}",
- [sample[f"responses_level_{next_level}"] for sample in samples],
- )
-
- shard_name_base = shard_name.split("_level_")[0]
- shard_name = f"{shard_name_base}_level_{next_level}"
- if args.debug:
- shard_name += "_debug"
- ds.to_parquet(f"data/raw_datasets/fw_qa_v2/{shard_name}.parquet")
- print(f"Saved to data/raw_datasets/fw_qa_v2/{shard_name}.parquet")
diff --git a/data/gutenburg_sample.txt b/data/gutenburg_sample.txt
deleted file mode 100644
index 12cde16..0000000
--- a/data/gutenburg_sample.txt
+++ /dev/null
@@ -1,387 +0,0 @@
-The Project Gutenberg eBook, Addison, by William John Courthope
-
-
-This eBook is for the use of anyone anywhere at no cost and with
-almost no restrictions whatsoever. You may copy it, give it away or
-re-use it under the terms of the Project Gutenberg License included
-with this eBook or online at www.gutenberg.org
-
-
-
-
-
-Title: Addison
-
-
-Author: William John Courthope
-
-
-
-Release Date: November 27, 2012 [eBook #41496]
-
-Language: English
-
-Character set encoding: ISO-8859-1
-
-
-***START OF THE PROJECT GUTENBERG EBOOK ADDISON***
-
-
-E-text prepared by the Online Distributed Proofreading Team
-(http://www.pgdp.net) from page images generously made available by
-Internet Archive (http://archive.org)
-
-
-
-Note: Images of the original pages are available through
- Internet Archive. See
- http://archive.org/details/addison_00cour
-
-
-Transcriber's note:
-
- Text enclosed by underscores is in italics (_italics_).
-
- Text enclosed by curly brackets is superscripted
- (example: y{e}).
-
-
-
-
-
-English Men of Letters
-
-Edited by John Morley
-
-ADDISON
-
-by
-
-W. J. COURTHOPE
-
-
-
-
-
-
-
-Harper & Brothers Publishers
-New York and London
-1902
-
- * * * * *
-
-ENGLISH MEN OF LETTERS.
-
-EDITED BY JOHN MORLEY.
-
- JOHNSON Leslie Stephen.
- GIBBON J. C. Morison.
- SCOTT R. H. Hutton.
- SHELLEY J. A. Symonds.
- HUME T. H. Huxley.
- GOLDSMITH William Black.
- DEFOE William Minto.
- BURNS J. C. Shairp.
- SPENSER R. W. Church.
- THACKERAY Anthony Trollope.
- BURKE John Morley.
- MILTON Mark Pattison.
- HAWTHORNE Henry James, Jr.
- SOUTHEY E. Dowden.
- CHAUCER A. W. Ward.
- BUNYAN J. A. Froude.
- COWPER Goldwin Smith.
- POPE Leslie Stephen.
- BYRON John Nichol.
- LOCKE Thomas Fowler.
- WORDSWORTH F. Myers.
- DRYDEN G. Saintsbury.
- LANDOR Sidney Colvin.
- DE QUINCEY David Masson.
- LAMB Alfred Ainger.
- BENTLEY R. C. Jebb.
- DICKENS A. W. Ward.
- GRAY E. W. Gosse.
- SWIFT Leslie Stephen.
- STERNE H. D. Traill.
- MACAULAY J. Cotter Morison.
- FIELDING Austin Dobson.
- SHERIDAN Mrs. Oliphant.
- ADDISON W. J. Courthope.
- BACON R. W. Church.
- COLERIDGE H. D. Traill.
- SIR PHILIP SIDNEY J. A. Symonds.
- KEATS Sidney Colvin.
- CARLYLE John Nichol.
-
-12mo, Cloth, 75 cents per volume.
-
-_Other volumes in preparation._
-
-PUBLISHED BY HARPER & BROTHERS, NEW YORK.
-
-_Any of the above works will be sent by mail, postage prepaid, to any part
-of the United States, Canada, or Mexico, on receipt of the price._
-
- * * * * *
-
-
-
-CONTENTS.
-
-
- PAGE
-
- CHAPTER I.
- THE STATE OF ENGLISH SOCIETY AND LETTERS
- AFTER THE RESTORATION 1
-
- CHAPTER II.
- ADDISON'S FAMILY AND EDUCATION 21
-
- CHAPTER III.
- ADDISON ON HIS TRAVELS 38
-
- CHAPTER IV.
- HIS EMPLOYMENT IN AFFAIRS OF STATE 53
-
- CHAPTER V.
- THE "TATLER" AND "SPECTATOR" 78
-
- CHAPTER VI.
- "CATO" 110
-
- CHAPTER VII.
- ADDISON'S QUARREL WITH POPE 125
-
- CHAPTER VIII.
- THE LAST YEARS OF HIS LIFE 139
-
- CHAPTER IX.
- THE GENIUS OF ADDISON 153
-
-
-
-
-ADDISON.
-
-
-
-
-CHAPTER I.
-
-THE STATE OF ENGLISH SOCIETY AND LETTERS AFTER THE RESTORATION.
-
-
-Of the four English men of letters whose writings most fully embody the
-spirit of the eighteenth century, the one who provides the biographer with
-the scantiest materials is Addison. In his _Journal to Stella_, his social
-verses, and his letters to his friends, we have a vivid picture of those
-relations with women and that protracted suffering which invest with such
-tragic interest the history of Swift. Pope, by the publication of his own
-correspondence, has enabled us, in a way that he never intended, to
-understand the strange moral twist which distorted a nature by no means
-devoid of noble instincts. Johnson was fortunate in the companionship of
-perhaps the best biographer who ever lived. But of the real life and
-character of Addison scarcely any contemporary record remains. The formal
-narrative prefixed to his works by Tickell is, by that writer's own
-admission, little more than a bibliography. Steele, who might have told us
-more than any man about his boyhood and his manner of life in London, had
-become estranged from his old friend before his death. No writer has
-taken the trouble to preserve any account of the wit and wisdom that
-enlivened the "little senate" at Button's. His own letters are, as a rule,
-compositions as finished as his papers in the _Spectator_. Those features
-in his character which excite the greatest interest have been delineated
-by the hand of an enemy--an enemy who possessed an unrivalled power of
-satirical portrait-painting, and was restrained by no regard for truth
-from creating in the public mind such impressions about others as might
-serve to heighten the favourable opinion of himself.
-
-This absence of dramatic incident in Addison's life would lead us
-naturally to conclude that he was deficient in the energy and passion
-which cause a powerful nature to leave a mark upon its age. Yet such a
-judgment would certainly be erroneous. Shy and reserved as he was, the
-unanimous verdict of his most illustrious contemporaries is decisive as to
-the respect and admiration which he excited among them. The man who could
-exert so potent an influence over the mercurial Steele, who could
-fascinate the haughty and cynical intellect of Swift, whose conversation,
-by the admission of his satirist Pope, had in it something more charming
-than that of any other man; of whom it was said that he might have been
-chosen king if he wished it; such a man, though to the coarse perception
-of Mandeville he might have seemed no more than "a parson in a tye-wig,"
-can hardly have been deficient in force of character.
-
-Nor would it have been possible for a writer distinguished by mere
-elegance and refinement to leave a lasting impress on the literature and
-society of his country. In one generation after another, men representing
-opposing elements of rank, class, interest, and taste, have agreed in
-acknowledging Addison's extraordinary merits. "Whoever wishes," says
-Johnson--at the end of a biography strongly coloured with the
-prepossessions of a semi-Jacobite Tory--"whoever wishes to attain an
-English style, familiar but not coarse, and elegant but not ostentatious,
-must give his days and nights to the volumes of Addison." "Such a mark of
-national respect," says Macaulay, the best representative of middle-class
-opinion in the present century, speaking of the statue erected to Addison
-in Westminster Abbey, "was due to the unsullied statesman, to the
-accomplished scholar, to the master of pure English eloquence, to the
-consummate painter of life and manners. It was due, above all, to the
-great satirist who alone knew how to use ridicule without abusing it; who,
-without inflicting a wound, effected a great social reform, and who
-reconciled wit and virtue after a long and disastrous separation, during
-which wit had been led astray by profligacy, and virtue by fanaticism."
-
-This verdict of a great critic is accepted by an age to which the grounds
-of it are, perhaps, not very apparent. The author of any ideal creation--a
-poem, a drama, or a novel--has an imprescriptible property in the fame of
-his work. But to harmonise conflicting social elements, to bring order out
-of chaos in the sphere of criticism, to form right ways of thinking about
-questions of morals, taste, and breeding, are operations of which the
-credit, though it is certainly to be ascribed to particular individuals,
-is generally absorbed by society itself. Macaulay's eulogy is as just as
-it is eloquent, but the pages of the _Spectator_ alone will hardly show
-the reader why Addison should be so highly praised for having reconciled
-wit with virtue. Nor, looking at him as a critic, will it appear a great
-achievement to have pointed out to English society the beauties of
-_Paradise Lost_, unless it be remembered that the taste of the preceding
-generation still influenced Addison's contemporaries, and that in that
-generation Cowley was accounted a greater poet than Milton.
-
-To estimate Addison at his real value we must regard him as the chief
-architect of Public Opinion in the eighteenth century. But here again we
-are met by an initial difficulty, because it has become almost a
-commonplace of contemporary criticism to represent the eighteenth century
-as a period of sheer destruction. It is tacitly assumed by a school of
-distinguished philosophical writers that we have arrived at a stage in the
-world's history in which it is possible to take a positive and scientific
-view of human affairs. As it is of course necessary that from such a
-system all belief in the supernatural shall be jealously excluded, it has
-not seemed impossible to write the history of Thought itself in the
-eighteenth century. And in tracing the course of this supposed continuous
-stream it is natural that all the great English writers of the period
-should be described as in one way or another helping to pull down, or
-vainly to strengthen, the theological barriers erected by centuries of
-bigotry against the irresistible tide of enlightened progress.
-
-It would be of course entirely out of place to discuss here the merits of
-this new school of history. Those who consider that, whatever glimpses we
-may obtain of the law and order of the universe, man is, as he always has
-been and always will be, a mystery to himself, will hardly allow that the
-operations of the human spirit can be traced in the dissecting-room. But
-it is, in any case, obvious that to treat the great _imaginative_ writers
-of any age as if they were only mechanical agents in an evolution of
-thought is to do them grave injustice. Such writers are, above all things,
-creative. Their first aim is to "show the very age and body of the time
-his form and pressure." No work of the eighteenth century, composed in a
-consciously destructive spirit, has taken its place among the acknowledged
-classics of the language. Even the _Tale of a Tub_ is to be regarded as a
-satire upon the aberrations of theologians from right reason, not upon the
-principles of Christianity itself. The _Essay on Man_ has, no doubt,
-logically a tendency towards Deism, but nobody ever read the poem for the
-sake of its philosophy; and it is well known that Pope was much alarmed
-when it was pointed out to him that his conclusions might be represented
-as incompatible with the doctrines of revealed religion.
-
-The truth indeed seems to be the exact converse of what is alleged by the
-scientific historians. So far from the eighteenth century in England being
-an age of destructive analysis, its energies were chiefly devoted to
-political, social, and literary reconstruction. Whatever revolution in
-faith and manners the English nation had undergone had been the work of
-the two preceding centuries, and though the historic foundations of
-society remained untouched, the whole form of the superstructure had been
-profoundly modified.
-
- "So tenacious are we," said Burke, towards the close of the last
- century, "of our old ecclesiastical modes and fashions of institution
- that very little change has been made in them since the fourteenth or
- fifteenth centuries, adhering in this particular as in all else to our
- old settled maxim never entirely nor at once to depart from antiquity.
- We found these institutions on the whole favourable to morality and
- discipline, and we thought they were susceptible of amendment without
- altering the ground. We thought they were capable of receiving and
- meliorating, and, above all, of preserving the accessories of science
- and literature as the order of Providence should successively produce
- them. And after all, with this Gothic and monkish education (for such
- it is the groundwork), we may put in our claim to as ample and early
- a share in all the improvements in science, in arts, and in literature
- which have illuminated the modern world as any other nation in Europe.
- We think one main cause of this improvement was our not despising the
- patrimony of knowledge which was left us by our forefathers."
-
-All this is, in substance, true of our political as well as our
-ecclesiastical institutions. And yet, when Burke wrote, the great feudal
-and mediรฆval structure of England had been so transformed by the Wars of
-the Roses, the Reformation, the Rebellion, and the Revolution, that its
-ancient outlines were barely visible. In so far, therefore, as his words
-seem to imply that the social evolution he describes was produced by an
-imperceptible and almost mechanical process of national instinct, the
-impression they tend to create is entirely erroneous.
-
-If we have been hitherto saved from such corruption as undermined the
-republics of Italy, from the religious wars that so long enfeebled and
-divided Germany, and from the Revolution that has severed modern France
-from her ancient history, thanks for this are due partly, no doubt, to
-favouring conditions of nature and society, but quite as much to the
-genius of great individuals who prepared the mind of the nation for the
-gradual assimilation of new ideas. Thus Langland and Wycliffe and their
-numerous followers, long before the Reformation, had so familiarised the
-minds of the people with their ideas of the Christian religion that the
-Sovereign was able to assume the Headship of the Church without the shock
-of a social convulsion. Fresh feelings and instincts grew up in the hearts
-of whole classes of the nation without at first producing any change in
-outward habits of life, and even without arousing a sense of their logical
-incongruity. These mixed ideas were constantly brought before the
-imagination in the works of the poets. Shakespeare abounds with passages
-in which, side by side with the old feudal, monarchical, catholic, and
-patriotic instincts of Englishmen, we find the sentiments of the Italian
-Renaissance. Spenser conveys Puritan doctrines sometimes by the mouth of
-shepherds, whose originals he had found in Theocritus and Virgil;
-sometimes under allegorical forms derived from books of chivalry and the
-ceremonial of the Catholic Church. Milton, the most rigidly Calvinistic of
-all the English poets in his opinions, is also the most severely classical
-in his style.
-
-It was the task of Addison to carry on the reconciling traditions of our
-literature. It is his praise to have accomplished his task under
-conditions far more difficult than any that his predecessors had
-experienced. What they had done was to give instinctive and characteristic
-expression to the floating ideas of the society about them; what Addison
-and his contemporaries did was to found a public opinion by a conscious
-effort of reason and persuasion. Before the Civil Wars there had been at
-least no visible breach in the principle of Authority in Church and State.
-At the beginning of the eighteenth century constituted authority had been
-recently overthrown; one king had been beheaded, another had been
-expelled; the Episcopalian form of Church Government had been violently
-displaced in favour of the Presbyterian, and had been with almost equal
-violence restored. Whole classes of the population had been drawn into
-opposing camps during the Civil War, and still stood confronting each
-other with all the harsh antagonism of sentiment inherited from that
-conflict. Such a bare summary alone is sufficient to indicate the nature
-of the difficulties Addison had to encounter in his efforts to harmonise
-public opinion; but a more detailed examination of the state of society
-after the Restoration is required to place in its full light the
-extraordinary merits of the success that he achieved.
-
-There was, to begin with, a vehement opposition between town and country.
-In the country the old ideas of Feudalism, modified by circumstances, but
-vigorous and deep-rooted, still prevailed. True, the military system of
-land-tenure had disappeared with the Restoration, but it was not so with
-the relations of life, and the habits of thought and feeling which the
-system had created. The features of surviving Feudalism have been
-inimitably preserved for us in the character of Sir Roger de Coverley.
-Living in the patriarchal fashion, in the midst of tenants and retainers,
-who looked up to him as their chief, and for whose welfare and protection
-he considered himself responsible, the country gentleman valued above all
-things the principle of Loyalty. To the moneyed classes in the towns he
-was instinctively opposed; he regarded their interests, both social and
-commercial, as contrary to his own; he looked with dislike and suspicion
-on the economical principles of government and conduct on which these
-classes naturally rely. Even the younger sons of county families had in
-Addison's day abandoned the custom, common enough in the feudal times, of
-seeking their fortune in trade. Many a Will Wimble now spent his whole
-life in the country, training dogs for his neighbours, fishing their
-streams, making whips for their young heirs, and even garters for their
-wives and daughters.[1]
-
-
-
diff --git a/data/sakana_wiki.txt b/data/sakana_wiki.txt
deleted file mode 100644
index 694d451..0000000
--- a/data/sakana_wiki.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-Sakana AI Co, Ltd. is a Japanese artificial intelligence company based in Tokyo.
-Overview
-
-Sakana AI's main research fields are evolution and collective intelligence of AI. The company's name is derived from the Japanese word ใใใช (sakana), which means fish. This represents the idea of a school of fish coming together and forming a coherent entity from simple rules, which is an analogy of collective intelligence.[2]
-
-The company was founded by David Ha, Llion Jones and Ren Ito. Llion Jones co-authored the famous paper "Attention Is All You Need" when he was working for Google in 2017. The company raised $30M in its seed funding round from Lux Capital and Khosla Ventures.[3] The company raised approximately $200M from companies such as Mitsubishi UFJ, SMBC, Mizuho, Itochu, KDDI, Nomura and Nvidia in its series A funding round in 2024.[4]
-
-In January 2024, Sakana AI developed a method to build new AI models by 'breeding' multiple existing models, which it sees as a means to democratise AI development, as this process does not require large computational resources.[5] Sakana AI is also developing a model called the AI Scientist, which automates the entire process of scientific research.[6] The Nikkei estimated the company's value at 19 billion yen in 2024.[7]
\ No newline at end of file
diff --git a/data/self_generate_qa.py b/data/self_generate_qa.py
deleted file mode 100644
index b52e909..0000000
--- a/data/self_generate_qa.py
+++ /dev/null
@@ -1,620 +0,0 @@
-import argparse
-import os
-import random
-import re
-from glob import glob
-
-import numpy as np
-import yaml
-from datasets import Dataset, load_dataset
-from vllm import LLM, SamplingParams
-
-from ctx_to_lora.data.definitions import (
- CLOSED_QA_INTX_TEMPLATES,
- RAW_DATA_DIR,
- SELF_GEN_DATA_DIR,
-)
-from ctx_to_lora.data.processing import (
- filter_none,
- get_preprocessing_fn,
- load_and_process_dataset,
- tokenize_ctx_text,
-)
-from ctx_to_lora.data.self_gen_template import (
- PRE_CTX,
- PROMPT_TEMPLATE,
- QA_PROMPT_TEMPLATE,
- SELF_GEN_SYSTEM_MSG,
- SELF_QA_INTX,
-)
-from ctx_to_lora.model_loading import get_tokenizer
-from ctx_to_lora.utils import clear_gpu
-
-STOP_STRINGS = {
- "google/gemma-2-2b-it": ["", ""],
-}
-
-MODEL_CTX_LEN = {
- "google/gemma-2-27b-it": 8192,
- "google/gemma-2-2b-it": 8192,
- "google/gemma-2-9b-it": 8192,
- # qwen 4b has 256k ctx length but using lower max lengths is faster
- "Qwen/Qwen3-4B-Instruct-2507": 2**13 + 2**12,
-}
-
-
-def truncate_middle_if_too_long(
- input_ids: list[int],
- max_length: int,
- max_new_tokens: int = 256,
-) -> list[int]:
- """
- Truncate the middle of a list of tokens to fit within a maximum length.
-
- Args:
- tokens: List of token IDs
- max_length: Maximum length for the truncated tokens
-
- Returns:
- List of truncated token IDs
- """
- max_new_tokens_half = max_new_tokens // 2
- # leave max_new_tokens for generation
- half = max_length // 2 - max_new_tokens_half
- if len(input_ids) > max_length:
- return input_ids[:half] + input_ids[-half:]
- return input_ids
-
-
-def get_prompt(context: str, q: str, remove_qa_template: bool) -> str:
- prompt = QA_PROMPT_TEMPLATE if not remove_qa_template else PROMPT_TEMPLATE
- return prompt.format(context=context, question=q)
-
-
-def add_closed_qa_prompt(q: str, closed_qa_prob: float = 0.1) -> str:
- if random.random() <= closed_qa_prob:
- q = random.choice(CLOSED_QA_INTX_TEMPLATES).format(input=q)
- return q
-
-
-def load_config(config_path: str) -> dict:
- """Load dataset names from YAML config file."""
- with open(config_path) as f:
- config = yaml.safe_load(f)
- return config
-
-
-def get_dataset_configs(
- ds_names: list[str] | None,
- config: dict | None,
- split: str | None,
-) -> list[tuple[str, str]]:
- assert not (ds_names and config), "Cannot provide both ds_names and config"
- if ds_names:
- assert split, "When using ds_names, --split must be provided"
- # Validate ds_names format
- for ds_name in ds_names:
- if not isinstance(ds_name, str):
- raise ValueError(f"Invalid dataset name: {ds_name}")
- return [(ds_name, split) for ds_name in ds_names]
-
- if config:
- dataset_configs = []
-
- # Process train datasets
- train_ds_names = config.get("train_ds_names", [])
- # self_gen_train_ds_names = [
- # (ds_name.split("/")[-1], "train")
- # for ds_name in train_ds_names
- # if ds_name.startswith("self_gen/")
- # ]
- self_gen_train_ds_names = [
- (ds_name, "train")
- for ds_name in train_ds_names
- if ds_name.startswith("self_gen/")
- ]
- if not self_gen_train_ds_names:
- print("No self_gen datasets found in train_ds_names")
- dataset_configs.extend(self_gen_train_ds_names)
-
- # Process validation datasets
- val_ds_names = config.get("val_ds_names", [])
- self_gen_val_ds_names = [
- (ds_name, "validation")
- for ds_name in val_ds_names
- if ds_name.startswith("self_gen/")
- ]
- if not self_gen_val_ds_names:
- print("No self_gen datasets found in val_ds_names")
- dataset_configs.extend(self_gen_val_ds_names)
-
- return dataset_configs
-
-
-def create_messages(
- ctxs: list[str],
- questions: list[list[str]],
- vllm_model: str,
- system_template: str,
- remove_qa_template: bool,
-) -> list[list[dict]]:
- """Create chat messages for the model."""
- # if "gemma" in vllm_model:
- # gemma models do not support system messages
- return [
- [
- {
- "role": "user",
- "content": (
- system_template + "\n\n\n" + get_prompt(ctx, q, remove_qa_template)
- ).strip(),
- }
- ]
- for ctx, q_list in zip(ctxs, questions)
- for q in q_list
- ]
- # else:
- # return [
- # [
- # {"role": "system", "content": system_template},
- # {"role": "user", "content": get_prompt(ctx, q)},
- # ]
- # for ctx, q_list in zip(ctxs, questions)
- # for q in q_list
- # ]
-
-
-def self_generate(
- ds_name: str,
- split: str,
- args: argparse.Namespace,
- llm: LLM,
- system_template: str,
- parquet_file: str | None = None,
- do_truncate: bool = False,
-) -> None:
- """Process a single dataset and generate QA pairs."""
-
- shard_name = ""
-
- # Conflict checks for ds_name-derived overrides
- if ds_name is not None:
- # temperature & closed_qa already handled later; add new ones
- if "_temp_" in ds_name and args.temp != 0.0:
- raise ValueError(
- f"Multiple sources of truth for temperature: CLI arg --temp={args.temp} and dataset name contains temp specification."
- )
- if "_closed_qa_prob_" in ds_name and args.closed_qa_prob != 0.0:
- raise ValueError(
- f"Multiple sources of truth for closed_qa_prob: CLI arg --closed_qa_prob={args.closed_qa_prob} and dataset name contains closed_qa_prob specification."
- )
-
- # Base values from args
- temp = args.temp
- closed_qa_prob = args.closed_qa_prob
-
- # Overrides from ds_name pattern if present
- if ds_name is not None:
- if "_temp_" in ds_name:
- m = re.search(r"_temp_([\d.]+)", ds_name)
- if m:
- temp = float(m.group(1))
- if "_closed_qa_prob_" in ds_name:
- m = re.search(r"_closed_qa_prob_([\d.]+)", ds_name)
- if m:
- closed_qa_prob = float(m.group(1))
-
- print(f"Processing dataset: {ds_name}, split: {split}")
- print(f"Using temperature: {temp}")
- print(f"Using closed QA prompt probability: {closed_qa_prob}")
-
- if parquet_file:
- print(f"Loading dataset from parquet file: {parquet_file}")
-
- split = "train"
- ds_name = "/".join(parquet_file.split(RAW_DATA_DIR)[-1].split("/")[:-1])
-
- shard_name = "_" + os.path.basename(parquet_file).replace(".parquet", "")
- ds = load_dataset(path="parquet", data_files=[parquet_file], split="train")
- processing_fn = get_preprocessing_fn(ds_name, is_eval=False)
- ds = ds.map(processing_fn, num_proc=8)
-
- else:
- ds_name = ds_name.split("/")[-1] # Extract just the dataset name
-
- print(f"Loading dataset: {ds_name} with split: {split}")
- kwargs = dict(ds_name=ds_name, split=split)
-
- ds = load_and_process_dataset(**kwargs, num_proc=8, remove_cols=False)
- print(f"Loaded dataset: {ds_name} with split: {split}")
-
- if args.debug:
- ds = ds.take(10)
-
- ds = ds.filter(filter_none, batched=False, num_proc=8)
-
- tk = get_tokenizer(args.vllm_model, train=True)
-
- self_qa_intx_tokens = tk(SELF_QA_INTX, add_special_tokens=False)["input_ids"][1:]
- if args.remove_qa_template:
- self_qa_intx_tokens = tk("\n\n", add_special_tokens=False)["input_ids"]
- n_self_qa_intx_tokens = len(self_qa_intx_tokens)
- pre_ctx_tokens = tk(PRE_CTX, add_special_tokens=False)["input_ids"]
- n_pre_ctx_tokens = len(pre_ctx_tokens)
- sys_tokens = tk(system_template.split("\n")[0], add_special_tokens=False)[
- "input_ids"
- ][:-1]
- n_sys_tokens = len(sys_tokens)
- os.environ["TOKENIZERS_PARALLELISM"] = "true"
- ds = ds.map(
- tokenize_ctx_text,
- fn_kwargs={"tokenizer": tk},
- batched=True,
- batch_size=50_000,
- keep_in_memory=True,
- )
-
- ctxs = [sample["context"] for sample in ds]
- questions = [
- [add_closed_qa_prompt(q, closed_qa_prob) for q in sample["prompts"] if q]
- for sample in ds
- ]
-
- questions = [q_list for q_list in ds["prompts"] if len(q_list) > 0]
-
- print(f"Loaded {len(ctxs)} contexts and {len(questions)} questions")
-
- k = 16
- fpath = f"{SELF_GEN_DATA_DIR}/{args.vllm_model}_temp_{temp}_closed_qa_prob_{closed_qa_prob}/{ds_name}/{split}/ds{shard_name}"
-
- chunk_size = 1_000
- for chunk_idx, start in enumerate(range(0, len(ctxs), chunk_size)):
- print(f"Processing chunk {chunk_idx}")
-
- chunk_ctxs = ctxs[start : start + chunk_size]
- chunk_questions = questions[start : start + chunk_size]
- chunk_messages = create_messages(
- chunk_ctxs,
- chunk_questions,
- args.vllm_model,
- SELF_GEN_SYSTEM_MSG,
- args.remove_qa_template,
- )
-
- if do_truncate:
- # we should only do this for evaluation data
- tokenized_contents = tk(
- [m[0]["content"] for m in chunk_messages],
- add_special_tokens=False,
- return_attention_mask=False,
- )
- tokenized_contents["input_ids"] = [
- truncate_middle_if_too_long(
- ids,
- max_length=MODEL_CTX_LEN[args.vllm_model],
- max_new_tokens=args.max_new_tokens,
- )
- for ids in tokenized_contents["input_ids"]
- ]
- contents = tk.batch_decode(
- tokenized_contents["input_ids"], skip_special_tokens=True
- )
- for c, m in zip(contents, chunk_messages):
- m[0]["content"] = c
-
- print(f"Generating from {len(chunk_messages)} contexts")
-
- # Clear GPU memory before processing the next chunk
- clear_gpu()
- execute_qa_generation(
- fpath + f"_{chunk_idx:04d}",
- args,
- llm,
- temp,
- tk,
- self_qa_intx_tokens,
- n_self_qa_intx_tokens,
- sys_tokens,
- n_sys_tokens,
- chunk_ctxs,
- ds[start : start + chunk_size]["ctx_ids"],
- chunk_questions,
- chunk_messages,
- k,
- )
-
-
-def execute_qa_generation(
- fpath,
- args,
- llm,
- temp,
- tk,
- self_qa_intx_tokens,
- n_self_qa_intx_tokens,
- sys_tokens,
- n_sys_tokens,
- ctxs,
- ctx_ids,
- questions,
- messages,
- k,
-):
- completions = llm.chat(
- messages,
- sampling_params=SamplingParams(
- max_tokens=args.max_new_tokens,
- logprobs=k,
- temperature=temp,
- seed=42,
- spaces_between_special_tokens=False,
- skip_special_tokens=False,
- include_stop_str_in_output=True,
- ),
- )
-
- self_gen_data = {
- ctx: {
- "ctx_ids": ctx_ids,
- "input_ids": [],
- "response_start_end": [],
- "logprobs_vals": [],
- "logprobs_indices": [],
- }
- for ctx, ctx_ids in zip(ctxs, ctx_ids)
- }
- c = 0
- n_skips = 0
- sys_start = None
- for ctx, q_list in zip(ctxs, questions):
- # self_gen_data[ctx]["ctx_ids"] = ctx_ids
- for i, _ in enumerate(q_list):
- # response = completions[c + i].outputs[0].text
- reason = completions[c + i].outputs[0].finish_reason
- if reason != "stop":
- # print(f"idx: {c + i}")
- print(f"finish_reason: {completions[c + i].outputs[0].finish_reason}")
- print(f"Skipping due to finish_reason={reason} != 'stop'")
- n_skips += 1
- continue
-
- # includes the logprob before the first response token
- # but excludes the logprob from eos token
- logp = completions[c + i].outputs[0].logprobs
-
- # len = num response tokens
- n_response_tokens = len(completions[c + i].outputs[0].token_ids)
-
- logp_indices = np.empty((n_response_tokens, k), dtype=np.int32)
- # float-16 is better for this range
- logp_vals = np.empty((n_response_tokens, k), dtype=np.float16)
- assert len(logp) == n_response_tokens, (
- f"Expected {n_response_tokens} logp entries, got {len(logp)}"
- )
-
- for li, info_d in enumerate(logp):
- for j, (idx, tok_info) in enumerate(info_d.items()):
- logp_indices[li, j] = idx
- logp_vals[li, j] = tok_info.logprob
-
- prompt_ids = completions[c + i].prompt_token_ids # 1d list
- # token_ids only includes generated tokens, not the prompt
- response_token_ids = completions[c + i].outputs[0].token_ids # 1d list
- all_ids = prompt_ids + response_token_ids
- res_start = len(prompt_ids)
- res_end = res_start + n_response_tokens
-
- if sys_start is None:
- for ii in range(len(prompt_ids) - n_sys_tokens):
- if prompt_ids[ii : ii + n_sys_tokens] == sys_tokens:
- # found the start of the system message
- sys_start = ii
- break
-
- q_start = None
- for ii in range(
- len(prompt_ids) - n_self_qa_intx_tokens,
- -1,
- -1,
- ):
- if prompt_ids[ii : ii + n_self_qa_intx_tokens] == self_qa_intx_tokens:
- # found the start of the user input
- q_start = ii + n_self_qa_intx_tokens
- break
-
- # bos + question + eos + start model turn + response + eos
- input_ids = all_ids[:sys_start] + all_ids[q_start:res_end]
-
- # relative to the input_ids
- res_start = res_start - q_start + sys_start
- res_end = res_start + n_response_tokens
-
- # arrays will be saved as nested lists of numbers
-
- self_gen_data[ctx]["input_ids"].append(input_ids)
- # assume single-turn chat
- self_gen_data[ctx]["response_start_end"].append((res_start, res_end))
- self_gen_data[ctx]["logprobs_vals"].append(logp_vals)
- self_gen_data[ctx]["logprobs_indices"].append(logp_indices)
-
- c += i + 1
-
- print(f"Skipped {n_skips} responses due to missing stop strings")
- samples = [
- {
- # "context": ctx,
- # "prompts": q_list,
- # "responses": self_gen_data[ctx]["responses"],
- "ctx_ids": self_gen_data[ctx]["ctx_ids"],
- "input_ids": self_gen_data[ctx]["input_ids"],
- "response_start_end": self_gen_data[ctx]["response_start_end"],
- # "prompt_start_end": self_gen_data[ctx]["prompt_start_end"],
- "logprobs_vals": self_gen_data[ctx]["logprobs_vals"],
- "logprobs_indices": self_gen_data[ctx]["logprobs_indices"],
- }
- for ctx, q_list in zip(ctxs, questions)
- ]
-
- if args.debug:
- for sample in samples:
- # print(f"context={tk.decode(sample['ctx_ids'])}")
- print(f"QA={[tk.decode(ids) for ids in sample['input_ids']]}")
-
- for input_ids, (start, end) in zip(
- sample["input_ids"], sample["response_start_end"]
- ):
- print(f"start={start}, end={end}")
- print(f"response={tk.decode(input_ids[start:end])}")
-
- print(f"logprobs_vals={[x.shape for x in sample['logprobs_vals']]}")
- print(f"logprobs_indices={[x.shape for x in sample['logprobs_indices']]}")
- for indices in sample["logprobs_indices"]:
- print(f"logprobs_indices={indices[-1]}")
- print("=" * 80)
-
- print(f"Generated {len(samples)} samples")
- # random.shuffle(samples)
-
- # Save results
- # df = pd.DataFrame(samples)
- # ds_out = Dataset.from_pandas(df)
- ds_out = Dataset.from_list(samples)
- # fpath = f"{SELF_GEN_DATA_DIR}/{args.vllm_model}_temp_{temp}_closed_qa_prob_{closed_qa_prob}/{ds_name}/{split}/ds{shard_name}"
-
- if args.debug:
- fpath += "_debug"
- os.makedirs(os.path.dirname(fpath), exist_ok=True)
-
- fpath = f"{fpath}.parquet"
- ds_out.to_parquet(fpath)
- print(f"Saved to {fpath}")
-
- # Cleanup
- del samples, ds_out, completions, messages, ctxs, questions
- clear_gpu()
-
-
-def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser(description="Generate QA pairs using VLLM")
- parser.add_argument(
- "--vllm_model",
- type=str,
- required=True,
- help="VLLM model name (e.g., google/gemma-2-2b-it)",
- )
- parser.add_argument(
- "--debug",
- action="store_true",
- help="Enable debug mode (process only 10 samples)",
- )
-
- # Either config file OR ds_names + split
- group = parser.add_mutually_exclusive_group(required=True)
- group.add_argument(
- "--config",
- type=str,
- help="Path to YAML config file with train_ds_names/val_ds_names",
- )
- group.add_argument(
- "--ds_names",
- type=str,
- nargs="+",
- help="List of dataset names/shard patterns",
- )
- group.add_argument(
- "--glob_pattern",
- type=str,
- help="Glob pattern to match dataset names (e.g., 'data/raw_datasets/fw_qa_3/*')",
- )
-
- parser.add_argument(
- "--split",
- type=str,
- help="Dataset split to use when using --ds_names (required with --ds_names)",
- )
- parser.add_argument(
- "--temp",
- type=float,
- default=0.0,
- help="Temperature for sampling (default: 0.0)",
- )
- parser.add_argument(
- "--closed_qa_prob",
- type=float,
- default=0.0,
- help="Probability of using closed QA prompt template (default: 0.0)",
- )
- parser.add_argument(
- "--do_truncate",
- action="store_true",
- help="Truncate contexts to fit model context length",
- )
- parser.add_argument(
- "--remove_qa_template",
- action="store_true",
- help="Remove QA template formatting from prompts",
- )
- parser.add_argument(
- "--max_new_tokens",
- type=int,
- default=256,
- help="Maximum number of new tokens to generate (default: 256)",
- )
- return parser.parse_args()
-
-
-if __name__ == "__main__":
- args = parse_args()
-
- # Validate arguments
- if args.ds_names and not args.split:
- raise ValueError("--split is required when using --ds_names")
-
- vllm_model = args.vllm_model
- print(f"Using model: {vllm_model}")
-
- # Setup model-specific configurations
- llm_kwargs = dict(
- model=vllm_model,
- dtype="bfloat16",
- enable_prefix_caching=True,
- enable_chunked_prefill=True,
- max_model_len=MODEL_CTX_LEN.get(vllm_model),
- max_num_batched_tokens=16384,
- max_num_seqs=32, # avoid oom when getting logprobs
- )
-
- print(f"{llm_kwargs=}")
- llm = LLM(**llm_kwargs)
-
- # Get dataset configs from config or CLI args
- config = load_config(args.config) if args.config else None
- if args.ds_names or args.config:
- dataset_configs = get_dataset_configs(
- ds_names=args.ds_names,
- config=config,
- split=args.split,
- )
-
- # Process each dataset
- for ds_name, split in dataset_configs:
- print(f"Processing dataset: {ds_name}, split: {split}")
- self_generate(
- ds_name, split, args, llm, SELF_GEN_SYSTEM_MSG, None, args.do_truncate
- )
- else:
- assert args.glob_pattern, (
- "glob_pattern must be provided if no ds_names or config"
- )
- files = glob(args.glob_pattern)
- for file in files:
- print(f"Processing file: {file}")
- self_generate(
- ds_name=None,
- parquet_file=file,
- split=args.split,
- args=args,
- llm=llm,
- system_template=SELF_GEN_SYSTEM_MSG,
- do_truncate=args.do_truncate,
- )
diff --git a/demo/app.py b/demo/app.py
deleted file mode 100644
index 5d571f1..0000000
--- a/demo/app.py
+++ /dev/null
@@ -1,685 +0,0 @@
-import os
-import sys
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-# Add the src directory to the path
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from ctx_to_lora.data.processing import tokenize_ctx_text
-from ctx_to_lora.model_loading import get_tokenizer
-from ctx_to_lora.modeling import hypernet
-
-sys.modules["ctx_to_lora.modeling_utils"] = hypernet
-
-# Global state
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-modulated_model = None
-chat_history = []
-ctx_tokenizer = None
-base_tokenizer = None
-
-try:
- DEFAULT_CONTEXT = Path("data/sakana_wiki.txt").read_text(encoding="utf-8").strip()
-except FileNotFoundError:
- DEFAULT_CONTEXT = ""
-
-WARNING_MESSAGE = (
- "โ ๏ธ **Caution**: This is an educational proof-of-concept demonstration.\n"
- "The model may generate inaccurate information or hallucinate facts."
-)
-
-FOOTER = """
-โ ๏ธ This model is an experimental prototype and is only available for educational and research and development purposes. It is not suitable for commercial use or in environments where failure can have significant effects (mission-critical environments).
-The use of this model is at the user's own risk and its performance and results is not guaranteed in any way.
-Sakana AI is not responsible for any direct or indirect loss resulting from using this model, regardless of the outcome.
-"""
-
-
-def load_custom_chat_template(tokenizer, model_name):
- if "gemma" in model_name.lower():
- template_path = "chat_templates/google/gemma-2-2b-it.jinja"
- if os.path.exists(template_path):
- with open(template_path) as f:
- template_content = f.read()
- tokenizer.chat_template = template_content
- print(f"Loaded custom chat template from {template_path}")
- return True
- return False
-
-
-def get_available_checkpoints():
- trained_d2l_checkpoints = {
- str(path)
- for path in Path().glob("trained_d2l/**/pytorch_model.bin")
- if path.is_file()
- }
- run_output_checkpoints = {
- str(path)
- for path in Path().glob("train_outputs/runs/**/pytorch_model.bin")
- if path.is_file()
- }
- checkpoints = sorted(trained_d2l_checkpoints) + sorted(
- run_output_checkpoints - trained_d2l_checkpoints
- )
- return checkpoints if checkpoints else ["No checkpoints found"]
-
-
-def load_checkpoint(
- checkpoint_path: str,
-) -> tuple[str, gr.update, gr.update, gr.update, gr.update]:
- global modulated_model, ctx_tokenizer, base_tokenizer, chat_history
-
- if not checkpoint_path or checkpoint_path == "No checkpoints found":
- return (
- "โ ๏ธ Please select a valid checkpoint",
- gr.update(),
- gr.update(),
- gr.update(),
- gr.update(),
- )
-
- try:
- print(f"Loading checkpoint: {checkpoint_path}")
- from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-
- state_dict = torch.load(checkpoint_path, weights_only=False)
- modulated_model = ModulatedPretrainedModel.from_state_dict(
- state_dict,
- train=False,
- use_flash_attn=True,
- use_sequence_packing=False,
- )
- modulated_model = modulated_model.to(device).to(torch.bfloat16)
- modulated_model.eval()
-
- ctx_encoder_model_name_or_path = (
- modulated_model.ctx_encoder_args.ctx_encoder_model_name_or_path
- or modulated_model.base_model.config.name_or_path
- )
- ctx_tokenizer = get_tokenizer(ctx_encoder_model_name_or_path, train=False)
- base_tokenizer = get_tokenizer(
- modulated_model.base_model.config.name_or_path, train=False
- )
-
- load_custom_chat_template(
- base_tokenizer, modulated_model.base_model.config.name_or_path
- )
-
- chat_history = [{"role": "system", "content": ""}]
-
- model_name = modulated_model.base_model.config.name_or_path
- success_msg = (
- f"โ
Successfully loaded checkpoint!\n\nBase Model: {model_name}\n\n"
- "You can now add context and start chatting."
- )
- return (
- success_msg,
- gr.update(interactive=True), # msg
- gr.update(interactive=True), # send_btn
- gr.update(interactive=True), # system_msg
- gr.update(interactive=True), # clear_btn
- )
-
- except Exception as e:
- import traceback
-
- error_msg = (
- f"โ Error loading checkpoint:\n{str(e)}\n\n{traceback.format_exc()}"
- )
- print(error_msg)
- return (
- error_msg,
- gr.update(interactive=False),
- gr.update(interactive=False),
- gr.update(interactive=False),
- gr.update(interactive=False),
- )
-
-
-def process_context(context: str) -> dict:
- context = context.strip() if context else ""
- tokenized_contexts = tokenize_ctx_text({"context": [context]}, ctx_tokenizer)
- ctx_ids = tokenized_contexts["ctx_ids"]
- ctx_ids = [
- torch.tensor(ctx_id, dtype=torch.long, device=device) for ctx_id in ctx_ids
- ]
- ctx_attn_mask = [torch.ones_like(ids) for ids in ctx_ids]
- ctx_attn_mask = [
- torch.tensor(mask, dtype=torch.long, device=device) for mask in ctx_attn_mask
- ]
- ctx_ids = torch.nn.utils.rnn.pad_sequence(
- ctx_ids,
- batch_first=True,
- padding_value=0,
- )
- ctx_attn_mask = torch.nn.utils.rnn.pad_sequence(
- ctx_attn_mask,
- batch_first=True,
- padding_value=0,
- )
- return {"ctx_ids": ctx_ids, "ctx_attn_mask": ctx_attn_mask}
-
-
-def add_user_message(message: str, history):
- if not message.strip():
- return history, ""
- return history + [[message, None]], ""
-
-
-def generate_response(
- history: list[list[str]],
- system_msg: str,
- context: str,
- context_scaler: float,
- bias_scaler: float,
-):
- global modulated_model, chat_history, ctx_tokenizer, base_tokenizer
-
- if modulated_model is None:
- history[-1][1] = "Please load a checkpoint first."
- yield history
- return
-
- if not history or history[-1][0] is None:
- yield history
- return
-
- try:
- user_message = history[-1][0]
-
- if system_msg.strip() and chat_history[0]["role"] == "system":
- chat_history[0]["content"] = system_msg.strip()
-
- chat_history.append({"role": "user", "content": user_message})
-
- context = context.strip() if context else ""
- print(f"Processing single context with scaler: {context_scaler}")
- print(f"Bias scaler: {bias_scaler}")
-
- with torch.inference_mode(), torch.amp.autocast(str(device)):
- ctx_inputs = process_context(context)
- ctx_ids = ctx_inputs["ctx_ids"].to(device)
- ctx_attn_mask = ctx_inputs["ctx_attn_mask"].to(device)
-
- scalers_tensor = torch.tensor(
- [context_scaler], dtype=torch.float32, device=device
- )
-
- model_inputs = base_tokenizer.apply_chat_template(
- chat_history, return_tensors="pt", add_generation_prompt=True
- ).to(device)
-
- print(f"Context: {context}")
- print(f"Chat history: {chat_history}")
-
- outputs = modulated_model.generate(
- ctx_ids=ctx_ids,
- ctx_attn_mask=ctx_attn_mask,
- n_ctx_chunks=torch.tensor([len(ctx_ids)], device=ctx_ids.device),
- scalers=scalers_tensor,
- bias_scaler=bias_scaler,
- input_ids=model_inputs,
- max_new_tokens=512,
- do_sample=False,
- temperature=0,
- )
-
- response = base_tokenizer.decode(
- outputs[0][model_inputs.shape[1] :], skip_special_tokens=True
- )
-
- chat_history.append({"role": "assistant", "content": response})
-
- words = response.split()
- partial_response = ""
- for word in words:
- partial_response += word + " "
- history[-1][1] = partial_response.strip()
- yield history
-
- history[-1][1] = response
- yield history
-
- except Exception as e:
- import traceback
-
- error_msg = f"โ Error: {str(e)}"
- print(f"Error generating response: {str(e)}\n\n{traceback.format_exc()}")
- history[-1][1] = error_msg
- yield history
-
-
-def reset_chat(system_msg: str):
- global chat_history
- chat_history = [
- {"role": "system", "content": system_msg.strip() if system_msg else ""}
- ]
- return [[None, WARNING_MESSAGE]], "Chat history reset successfully!"
-
-
-custom_css = """
-:root {
- color-scheme: light;
-}
-
-.gradio-container {
- font-family: 'Inter', sans-serif;
-}
-
-.chat-container {
- border-radius: 10px;
- border: 2px solid #d1d5db;
-}
-
-.context-field {
- border-radius: 8px;
- padding: 15px;
- margin-bottom: 10px;
- border: 2px solid #d1d5db;
-}
-
-.status-box {
- border-radius: 8px;
- padding: 15px;
- margin: 10px 0;
- border: 2px solid #e5e7eb;
-}
-
-.primary-button {
- background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
- border: none;
- border-radius: 6px;
- color: white;
- font-weight: 600;
-}
-
-.secondary-button {
- background-color: #607d8b;
- border: none;
- border-radius: 6px;
- color: white;
-}
-
-#chatbot {
- height: 500px;
-}
-
-.instruction-text {
- font-style: italic;
- color: #666;
- font-size: 0.9em;
- margin-bottom: 10px;
-}
-
-.warning-box {
- background-color: #fff3cd;
- border: 2px solid #ffc107;
- border-radius: 8px;
- padding: 12px;
- margin: 10px 0;
- color: #856404;
- font-size: 0.95em;
-}
-
-.warning-box strong {
- color: #d97706;
-}
-
-.disabled-overlay {
- background-color: #f5f5f5;
- border: 3px dashed #999;
- border-radius: 10px;
- padding: 20px;
- text-align: center;
- color: #999;
-}
-
-.chat-disabled-notice {
- border: 3px solid #f59e0b;
- border-radius: 8px;
- padding: 15px;
- margin-bottom: 15px;
- color: #92400e;
- font-weight: 600;
- text-align: center;
- background-color: #fef3c7;
- box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-}
-
-.internalization-banner {
- background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
- color: white;
- border-radius: 10px;
- padding: 20px;
- margin-bottom: 15px;
- text-align: center;
- font-weight: 600;
- box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);
- border: 3px solid #5568d3;
-}
-
-.internalization-banner h3 {
- margin: 0 0 10px 0;
- font-size: 1.2em;
- color: white;
-}
-
-.internalization-banner p {
- margin: 5px 0;
- font-size: 0.95em;
- font-weight: 400;
- color: white;
-}
-
-.context-section-header {
- background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
- border: 3px solid #5568d3;
- border-left: 6px solid #4c51bf;
- padding: 15px;
- margin-bottom: 15px;
- border-radius: 6px;
- color: white;
- box-shadow: 0 2px 6px rgba(102, 126, 234, 0.3);
-}
-
-.context-section-header strong,
-.context-section-header small {
- color: white;
-}
-
-.chat-section-header {
- background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
- border: 3px solid #2563eb;
- border-left: 6px solid #1d4ed8;
- padding: 15px;
- margin-bottom: 15px;
- border-radius: 6px;
- color: white;
- box-shadow: 0 2px 6px rgba(59, 130, 246, 0.3);
-}
-
-.chat-section-header strong,
-.chat-section-header small {
- color: white;
-}
-
-.panel-box {
- background-color: rgba(249, 250, 251, 0.5);
- border: 2px solid rgba(209, 213, 219, 0.5);
- border-radius: 12px;
- padding: 20px;
- box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-}
-
-.chat-panel-box {
- background-color: rgba(249, 250, 251, 0.5);
- border: 2px solid rgba(209, 213, 219, 0.5);
- border-radius: 12px;
- padding: 20px;
- box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-}
-
-#checkpoint-dropdown {
- position: relative;
- z-index: 20;
-}
-
-#checkpoint-dropdown [role="listbox"] {
- z-index: 9999 !important;
-}
-
-/* Dark mode support */
-.dark .panel-box,
-.dark .chat-panel-box {
- background-color: rgba(31, 41, 55, 0.5);
- border: 2px solid rgba(75, 85, 99, 0.5);
-}
-
-.dark .context-field,
-.dark .chat-container {
- border-color: rgba(75, 85, 99, 0.5);
-}
-
-.dark .status-box {
- border-color: rgba(55, 65, 81, 0.5);
-}
-"""
-
-
-def create_demo():
- with gr.Blocks(
- title="Doc-to-LoRA Chat Interface",
- theme=gr.themes.Soft(),
- css=custom_css,
- ) as demo:
- gr.Markdown(
- """
- # ๐ Doc-to-LoRA Chat Interface
-
- Load a hypernetwork checkpoint and chat with a context-modulated language model.
- Add one context with a scaling parameter to influence the model's responses.
- """
- )
-
- gr.HTML(
- """
-
-
๐ง How Context Internalization Works
-
๐ฅ Contexts are processed by the hypernetworkto dynamically modulate the base model's parameters
-
๐ซ Contexts are NOT passed as text to the base model โ they influence behavior internally
-
๐ฌ Only your chat messages (below) are sent to the language model
-
- """
- )
-
- gr.Markdown(
- """
- ### ๐ Usage Instructions
-
- 1. **Load a Checkpoint**: Select a hypernetwork checkpoint from the dropdown and click "Load Checkpoint"
- 2. **Configure Context**:
- - Enter your context information in the text field
- - Adjust the scaling slider to control context influence
- 3. **Set Bias Scaler**: Adjust the bias scaler to control overall model behavior
- 4. **Start Chatting**: Once the model is loaded, type your message and press Shift+Enter or click Send
- 5. **Reset**: Use the "Reset Chat" button to start a new conversation
-
- ๐ก **Tip**: You can use context to provide background information or specific knowledge
- that should influence the model's responses.
- """
- )
-
- with gr.Row():
- with gr.Column(scale=1, elem_classes="panel-box"):
- gr.Markdown("### ๐ฆ Load Checkpoint")
- gr.Markdown(
- "*Select a trained hypernetwork checkpoint to begin.*",
- elem_classes="instruction-text",
- )
-
- checkpoint_dropdown = gr.Dropdown(
- choices=get_available_checkpoints(),
- label="Select Checkpoint",
- value=None,
- interactive=True,
- elem_id="checkpoint-dropdown",
- )
-
- load_btn = gr.Button("Load Checkpoint", variant="primary", size="lg")
-
- status_box = gr.Textbox(
- label="Status",
- lines=8,
- interactive=False,
- elem_classes="status-box",
- )
-
- gr.Markdown("---")
-
- gr.HTML(
- """
-
- """
- )
-
- context = gr.Textbox(
- label="๐ง Context (Internalized via Hypernetwork)",
- placeholder="Enter context to be internalized by the hypernetwork...",
- lines=4,
- value=DEFAULT_CONTEXT,
- )
- context_scaler = gr.Slider(
- minimum=-2.0,
- maximum=2.0,
- step=0.01,
- value=1.0,
- label="Context Scaling",
- )
-
- gr.Markdown("---")
-
- bias_scaler = gr.Slider(
- minimum=-2.0,
- maximum=2.0,
- step=0.01,
- value=1.0,
- label="Bias Scaler",
- info="A single scalar applied to bias parameters (independent of contexts)",
- )
-
- with gr.Column(scale=2, elem_classes="chat-panel-box"):
- gr.HTML(
- """
-
- """
- )
-
- chat_status_notice = gr.HTML(
- """
-
- ๐ Chat Disabled: Please load a checkpoint first to enable chat functionality.
-
- """,
- visible=True,
- )
-
- system_msg = gr.Textbox(
- label="System Message (Optional - Sent to Base Model)",
- placeholder="Load a checkpoint to enable chat...",
- lines=2,
- interactive=False,
- )
-
- chatbot = gr.Chatbot(
- label="Conversation",
- show_copy_button=True,
- height=500,
- elem_id="chatbot",
- elem_classes="chat-container",
- value=[
- [
- None,
- "๐ Chat is currently disabled. Please load a checkpoint from the left panel to begin chatting.",
- ]
- ],
- )
-
- with gr.Row():
- msg = gr.Textbox(
- label="Your Message (Sent Directly to Base Model)",
- placeholder="โ ๏ธ Load a checkpoint first to start chatting...",
- lines=2,
- scale=4,
- interactive=False,
- )
- send_btn = gr.Button(
- "๐ Send (Disabled)",
- variant="primary",
- scale=1,
- interactive=False,
- )
-
- with gr.Row():
- clear_btn = gr.Button(
- "๐ Reset Chat (Disabled)",
- variant="secondary",
- interactive=False,
- )
- reset_status = gr.Textbox(label="Reset Status", visible=False)
-
- load_btn.click(
- fn=load_checkpoint,
- inputs=[checkpoint_dropdown],
- outputs=[status_box, msg, send_btn, system_msg, clear_btn],
- ).then(
- fn=lambda: (
- gr.update(visible=False),
- gr.update(
- placeholder="Type your message here... (Shift+Enter for new line)"
- ),
- gr.update(value="Send"),
- gr.update(value="๐ Reset Chat"),
- gr.update(value=[[None, WARNING_MESSAGE]]),
- ),
- outputs=[chat_status_notice, msg, send_btn, clear_btn, chatbot],
- )
-
- msg.submit(
- fn=add_user_message,
- inputs=[msg, chatbot],
- outputs=[chatbot, msg],
- ).then(
- fn=generate_response,
- inputs=[
- chatbot,
- system_msg,
- context,
- context_scaler,
- bias_scaler,
- ],
- outputs=[chatbot],
- )
-
- send_btn.click(
- fn=add_user_message,
- inputs=[msg, chatbot],
- outputs=[chatbot, msg],
- ).then(
- fn=generate_response,
- inputs=[
- chatbot,
- system_msg,
- context,
- context_scaler,
- bias_scaler,
- ],
- outputs=[chatbot],
- )
-
- clear_btn.click(
- fn=reset_chat,
- inputs=[system_msg],
- outputs=[chatbot, reset_status],
- )
-
- gr.Markdown(f"---\n{FOOTER.strip()}")
- return demo
-
-
-if __name__ == "__main__":
- demo = create_demo()
- demo.launch(
- server_name="0.0.0.0",
- server_port=7861,
- share=False,
- debug=True,
- )
diff --git a/examples/python_api.py b/examples/python_api.py
deleted file mode 100644
index be56230..0000000
--- a/examples/python_api.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# caveat: this interface only supports non-batched inputs
-# for batched inference please see `src/ctx_to_lora/modeling/hypernet.py`
-import torch
-
-from ctx_to_lora.model_loading import get_tokenizer
-from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-
-# model loading
-checkpoint_path = "trained_d2l/gemma_demo/checkpoint-80000/pytorch_model.bin"
-state_dict = torch.load(checkpoint_path, weights_only=False)
-model = ModulatedPretrainedModel.from_state_dict(
- state_dict, train=False, use_sequence_packing=False
-)
-model.reset()
-tokenizer = get_tokenizer(model.base_model.name_or_path)
-
-# prepare data
-doc = open("data/sakana_wiki.txt", "r").read()
-chat = [{"role": "user", "content": "Tell me about Sakana AI."}]
-chat_ids = tokenizer.apply_chat_template(
- chat,
- add_special_tokens=False,
- return_attention_mask=False,
- add_generation_prompt=True,
- return_tensors="pt",
-).to(model.device)
-
-
-# calls after internalization will be influenced by internalized info
-model.internalize(doc)
-
-outputs = model.generate(input_ids=chat_ids, max_new_tokens=512)
-print(tokenizer.decode(outputs[0]))
-
-
-# remove internalized info
-# model.reset()
-
-# without internalized info, the model will halucinate
-# outputs = model.generate(input_ids=chat_ids, max_new_tokens=512)
-# print(tokenizer.decode(outputs[0]))
diff --git a/install.sh b/install.sh
deleted file mode 100755
index 2f52443..0000000
--- a/install.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-curl -LsSf https://astral.sh/uv/install.sh | sh
-uv self update
-uv venv --python 3.10 --seed
-uv pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --torch-backend=cu124
-uv sync
-uv pip install tokenizers==0.21.0
-uv pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
-uv pip install flashinfer-python==0.2.2 -i https://flashinfer.ai/whl/cu124/torch2.6
-
-# download squad dataset
-HF_HUB_ENABLE_HF_TRANSFER=1 uv run huggingface-cli download --repo-type dataset rajpurkar/squad --local-dir data/raw_datasets/squad
-uv run data/build_drop_compact.py
-uv run data/build_pwc_compact.py
-uv run data/build_ropes_compact.py
-uv run data/build_squad_compact.py
-
-# optional: needed for gated models
-# uv run huggingface-cli login
-
-# optional: needed for logging with wandb
-# wandb login
-
-# optional: dev
-# uv run pre-commit install
diff --git a/pyproject.toml b/pyproject.toml
index 0cb77c7..8fe0f5b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,50 +1,49 @@
[project]
-name = "ctx-to-lora"
-version = "0.0.1"
-authors = [{ name = "Rujikorn Charakorn" }]
-description = ""
+name = "video2lora"
+version = "0.1.0"
+authors = [{ name = "Video2LoRA authors" }]
+description = "Video-conditioned LoRA generation for vision-language models."
readme = "README.md"
-requires-python = ">= 3.10"
+requires-python = ">=3.10"
dependencies = [
- "transformers==4.51.3",
- "deepspeed==0.17.1",
"accelerate==1.6.0",
+ "av>=17.0.0",
+ "bitsandbytes>=0.46.1",
"datasets==3.6.0",
- "setuptools",
- "peft",
- "jupyter",
- "matplotlib",
- "hf_transfer",
- "torchmetrics",
- "inflect",
- "pre-commit",
- "tensorboardX",
- "wandb",
- "fasttext-wheel",
+ "decord",
"einops",
+ "gdown>=5.1.0",
+ "hf_transfer",
+ "huggingface-hub[hf-transfer]>=0.32.0",
"jaxtyping",
"liger-kernel",
- "tensorboard",
- "flask",
- "gradio>=4.40.0",
- "pandas",
- "plotly",
- "rouge-score",
- "vllm==0.8.5.post1",
- "huggingface-hub[hf-transfer]>=0.32.0",
+ "matplotlib",
+ "num2words",
+ "numpy",
"opt-einsum>=3.4.0",
- "kagglehub[hf-datasets]>=0.3.12",
- "kaggle>=1.7.4.5",
- "bitsandbytes>=0.46.1",
- "google-cloud-storage>=3.2.0",
- "wonderwords>=2.2.0",
- "llmlingua>=0.2.2",
+ "pandas",
+ "peft",
+ "plotly",
+ "pyyaml",
+ "rouge-score",
+ "setuptools",
+ "tensorboard",
+ "tensorboardX",
+ "tokenizers",
+ "torch>=2.6.0",
+ "torchmetrics",
+ "torchvision>=0.21.0",
+ "transformers==4.51.3",
+ "wandb",
]
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
+[tool.setuptools.packages.find]
+where = ["src"]
+
[tool.pyright]
exclude = [
"**/node_modules",
@@ -53,32 +52,19 @@ exclude = [
".venv",
".github",
".vscode",
- "chat_templates",
- "eval_results",
- "configs",
- "EditingLlama",
- "icae_v2",
- "lm-evaluation-harness",
- "llm-comparator",
- "LongBench",
- "scripts",
- "train_outputs",
- "./data/",
- "/data/",
- "generated_tasks",
+ "assets",
+ "checkpoints",
+ "data",
"outputs",
- "plots",
+ "runs",
"tmp",
"wandb",
- ".wandb",
- ".ruff_cache",
- "assets",
]
typeCheckingMode = "off"
[tool.ruff]
line-length = 88
-select = ["F401"] # remove unused imports
+select = ["F401"]
ignore = ["E", "F"]
[tool.isort]
diff --git a/run_eval.py b/run_eval.py
deleted file mode 100644
index ca37754..0000000
--- a/run_eval.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import logging
-
-from ctx_to_lora.eval_utils import run_eval
-
-logger = logging.getLogger()
-
-
-if __name__ == "__main__":
- import argparse
-
- parser = argparse.ArgumentParser(description="Evaluate a checkpoint")
- parser.add_argument(
- "--model_name_or_path",
- type=str,
- default=None,
- help="Evaluate a base model from HuggingFace Hub, without loading checkpoint",
- )
- parser.add_argument(
- "--checkpoint_path",
- type=str,
- default=None,
- help="Path to the checkpoint to evaluate",
- )
- parser.add_argument(
- "--split",
- type=str,
- choices=["validation", "test"],
- default="validation",
- help="Which split to evaluate on",
- )
- parser.add_argument(
- "--datasets",
- type=str,
- nargs="+",
- help=(
- "Specific datasets to evaluate on."
- "If not provided, uses default from args.yaml"
- ),
- )
- parser.add_argument(
- "--eval_batch_size",
- type=int,
- default=8,
- help="Eval batch size for teacher forcing",
- )
- parser.add_argument(
- "--eval_batch_size_gen",
- type=int,
- default=32,
- help="Eval batch size for generation",
- )
- parser.add_argument(
- "--max_val_samples_per_ds",
- type=int,
- default=-1,
- help=(
- "Maximum number of validation samples per dataset. "
- "If -1, uses values from checkpoint config."
- ),
- )
- parser.add_argument(
- "--max_test_samples_per_ds",
- type=int,
- default=500,
- help=(
- "Maximum number of validation samples per dataset. "
- "If -1, uses values from checkpoint config."
- ),
- )
- parser.add_argument(
- "--max_ctx_chunk_len",
- type=int,
- default=-1,
- help="Maximum length of context chunk for evaluation",
- )
- parser.add_argument(
- "--max_new_tokens",
- type=int,
- default=256,
- help="Maximum number of new tokens to generate during evaluation",
- )
- parser.add_argument(
- "--remove_context",
- action="store_true",
- help="Remove context when evaluating the base model.",
- )
- parser.add_argument(
- "--use_cd",
- action="store_true",
- help="Use context distillation model for evaluation.",
- )
- parser.add_argument(
- "--cd_update_iterations",
- type=int,
- default=20,
- help="Number of update iterations for context distillation during evaluation",
- )
- parser.add_argument(
- "--cd_use_gen_q",
- action="store_true",
- help="Use generated queries for context distillation training.",
- )
- parser.add_argument(
- "--q_gen_rounds",
- type=int,
- default=4,
- help="Number of rounds of query generation for context distillation.",
- )
- parser.add_argument(
- "--cd_batch_size",
- type=int,
- default=16,
- help="Batch size for context distillation.",
- )
- parser.add_argument(
- "--use_iterative_mode",
- action="store_true",
- help="Use iterative mode LoRA layer-by-layer generation",
- )
- parser.add_argument(
- "--use_llmlingua",
- action="store_true",
- help="Use LLMLingua compression for evaluation",
- )
- parser.add_argument(
- "--llmlingua_compression_rate",
- type=float,
- default=0.9,
- help="Compression rate for LLMLingua",
- )
- parser.add_argument(
- "--use_t2l",
- action="store_true",
- help="Use Text-to-LoRA model for evaluation",
- )
- parser.add_argument(
- "--add_ctx_to_input",
- action="store_true",
- help="Add ctx to base model's input",
- )
- parser.add_argument(
- "--truncate_if_too_long_inp",
- action="store_true",
- help="Truncate input sequences that are too long",
- )
- parser.add_argument(
- "--truncate_if_too_long_ctx",
- action="store_true",
- help="Truncate ctx sequences that are too long",
- )
- parser.add_argument(
- "--gen_lora_scaling",
- type=float,
- default=1.0,
- )
- parser.add_argument(
- "--flip_ctx_inp",
- action="store_true",
- help="Flip the order of context and input",
- )
- parser.add_argument(
- "--use_generative_adapter",
- action="store_true",
- help="Use generative adapter for evaluation",
- )
-
- cli_args = vars(parser.parse_args())
-
- if cli_args["model_name_or_path"]:
- assert cli_args["max_ctx_chunk_len"] <= 0, (
- f"Evaluating base model shouldn't be used with `max_ctx_chunk_len`"
- )
-
- eval_batch_size_gen = cli_args.pop("eval_batch_size_gen")
- eval_batch_size = cli_args.pop("eval_batch_size")
- run_eval(
- **cli_args,
- eval_batch_size=eval_batch_size_gen,
- generative=True,
- )
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/scripts/__init__.py
@@ -0,0 +1 @@
+
diff --git a/scripts/main_exp/0-download_data.py b/scripts/main_exp/0-download_data.py
deleted file mode 100644
index ddb81f2..0000000
--- a/scripts/main_exp/0-download_data.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from huggingface_hub import snapshot_download
-
-if __name__ == "__main__":
- self_gen_data_dir = "./data/raw_datasets/self_gen/"
- snapshot_download(
- "SakanaAI/self_gen_qa_d2l",
- repo_type="dataset",
- local_dir=self_gen_data_dir,
- # we can filter based on model by using the `allow_patterns` argument
- # based on https://huggingface.co/datasets/SakanaAI/self_gen_qa_d2l/tree/main
- # we can use
- # - `Qwen` for downloading the data for `Qwen/Qwen3-4B-Instruct-2507`
- # - `google` for downloading the data for `google/gemma-2-2b-it`
- # - `mistralai` for downloading the data for `mistralai/Mistral-7B-Instruct-v0.2`
- #
- # allow_patterns="google/*", # downloading the data for `google/gemma-2-2b-it`
- )
diff --git a/scripts/main_exp/1-train.sh b/scripts/main_exp/1-train.sh
deleted file mode 100755
index cc136af..0000000
--- a/scripts/main_exp/1-train.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-port=29051
-
-uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
---num_processes=8 --gpu_ids all train.py \
-configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---target_modules=down_proj --lora_r=8 \
---eval_strategy=no --max_qas_len=2048 --max_qas_per_sample=1 \
---per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1 \
---max_steps=80000 --gradient_accumulation_steps=8 --max_packed_inp_len=4096 \
---max_packed_ctx_len=4096 --use_per_ctx_average_loss=True --use_kl_loss=True \
---quantize_ctx_encoder=True
diff --git a/scripts/main_exp/2-train-chunk.sh b/scripts/main_exp/2-train-chunk.sh
deleted file mode 100755
index 4131e1a..0000000
--- a/scripts/main_exp/2-train-chunk.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-
-port=29051
-
-uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
---num_processes=8 --gpu_ids all train.py \
-configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---target_modules=down_proj \
---lora_r=8 \
---eval_strategy=no \
---max_qas_len=512 \
---max_qas_per_sample=1 \
---per_rank_gen=True \
---per_layer_processing=True \
---gen_lora_l1_reg_coef=0.1 \
---max_steps=20000 \
---gradient_accumulation_steps=16 \
---max_packed_inp_len=1024 \
---max_packed_ctx_len=2048 \
---use_per_ctx_average_loss=True \
---use_kl_loss=True \
---quantize_ctx_encoder=True \
---torch_empty_cache_steps=10 \
---from_pretrained_checkpoint=train_outputs/runs/$RUN_NAME/checkpoint-80000/pytorch_model.bin \
---max_ctx_chunk_len=512 \
---min_ctx_chunk_len=25 \
---num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
---warmup_steps=2000 \
---learning_rate=2e-5
diff --git a/scripts/main_exp/README.md b/scripts/main_exp/README.md
deleted file mode 100644
index 2309d3c..0000000
--- a/scripts/main_exp/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# D2L pipeline
-### Data
-You can either download the generated data (recommended, ~100 GB for each model) or generate them by youself.
-Please see [`0-download_data.sh`](0-download_data.sh) for how to do model-specific data download.
-```bash
-# download training data for all three models (328GB)
-uv run bash scripts/main_exp/0-download_data.sh
-```
-
-Generating data from scratch can take very long if not parallelized across multiple gpus.
-```bash
-# generate training data (takes very long if not parallelized across multiple gpus)
-# optional: use the command below for generating data from scratch
-# uv run bash scripts/main_exp/gen_data.sh
-```
-
-### Training
-Simply run the training script once the data is ready.
-```bash
-# train
-uv run bash scripts/main_exp/1-train.sh
-```
-
-### Evaluation
-All evaluation scripts for reproducing the main results in the paper are included in [eval](eval/) directory.
diff --git a/scripts/main_exp/eval/base_model.sh b/scripts/main_exp/eval/base_model.sh
deleted file mode 100755
index dc4ceef..0000000
--- a/scripts/main_exp/eval/base_model.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-# no truncation
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --eval_batch_size_gen 1
-
-# w/ truncation
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --eval_batch_size_gen 1 --truncate_if_too_long_inp
-
-# no context
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --eval_batch_size_gen 1 --remove_context
diff --git a/scripts/main_exp/eval/cd.sh b/scripts/main_exp/eval/cd.sh
deleted file mode 100755
index 3f239af..0000000
--- a/scripts/main_exp/eval/cd.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-# qa
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets squad drop ropes --split test --use_cd --cd_update_iterations 300 --eval_batch_size_gen=1 --truncate_if_too_long_inp --cd_use_gen_q --q_gen_rounds=4
-
-
-# longbench
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets longbench/multifieldqa_en_e longbench/2wikimqa_e longbench/qasper_e --split test --use_cd --cd_update_iterations 300 --eval_batch_size_gen=1 --truncate_if_too_long_inp --cd_use_gen_q --q_gen_rounds=1
diff --git a/scripts/main_exp/eval/cd_minibatch.sh b/scripts/main_exp/eval/cd_minibatch.sh
deleted file mode 100644
index 9f14680..0000000
--- a/scripts/main_exp/eval/cd_minibatch.sh
+++ /dev/null
@@ -1 +0,0 @@
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets $1 --split test --use_cd --cd_update_iterations 50 --eval_batch_size_gen=1 --truncate_if_too_long_inp --cd_use_gen_q --q_gen_rounds=5 --cd_batch_size=2
\ No newline at end of file
diff --git a/scripts/main_exp/eval/cd_oracle.sh b/scripts/main_exp/eval/cd_oracle.sh
deleted file mode 100755
index 22da250..0000000
--- a/scripts/main_exp/eval/cd_oracle.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-# qa
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets squad drop ropes --split test --use_cd --cd_update_iterations 300 --eval_batch_size_gen=1 --truncate_if_too_long_inp
-
-
-# longbench
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets longbench/multifieldqa_en_e longbench/2wikimqa_e longbench/qasper_e --split test --use_cd --cd_update_iterations 300 --eval_batch_size_gen=1 --truncate_if_too_long_inp
diff --git a/scripts/main_exp/eval/d2l.sh b/scripts/main_exp/eval/d2l.sh
deleted file mode 100755
index aec7921..0000000
--- a/scripts/main_exp/eval/d2l.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-# main results
-# batched
-WANDB_MODE=disabled uv run run_eval.py --checkpoint_path train_outputs/runs/$RUN_NAME/checkpoint-$step/pytorch_model.bin --datasets squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --max_ctx_chunk_len 8192 --eval_batch_size_gen 1
-
-# iterative
-WANDB_MODE=disabled uv run run_eval.py --checkpoint_path train_outputs/runs/$RUN_NAME/checkpoint-$step/pytorch_model.bin --datasets squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --max_ctx_chunk_len 8192 --eval_batch_size_gen 1 --use_iterative_mode
-
-# query internalization
-WANDB_MODE=disabled uv run run_eval.py --checkpoint_path train_outputs/runs/$RUN_NAME/checkpoint-$step/pytorch_model.bin --datasets squad --split test --eval_batch_size_gen=1 --flip_ctx_inp
-
-# replaced squad context
-WANDB_MODE=disabled uv run python run_eval.py --checkpoint_path train_outputs/runs/$RUN_NAME/checkpoint-$step/pytorch_model.bin --datasets squad_assistant_ctx_no_passage --split test
-WANDB_MODE=disabled uv run python run_eval.py --checkpoint_path train_outputs/runs/$RUN_NAME/checkpoint-$step/pytorch_model.bin --datasets squad_negative_no_passage --split test
diff --git a/scripts/main_exp/eval/imagenette_eval.py b/scripts/main_exp/eval/imagenette_eval.py
deleted file mode 100644
index a89aaa1..0000000
--- a/scripts/main_exp/eval/imagenette_eval.py
+++ /dev/null
@@ -1,279 +0,0 @@
-import json
-import os
-import re
-from argparse import Namespace
-from difflib import SequenceMatcher
-
-import torch
-from datasets import load_dataset
-from tqdm import tqdm
-from transformers import AutoProcessor, Gemma3ForConditionalGeneration
-
-from ctx_to_lora.model_loading import get_tokenizer
-from ctx_to_lora.modeling.ctx_encoder import PerLayerActivations
-from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-from ctx_to_lora.modeling.lora_layer import apply_lora_to_layers
-from ctx_to_lora.modeling.lora_merger import combine_lora
-
-CLASS_NAMES = [
- "tench",
- "English springer",
- "cassette player",
- "chain saw",
- "church",
- "French horn",
- "garbage truck",
- "gas pump",
- "golf ball",
- "parachute",
-]
-CLASS_TO_INT = {name: i for i, name in enumerate(CLASS_NAMES)}
-INPUT_TXT = f"What is in this image? Choose exactly one of the following classes: {', '.join(CLASS_NAMES)}. Response with only the correct class without any other text."
-RUN_DIR = "train_outputs/runs/Oct16_02-37-04_slurm0-a3nodeset-8_94074_1d62ecb8"
-
-
-def _normalize_text(text: str) -> str:
- text = re.sub(r"[^a-z0-9\s]", " ", text.lower())
- return " ".join(text.split())
-
-
-def _normalize_compact(text: str) -> str:
- return re.sub(r"[^a-z0-9]", "", text.lower())
-
-
-def _build_alias_map():
- alias_overrides = {
- "english springer spaniel": "English springer",
- "springer spaniel": "English springer",
- "chainsaw": "chain saw",
- "dump truck": "garbage truck",
- "refuse truck": "garbage truck",
- "garbage lorry": "garbage truck",
- "fuel pump": "gas pump",
- "gas station pump": "gas pump",
- "cassette deck": "cassette player",
- "cassette recorder": "cassette player",
- "fish": "tench",
- "tench fish": "tench",
- "french horn instrument": "French horn",
- "golfball": "golf ball",
- "skydiving": "parachute",
- "parachutist": "parachute",
- }
-
- alias_map = {}
-
- def register(alias: str, canonical: str):
- alias = _normalize_text(alias)
- if alias:
- alias_map[alias] = canonical
- alias_map[_normalize_compact(alias)] = canonical
-
- for name in CLASS_NAMES:
- register(name, name)
- register(name.replace(" ", ""), name)
- register(name.replace(" ", "-"), name)
-
- for alias, canonical in alias_overrides.items():
- register(alias, canonical)
-
- return alias_map
-
-
-CLASS_ALIAS_MAP = _build_alias_map()
-
-
-def pred_to_class_id(pred_txt: str) -> int:
- norm_pred = _normalize_text(pred_txt)
- compact_pred = _normalize_compact(pred_txt)
-
- for alias, canonical in CLASS_ALIAS_MAP.items():
- if alias and (alias in norm_pred or alias in compact_pred):
- return CLASS_TO_INT[canonical]
-
- pred_tokens = set(norm_pred.split())
- best_class = None
- best_token_hits = -1
- for name in CLASS_NAMES:
- class_tokens = set(_normalize_text(name).split())
- if class_tokens and class_tokens.issubset(pred_tokens):
- return CLASS_TO_INT[name]
- hits = sum(token in pred_tokens for token in class_tokens)
- if hits > best_token_hits:
- best_token_hits = hits
- best_class = name
-
- best_ratio = -1.0
- for name in CLASS_NAMES:
- ratio = SequenceMatcher(None, norm_pred, _normalize_text(name)).ratio()
- if ratio > best_ratio:
- best_ratio = ratio
- best_class = name
-
- return CLASS_TO_INT[best_class]
-
-
-def load_checkpoint():
- checkpoint_path = f"{RUN_DIR}/checkpoint-80000/pytorch_model.bin"
- state_dict = torch.load(checkpoint_path)
-
- model = ModulatedPretrainedModel.from_state_dict(
- state_dict,
- train=False,
- base_model_kwargs=dict(attn_implementation="flash_attention_2"),
- use_flash_attn=True,
- use_sequence_packing=False, # for generation
- )
- tokenizer = get_tokenizer("google/gemma-2-2b-it")
- model.eval()
- return model, tokenizer
-
-
-def load_ctx_encoder():
- model_id = "google/gemma-3-4b-it"
- ctx_model = Gemma3ForConditionalGeneration.from_pretrained(
- model_id, device_map="auto"
- ).eval()
- ctx_encoder_config = Namespace(ctx_encoder_last_layer=26, keep_lm_head=True)
- ctx_model.language_model = PerLayerActivations(
- ctx_model.language_model, ctx_encoder_config
- )
- processor = AutoProcessor.from_pretrained(model_id)
- return ctx_model, processor
-
-
-def template_image(img, ctx_processor):
- messages = [
- {
- "role": "system",
- "content": [{"type": "text", "text": ""}],
- },
- {
- "role": "user",
- "content": [
- {"type": "image", "image": img},
- ],
- },
- ]
-
- inputs = ctx_processor.apply_chat_template(
- messages,
- add_generation_prompt=True,
- tokenize=True,
- return_dict=True,
- return_tensors="pt",
- )
- return inputs
-
-
-@torch.inference_mode
-def get_ctx_features(ctx_inputs, ctx_encoder):
- forward_outputs = ctx_encoder(**ctx_inputs, output_hidden_states=True)
- ctx_features = torch.stack(forward_outputs.hidden_states, dim=1)
- return ctx_features
-
-
-def generate_loras(ctx_inputs, ctx_features):
- generated_loras, _ = model.hypernet.generate_weights(
- ctx_features, attn_mask=torch.ones_like(ctx_inputs["input_ids"])
- )
- generated_loras = combine_lora(
- generated_loras,
- n_chunks=torch.tensor((1,), device=model.device),
- lora_bias=model.hypernet.get_head_bias()
- if model.hypernet.config.use_bias
- else None,
- )
- return generated_loras
-
-
-def apply_loras(model, generated_loras):
- n_queries = torch.ones(1, dtype=torch.int32, device=model.device)
-
- apply_lora_to_layers(
- model.base_model,
- model.hypernet.layer_indices,
- generated_loras,
- n_queries,
- )
-
-
-if __name__ == "__main__":
- model, base_tokenizer = load_checkpoint()
- ctx_encoder, ctx_processor = load_ctx_encoder()
- ds = load_dataset("frgfm/imagenette", "full_size", split="validation")
- # ds = ds.shuffle().select(range(int(0.05 * len(ds))))
- input_ids = base_tokenizer.apply_chat_template(
- [{"role": "user", "content": INPUT_TXT}],
- add_special_tokens=False,
- return_attention_mask=False,
- add_generation_prompt=True,
- return_tensors="pt",
- ).to(model.device)
-
- preds = []
- pred_txts = []
- corrects = []
- labels = ds["label"]
- for sample in tqdm(ds):
- img = sample["image"]
- ctx_inputs = template_image(img, ctx_processor).to(ctx_encoder.device)
- ctx_features = get_ctx_features(ctx_inputs, ctx_encoder)
- generated_loras = generate_loras(ctx_inputs, ctx_features)
- apply_loras(model, generated_loras)
-
- model_outputs = model.base_model.generate(
- input_ids, max_new_tokens=256, do_sample=False
- )
- pred_txt = base_tokenizer.decode(
- model_outputs[0][len(input_ids[0]) :], skip_special_tokens=True
- )
-
- pred_txts.append(pred_txt)
- preds.append(pred_to_class_id(pred_txt))
- is_correct = preds[-1] == labels[len(preds) - 1]
- corrects.append(is_correct)
- print(
- f"GT: {CLASS_NAMES[labels[len(preds) - 1]]}, Pred: {pred_txt} -> {CLASS_NAMES[preds[-1]]}, Correct: {is_correct}"
- )
-
- acc = sum(corrects) / len(corrects)
- print(f"Final accuracy: {acc:4f}")
-
- jsonl_path = os.path.join(RUN_DIR, "imagenette_eval.jsonl")
- meta_path = os.path.join(RUN_DIR, "imagenette_eval.meta.json")
-
- with open(jsonl_path, "w") as f:
- for i, (pred_txt, pred_id, label_id) in enumerate(
- zip(pred_txts, preds, labels)
- ):
- f.write(
- json.dumps(
- {
- "index": i,
- "label": int(label_id),
- "label_name": CLASS_NAMES[label_id],
- "pred_text": pred_txt,
- "pred_class_id": int(pred_id),
- "pred_class_name": CLASS_NAMES[pred_id],
- "correct": bool(pred_id == label_id),
- }
- )
- + "\n"
- )
-
- meta = {
- "dataset": "frgfm/imagenette",
- "subset": "full_size",
- "split": "validation",
- "run_dir": RUN_DIR,
- "prompt": INPUT_TXT,
- "accuracy": float(acc),
- "num_samples": len(preds),
- "class_names": CLASS_NAMES,
- }
- with open(meta_path, "w") as f:
- json.dump(meta, f, indent=2)
-
- print(f"Wrote samples to {jsonl_path}")
- print(f"Wrote metadata to {meta_path}")
diff --git a/scripts/main_exp/eval/llmlingua.sh b/scripts/main_exp/eval/llmlingua.sh
deleted file mode 100755
index 26e0087..0000000
--- a/scripts/main_exp/eval/llmlingua.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-for dataset in squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e; do
- for rate in 0.9 0.8 0.6 0.4 0.2 0.1; do
- WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets "$dataset" --split test --eval_batch_size_gen=1 --use_llmlingua --llmlingua_compression_rate "$rate" --truncate_if_too_long_ctx
- done
-done
diff --git a/scripts/main_exp/eval/t2l.sh b/scripts/main_exp/eval/t2l.sh
deleted file mode 100755
index 73be08d..0000000
--- a/scripts/main_exp/eval/t2l.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-# download t2l checkpoint
-uv run huggingface-cli download SakanaAI/text-to-lora --local-dir . --include "trained_t2l/gemma_2b_t2l"
-
-WANDB_MODE=disabled uv run run_eval.py --model_name_or_path google/gemma-2-2b-it --datasets squad drop ropes longbench/qasper_e longbench/2wikimqa_e longbench/multifieldqa_en_e --split test --eval_batch_size_gen=1 --use_t2l
diff --git a/scripts/main_exp/gen_data.sh b/scripts/main_exp/gen_data.sh
deleted file mode 100755
index 78e7eb2..0000000
--- a/scripts/main_exp/gen_data.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-# download fineweb_edu to `data/raw_datasets/fineweb_edu
-uv run data/download_fineweb_edu.py
-
-# generate qa data
-# run from 000 to 013
-for shard_id in $(seq -f "%03g" 0 13); do
- uv run data/generate_fw_edu_qa_v2.py --shard_pattern "${shard_id}_00000" --n_qa_pairs=5 --vllm_model=google/gemma-3-12b-it --max_length=2000 --max_model_length=2048
- uv run data/generate_fw_edu_qa_v2_repeat.py --shard_pattern "min_0_to_2000/${shard_id}*level_0" --n_qa_pairs=5 --vllm_model=google/gemma-3-12b-it
-
- # self-generated response QA data
- uv run data/self_generate_qa.py --vllm_model google/gemma-2-2b-it --glob_pattern "data/raw_datasets/fw_qa_v2/min_0_to_2000/${shard_id}*_level_1*" --closed_qa_prob 1.0
-done
-
-
-# val split
-uv run data/self_generate_qa.py --vllm_model google/gemma-2-2b-it --glob_pattern 'data/raw_datasets/fw_qa_v2/min_0_to_2000/*_level_0_val.parquet'
-
-# self-gen data for other ds
-uv run data/self_generate_qa.py --vllm_model google/gemma-2-2b-it --ds_names squad_compact ropes_compact drop_compact --split train --closed_qa_prob 1.0
-uv run data/self_generate_qa.py --vllm_model google/gemma-2-2b-it --ds_names pwc_compact --split train --closed_qa_prob 0.0
diff --git a/scripts/main_exp/train-cross-enc-chunk-slurm.sh b/scripts/main_exp/train-cross-enc-chunk-slurm.sh
deleted file mode 100644
index 095a3f1..0000000
--- a/scripts/main_exp/train-cross-enc-chunk-slurm.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=ctxlora
-#SBATCH --nodes=1
-#SBATCH --partition=a3
-#SBATCH --gpus=8
-#SBATCH --output=slurm_logs/%x-%j.out
-#SBATCH --error=slurm_logs/%x-%j.out
-
-port=$((10000 + ($SLURM_JOBID % 50000)))
-echo "Using port: $port"
-
-# port=29051
-
-uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
---num_processes=8 --gpu_ids all train.py \
-configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---target_modules=down_proj --lora_r=8 \
---eval_strategy=no --max_qas_len=2048 --max_qas_per_sample=1 \
---per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1 \
---max_steps=20000 --gradient_accumulation_steps=8 --max_packed_inp_len=4096 \
---max_packed_ctx_len=4096 --use_per_ctx_average_loss=True --use_kl_loss=True \
---quantize_ctx_encoder=True --ctx_encoder_model_name_or_path=google/gemma-3-4b-it \
---max_ctx_chunk_len=512 \
---min_ctx_chunk_len=25 \
---num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
---warmup_steps=2000 \
---learning_rate=2e-5 \
-"$@"
diff --git a/scripts/main_exp/train-cross-enc-slurm.sh b/scripts/main_exp/train-cross-enc-slurm.sh
deleted file mode 100644
index e48ac0e..0000000
--- a/scripts/main_exp/train-cross-enc-slurm.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=ctxlora
-#SBATCH --nodes=1
-#SBATCH --partition=a3
-#SBATCH --gpus=8
-#SBATCH --output=slurm_logs/%x-%j.out
-#SBATCH --error=slurm_logs/%x-%j.out
-
-port=$((10000 + ($SLURM_JOBID % 50000)))
-echo "Using port: $port"
-
-# port=29051
-
-uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
---num_processes=8 --gpu_ids all train.py \
-configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---target_modules=down_proj --lora_r=8 \
---eval_strategy=no --max_qas_len=2048 --max_qas_per_sample=1 \
---per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1 \
---max_steps=80000 --gradient_accumulation_steps=8 --max_packed_inp_len=4096 \
---max_packed_ctx_len=4096 --use_per_ctx_average_loss=True --use_kl_loss=True \
---quantize_ctx_encoder=True --ctx_encoder_model_name_or_path=google/gemma-3-4b-it \
-"$@"
diff --git a/scripts/main_exp/train_no_qa.sh b/scripts/main_exp/train_no_qa.sh
deleted file mode 100755
index 9e61d15..0000000
--- a/scripts/main_exp/train_no_qa.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-port=29051
-
-uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
---num_processes=8 --gpu_ids all train.py \
-configs/main_exp/self_gen_lv1_closed_qa_1_no_qa_l2l.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---target_modules=down_proj --lora_r=8 \
---eval_strategy=no --max_qas_len=2048 --max_qas_per_sample=1 \
---per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1 \
---max_steps=80000 --gradient_accumulation_steps=8 --max_packed_inp_len=4096 \
---max_packed_ctx_len=4096 --use_per_ctx_average_loss=True --use_kl_loss=True \
---quantize_ctx_encoder=True \
-"$@"
diff --git a/scripts/niah/0-gen_data.sh b/scripts/niah/0-gen_data.sh
deleted file mode 100755
index 2359077..0000000
--- a/scripts/niah/0-gen_data.sh
+++ /dev/null
@@ -1 +0,0 @@
-uv run data/generate_ctx_magic_number.py
diff --git a/scripts/niah/1-train.sh b/scripts/niah/1-train.sh
deleted file mode 100755
index 208f602..0000000
--- a/scripts/niah/1-train.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/bin/bash
-WANDB_MODE=disabled uv run train.py \
- configs/niah_exp/ctx_magic_number_32_256.yaml \
- --model_name_or_path=google/gemma-2-2b-it \
- --num_train_epochs=1 \
- --per_device_train_batch_size=-1 \
- --gradient_accumulation_steps=16 \
- --per_device_eval_batch_size=16 \
- --exp_setup=hyper_lora \
- --aggregator_type=perceiver \
- --target_modules=down_proj \
- --num_blocks=8 \
- --num_self_attn_per_block=0 \
- --num_pre_head_layers=1 \
- --lora_r=8 \
- --eval_steps=100 \
- --logging_steps=10 \
- --save_steps=1000 \
- --learning_rate=4e-5 \
- --lora_dropout=0.0 \
- --neftune_noise_alpha=0 \
- --per_rank_gen=True \
- --per_layer_processing=True \
- --gen_lora_l1_reg_coef=1.5 \
- --use_sequence_packing=True \
- --max_packed_inp_len=4096 \
- --max_packed_ctx_len=4096 \
- --dataloader_num_workers=0 \
- --dataloader_prefetch_factor=None \
- --eval_on_start=False \
- --ctx_encoder_type=early_exit \
- --n_latent_queries=208 \
- --use_kl_loss=False \
- --eval_on_start=True \
- --max_ctx_chunk_len=512 \
- --min_ctx_chunk_len=25 \
- --num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
- --max_val_samples_per_ds=100 \
- --seed=1 \
- --use_per_ctx_average_loss=True \
- --torch_empty_cache_steps=10 \
- "$@"
\ No newline at end of file
diff --git a/scripts/niah/2-eval-test.sh b/scripts/niah/2-eval-test.sh
deleted file mode 100755
index ef88394..0000000
--- a/scripts/niah/2-eval-test.sh
+++ /dev/null
@@ -1 +0,0 @@
-WANDB_MODE=disabled uv run run_eval.py --checkpoint_path CHECKPOINT_PATH --datasets ctx_magic_number_32_1024 ctx_magic_number_1024_2048 ctx_magic_number_3072_4096 ctx_magic_number_7168_8192 ctx_magic_number_15360_16384 ctx_magic_number_28672_32768 ctx_magic_number_57344_65536 ctx_magic_number_122880_131072 --max_ctx_chunk_len=1024 --split test --eval_batch_size_gen=4
\ No newline at end of file
diff --git a/scripts/niah/2-eval.sh b/scripts/niah/2-eval.sh
deleted file mode 100755
index dc082a8..0000000
--- a/scripts/niah/2-eval.sh
+++ /dev/null
@@ -1 +0,0 @@
-WANDB_MODE=disabled uv run run_eval.py --checkpoint_path $CHECKPOINT_PATH --datasets ctx_magic_number_32_1024 ctx_magic_number_1024_2048 ctx_magic_number_2048_3072 ctx_magic_number_3072_4096 ctx_magic_number_4096_5120 ctx_magic_number_5120_6144 ctx_magic_number_6144_7168 ctx_magic_number_7168_8192 ctx_magic_number_8192_9216 ctx_magic_number_9216_10240 ctx_magic_number_10240_11264 ctx_magic_number_11264_12288 ctx_magic_number_12288_13312 ctx_magic_number_13312_14336 ctx_magic_number_14336_15360 ctx_magic_number_15360_16384 ctx_magic_number_16384_20480 ctx_magic_number_20480_24576 ctx_magic_number_24576_28672 ctx_magic_number_28672_32768 ctx_magic_number_32768_40960 ctx_magic_number_40960_49152 ctx_magic_number_49152_57344 ctx_magic_number_57344_65536 ctx_magic_number_65536_73728 ctx_magic_number_73728_81920 ctx_magic_number_81920_90112 ctx_magic_number_90112_98304 ctx_magic_number_98304_106496 ctx_magic_number_106496_114688 ctx_magic_number_114688_122880 ctx_magic_number_122880_131072 --max_ctx_chunk_len=1024 --split test --eval_batch_size_gen=4
\ No newline at end of file
diff --git a/scripts/niah/README.md b/scripts/niah/README.md
deleted file mode 100644
index 24403bc..0000000
--- a/scripts/niah/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# NIAH experiment
-```bash
-# run the scripts in this order
-# data generation is only needed to be run once
-uv run bash scripts/niah/0-gen_data.sh
-uv run bash scripts/niah/1-train.sh
-uv run bash scripts/niah/2-eval.sh
-```
\ No newline at end of file
diff --git a/scripts/niah/train_mistral_7b.sh b/scripts/niah/train_mistral_7b.sh
deleted file mode 100644
index 3d2eaab..0000000
--- a/scripts/niah/train_mistral_7b.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-WANDB_MODE=disabled run uv run train.py configs/niah_exp/ctx_magic_number_32_256.yaml \
---model_name_or_path=mistralai/Mistral-7B-Instruct-v0.2 \
---num_train_epochs=1 \
---per_device_train_batch_size=-1 \
---gradient_accumulation_steps=64 \
---per_device_eval_batch_size=16 \
---exp_setup=hyper_lora \
---aggregator_type=perceiver \
---target_modules=down_proj \
---num_blocks=8 \
---num_self_attn_per_block=0 \
---num_pre_head_layers=1 \
---lora_r=8 \
---eval_steps=100 \
---logging_steps=10 \
---save_steps=1000 \
---learning_rate=4e-5 \
---lora_dropout=0.0 \
---neftune_noise_alpha=0 \
---per_rank_gen=True \
---per_layer_processing=True \
---gen_lora_l1_reg_coef=2.0 \
---use_sequence_packing=True \
---max_packed_inp_len=1024 \
---max_packed_ctx_len=1024 \
---dataloader_num_workers=0 \
---dataloader_prefetch_factor=None \
---eval_on_start=False \
---ctx_encoder_type=early_exit \
---n_latent_queries=208 \
---use_kl_loss=False \
---eval_on_start=True \
---max_ctx_chunk_len=512 \
---min_ctx_chunk_len=25 \
---num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
---max_val_samples_per_ds=100 \
---seed=1 \
---use_per_ctx_average_loss=True \
---torch_empty_cache_steps=10
diff --git a/scripts/niah/train_qwen3_4b.sh b/scripts/niah/train_qwen3_4b.sh
deleted file mode 100644
index 78c32be..0000000
--- a/scripts/niah/train_qwen3_4b.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-WANDB_MODE=disabled run uv run train.py configs/niah_exp/ctx_magic_number_32_256.yaml \
---model_name_or_path=Qwen/Qwen3-4B-Instruct-2507 \
---num_train_epochs=1 \
---per_device_train_batch_size=-1 \
---gradient_accumulation_steps=32 \
---per_device_eval_batch_size=16 \
---exp_setup=hyper_lora \
---aggregator_type=perceiver \
---target_modules=down_proj \
---num_blocks=8 \
---num_self_attn_per_block=0 \
---num_pre_head_layers=1 \
---lora_r=8 \
---eval_steps=100 \
---logging_steps=10 \
---save_steps=1000 \
---learning_rate=4e-5 \
---lora_dropout=0.0 \
---neftune_noise_alpha=0 \
---per_rank_gen=True \
---per_layer_processing=True \
---gen_lora_l1_reg_coef=0.5 \
---use_sequence_packing=True \
---max_packed_inp_len=2048 \
---max_packed_ctx_len=2048 \
---dataloader_num_workers=0 \
---dataloader_prefetch_factor=None \
---eval_on_start=False \
---ctx_encoder_type=early_exit \
---n_latent_queries=208 \
---use_kl_loss=False \
---eval_on_start=True \
---max_ctx_chunk_len=512 \
---min_ctx_chunk_len=25 \
---num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
---max_val_samples_per_ds=100 \
---seed=1 \
---use_per_ctx_average_loss=True \
---torch_empty_cache_steps=10
diff --git a/scripts/video2lora/__init__.py b/scripts/video2lora/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/scripts/video2lora/__init__.py
@@ -0,0 +1 @@
+
diff --git a/scripts/video2lora/build_finevideo_stage1_manifest.py b/scripts/video2lora/build_finevideo_stage1_manifest.py
new file mode 100644
index 0000000..57ff3a6
--- /dev/null
+++ b/scripts/video2lora/build_finevideo_stage1_manifest.py
@@ -0,0 +1,249 @@
+import argparse
+import os
+import random
+from pathlib import Path
+
+from ctx_to_lora.data.finevideo import (
+ candidates_to_rows,
+ collect_finevideo_span_pools,
+ materialize_span_clips,
+ sample_fixed_count,
+ sample_mixture,
+ write_finevideo_manifests,
+)
+
+
+def parse_args():
+ data_root = Path(os.environ.get("VIDEO2LORA_DATA_ROOT", "data/video2lora"))
+ finevideo_root = data_root / "raw" / "finevideo"
+ processed_root = data_root / "processed" / "finevideo"
+ parser = argparse.ArgumentParser(
+ description="Build exact-span Stage 1 manifests for FineVideo with ffmpeg-cut scene/adjacent clips."
+ )
+ parser.add_argument("--train-root", default=str(finevideo_root / "train"))
+ parser.add_argument("--val-root", default=str(finevideo_root / "val"))
+ parser.add_argument(
+ "--clips-root",
+ default=str(data_root / "raw" / "finevideo_stage1_spans"),
+ )
+ parser.add_argument(
+ "--train-out",
+ default=str(processed_root / "train.jsonl"),
+ )
+ parser.add_argument(
+ "--val-out",
+ default=str(processed_root / "val.jsonl"),
+ )
+ parser.add_argument(
+ "--val-scene-out",
+ default=str(processed_root / "val_scene.jsonl"),
+ )
+ parser.add_argument(
+ "--val-adjacent-out",
+ default=str(processed_root / "val_adjacent.jsonl"),
+ )
+ parser.add_argument(
+ "--val-full-out",
+ default=str(processed_root / "val_full.jsonl"),
+ )
+ parser.add_argument(
+ "--val-core-out",
+ default=str(processed_root / "val_core.jsonl"),
+ )
+ parser.add_argument(
+ "--val-gen-out",
+ default=str(processed_root / "val_gen_100.jsonl"),
+ )
+ parser.add_argument(
+ "--train-target",
+ type=int,
+ default=0,
+ help="Target number of train examples with 60/30/10 span mix. 0 means max unique possible.",
+ )
+ parser.add_argument("--scene-ratio", type=float, default=0.60)
+ parser.add_argument("--adjacent-ratio", type=float, default=0.30)
+ parser.add_argument("--full-ratio", type=float, default=0.10)
+ parser.add_argument("--val-scene-size", type=int, default=384)
+ parser.add_argument("--val-adjacent-size", type=int, default=384)
+ parser.add_argument("--val-full-size", type=int, default=384)
+ parser.add_argument("--val-gen-size", type=int, default=100)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--num-workers", type=int, default=24)
+ parser.add_argument("--ffmpeg-threads", type=int, default=1)
+ parser.add_argument("--ffmpeg-preset", default="ultrafast")
+ parser.add_argument("--ffmpeg-crf", type=int, default=30)
+ parser.add_argument("--ffmpeg-include-audio", action="store_true")
+ parser.add_argument("--overwrite-clips", action="store_true")
+ parser.add_argument("--skip-clip-materialization", action="store_true")
+ return parser.parse_args()
+
+
+def _stratified_val_gen_rows(
+ val_scene_rows: list[dict],
+ val_adjacent_rows: list[dict],
+ val_full_rows: list[dict],
+ *,
+ val_gen_size: int,
+ seed: int,
+) -> list[dict]:
+ if val_gen_size <= 0:
+ return []
+ pools = [
+ rows
+ for rows in (val_scene_rows, val_adjacent_rows, val_full_rows)
+ if rows
+ ]
+ if not pools:
+ return []
+ base = val_gen_size // 3
+ remainder = val_gen_size - base * 3
+ scene_n = base + (1 if remainder > 0 else 0)
+ adjacent_n = base + (1 if remainder > 1 else 0)
+ full_n = base
+ rng = random.Random(seed + 91)
+ out: list[dict] = []
+ if val_scene_rows:
+ out.extend(rng.sample(val_scene_rows, min(scene_n, len(val_scene_rows))))
+ if val_adjacent_rows:
+ out.extend(rng.sample(val_adjacent_rows, min(adjacent_n, len(val_adjacent_rows))))
+ if val_full_rows:
+ out.extend(rng.sample(val_full_rows, min(full_n, len(val_full_rows))))
+ if len(out) < val_gen_size:
+ selected_ids = {row["id"] for row in out}
+ remaining = [
+ row
+ for rows in pools
+ for row in rows
+ if row["id"] not in selected_ids
+ ]
+ rng.shuffle(remaining)
+ out.extend(remaining[: val_gen_size - len(out)])
+ rng.shuffle(out)
+ return out[:val_gen_size]
+
+
+def main():
+ args = parse_args()
+ random.seed(args.seed)
+
+ train_pools = collect_finevideo_span_pools(
+ args.train_root,
+ split="train",
+ clips_root=args.clips_root,
+ )
+ val_pools = collect_finevideo_span_pools(
+ args.val_root,
+ split="val",
+ clips_root=args.clips_root,
+ )
+
+ print(
+ "[pool] train scene/adjacent/full = "
+ f"{len(train_pools['scene'])}/{len(train_pools['adjacent'])}/{len(train_pools['full'])}"
+ )
+ print(
+ "[pool] val scene/adjacent/full = "
+ f"{len(val_pools['scene'])}/{len(val_pools['adjacent'])}/{len(val_pools['full'])}"
+ )
+
+ train_candidates = sample_mixture(
+ train_pools,
+ target_total=args.train_target,
+ scene_ratio=args.scene_ratio,
+ adjacent_ratio=args.adjacent_ratio,
+ full_ratio=args.full_ratio,
+ seed=args.seed,
+ )
+
+ val_scene = sample_fixed_count(
+ val_pools["scene"],
+ count=args.val_scene_size,
+ seed=args.seed + 1,
+ )
+ val_adjacent = sample_fixed_count(
+ val_pools["adjacent"],
+ count=args.val_adjacent_size,
+ seed=args.seed + 2,
+ )
+ val_full = sample_fixed_count(
+ val_pools["full"],
+ count=args.val_full_size,
+ seed=args.seed + 3,
+ )
+ val_candidates = list(val_scene) + list(val_adjacent) + list(val_full)
+ random.Random(args.seed + 4).shuffle(val_candidates)
+
+ print(f"[select] train candidates: {len(train_candidates)}")
+ print(
+ "[select] val scene/adjacent/full = "
+ f"{len(val_scene)}/{len(val_adjacent)}/{len(val_full)} (total={len(val_candidates)})"
+ )
+
+ if not args.skip_clip_materialization:
+ all_candidates = train_candidates + val_candidates
+ failures = materialize_span_clips(
+ all_candidates,
+ num_workers=args.num_workers,
+ overwrite=args.overwrite_clips,
+ ffmpeg_threads=args.ffmpeg_threads,
+ ffmpeg_preset=args.ffmpeg_preset,
+ ffmpeg_crf=args.ffmpeg_crf,
+ include_audio=args.ffmpeg_include_audio,
+ )
+ if failures:
+ print(f"[clip] failures: {len(failures)}")
+ print("[clip] first failures:")
+ for sample_id, error in failures[:20]:
+ print(f" {sample_id}: {error}")
+ failure_ids = {sample_id for sample_id, _ in failures}
+ train_candidates = [
+ candidate for candidate in train_candidates if candidate.sample_id not in failure_ids
+ ]
+ val_scene = [candidate for candidate in val_scene if candidate.sample_id not in failure_ids]
+ val_adjacent = [
+ candidate for candidate in val_adjacent if candidate.sample_id not in failure_ids
+ ]
+ val_full = [candidate for candidate in val_full if candidate.sample_id not in failure_ids]
+ val_candidates = list(val_scene) + list(val_adjacent) + list(val_full)
+ random.Random(args.seed + 4).shuffle(val_candidates)
+
+ train_rows = candidates_to_rows(train_candidates)
+ val_scene_rows = candidates_to_rows(val_scene)
+ val_adjacent_rows = candidates_to_rows(val_adjacent)
+ val_full_rows = candidates_to_rows(val_full)
+ val_rows = candidates_to_rows(val_candidates)
+ val_gen_rows = _stratified_val_gen_rows(
+ val_scene_rows,
+ val_adjacent_rows,
+ val_full_rows,
+ val_gen_size=args.val_gen_size,
+ seed=args.seed + 10,
+ )
+
+ write_finevideo_manifests(
+ train_rows=train_rows,
+ val_rows=val_rows,
+ val_scene_rows=val_scene_rows,
+ val_adjacent_rows=val_adjacent_rows,
+ val_full_rows=val_full_rows,
+ val_gen_rows=val_gen_rows,
+ train_out=args.train_out,
+ val_out=args.val_out,
+ val_scene_out=args.val_scene_out,
+ val_adjacent_out=args.val_adjacent_out,
+ val_full_out=args.val_full_out,
+ val_core_out=args.val_core_out,
+ val_gen_out=args.val_gen_out,
+ )
+
+ print(f"Wrote train rows: {len(train_rows)} -> {args.train_out}")
+ print(f"Wrote val rows: {len(val_rows)} -> {args.val_out}")
+ print(f"Wrote val_scene rows: {len(val_scene_rows)} -> {args.val_scene_out}")
+ print(f"Wrote val_adjacent rows: {len(val_adjacent_rows)} -> {args.val_adjacent_out}")
+ print(f"Wrote val_full rows: {len(val_full_rows)} -> {args.val_full_out}")
+ print(f"Wrote val_core rows: {min(1024, len(val_rows))} -> {args.val_core_out}")
+ print(f"Wrote val_gen rows: {len(val_gen_rows)} -> {args.val_gen_out}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/video2lora/generate_finevideo_teacher_targets.py b/scripts/video2lora/generate_finevideo_teacher_targets.py
new file mode 100644
index 0000000..477e82d
--- /dev/null
+++ b/scripts/video2lora/generate_finevideo_teacher_targets.py
@@ -0,0 +1,275 @@
+import argparse
+import json
+import os
+import time
+from pathlib import Path
+from typing import Any
+
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+from transformers.utils import is_av_available, is_cv2_available, is_decord_available
+
+from ctx_to_lora.data.video_manifest import load_video_manifest, dump_jsonl, resolve_video_path
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='Generate offline teacher targets for FineVideo manifests.')
+ parser.add_argument('--input-manifest', required=True)
+ parser.add_argument('--output-manifest', required=True)
+ parser.add_argument('--smolvlm-name-or-path', default='HuggingFaceTB/SmolVLM2-2.2B-Instruct')
+ parser.add_argument('--per-device-batch-size', type=int, default=8)
+ parser.add_argument('--max-new-tokens', type=int, default=64)
+ parser.add_argument('--max-frames', type=int, default=12)
+ parser.add_argument('--video-size-longest-edge', type=int, default=384)
+ parser.add_argument('--video-fps', type=float, default=None)
+ parser.add_argument(
+ '--video-load-backend',
+ default='auto',
+ choices=('auto', 'decord', 'pyav', 'opencv', 'torchvision'),
+ )
+ parser.add_argument('--max-samples', type=int, default=None)
+ parser.add_argument('--flush-every', type=int, default=256)
+ parser.add_argument('--output-dir', default='')
+ parser.add_argument('--shard-index', type=int, default=0)
+ parser.add_argument('--num-shards', type=int, default=1)
+ parser.add_argument('--merge-only', action='store_true')
+ return parser.parse_args()
+
+
+def is_skippable_video_error(exc: BaseException) -> bool:
+ seen = set()
+ cur: BaseException | None = exc
+ while cur is not None and id(cur) not in seen:
+ seen.add(id(cur))
+ if isinstance(cur, UnicodeDecodeError):
+ return True
+ if isinstance(cur, IndexError) and 'tuple index out of range' in str(cur).lower():
+ return True
+ err_name = cur.__class__.__name__.lower()
+ err_mod = cur.__class__.__module__.lower()
+ msg = str(cur).lower()
+ if 'av.' in err_mod or 'ffmpeg' in err_mod or 'pyav' in err_mod:
+ return True
+ if 'invalid total_num_frames' in msg:
+ return True
+ if 'cannot open' in msg and 'video' in msg:
+ return True
+ if 'error opening input file' in msg:
+ return True
+ if 'moov atom not found' in msg:
+ return True
+ if 'invalid data found when processing input' in msg:
+ return True
+ if 'unsupported codec' in msg:
+ return True
+ if 'decode' in err_name and 'error' in err_name:
+ return True
+ cur = cur.__cause__ or cur.__context__
+ return False
+
+
+def prepare_generation_inputs(
+ processor,
+ messages,
+ device,
+ *,
+ max_frames,
+ video_fps,
+ video_size_longest_edge,
+ video_load_backend,
+):
+ processor.tokenizer.padding_side = 'left'
+ chat_template_kwargs = dict(padding=True)
+ processor_name = type(processor).__name__.lower()
+ if 'idefics3' in processor_name:
+ chat_template_kwargs['num_frames'] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs['video_fps'] = video_fps
+ else:
+ chat_template_kwargs['max_frames'] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs['target_fps'] = video_fps
+ if video_load_backend == 'auto':
+ if is_decord_available():
+ video_load_backend = 'decord'
+ elif is_av_available():
+ video_load_backend = 'pyav'
+ elif is_cv2_available():
+ video_load_backend = 'opencv'
+ else:
+ video_load_backend = 'torchvision'
+ chat_template_kwargs['video_load_backend'] = video_load_backend
+ if video_size_longest_edge is not None:
+ video_size = {'longest_edge': video_size_longest_edge}
+ processor.video_size = video_size
+ processor.image_processor.size = video_size
+ prompt_inputs = processor.apply_chat_template(
+ messages,
+ add_generation_prompt=True,
+ tokenize=True,
+ return_dict=True,
+ return_tensors='pt',
+ **chat_template_kwargs,
+ )
+ moved = {}
+ for key, value in prompt_inputs.items():
+ if isinstance(value, torch.Tensor):
+ if value.is_floating_point():
+ moved[key] = value.to(device=device, dtype=torch.bfloat16)
+ else:
+ moved[key] = value.to(device=device)
+ else:
+ moved[key] = value
+ return moved
+
+
+def chunked(seq, size):
+ for i in range(0, len(seq), size):
+ yield seq[i:i + size]
+
+
+def generate_rows(
+ model,
+ processor,
+ tokenizer,
+ rows: list[dict[str, Any]],
+ device: str,
+ args,
+) -> list[dict[str, Any]]:
+ messages = []
+ for row in rows:
+ messages.append([
+ {
+ 'role': 'user',
+ 'content': [
+ {'type': 'video', 'path': row['video_path']},
+ {'type': 'text', 'text': row['prompt']},
+ ],
+ }
+ ])
+ inputs = prepare_generation_inputs(
+ processor,
+ messages,
+ device,
+ max_frames=args.max_frames,
+ video_fps=args.video_fps,
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ generated = model.generate(
+ **inputs,
+ max_new_tokens=args.max_new_tokens,
+ do_sample=False,
+ pad_token_id=tokenizer.pad_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ )
+ input_len = inputs['input_ids'].shape[1]
+ generated_only = generated[:, input_len:]
+ decoded = tokenizer.batch_decode(generated_only, skip_special_tokens=True)
+
+ out_rows = []
+ for row, target_text in zip(rows, decoded, strict=True):
+ out_row = dict(row)
+ out_row['target_text'] = target_text.strip() or 'Unable to determine.'
+ metadata = dict(out_row.get('metadata') or {})
+ metadata['teacher_model'] = args.smolvlm_name_or_path
+ metadata['teacher_generated'] = True
+ metadata['teacher_prompt'] = out_row['prompt']
+ metadata['teacher_max_new_tokens'] = args.max_new_tokens
+ out_row['metadata'] = metadata
+ out_rows.append(out_row)
+ return out_rows
+
+
+def merge_shards(output_path: Path, tmp_dir: Path) -> int:
+ merged = []
+ for shard_file in sorted(tmp_dir.glob(f'{output_path.stem}.rank*.jsonl')):
+ with open(shard_file) as f:
+ for line in f:
+ line = line.strip()
+ if line:
+ merged.append(json.loads(line))
+ dump_jsonl(output_path, merged)
+ return len(merged)
+
+
+def main():
+ args = parse_args()
+ output_path = Path(args.output_manifest)
+ tmp_dir = Path(args.output_dir) if args.output_dir else output_path.parent / f'.tmp-{output_path.stem}'
+ tmp_dir.mkdir(parents=True, exist_ok=True)
+ if args.merge_only:
+ merged = merge_shards(output_path, tmp_dir)
+ print(f'[merge] wrote {merged} rows to {output_path}', flush=True)
+ return
+
+ rows = load_video_manifest(args.input_manifest, max_samples=args.max_samples)
+ world = args.num_shards
+ rank = args.shard_index
+ if rank < 0 or rank >= world:
+ raise ValueError(f'Invalid shard index {rank} for num_shards={world}')
+ shard = rows[rank::world]
+ shard_path = tmp_dir / f'{output_path.stem}.rank{rank:02d}.jsonl'
+
+ processor = AutoProcessor.from_pretrained(args.smolvlm_name_or_path, trust_remote_code=True)
+ tokenizer = processor.tokenizer
+ if tokenizer.pad_token_id is None:
+ tokenizer.pad_token_id = tokenizer.eos_token_id
+ model = AutoModelForImageTextToText.from_pretrained(
+ args.smolvlm_name_or_path,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True,
+ ).to('cuda').eval()
+ model.config.pad_token_id = tokenizer.pad_token_id
+ if getattr(model, 'generation_config', None):
+ model.generation_config.pad_token_id = tokenizer.pad_token_id
+
+ written = 0
+ skipped = 0
+ started = time.time()
+ buffer: list[dict[str, Any]] = []
+
+ with open(shard_path, 'w') as fout:
+ for batch_rows in chunked(shard, args.per_device_batch_size):
+ normalized_rows = []
+ for row in batch_rows:
+ row = dict(row)
+ row['video_path'] = resolve_video_path(row['video_path'])
+ normalized_rows.append(row)
+ try:
+ out_rows = generate_rows(model, processor, tokenizer, normalized_rows, 'cuda', args)
+ buffer.extend(out_rows)
+ written += len(out_rows)
+ except Exception as exc: # pylint: disable=broad-except
+ if len(normalized_rows) > 1:
+ for row in normalized_rows:
+ try:
+ out_rows = generate_rows(model, processor, tokenizer, [row], 'cuda', args)
+ buffer.extend(out_rows)
+ written += len(out_rows)
+ except Exception as single_exc: # pylint: disable=broad-except
+ if not is_skippable_video_error(single_exc):
+ raise
+ skipped += 1
+ print(f'[rank{rank}] skipped {row["id"]}: {type(single_exc).__name__}: {single_exc}', flush=True)
+ continue
+ if not is_skippable_video_error(exc):
+ raise
+ skipped += len(normalized_rows)
+ for row in normalized_rows:
+ print(f'[rank{rank}] skipped {row["id"]}: {type(exc).__name__}: {exc}', flush=True)
+ if len(buffer) >= args.flush_every:
+ for row in buffer:
+ fout.write(json.dumps(row) + '\n')
+ fout.flush()
+ buffer.clear()
+ elapsed = max(time.time() - started, 1e-6)
+ rate = written / elapsed
+ print(f'[rank{rank}] written={written} skipped={skipped} rate={rate:.2f} rows/s', flush=True)
+ for row in buffer:
+ fout.write(json.dumps(row) + '\n')
+ fout.flush()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/scripts/video2lora/infer.py b/scripts/video2lora/infer.py
new file mode 100644
index 0000000..f6bede2
--- /dev/null
+++ b/scripts/video2lora/infer.py
@@ -0,0 +1,207 @@
+import argparse
+import json
+import re
+from dataclasses import fields
+from pathlib import Path
+from typing import Any
+
+import torch
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+
+from ctx_to_lora.data.video_manifest import dump_jsonl, load_video_manifest
+from scripts.video2lora.train_smolvlm_stage1 import (
+ TrainArgs,
+ build_stage1_model,
+ generate_fixed_examples,
+)
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ description="Run Video2LoRA inference from a Stage 1 checkpoint."
+ )
+ parser.add_argument(
+ "--run-dir",
+ default="",
+ help="Directory containing train_args.json and checkpoints/.",
+ )
+ parser.add_argument(
+ "--checkpoint",
+ required=True,
+ help="Checkpoint filename under RUN_DIR/checkpoints/ or a direct path.",
+ )
+ parser.add_argument(
+ "--preset",
+ default="auto",
+ choices=("auto", "500m", "2.2b"),
+ help="Use built-in public checkpoint settings when train_args.json is absent.",
+ )
+ parser.add_argument("--manifest", required=True, help="JSONL video manifest.")
+ parser.add_argument("--output", required=True, help="Output JSONL path.")
+ parser.add_argument("--max-samples", type=int, default=None)
+ parser.add_argument("--video-size-longest-edge", type=int, default=None)
+ parser.add_argument("--generation-max-new-tokens", type=int, default=None)
+ parser.add_argument("--seed", type=int, default=None)
+ return parser.parse_args()
+
+
+def checkpoint_path(run_dir: Path | None, checkpoint: str) -> Path:
+ path = Path(checkpoint)
+ if path.exists():
+ return path
+ if run_dir is None:
+ return path
+ return run_dir / "checkpoints" / checkpoint
+
+
+def checkpoint_label(path: Path) -> str:
+ match = re.search(r"step-(\d+)\.pt$", path.name)
+ if match:
+ return str(int(match.group(1)))
+ return path.stem
+
+
+def infer_preset(checkpoint: str, requested: str) -> str:
+ if requested != "auto":
+ return requested
+ lower = checkpoint.lower()
+ if "500m" in lower:
+ return "500m"
+ if "2.2b" in lower or "2p2b" in lower or "22b" in lower:
+ return "2.2b"
+ raise ValueError(
+ "Could not infer checkpoint preset from filename. Pass --preset 500m or --preset 2.2b."
+ )
+
+
+def default_train_args(preset: str) -> TrainArgs:
+ if preset == "500m":
+ model_name = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
+ video_load_backend = "auto"
+ elif preset == "2.2b":
+ model_name = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
+ video_load_backend = "auto"
+ else:
+ raise ValueError(f"Unknown preset: {preset}")
+ return TrainArgs(
+ smolvlm_name_or_path=model_name,
+ train_manifest="",
+ val_manifest="",
+ val_core_manifest=None,
+ val_gen_manifest=None,
+ output_dir="",
+ per_device_batch_size=1,
+ eval_batch_size=1,
+ gradient_accumulation_steps=1,
+ max_steps=0,
+ learning_rate=0.0,
+ weight_decay=0.0,
+ warmup_ratio=0.0,
+ max_grad_norm=1.0,
+ seed=42,
+ log_every=1,
+ eval_every=1,
+ save_every=1,
+ max_train_samples=None,
+ max_val_samples=None,
+ num_workers=0,
+ lora_r=16,
+ lora_dropout=0.0,
+ target_modules=["down_proj"],
+ latent_size=512,
+ dropout_rate=0.0,
+ n_latent_queries=8,
+ num_blocks=9,
+ num_self_attn_per_block=0,
+ video_fps=None,
+ max_frames=12,
+ video_size_longest_edge=384,
+ video_load_backend=video_load_backend,
+ internalization_prompt="Internalize this video for later captioning.",
+ kl_weight=0.0,
+ kl_temperature=1.0,
+ generation_max_new_tokens=128,
+ legacy_sanity_manifests=[],
+ legacy_sanity_max_samples_per_manifest=0,
+ wandb_project="video2lora",
+ wandb_mode="disabled",
+ wandb_group=None,
+ wandb_run_name=None,
+ wandb_notes=None,
+ resume_checkpoint=None,
+ resume_trainer_state=None,
+ resume_global_step=None,
+ resume_ignore_scheduler_state=False,
+ )
+
+
+def load_train_args(run_dir: Path | None, overrides: argparse.Namespace) -> TrainArgs:
+ args_path = run_dir / "train_args.json" if run_dir is not None else None
+ if args_path is None or not args_path.exists():
+ train_args = default_train_args(infer_preset(overrides.checkpoint, overrides.preset))
+ if overrides.video_size_longest_edge is not None:
+ train_args.video_size_longest_edge = overrides.video_size_longest_edge
+ if overrides.generation_max_new_tokens is not None:
+ train_args.generation_max_new_tokens = overrides.generation_max_new_tokens
+ if overrides.seed is not None:
+ train_args.seed = overrides.seed
+ return train_args
+
+ with open(args_path) as f:
+ raw_args: dict[str, Any] = json.load(f)
+
+ raw_args.setdefault("video_size_longest_edge", None)
+ raw_args.setdefault("resume_ignore_scheduler_state", False)
+
+ if overrides.video_size_longest_edge is not None:
+ raw_args["video_size_longest_edge"] = overrides.video_size_longest_edge
+ if overrides.generation_max_new_tokens is not None:
+ raw_args["generation_max_new_tokens"] = overrides.generation_max_new_tokens
+ if overrides.seed is not None:
+ raw_args["seed"] = overrides.seed
+
+ allowed = {field.name for field in fields(TrainArgs)}
+ filtered_args = {key: value for key, value in raw_args.items() if key in allowed}
+ missing = sorted(allowed - filtered_args.keys())
+ if missing:
+ raise ValueError(f"train_args.json is missing required fields: {missing}")
+ return TrainArgs(**filtered_args)
+
+
+def main() -> None:
+ args = parse_args()
+ run_dir = Path(args.run_dir) if args.run_dir else None
+ ckpt_path = checkpoint_path(run_dir, args.checkpoint)
+ if not ckpt_path.exists():
+ raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
+
+ train_args = load_train_args(run_dir, args)
+ set_seed(train_args.seed)
+ rows = load_video_manifest(args.manifest, max_samples=args.max_samples)
+
+ accelerator = Accelerator(mixed_precision="bf16")
+ model, raw_model, processor, tokenizer = build_stage1_model(train_args)
+ state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+ model.load_state_dict(state_dict)
+ model.to(accelerator.device)
+ raw_model.eval()
+
+ outputs = generate_fixed_examples(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ rows,
+ train_args,
+ )
+ for row in outputs:
+ row["checkpoint"] = checkpoint_label(ckpt_path)
+
+ dump_jsonl(args.output, outputs)
+ print(f"[infer] wrote {len(outputs)} rows to {args.output}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/video2lora/train_smolvlm_online.py b/scripts/video2lora/train_smolvlm_online.py
new file mode 100644
index 0000000..20bc737
--- /dev/null
+++ b/scripts/video2lora/train_smolvlm_online.py
@@ -0,0 +1,1496 @@
+import argparse
+import json
+import math
+import os
+import random
+import time
+from collections import OrderedDict
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any
+
+import torch
+try:
+ import wandb
+except ModuleNotFoundError:
+ class _MissingWandb:
+ def init(self, *args, **kwargs):
+ if kwargs.get("mode") != "disabled":
+ raise ModuleNotFoundError(
+ "wandb is not installed. Install it or pass --wandb-mode disabled."
+ )
+
+ def log(self, *args, **kwargs):
+ return None
+
+ def finish(self, *args, **kwargs):
+ return None
+
+ wandb = _MissingWandb()
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, Dataset
+from transformers import (
+ AutoModelForCausalLM,
+ AutoModelForImageTextToText,
+ AutoProcessor,
+ AutoTokenizer,
+ PretrainedConfig,
+ get_cosine_schedule_with_warmup,
+)
+from transformers.utils import is_av_available, is_cv2_available, is_decord_available
+
+from ctx_to_lora.configs import (
+ AggregatorArguments,
+ CtxEncoderArguments,
+ HypernetArguments,
+)
+from ctx_to_lora.model_loading import get_lora_config
+from ctx_to_lora.modeling.hypernet import (
+ ModulatedPretrainedModel,
+ get_hypernet_config,
+)
+
+
+DATA_ROOT = Path(os.environ.get("VIDEO2LORA_DATA_ROOT", "data/video2lora"))
+
+
+@dataclass
+class TrainArgs:
+ smolvlm_name_or_path: str
+ base_lm_name_or_path: str
+ train_manifest: str
+ val_manifest: str | None
+ output_dir: str
+ epochs: int
+ batch_size: int
+ eval_batch_size: int
+ grad_accum_steps: int
+ max_steps: int | None
+ learning_rate: float
+ weight_decay: float
+ warmup_steps: int
+ max_grad_norm: float
+ seed: int
+ log_every: int
+ eval_every: int
+ save_every: int
+ max_train_samples: int | None
+ max_val_samples: int | None
+ num_workers: int
+ lora_r: int
+ lora_dropout: float
+ target_modules: list[str]
+ latent_size: int
+ dropout_rate: float
+ n_latent_queries: int
+ num_blocks: int
+ num_self_attn_per_block: int
+ video_fps: float | None
+ max_frames: int
+ video_size_longest_edge: int | None
+ video_load_backend: str
+ questions_per_video: int
+ frame_pooling: str
+ ctx_feature_mode: str
+ kl_weight: float
+ kl_temperature: float
+ wandb_project: str
+ wandb_mode: str
+ wandb_group: str | None
+ wandb_run_name: str | None
+ wandb_notes: str | None
+
+
+def parse_args() -> TrainArgs:
+ parser = argparse.ArgumentParser(
+ description="Train Video2LoRA with online SmolVLM video feature extraction."
+ )
+ parser.add_argument(
+ "--smolvlm-name-or-path",
+ default="HuggingFaceTB/SmolVLM2-2.2B-Instruct",
+ )
+ parser.add_argument(
+ "--base-lm-name-or-path",
+ default="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+ )
+ parser.add_argument(
+ "--train-manifest",
+ default=str(DATA_ROOT / "processed" / "train.jsonl"),
+ )
+ parser.add_argument(
+ "--val-manifest",
+ default=str(DATA_ROOT / "processed" / "val.jsonl"),
+ )
+ parser.add_argument("--output-dir", default="")
+ parser.add_argument("--epochs", type=int, default=1)
+ parser.add_argument("--batch-size", type=int, default=1)
+ parser.add_argument("--eval-batch-size", type=int, default=None)
+ parser.add_argument("--grad-accum-steps", type=int, default=4)
+ parser.add_argument("--max-steps", type=int, default=None)
+ parser.add_argument("--learning-rate", type=float, default=2e-4)
+ parser.add_argument("--weight-decay", type=float, default=0.01)
+ parser.add_argument("--warmup-steps", type=int, default=50)
+ parser.add_argument("--max-grad-norm", type=float, default=1.0)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--log-every", type=int, default=10)
+ parser.add_argument("--eval-every", type=int, default=100)
+ parser.add_argument("--save-every", type=int, default=200)
+ parser.add_argument("--max-train-samples", type=int, default=None)
+ parser.add_argument("--max-val-samples", type=int, default=1000)
+ parser.add_argument("--num-workers", type=int, default=0)
+ parser.add_argument("--lora-r", type=int, default=16)
+ parser.add_argument("--lora-dropout", type=float, default=0.0)
+ parser.add_argument(
+ "--target-modules",
+ default="q_proj,v_proj,down_proj",
+ )
+ parser.add_argument("--latent-size", type=int, default=512)
+ parser.add_argument("--dropout-rate", type=float, default=0.0)
+ parser.add_argument("--n-latent-queries", type=int, default=208)
+ parser.add_argument("--num-blocks", type=int, default=6)
+ parser.add_argument("--num-self-attn-per-block", type=int, default=1)
+ parser.add_argument("--video-fps", type=float, default=None)
+ parser.add_argument("--max-frames", type=int, default=24)
+ parser.add_argument(
+ "--video-size-longest-edge",
+ type=int,
+ default=None,
+ help="Optional SmolVLM processor resize longest edge for video frames.",
+ )
+ parser.add_argument(
+ "--video-load-backend",
+ default="auto",
+ choices=("auto", "decord", "pyav", "opencv", "torchvision"),
+ help="Video decode backend for processor.apply_chat_template.",
+ )
+ parser.add_argument("--questions-per-video", type=int, default=4)
+ parser.add_argument(
+ "--frame-pooling",
+ default="mean",
+ choices=("mean", "flatten"),
+ )
+ parser.add_argument(
+ "--ctx-feature-mode",
+ default="visual_pooled",
+ choices=("visual_pooled", "l2l_fused_text"),
+ )
+ parser.add_argument("--kl-weight", type=float, default=0.0)
+ parser.add_argument("--kl-temperature", type=float, default=1.0)
+ parser.add_argument("--wandb-project", default="video2lora-video-centric")
+ parser.add_argument(
+ "--wandb-mode",
+ default="auto",
+ choices=("auto", "online", "offline", "disabled"),
+ )
+ parser.add_argument("--wandb-group", default=None)
+ parser.add_argument("--wandb-run-name", default=None)
+ parser.add_argument("--wandb-notes", default=None)
+ parsed = parser.parse_args()
+
+ output_dir = parsed.output_dir
+ if not output_dir:
+ timestamp = time.strftime("%Y%m%d-%H%M%S", time.gmtime())
+ output_dir = str(DATA_ROOT / "runs" / f"{timestamp}-smolvlm-online")
+
+ val_manifest = parsed.val_manifest
+ if val_manifest and not os.path.exists(val_manifest):
+ val_manifest = None
+
+ return TrainArgs(
+ smolvlm_name_or_path=parsed.smolvlm_name_or_path,
+ base_lm_name_or_path=parsed.base_lm_name_or_path,
+ train_manifest=parsed.train_manifest,
+ val_manifest=val_manifest,
+ output_dir=output_dir,
+ epochs=parsed.epochs,
+ batch_size=parsed.batch_size,
+ eval_batch_size=parsed.eval_batch_size or parsed.batch_size,
+ grad_accum_steps=parsed.grad_accum_steps,
+ max_steps=parsed.max_steps,
+ learning_rate=parsed.learning_rate,
+ weight_decay=parsed.weight_decay,
+ warmup_steps=parsed.warmup_steps,
+ max_grad_norm=parsed.max_grad_norm,
+ seed=parsed.seed,
+ log_every=parsed.log_every,
+ eval_every=parsed.eval_every,
+ save_every=parsed.save_every,
+ max_train_samples=parsed.max_train_samples,
+ max_val_samples=parsed.max_val_samples,
+ num_workers=parsed.num_workers,
+ lora_r=parsed.lora_r,
+ lora_dropout=parsed.lora_dropout,
+ target_modules=[x.strip() for x in parsed.target_modules.split(",") if x.strip()],
+ latent_size=parsed.latent_size,
+ dropout_rate=parsed.dropout_rate,
+ n_latent_queries=parsed.n_latent_queries,
+ num_blocks=parsed.num_blocks,
+ num_self_attn_per_block=parsed.num_self_attn_per_block,
+ video_fps=parsed.video_fps,
+ max_frames=parsed.max_frames,
+ video_size_longest_edge=parsed.video_size_longest_edge,
+ video_load_backend=parsed.video_load_backend,
+ questions_per_video=parsed.questions_per_video,
+ frame_pooling=parsed.frame_pooling,
+ ctx_feature_mode=parsed.ctx_feature_mode,
+ kl_weight=parsed.kl_weight,
+ kl_temperature=parsed.kl_temperature,
+ wandb_project=parsed.wandb_project,
+ wandb_mode=parsed.wandb_mode,
+ wandb_group=parsed.wandb_group,
+ wandb_run_name=parsed.wandb_run_name,
+ wandb_notes=parsed.wandb_notes,
+ )
+
+
+def ensure_layout(output_dir: Path) -> None:
+ for path in (
+ DATA_ROOT,
+ DATA_ROOT / "raw",
+ DATA_ROOT / "processed",
+ DATA_ROOT / "features",
+ DATA_ROOT / "runs",
+ DATA_ROOT / "cache",
+ output_dir,
+ output_dir / "checkpoints",
+ ):
+ path.mkdir(parents=True, exist_ok=True)
+
+
+def set_seed(seed: int) -> None:
+ random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+
+def load_jsonl(path: str, max_samples: int | None = None) -> list[dict[str, Any]]:
+ rows: list[dict[str, Any]] = []
+ with open(path) as f:
+ for line in f:
+ line = line.strip()
+ if not line:
+ continue
+ rows.append(json.loads(line))
+ if max_samples is not None and len(rows) >= max_samples:
+ break
+ return rows
+
+
+def resolve_video_path(video_path_str: str) -> str:
+ video_path = Path(video_path_str)
+ if not video_path.is_absolute():
+ video_path = DATA_ROOT / video_path
+ return str(video_path)
+
+
+class VideoQADataset(Dataset):
+ def __init__(
+ self,
+ rows: list[dict[str, Any]],
+ *,
+ questions_per_video: int,
+ sample_questions_randomly: bool,
+ ):
+ grouped: OrderedDict[str, dict[str, Any]] = OrderedDict()
+ for row in rows:
+ video_path = resolve_video_path(row["video_path"])
+ if video_path not in grouped:
+ grouped[video_path] = {
+ "id": row.get("id", video_path),
+ "video_path": video_path,
+ "metadata": row.get("metadata", {}),
+ "qas": [],
+ }
+ grouped[video_path]["qas"].append(
+ {
+ "id": row.get("id"),
+ "question": row["question"],
+ "answer": row["answer"],
+ }
+ )
+ self.rows = list(grouped.values())
+ self.questions_per_video = questions_per_video
+ self.sample_questions_randomly = sample_questions_randomly
+
+ def __len__(self) -> int:
+ return len(self.rows)
+
+ def __getitem__(self, idx: int) -> dict[str, Any]:
+ row = self.rows[idx]
+ qas = row["qas"]
+ if 0 < self.questions_per_video < len(qas):
+ if self.sample_questions_randomly:
+ qas = random.sample(qas, k=self.questions_per_video)
+ else:
+ qas = qas[: self.questions_per_video]
+ return {
+ "id": row.get("id", str(idx)),
+ "video_path": row["video_path"],
+ "qas": qas,
+ "metadata": row.get("metadata", {}),
+ }
+
+
+def build_labels(tokenizer, question: str, answer: str):
+ prompt_messages = [{"role": "user", "content": question}]
+ full_messages = [
+ {"role": "user", "content": question},
+ {"role": "assistant", "content": answer},
+ ]
+ prompt_ids = tokenizer.apply_chat_template(
+ prompt_messages,
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors=None,
+ )
+ full_ids = tokenizer.apply_chat_template(
+ full_messages,
+ tokenize=True,
+ add_generation_prompt=False,
+ return_tensors=None,
+ )
+ input_ids = torch.tensor(full_ids, dtype=torch.long)
+ attention_mask = torch.ones_like(input_ids)
+ labels = input_ids.clone()
+ labels[: len(prompt_ids)] = -100
+ return input_ids, attention_mask, labels
+
+
+class SmolVLMOnlineCollator:
+ def __init__(
+ self,
+ base_tokenizer,
+ video_fps: float | None,
+ max_frames: int,
+ video_size_longest_edge: int | None = None,
+ ):
+ self.base_tokenizer = base_tokenizer
+ self.video_fps = video_fps
+ self.max_frames = max_frames
+ self.video_size_longest_edge = video_size_longest_edge
+
+ def __call__(self, batch: list[dict[str, Any]]) -> dict[str, Any]:
+ input_ids, attention_masks, labels = [], [], []
+ qa_ids = []
+ video_ids = []
+ video_messages = []
+ teacher_prompt_messages = []
+ teacher_full_messages = []
+ question_counts = []
+
+ for example in batch:
+ qas = example["qas"]
+ question_counts.append(len(qas))
+ video_ids.append(example["id"])
+ video_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {
+ "type": "text",
+ "text": "Internalize this video for later question answering.",
+ },
+ ],
+ }
+ ]
+ )
+ for qa_idx, qa in enumerate(qas):
+ inp_ids, attn_mask, lbl = build_labels(
+ self.base_tokenizer,
+ question=qa["question"],
+ answer=qa["answer"],
+ )
+ input_ids.append(inp_ids)
+ attention_masks.append(attn_mask)
+ labels.append(lbl)
+ qa_ids.append(qa.get("id") or f"{example['id']}-q{qa_idx}")
+ teacher_prompt_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {"type": "text", "text": qa["question"]},
+ ],
+ }
+ ]
+ )
+ teacher_full_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {"type": "text", "text": qa["question"]},
+ ],
+ },
+ {
+ "role": "assistant",
+ "content": [{"type": "text", "text": qa["answer"]}],
+ },
+ ]
+ )
+
+ batch_input_ids = pad_sequence(
+ input_ids,
+ batch_first=True,
+ padding_value=self.base_tokenizer.pad_token_id,
+ )
+ batch_attention_mask = pad_sequence(
+ attention_masks,
+ batch_first=True,
+ padding_value=0,
+ )
+ batch_labels = pad_sequence(
+ labels,
+ batch_first=True,
+ padding_value=-100,
+ )
+
+ return {
+ "ids": qa_ids,
+ "video_ids": video_ids,
+ "question_counts": torch.tensor(question_counts, dtype=torch.long),
+ "input_ids": batch_input_ids,
+ "attention_mask": batch_attention_mask,
+ "labels": batch_labels,
+ "video_messages": video_messages,
+ "teacher_prompt_messages": teacher_prompt_messages,
+ "teacher_full_messages": teacher_full_messages,
+ "video_fps": self.video_fps,
+ "max_frames": self.max_frames,
+ "video_size_longest_edge": self.video_size_longest_edge,
+ }
+
+
+def move_text_batch_to_device(batch: dict[str, Any], device: torch.device) -> dict[str, Any]:
+ out = {}
+ for key, value in batch.items():
+ if isinstance(value, torch.Tensor):
+ out[key] = value.to(device)
+ else:
+ out[key] = value
+ return out
+
+
+def build_base_model(args: TrainArgs, device: torch.device):
+ tokenizer = AutoTokenizer.from_pretrained(
+ args.base_lm_name_or_path,
+ trust_remote_code=True,
+ padding_side="right",
+ )
+ if tokenizer.pad_token_id is None:
+ tokenizer.pad_token_id = tokenizer.eos_token_id
+
+ peft_config = get_lora_config(
+ args.base_lm_name_or_path,
+ lora_r=args.lora_r,
+ lora_dropout=args.lora_dropout,
+ target_modules=args.target_modules,
+ )
+ base_model = AutoModelForCausalLM.from_pretrained(
+ args.base_lm_name_or_path,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True,
+ device_map={"": device.index if device.index is not None else 0},
+ )
+ from peft import PeftModel
+
+ base_model = PeftModel(base_model, peft_config)
+ base_model.train()
+ for param in base_model.parameters():
+ param.requires_grad = False
+ base_model.config.pad_token_id = tokenizer.pad_token_id
+ if getattr(base_model, "generation_config", None):
+ base_model.generation_config.pad_token_id = tokenizer.pad_token_id
+ return base_model, tokenizer
+
+
+def build_video2lora_model(args: TrainArgs, device: torch.device):
+ base_model, base_tokenizer = build_base_model(args, device)
+
+ processor = AutoProcessor.from_pretrained(
+ args.smolvlm_name_or_path,
+ trust_remote_code=True,
+ )
+ vlm = AutoModelForImageTextToText.from_pretrained(
+ args.smolvlm_name_or_path,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True,
+ device_map={"": device.index if device.index is not None else 0},
+ )
+ vlm.eval()
+ for param in vlm.parameters():
+ param.requires_grad = False
+
+ ctx_hidden_size = vlm.config.text_config.hidden_size
+ hypernet_args = HypernetArguments(
+ latent_size=args.latent_size,
+ dropout_rate=args.dropout_rate,
+ per_rank_gen=True,
+ )
+ aggregator_args = AggregatorArguments(
+ aggregator_type="perceiver",
+ n_latent_queries=args.n_latent_queries,
+ num_blocks=args.num_blocks,
+ num_self_attn_per_block=args.num_self_attn_per_block,
+ )
+ ctx_encoder_args = CtxEncoderArguments(
+ ctx_encoder_model_name_or_path="precomputed",
+ ctx_encoder_type=(
+ "per_layer_activations"
+ if args.ctx_feature_mode == "l2l_fused_text"
+ else "early_exit"
+ ),
+ )
+ ctx_config = PretrainedConfig(hidden_size=ctx_hidden_size)
+ hypernet_config = get_hypernet_config(
+ base_model,
+ ctx_config,
+ hypernet_args,
+ aggregator_args,
+ ctx_encoder_args,
+ )
+ model = ModulatedPretrainedModel(
+ base_model,
+ hypernet_config,
+ ctx_encoder_args,
+ use_sequence_packing=False,
+ )
+ model.to(device)
+ model.train()
+ return model, base_tokenizer, processor, vlm
+
+
+def prepare_smolvlm_inputs(
+ processor,
+ video_messages,
+ device,
+ video_fps: float | None,
+ max_frames: int,
+ video_size_longest_edge: int | None = None,
+ video_load_backend: str = "auto",
+):
+ def _message_requests_visuals(messages) -> bool:
+ for conversation in messages:
+ for message in conversation:
+ for content in message.get("content", []):
+ if content.get("type") in {"image", "video"}:
+ return True
+ return False
+
+ def _has_multimodal_payload(vlm_inputs) -> bool:
+ for key, value in vlm_inputs.items():
+ if not isinstance(value, torch.Tensor):
+ continue
+ lowered = key.lower()
+ if "pixel" in lowered or "image" in lowered or "video" in lowered:
+ return True
+ return False
+
+ chat_template_kwargs = dict(padding=True)
+ processor_name = type(processor).__name__.lower()
+ # SmolVLM2 and Idefics3 use different multimodal kwargs for video sampling.
+ if "idefics3" in processor_name:
+ chat_template_kwargs["num_frames"] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs["video_fps"] = video_fps
+ else:
+ chat_template_kwargs["max_frames"] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs["target_fps"] = video_fps
+ if video_load_backend == "auto":
+ if is_decord_available():
+ video_load_backend = "decord"
+ elif is_av_available():
+ video_load_backend = "pyav"
+ elif is_cv2_available():
+ video_load_backend = "opencv"
+ else:
+ video_load_backend = "torchvision"
+ chat_template_kwargs["video_load_backend"] = video_load_backend
+ if video_size_longest_edge is not None:
+ video_size = {"longest_edge": video_size_longest_edge}
+ processor.video_size = video_size
+ processor.image_processor.size = video_size
+
+ vlm_inputs = processor.apply_chat_template(
+ video_messages,
+ add_generation_prompt=False,
+ tokenize=True,
+ return_dict=True,
+ return_tensors="pt",
+ **chat_template_kwargs,
+ )
+ if _message_requests_visuals(video_messages) and not _has_multimodal_payload(vlm_inputs):
+ raise ValueError(
+ "Processor produced no multimodal tensors for a visual prompt. "
+ "This model/processor path is not consuming the provided image/video input."
+ )
+ moved = {}
+ for key, value in vlm_inputs.items():
+ if isinstance(value, torch.Tensor):
+ if value.is_floating_point():
+ moved[key] = value.to(device=device, dtype=torch.bfloat16)
+ else:
+ moved[key] = value.to(device=device)
+ else:
+ moved[key] = value
+ return moved
+
+
+def prepare_smolvlm_teacher_batch(
+ processor,
+ prompt_messages,
+ full_messages,
+ device,
+ video_fps: float | None,
+ max_frames: int,
+ video_size_longest_edge: int | None = None,
+ video_load_backend: str = "auto",
+):
+ chat_template_kwargs = dict(padding=True)
+ processor_name = type(processor).__name__.lower()
+ if "idefics3" in processor_name:
+ chat_template_kwargs["num_frames"] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs["video_fps"] = video_fps
+ else:
+ chat_template_kwargs["max_frames"] = max_frames
+ if video_fps is not None:
+ chat_template_kwargs["target_fps"] = video_fps
+ if video_load_backend == "auto":
+ if is_decord_available():
+ video_load_backend = "decord"
+ elif is_av_available():
+ video_load_backend = "pyav"
+ elif is_cv2_available():
+ video_load_backend = "opencv"
+ else:
+ video_load_backend = "torchvision"
+ chat_template_kwargs["video_load_backend"] = video_load_backend
+ if video_size_longest_edge is not None:
+ video_size = {"longest_edge": video_size_longest_edge}
+ processor.video_size = video_size
+ processor.image_processor.size = video_size
+
+ prompt_inputs = processor.apply_chat_template(
+ prompt_messages,
+ add_generation_prompt=True,
+ tokenize=True,
+ return_dict=True,
+ return_tensors="pt",
+ **chat_template_kwargs,
+ )
+ full_inputs = processor.apply_chat_template(
+ full_messages,
+ add_generation_prompt=False,
+ tokenize=True,
+ return_dict=True,
+ return_tensors="pt",
+ **chat_template_kwargs,
+ )
+ labels = full_inputs["input_ids"].clone()
+ full_attention_mask = full_inputs.get("attention_mask")
+ prompt_attention_mask = prompt_inputs.get("attention_mask")
+ if full_attention_mask is None or prompt_attention_mask is None:
+ raise ValueError("Teacher SmolVLM inputs require attention masks.")
+ labels[full_attention_mask == 0] = -100
+ prompt_lens = prompt_attention_mask.sum(dim=1).tolist()
+ for row_idx, prompt_len in enumerate(prompt_lens):
+ labels[row_idx, :prompt_len] = -100
+
+ moved = {}
+ for key, value in full_inputs.items():
+ if isinstance(value, torch.Tensor):
+ if value.is_floating_point():
+ moved[key] = value.to(device=device, dtype=torch.bfloat16)
+ else:
+ moved[key] = value.to(device=device)
+ else:
+ moved[key] = value
+ moved["labels"] = labels.to(device=device)
+ return moved
+
+
+@torch.no_grad()
+def extract_video_features(vlm, vlm_inputs, frame_pooling: str):
+ outputs = vlm.model(
+ input_ids=vlm_inputs["input_ids"],
+ attention_mask=vlm_inputs.get("attention_mask"),
+ pixel_values=vlm_inputs.get("pixel_values"),
+ pixel_attention_mask=vlm_inputs.get("pixel_attention_mask"),
+ output_hidden_states=False,
+ return_dict=True,
+ use_cache=False,
+ )
+ ctx_features = outputs.image_hidden_states
+ if ctx_features is None:
+ raise ValueError("SmolVLM did not return image_hidden_states for the provided videos.")
+ batch_size = vlm_inputs["input_ids"].shape[0]
+ if ctx_features.ndim != 3:
+ raise ValueError(
+ f"Expected SmolVLM image_hidden_states to be rank-3, got shape {tuple(ctx_features.shape)}."
+ )
+ pixel_attention_mask = vlm_inputs.get("pixel_attention_mask")
+ if pixel_attention_mask is None:
+ raise ValueError("Expected pixel_attention_mask in SmolVLM inputs for video batching.")
+
+ # `image_hidden_states` is flattened across all valid visual units in the batch.
+ # Recover per-example counts from the pixel attention mask, then concatenate each
+ # example's valid visual units into one long token sequence and pad across examples.
+ valid_visual_units = pixel_attention_mask.view(batch_size, pixel_attention_mask.shape[1], -1)
+ valid_visual_units = valid_visual_units.any(dim=-1).sum(dim=-1).tolist()
+ if sum(valid_visual_units) != ctx_features.shape[0]:
+ raise ValueError(
+ "SmolVLM visual feature count mismatch: "
+ f"sum(valid_visual_units)={sum(valid_visual_units)} "
+ f"vs image_hidden_states.shape[0]={ctx_features.shape[0]}."
+ )
+
+ split_features = []
+ offset = 0
+ for n_units in valid_visual_units:
+ sample_features = ctx_features[offset : offset + n_units]
+ if frame_pooling == "mean":
+ pooled_features = sample_features.mean(dim=1)
+ elif frame_pooling == "flatten":
+ pooled_features = sample_features.reshape(
+ n_units * sample_features.shape[1],
+ ctx_features.shape[-1],
+ )
+ else:
+ raise ValueError(f"Unsupported frame_pooling={frame_pooling!r}")
+ split_features.append(pooled_features)
+ offset += n_units
+
+ ctx_features = pad_sequence(split_features, batch_first=True, padding_value=0.0)
+ ctx_attn_mask = torch.zeros(
+ ctx_features.shape[:2], dtype=torch.long, device=ctx_features.device
+ )
+ ctx_position_ids = torch.zeros_like(ctx_attn_mask)
+ for sample_idx, sample_features in enumerate(split_features):
+ sample_len = sample_features.shape[0]
+ ctx_attn_mask[sample_idx, :sample_len] = 1
+ ctx_position_ids[sample_idx, :sample_len] = torch.arange(
+ sample_len, dtype=torch.long, device=ctx_features.device
+ )
+ return ctx_features, ctx_attn_mask, ctx_position_ids
+
+
+@torch.no_grad()
+def extract_l2l_fused_text_features(
+ vlm,
+ vlm_inputs,
+ num_target_layers: int,
+):
+ outputs = vlm.model(
+ input_ids=vlm_inputs.get("input_ids"),
+ attention_mask=vlm_inputs.get("attention_mask"),
+ pixel_values=vlm_inputs.get("pixel_values"),
+ pixel_attention_mask=vlm_inputs.get("pixel_attention_mask"),
+ output_hidden_states=True,
+ output_attentions=False,
+ use_cache=False,
+ return_dict=True,
+ )
+ hidden_states = outputs.hidden_states
+ if hidden_states is None:
+ raise ValueError("SmolVLM text model did not return hidden_states.")
+
+ # Match the existing Doc2LoRA l2l bias as closely as possible by using
+ # embeddings through the input of the last attention block when available.
+ if len(hidden_states) == num_target_layers + 1:
+ selected_hidden_states = hidden_states[:-1]
+ elif len(hidden_states) >= num_target_layers:
+ selected_hidden_states = hidden_states[-num_target_layers:]
+ else:
+ raise ValueError(
+ f"Need at least {num_target_layers} hidden-state tensors for layer-to-layer mode, "
+ f"but only received {len(hidden_states)}."
+ )
+
+ ctx_features = torch.stack(selected_hidden_states, dim=1)
+ ctx_attn_mask = vlm_inputs.get("attention_mask")
+ if ctx_attn_mask is None:
+ raise ValueError("Layer-to-layer mode requires attention_mask from SmolVLM inputs.")
+ return ctx_features, ctx_attn_mask, None
+
+
+def extract_context_features(
+ vlm,
+ vlm_inputs,
+ *,
+ ctx_feature_mode: str,
+ frame_pooling: str,
+ num_target_layers: int,
+):
+ if ctx_feature_mode == "visual_pooled":
+ return extract_video_features(vlm, vlm_inputs, frame_pooling=frame_pooling)
+ if ctx_feature_mode == "l2l_fused_text":
+ return extract_l2l_fused_text_features(
+ vlm,
+ vlm_inputs,
+ num_target_layers=num_target_layers,
+ )
+ raise ValueError(f"Unsupported ctx_feature_mode={ctx_feature_mode!r}")
+
+
+def repeat_video_features_per_question(
+ ctx_features: torch.Tensor,
+ ctx_attn_mask: torch.Tensor,
+ ctx_position_ids: torch.Tensor | None,
+ question_counts: torch.Tensor,
+):
+ repeat_counts = question_counts.to(device=ctx_features.device, dtype=torch.long)
+ repeated_position_ids = None
+ if ctx_position_ids is not None:
+ repeated_position_ids = ctx_position_ids.repeat_interleave(repeat_counts, dim=0)
+ return (
+ ctx_features.repeat_interleave(repeat_counts, dim=0),
+ ctx_attn_mask.repeat_interleave(repeat_counts, dim=0),
+ repeated_position_ids,
+ )
+
+
+def compute_video_centric_ce_loss(
+ logits: torch.Tensor,
+ labels: torch.Tensor,
+ question_counts: torch.Tensor,
+):
+ shift_labels = nn.functional.pad(labels, (0, 1), value=-100)[..., 1:].contiguous()
+ vocab_size = logits.shape[-1]
+ token_losses = nn.functional.cross_entropy(
+ logits.float().view(-1, vocab_size),
+ shift_labels.view(-1),
+ reduction="none",
+ ).view(labels.shape[0], -1)
+ token_mask = shift_labels.ne(-100)
+ per_qa_loss = (token_losses * token_mask).sum(dim=1) / token_mask.sum(dim=1).clamp_min(1)
+ per_video_loss = torch.stack(
+ [
+ chunk.mean()
+ for chunk in torch.split(
+ per_qa_loss,
+ question_counts.detach().to("cpu", dtype=torch.long).tolist(),
+ )
+ ]
+ )
+ return per_video_loss.mean(), per_qa_loss, per_video_loss
+
+
+def compute_teacher_kl_loss(
+ student_logits: torch.Tensor,
+ student_labels: torch.Tensor,
+ teacher_logits: torch.Tensor,
+ teacher_labels: torch.Tensor,
+ question_counts: torch.Tensor,
+ *,
+ temperature: float,
+):
+ shared_vocab_size = min(student_logits.shape[-1], teacher_logits.shape[-1])
+ student_shift_labels = nn.functional.pad(student_labels, (0, 1), value=-100)[
+ ..., 1:
+ ].contiguous()
+ teacher_shift_labels = nn.functional.pad(teacher_labels, (0, 1), value=-100)[
+ ..., 1:
+ ].contiguous()
+ student_shift_logits = student_logits[..., :shared_vocab_size]
+ teacher_shift_logits = teacher_logits[..., :shared_vocab_size]
+
+ per_qa_kl = []
+ for qa_idx in range(student_shift_logits.shape[0]):
+ student_mask = student_shift_labels[qa_idx].ne(-100)
+ teacher_mask = teacher_shift_labels[qa_idx].ne(-100)
+ student_qa_logits = student_shift_logits[qa_idx][student_mask]
+ teacher_qa_logits = teacher_shift_logits[qa_idx][teacher_mask]
+ if student_qa_logits.shape[0] == 0 or teacher_qa_logits.shape[0] == 0:
+ continue
+ if student_qa_logits.shape[0] != teacher_qa_logits.shape[0]:
+ min_len = min(student_qa_logits.shape[0], teacher_qa_logits.shape[0])
+ student_qa_logits = student_qa_logits[:min_len]
+ teacher_qa_logits = teacher_qa_logits[:min_len]
+ teacher_logp = nn.functional.log_softmax(
+ teacher_qa_logits.float() / temperature,
+ dim=-1,
+ )
+ student_logp = nn.functional.log_softmax(
+ student_qa_logits.float() / temperature,
+ dim=-1,
+ )
+ teacher_p = teacher_logp.exp()
+ kl_tokens = (teacher_p * (teacher_logp - student_logp)).sum(dim=-1)
+ per_qa_kl.append(kl_tokens.mean())
+
+ if not per_qa_kl:
+ zero = student_logits.new_zeros(())
+ return zero, zero.new_zeros((0,))
+
+ per_qa_kl = torch.stack(per_qa_kl)
+ per_video_kl = torch.stack(
+ [
+ chunk.mean()
+ for chunk in torch.split(
+ per_qa_kl,
+ question_counts.detach().to("cpu", dtype=torch.long).tolist(),
+ )
+ ]
+ )
+ return per_video_kl.mean() * (temperature**2), per_video_kl
+
+
+@torch.no_grad()
+def evaluate(
+ model,
+ processor,
+ vlm,
+ data_loader: DataLoader,
+ device: torch.device,
+ frame_pooling: str,
+ ctx_feature_mode: str,
+ num_target_layers: int,
+) -> dict[str, float]:
+ model.eval()
+ if device.type == "cuda":
+ torch.cuda.reset_peak_memory_stats(device)
+ total_loss = 0.0
+ total_videos = 0
+ total_questions = 0
+ total_batches = 0
+ total_answer_tokens = 0.0
+ total_context_tokens = 0.0
+ for batch in data_loader:
+ text_batch = move_text_batch_to_device(batch, device)
+ vlm_inputs = prepare_smolvlm_inputs(
+ processor,
+ batch["video_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=batch["video_size_longest_edge"],
+ video_load_backend=args.video_load_backend,
+ )
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_context_features(
+ vlm,
+ vlm_inputs,
+ ctx_feature_mode=ctx_feature_mode,
+ frame_pooling=frame_pooling,
+ num_target_layers=num_target_layers,
+ )
+ ctx_features, ctx_attn_mask, ctx_position_ids = repeat_video_features_per_question(
+ ctx_features,
+ ctx_attn_mask,
+ ctx_position_ids,
+ text_batch["question_counts"],
+ )
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(ctx_features.shape[0], dtype=torch.int32, device=device),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ raw_loss, _, _ = compute_video_centric_ce_loss(
+ outputs.logits,
+ text_batch["labels"],
+ text_batch["question_counts"],
+ )
+ num_videos = text_batch["question_counts"].numel()
+ num_questions = int(text_batch["question_counts"].sum().item())
+ total_loss += raw_loss.item() * num_videos
+ total_videos += num_videos
+ total_questions += num_questions
+ total_batches += 1
+ total_answer_tokens += float((text_batch["labels"] != -100).sum().item())
+ total_context_tokens += float(ctx_attn_mask.sum().item())
+ model.train()
+ if total_videos == 0:
+ return {
+ "loss": float("nan"),
+ "ppl": float("nan"),
+ "videos": 0.0,
+ "questions": 0.0,
+ "batches": 0.0,
+ }
+ mean_loss = total_loss / total_videos
+ metrics = {
+ "loss": mean_loss,
+ "ppl": math.exp(min(mean_loss, 20)),
+ "videos": float(total_videos),
+ "questions": float(total_questions),
+ "batches": float(total_batches),
+ "questions_per_video_mean": total_questions / max(total_videos, 1),
+ "answer_tokens_per_question_mean": total_answer_tokens / max(total_questions, 1),
+ "context_tokens_per_video_mean": total_context_tokens / max(total_videos, 1),
+ "context_layers_per_video_mean": float(num_target_layers)
+ if ctx_feature_mode == "l2l_fused_text"
+ else 1.0,
+ }
+ memory_metrics = get_peak_cuda_memory_metrics(device)
+ if memory_metrics:
+ metrics["memory_peak_allocated_gb"] = memory_metrics["memory/peak_allocated_gb"]
+ metrics["memory_peak_reserved_gb"] = memory_metrics["memory/peak_reserved_gb"]
+ return metrics
+
+
+def save_checkpoint(model, output_dir: Path, step: int) -> Path:
+ ckpt_path = output_dir / "checkpoints" / f"step-{step}.pt"
+ torch.save(model.state_dict(), ckpt_path)
+ return ckpt_path
+
+
+def resolve_wandb_mode(requested_mode: str) -> str:
+ if requested_mode != "auto":
+ return requested_mode
+ if os.environ.get("WANDB_DISABLED", "").lower() in {"true", "1", "yes"}:
+ return "disabled"
+ if os.environ.get("WANDB_API_KEY"):
+ return "online"
+ return "offline"
+
+
+def dataset_tag_from_manifest(manifest_path: str | None) -> str:
+ if not manifest_path:
+ return "unknown-dataset"
+ stem = Path(manifest_path).stem
+ for suffix in ("-train", "-val", "_train", "_val"):
+ if stem.endswith(suffix):
+ return stem[: -len(suffix)]
+ return stem
+
+
+def get_peak_cuda_memory_metrics(device: torch.device) -> dict[str, float]:
+ if device.type != "cuda":
+ return {}
+ return {
+ "memory/peak_allocated_gb": torch.cuda.max_memory_allocated(device) / (1024**3),
+ "memory/peak_reserved_gb": torch.cuda.max_memory_reserved(device) / (1024**3),
+ }
+
+
+def main():
+ args = parse_args()
+ output_dir = Path(args.output_dir)
+ ensure_layout(output_dir)
+ set_seed(args.seed)
+
+ if not os.path.exists(args.train_manifest):
+ raise FileNotFoundError(f"Train manifest not found: {args.train_manifest}")
+
+ if torch.cuda.is_available():
+ torch.cuda.set_device(0)
+ device = torch.device("cuda:0")
+ print(
+ "[gpu] "
+ f"CUDA_VISIBLE_DEVICES={os.environ.get('CUDA_VISIBLE_DEVICES', '')} "
+ f"current_device={torch.cuda.current_device()} "
+ f"device_name={torch.cuda.get_device_name(torch.cuda.current_device())}"
+ )
+ else:
+ device = torch.device("cpu")
+ train_rows = load_jsonl(args.train_manifest, args.max_train_samples)
+ val_rows = (
+ load_jsonl(args.val_manifest, args.max_val_samples) if args.val_manifest else []
+ )
+
+ model, base_tokenizer, processor, vlm = build_video2lora_model(args, device)
+ collator = SmolVLMOnlineCollator(
+ base_tokenizer=base_tokenizer,
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+
+ train_loader = DataLoader(
+ VideoQADataset(
+ train_rows,
+ questions_per_video=args.questions_per_video,
+ sample_questions_randomly=True,
+ ),
+ batch_size=args.batch_size,
+ shuffle=True,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ prefetch_factor=2 if args.num_workers > 0 else None,
+ )
+ val_loader = None
+ if val_rows:
+ val_loader = DataLoader(
+ VideoQADataset(
+ val_rows,
+ questions_per_video=args.questions_per_video,
+ sample_questions_randomly=False,
+ ),
+ batch_size=args.eval_batch_size,
+ shuffle=False,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ prefetch_factor=2 if args.num_workers > 0 else None,
+ )
+
+ trainable_params = [p for p in model.parameters() if p.requires_grad]
+ optimizer = torch.optim.AdamW(
+ trainable_params,
+ lr=args.learning_rate,
+ weight_decay=args.weight_decay,
+ )
+ total_steps = (
+ args.max_steps
+ if args.max_steps is not None
+ else math.ceil(len(train_loader) * args.epochs / args.grad_accum_steps)
+ )
+ scheduler = get_cosine_schedule_with_warmup(
+ optimizer,
+ num_warmup_steps=args.warmup_steps,
+ num_training_steps=max(total_steps, 1),
+ )
+
+ run_name = args.wandb_run_name or output_dir.name
+ wandb_mode = resolve_wandb_mode(args.wandb_mode)
+ if wandb_mode == "disabled":
+ os.environ["WANDB_DISABLED"] = "true"
+ print(f"Using wandb mode: {wandb_mode}")
+ dataset_tag = dataset_tag_from_manifest(args.train_manifest)
+ wandb_tags = [
+ dataset_tag,
+ f"base:{Path(args.base_lm_name_or_path).name}",
+ f"vlm:{Path(args.smolvlm_name_or_path).name}",
+ f"rank:{args.lora_r}",
+ f"targets:{'-'.join(args.target_modules)}",
+ f"qpv:{args.questions_per_video}",
+ f"ctx:{args.ctx_feature_mode}",
+ f"pool:{args.frame_pooling}",
+ ]
+ wandb.init(
+ project=args.wandb_project,
+ name=run_name,
+ group=args.wandb_group,
+ notes=args.wandb_notes,
+ tags=wandb_tags,
+ config=asdict(args),
+ mode=wandb_mode,
+ )
+ with open(output_dir / "train_args.json", "w") as f:
+ json.dump(asdict(args), f, indent=2)
+ dataset_metrics = {
+ "data/train_questions": float(len(train_rows)),
+ "data/train_videos": float(len(train_loader.dataset)),
+ "data/val_questions": float(len(val_rows)),
+ "data/val_videos": float(len(val_loader.dataset) if val_loader is not None else 0),
+ "data/questions_per_video_cap": float(args.questions_per_video),
+ "data/nominal_videos_per_optimizer_step": float(args.batch_size * args.grad_accum_steps),
+ "data/nominal_questions_per_optimizer_step": float(
+ args.batch_size * args.grad_accum_steps * args.questions_per_video
+ ),
+ "data/eval_videos_per_batch": float(args.eval_batch_size or 0),
+ }
+ wandb.log(dataset_metrics, step=0)
+ print(
+ "[data] "
+ f"train_videos={int(dataset_metrics['data/train_videos'])} "
+ f"train_questions={int(dataset_metrics['data/train_questions'])} "
+ f"val_videos={int(dataset_metrics['data/val_videos'])} "
+ f"val_questions={int(dataset_metrics['data/val_questions'])} "
+ f"nominal_videos_per_optimizer_step={int(dataset_metrics['data/nominal_videos_per_optimizer_step'])} "
+ f"nominal_questions_per_optimizer_step={int(dataset_metrics['data/nominal_questions_per_optimizer_step'])}"
+ )
+
+ def log_eval_metrics(metrics: dict[str, float], *, step: int, prefix: str) -> None:
+ payload = {
+ f"{prefix}/loss": metrics["loss"],
+ f"{prefix}/ppl": metrics["ppl"],
+ f"{prefix}/videos": metrics["videos"],
+ f"{prefix}/questions": metrics["questions"],
+ f"{prefix}/batches": metrics["batches"],
+ f"{prefix}/questions_per_video_mean": metrics.get("questions_per_video_mean", 0.0),
+ f"{prefix}/answer_tokens_per_question_mean": metrics.get(
+ "answer_tokens_per_question_mean", 0.0
+ ),
+ f"{prefix}/context_tokens_per_video_mean": metrics.get(
+ "context_tokens_per_video_mean", 0.0
+ ),
+ f"{prefix}/context_layers_per_video_mean": metrics.get(
+ "context_layers_per_video_mean", 0.0
+ ),
+ f"{prefix}/memory_peak_allocated_gb": metrics.get("memory_peak_allocated_gb", 0.0),
+ f"{prefix}/memory_peak_reserved_gb": metrics.get("memory_peak_reserved_gb", 0.0),
+ }
+ wandb.log(payload, step=step)
+ print(
+ f"[{prefix}] "
+ f"step={step} loss={metrics['loss']:.4f} "
+ f"videos={int(metrics['videos'])} "
+ f"questions={int(metrics['questions'])} "
+ f"peak_reserved_gb={metrics.get('memory_peak_reserved_gb', 0.0):.2f}"
+ )
+
+ global_step = 0
+ optimizer.zero_grad(set_to_none=True)
+ stop_training = False
+ accumulated_videos = 0
+ accumulated_questions = 0
+ accumulated_microbatches = 0
+ accumulated_ce_loss = 0.0
+ accumulated_kl_loss = 0.0
+ accumulated_total_loss = 0.0
+ accumulated_per_video_loss = 0.0
+ accumulated_answer_tokens = 0.0
+ accumulated_context_tokens = 0.0
+ for epoch in range(args.epochs):
+ for step, batch in enumerate(train_loader, start=1):
+ if device.type == "cuda" and accumulated_microbatches == 0:
+ torch.cuda.reset_peak_memory_stats(device)
+ text_batch = move_text_batch_to_device(batch, device)
+ question_counts_f = text_batch["question_counts"].to(dtype=torch.float32)
+ microbatch_videos = int(question_counts_f.numel())
+ microbatch_questions = int(question_counts_f.sum().item())
+ vlm_inputs = prepare_smolvlm_inputs(
+ processor,
+ batch["video_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=batch["video_size_longest_edge"],
+ video_load_backend=args.video_load_backend,
+ )
+ with torch.no_grad():
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_context_features(
+ vlm,
+ vlm_inputs,
+ ctx_feature_mode=args.ctx_feature_mode,
+ frame_pooling=args.frame_pooling,
+ num_target_layers=model.hypernet.n_layers,
+ )
+ per_video_ctx_attn_mask = ctx_attn_mask
+ ctx_features, ctx_attn_mask, ctx_position_ids = repeat_video_features_per_question(
+ ctx_features,
+ ctx_attn_mask,
+ ctx_position_ids,
+ text_batch["question_counts"],
+ )
+
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(
+ ctx_features.shape[0],
+ dtype=torch.int32,
+ device=device,
+ ),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ raw_loss, _, per_video_loss = compute_video_centric_ce_loss(
+ outputs.logits,
+ text_batch["labels"],
+ text_batch["question_counts"],
+ )
+ kl_loss = outputs.logits.new_zeros(())
+ if args.kl_weight > 0:
+ with torch.no_grad():
+ teacher_batch = prepare_smolvlm_teacher_batch(
+ processor,
+ batch["teacher_prompt_messages"],
+ batch["teacher_full_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=batch["video_size_longest_edge"],
+ video_load_backend=args.video_load_backend,
+ )
+ teacher_outputs = vlm(
+ input_ids=teacher_batch["input_ids"],
+ attention_mask=teacher_batch.get("attention_mask"),
+ pixel_values=teacher_batch.get("pixel_values"),
+ pixel_attention_mask=teacher_batch.get("pixel_attention_mask"),
+ labels=None,
+ return_dict=True,
+ use_cache=False,
+ )
+ kl_loss, _ = compute_teacher_kl_loss(
+ outputs.logits,
+ text_batch["labels"],
+ teacher_outputs.logits,
+ teacher_batch["labels"],
+ text_batch["question_counts"],
+ temperature=args.kl_temperature,
+ )
+ total_loss = raw_loss + args.kl_weight * kl_loss
+ loss = total_loss / args.grad_accum_steps
+ loss.backward()
+
+ accumulated_videos += microbatch_videos
+ accumulated_questions += microbatch_questions
+ accumulated_microbatches += 1
+ accumulated_ce_loss += raw_loss.item()
+ accumulated_kl_loss += kl_loss.item() if args.kl_weight > 0 else 0.0
+ accumulated_total_loss += total_loss.item()
+ accumulated_per_video_loss += per_video_loss.mean().item()
+ accumulated_answer_tokens += float((text_batch["labels"] != -100).sum().item())
+ accumulated_context_tokens += float(per_video_ctx_attn_mask.sum().item())
+
+ question_counts_min = question_counts_f.min().item()
+ question_counts_max = question_counts_f.max().item()
+ question_counts_mean = question_counts_f.mean().item()
+
+ def log_train_metrics(epoch_value: float) -> None:
+ memory_metrics = get_peak_cuda_memory_metrics(device)
+ avg_den = max(accumulated_microbatches, 1)
+ metrics = {
+ "train/loss": accumulated_total_loss / avg_den,
+ "train/ce_loss": accumulated_ce_loss / avg_den,
+ "train/total_loss": accumulated_total_loss / avg_den,
+ "train/kl_loss": accumulated_kl_loss / avg_den,
+ "train/weighted_kl_loss": args.kl_weight * (accumulated_kl_loss / avg_den),
+ "train/per_video_loss": accumulated_per_video_loss / avg_den,
+ "train/videos_per_microbatch": float(microbatch_videos),
+ "train/questions_per_microbatch": float(microbatch_questions),
+ "train/questions_per_video_mean": question_counts_mean,
+ "train/questions_per_video_min": question_counts_min,
+ "train/questions_per_video_max": question_counts_max,
+ "train/answer_tokens_per_question_mean": accumulated_answer_tokens
+ / max(accumulated_questions, 1),
+ "train/context_tokens_per_video_mean": accumulated_context_tokens
+ / max(accumulated_videos, 1),
+ "train/context_layers_per_video_mean": float(model.hypernet.n_layers)
+ if args.ctx_feature_mode == "l2l_fused_text"
+ else 1.0,
+ "train/microbatches_per_optimizer_step": float(accumulated_microbatches),
+ "train/videos_per_optimizer_step": float(accumulated_videos),
+ "train/questions_per_optimizer_step": float(accumulated_questions),
+ "train/lr": scheduler.get_last_lr()[0],
+ "train/epoch": epoch_value,
+ "train/step": global_step,
+ **memory_metrics,
+ }
+ wandb.log(metrics, step=global_step)
+ if memory_metrics:
+ print(
+ "[train] "
+ f"step={global_step} total_loss={metrics['train/total_loss']:.4f} "
+ f"ce={metrics['train/ce_loss']:.4f} "
+ f"kl={metrics['train/kl_loss']:.4f} "
+ f"videos={microbatch_videos} "
+ f"questions={microbatch_questions} "
+ f"opt_videos={accumulated_videos} "
+ f"opt_questions={accumulated_questions} "
+ f"ctx_tokens_per_video={metrics['train/context_tokens_per_video_mean']:.2f} "
+ f"peak_reserved_gb={memory_metrics['memory/peak_reserved_gb']:.2f}"
+ )
+
+ if step % args.grad_accum_steps == 0:
+ torch.nn.utils.clip_grad_norm_(trainable_params, args.max_grad_norm)
+ optimizer.step()
+ scheduler.step()
+ optimizer.zero_grad(set_to_none=True)
+ global_step += 1
+
+ if global_step % args.log_every == 0:
+ log_train_metrics(epoch + (step / max(len(train_loader), 1)))
+
+ if val_loader is not None and global_step % args.eval_every == 0:
+ metrics = evaluate(
+ model,
+ processor,
+ vlm,
+ val_loader,
+ device,
+ frame_pooling=args.frame_pooling,
+ ctx_feature_mode=args.ctx_feature_mode,
+ num_target_layers=model.hypernet.n_layers,
+ )
+ log_eval_metrics(metrics, step=global_step, prefix="val")
+
+ if global_step % args.save_every == 0:
+ ckpt_path = save_checkpoint(model, output_dir, global_step)
+ wandb.log({"checkpoint/step": global_step}, step=global_step)
+ print(f"Saved checkpoint to {ckpt_path}")
+
+ accumulated_videos = 0
+ accumulated_questions = 0
+ accumulated_microbatches = 0
+ accumulated_ce_loss = 0.0
+ accumulated_kl_loss = 0.0
+ accumulated_total_loss = 0.0
+ accumulated_per_video_loss = 0.0
+ accumulated_answer_tokens = 0.0
+ accumulated_context_tokens = 0.0
+
+ if args.max_steps is not None and global_step >= args.max_steps:
+ stop_training = True
+ break
+
+ if stop_training:
+ break
+
+ if step % args.grad_accum_steps != 0:
+ torch.nn.utils.clip_grad_norm_(trainable_params, args.max_grad_norm)
+ optimizer.step()
+ scheduler.step()
+ optimizer.zero_grad(set_to_none=True)
+ global_step += 1
+
+ if global_step % args.log_every == 0:
+ log_train_metrics(epoch + 1)
+
+ if val_loader is not None and global_step % args.eval_every == 0:
+ metrics = evaluate(
+ model,
+ processor,
+ vlm,
+ val_loader,
+ device,
+ frame_pooling=args.frame_pooling,
+ ctx_feature_mode=args.ctx_feature_mode,
+ num_target_layers=model.hypernet.n_layers,
+ )
+ log_eval_metrics(metrics, step=global_step, prefix="val")
+
+ if global_step % args.save_every == 0:
+ ckpt_path = save_checkpoint(model, output_dir, global_step)
+ wandb.log({"checkpoint/step": global_step}, step=global_step)
+ print(f"Saved checkpoint to {ckpt_path}")
+
+ accumulated_videos = 0
+ accumulated_questions = 0
+ accumulated_microbatches = 0
+ accumulated_ce_loss = 0.0
+ accumulated_kl_loss = 0.0
+ accumulated_total_loss = 0.0
+ accumulated_per_video_loss = 0.0
+ accumulated_answer_tokens = 0.0
+ accumulated_context_tokens = 0.0
+
+ if args.max_steps is not None and global_step >= args.max_steps:
+ break
+
+ final_ckpt = save_checkpoint(model, output_dir, global_step or 0)
+ if val_loader is not None:
+ metrics = evaluate(
+ model,
+ processor,
+ vlm,
+ val_loader,
+ device,
+ frame_pooling=args.frame_pooling,
+ ctx_feature_mode=args.ctx_feature_mode,
+ num_target_layers=model.hypernet.n_layers,
+ )
+ log_eval_metrics(metrics, step=max(global_step, 1), prefix="val/final")
+ wandb.finish()
+ print(f"Training finished. Final checkpoint: {final_ckpt}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/video2lora/train_smolvlm_stage1.py b/scripts/video2lora/train_smolvlm_stage1.py
new file mode 100644
index 0000000..662fbf4
--- /dev/null
+++ b/scripts/video2lora/train_smolvlm_stage1.py
@@ -0,0 +1,1600 @@
+import argparse
+import json
+import math
+import os
+import random
+import time
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any
+
+import torch
+try:
+ import wandb
+except ModuleNotFoundError:
+ class _MissingWandb:
+ def init(self, *args, **kwargs):
+ if kwargs.get("mode") != "disabled":
+ raise ModuleNotFoundError(
+ "wandb is not installed. Install it or pass --wandb-mode disabled."
+ )
+
+ def log(self, *args, **kwargs):
+ return None
+
+ def finish(self, *args, **kwargs):
+ return None
+
+ wandb = _MissingWandb()
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from peft import PeftModel
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, Dataset
+from transformers import (
+ AutoModelForImageTextToText,
+ AutoProcessor,
+ PretrainedConfig,
+ get_cosine_schedule_with_warmup,
+)
+
+from ctx_to_lora.configs import (
+ AggregatorArguments,
+ CtxEncoderArguments,
+ HypernetArguments,
+)
+from ctx_to_lora.data.video_manifest import (
+ DATA_ROOT,
+ DEFAULT_INTERNALIZATION_PROMPT,
+ dump_jsonl,
+ load_video_manifest,
+ normalize_manifest_row,
+ resolve_video_path,
+)
+from ctx_to_lora.model_loading import get_lora_config
+from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel, get_hypernet_config
+from ctx_to_lora.modeling.lora_layer import apply_lora_to_layers
+from ctx_to_lora.modeling.lora_merger import combine_lora
+from scripts.video2lora.train_smolvlm_online import (
+ extract_l2l_fused_text_features,
+ prepare_smolvlm_inputs,
+ prepare_smolvlm_teacher_batch,
+)
+
+
+def debug_rank_log(accelerator: Accelerator | None, message: str) -> None:
+ if os.environ.get("VIDEO2LORA_DEBUG_STARTUP", "").lower() not in {"1", "true", "yes"}:
+ return
+ rank = "na"
+ if accelerator is not None:
+ try:
+ rank = str(accelerator.process_index)
+ except Exception: # pragma: no cover - defensive debug helper
+ rank = "unknown"
+ line = f"[debug rank={rank} pid={os.getpid()}] {message}"
+ print(line, flush=True)
+ try:
+ with open(f"/tmp/video2lora_startup_rank_{rank}_pid_{os.getpid()}.log", "a") as f:
+ f.write(line + "\n")
+ except OSError:
+ pass
+
+
+@dataclass
+class TrainArgs:
+ smolvlm_name_or_path: str
+ train_manifest: str
+ val_manifest: str
+ val_core_manifest: str | None
+ val_gen_manifest: str | None
+ output_dir: str
+ per_device_batch_size: int
+ eval_batch_size: int
+ gradient_accumulation_steps: int
+ max_steps: int
+ learning_rate: float
+ weight_decay: float
+ warmup_ratio: float
+ max_grad_norm: float
+ seed: int
+ log_every: int
+ eval_every: int
+ save_every: int
+ max_train_samples: int | None
+ max_val_samples: int | None
+ num_workers: int
+ lora_r: int
+ lora_dropout: float
+ target_modules: list[str]
+ latent_size: int
+ dropout_rate: float
+ n_latent_queries: int
+ num_blocks: int
+ num_self_attn_per_block: int
+ video_fps: float | None
+ max_frames: int
+ video_size_longest_edge: int | None
+ video_load_backend: str
+ internalization_prompt: str
+ kl_weight: float
+ kl_temperature: float
+ generation_max_new_tokens: int
+ legacy_sanity_manifests: list[str]
+ legacy_sanity_max_samples_per_manifest: int
+ wandb_project: str
+ wandb_mode: str
+ wandb_group: str | None
+ wandb_run_name: str | None
+ wandb_notes: str | None
+ resume_checkpoint: str | None
+ resume_trainer_state: str | None
+ resume_global_step: int | None
+ resume_ignore_scheduler_state: bool
+
+
+def parse_args() -> TrainArgs:
+ parser = argparse.ArgumentParser(
+ description="Stage 1 SmolVLM caption pretraining with generated LoRA."
+ )
+ parser.add_argument(
+ "--smolvlm-name-or-path",
+ default="HuggingFaceTB/SmolVLM2-2.2B-Instruct",
+ )
+ parser.add_argument(
+ "--train-manifest",
+ default=str(DATA_ROOT / "processed" / "finevideo" / "train.teacher_visual_smolvlm.jsonl"),
+ )
+ parser.add_argument(
+ "--val-manifest",
+ default=str(DATA_ROOT / "processed" / "finevideo" / "val.teacher_visual_smolvlm.jsonl"),
+ )
+ parser.add_argument(
+ "--val-core-manifest",
+ default="",
+ )
+ parser.add_argument(
+ "--val-gen-manifest",
+ default="",
+ )
+ parser.add_argument("--output-dir", default="")
+ parser.add_argument("--per-device-batch-size", type=int, default=2)
+ parser.add_argument("--eval-batch-size", type=int, default=2)
+ parser.add_argument("--gradient-accumulation-steps", type=int, default=64)
+ parser.add_argument("--max-steps", type=int, default=60000)
+ parser.add_argument("--learning-rate", type=float, default=1e-4)
+ parser.add_argument("--weight-decay", type=float, default=0.01)
+ parser.add_argument("--warmup-ratio", type=float, default=0.03)
+ parser.add_argument("--max-grad-norm", type=float, default=1.0)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--log-every", type=int, default=10)
+ parser.add_argument("--eval-every", type=int, default=1000)
+ parser.add_argument("--save-every", type=int, default=1000)
+ parser.add_argument("--max-train-samples", type=int, default=None)
+ parser.add_argument("--max-val-samples", type=int, default=None)
+ parser.add_argument("--num-workers", type=int, default=8)
+ parser.add_argument("--lora-r", type=int, default=16)
+ parser.add_argument("--lora-dropout", type=float, default=0.0)
+ parser.add_argument("--target-modules", default="down_proj")
+ parser.add_argument("--latent-size", type=int, default=512)
+ parser.add_argument("--dropout-rate", type=float, default=0.0)
+ parser.add_argument("--n-latent-queries", type=int, default=8)
+ parser.add_argument("--num-blocks", type=int, default=9)
+ parser.add_argument("--num-self-attn-per-block", type=int, default=0)
+ parser.add_argument("--video-fps", type=float, default=None)
+ parser.add_argument("--max-frames", type=int, default=16)
+ parser.add_argument(
+ "--video-size-longest-edge",
+ type=int,
+ default=None,
+ help="Optional SmolVLM processor resize longest edge for video frames.",
+ )
+ parser.add_argument(
+ "--video-load-backend",
+ default="auto",
+ choices=("auto", "decord", "pyav", "opencv", "torchvision"),
+ help="Video decode backend for processor.apply_chat_template.",
+ )
+ parser.add_argument(
+ "--internalization-prompt",
+ default=DEFAULT_INTERNALIZATION_PROMPT,
+ )
+ parser.add_argument("--kl-weight", type=float, default=0.05)
+ parser.add_argument("--kl-temperature", type=float, default=1.0)
+ parser.add_argument("--generation-max-new-tokens", type=int, default=48)
+ parser.add_argument(
+ "--legacy-sanity-manifest",
+ action="append",
+ default=[],
+ )
+ parser.add_argument(
+ "--legacy-sanity-max-samples-per-manifest",
+ type=int,
+ default=16,
+ )
+ parser.add_argument("--wandb-project", default="video2lora-stage1")
+ parser.add_argument(
+ "--wandb-mode",
+ default="auto",
+ choices=("auto", "online", "offline", "disabled"),
+ )
+ parser.add_argument("--wandb-group", default="finevideo-stage1")
+ parser.add_argument("--wandb-run-name", default=None)
+ parser.add_argument("--wandb-notes", default=None)
+ parser.add_argument("--resume-checkpoint", default="")
+ parser.add_argument("--resume-trainer-state", default="")
+ parser.add_argument("--resume-global-step", type=int, default=None)
+ parser.add_argument(
+ "--resume-ignore-scheduler-state",
+ action="store_true",
+ help="Resume optimizer/global_step/RNG but rebuild the LR scheduler from step count instead of loading its saved state.",
+ )
+ parsed = parser.parse_args()
+
+ output_dir = parsed.output_dir
+ if not output_dir:
+ timestamp = time.strftime("%Y%m%d-%H%M%S", time.gmtime())
+ output_dir = str(DATA_ROOT / "runs" / f"{timestamp}-smolvlm-stage1")
+
+ def maybe_existing(path: str) -> str | None:
+ if path and os.path.exists(path):
+ return path
+ return None
+
+ return TrainArgs(
+ smolvlm_name_or_path=parsed.smolvlm_name_or_path,
+ train_manifest=parsed.train_manifest,
+ val_manifest=parsed.val_manifest,
+ val_core_manifest=maybe_existing(parsed.val_core_manifest),
+ val_gen_manifest=maybe_existing(parsed.val_gen_manifest),
+ output_dir=output_dir,
+ per_device_batch_size=parsed.per_device_batch_size,
+ eval_batch_size=parsed.eval_batch_size,
+ gradient_accumulation_steps=parsed.gradient_accumulation_steps,
+ max_steps=parsed.max_steps,
+ learning_rate=parsed.learning_rate,
+ weight_decay=parsed.weight_decay,
+ warmup_ratio=parsed.warmup_ratio,
+ max_grad_norm=parsed.max_grad_norm,
+ seed=parsed.seed,
+ log_every=parsed.log_every,
+ eval_every=parsed.eval_every,
+ save_every=parsed.save_every,
+ max_train_samples=parsed.max_train_samples,
+ max_val_samples=parsed.max_val_samples,
+ num_workers=parsed.num_workers,
+ lora_r=parsed.lora_r,
+ lora_dropout=parsed.lora_dropout,
+ target_modules=[x.strip() for x in parsed.target_modules.split(",") if x.strip()],
+ latent_size=parsed.latent_size,
+ dropout_rate=parsed.dropout_rate,
+ n_latent_queries=parsed.n_latent_queries,
+ num_blocks=parsed.num_blocks,
+ num_self_attn_per_block=parsed.num_self_attn_per_block,
+ video_fps=parsed.video_fps,
+ max_frames=parsed.max_frames,
+ video_size_longest_edge=parsed.video_size_longest_edge,
+ video_load_backend=parsed.video_load_backend,
+ internalization_prompt=parsed.internalization_prompt,
+ kl_weight=parsed.kl_weight,
+ kl_temperature=parsed.kl_temperature,
+ generation_max_new_tokens=parsed.generation_max_new_tokens,
+ legacy_sanity_manifests=parsed.legacy_sanity_manifest,
+ legacy_sanity_max_samples_per_manifest=parsed.legacy_sanity_max_samples_per_manifest,
+ wandb_project=parsed.wandb_project,
+ wandb_mode=parsed.wandb_mode,
+ wandb_group=parsed.wandb_group,
+ wandb_run_name=parsed.wandb_run_name,
+ wandb_notes=parsed.wandb_notes,
+ resume_checkpoint=maybe_existing(parsed.resume_checkpoint),
+ resume_trainer_state=maybe_existing(parsed.resume_trainer_state),
+ resume_global_step=parsed.resume_global_step,
+ resume_ignore_scheduler_state=parsed.resume_ignore_scheduler_state,
+ )
+
+
+def ensure_layout(output_dir: Path) -> None:
+ for path in (
+ DATA_ROOT,
+ DATA_ROOT / "raw",
+ DATA_ROOT / "processed",
+ DATA_ROOT / "runs",
+ output_dir,
+ output_dir / "checkpoints",
+ output_dir / "generations",
+ ):
+ path.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_wandb_mode(requested_mode: str) -> str:
+ if requested_mode != "auto":
+ return requested_mode
+ if os.environ.get("WANDB_DISABLED", "").lower() in {"true", "1", "yes"}:
+ return "disabled"
+ if os.environ.get("WANDB_API_KEY"):
+ return "online"
+ return "offline"
+
+
+def build_labels(tokenizer, prompt: str, target_text: str):
+ prompt_messages = [
+ {"role": "user", "content": [{"type": "text", "text": prompt}]}
+ ]
+ full_messages = [
+ {"role": "user", "content": [{"type": "text", "text": prompt}]},
+ {"role": "assistant", "content": [{"type": "text", "text": target_text}]},
+ ]
+ prompt_ids = tokenizer.apply_chat_template(
+ prompt_messages,
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors=None,
+ )
+ full_ids = tokenizer.apply_chat_template(
+ full_messages,
+ tokenize=True,
+ add_generation_prompt=False,
+ return_tensors=None,
+ )
+ input_ids = torch.tensor(full_ids, dtype=torch.long)
+ attention_mask = torch.ones_like(input_ids)
+ labels = input_ids.clone()
+ labels[: len(prompt_ids)] = -100
+ return input_ids, attention_mask, labels
+
+
+class VideoCaptionDataset(Dataset):
+ def __init__(self, rows: list[dict[str, Any]], *, internalization_prompt: str):
+ self.rows = rows
+ self.internalization_prompt = internalization_prompt
+
+ def __len__(self) -> int:
+ return len(self.rows)
+
+ def __getitem__(self, idx: int) -> dict[str, Any]:
+ row = self.rows[idx]
+ return {
+ "id": row["id"],
+ "video_path": resolve_video_path(row["video_path"]),
+ "prompt": row["prompt"],
+ "target_text": row["target_text"],
+ "dataset": row["dataset"],
+ "task_type": row["task_type"],
+ "split": row.get("split"),
+ "metadata": row.get("metadata", {}),
+ "internalization_prompt": self.internalization_prompt,
+ }
+
+
+class Stage1Collator:
+ def __init__(self, tokenizer, video_fps: float | None, max_frames: int):
+ self.tokenizer = tokenizer
+ self.video_fps = video_fps
+ self.max_frames = max_frames
+
+ def __call__(self, batch: list[dict[str, Any]]) -> dict[str, Any]:
+ input_ids, attention_masks, labels = [], [], []
+ prompt_only_ids, prompt_only_masks = [], []
+ video_messages = []
+ teacher_prompt_messages = []
+ teacher_full_messages = []
+
+ for example in batch:
+ inp_ids, attn_mask, lbl = build_labels(
+ self.tokenizer,
+ prompt=example["prompt"],
+ target_text=example["target_text"],
+ )
+ prompt_ids = self.tokenizer.apply_chat_template(
+ [
+ {
+ "role": "user",
+ "content": [{"type": "text", "text": example["prompt"]}],
+ }
+ ],
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors=None,
+ )
+ prompt_only_ids.append(torch.tensor(prompt_ids, dtype=torch.long))
+ prompt_only_masks.append(torch.ones(len(prompt_ids), dtype=torch.long))
+ input_ids.append(inp_ids)
+ attention_masks.append(attn_mask)
+ labels.append(lbl)
+ video_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {
+ "type": "text",
+ "text": example["internalization_prompt"],
+ },
+ ],
+ }
+ ]
+ )
+ teacher_prompt_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {"type": "text", "text": example["prompt"]},
+ ],
+ }
+ ]
+ )
+ teacher_full_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {"type": "text", "text": example["prompt"]},
+ ],
+ },
+ {
+ "role": "assistant",
+ "content": [{"type": "text", "text": example["target_text"]}],
+ },
+ ]
+ )
+
+ batch_input_ids = pad_sequence(
+ input_ids,
+ batch_first=True,
+ padding_value=self.tokenizer.pad_token_id,
+ )
+ batch_attention_mask = pad_sequence(
+ attention_masks,
+ batch_first=True,
+ padding_value=0,
+ )
+ batch_labels = pad_sequence(
+ labels,
+ batch_first=True,
+ padding_value=-100,
+ )
+ batch_prompt_ids = pad_sequence(
+ prompt_only_ids,
+ batch_first=True,
+ padding_value=self.tokenizer.pad_token_id,
+ )
+ batch_prompt_masks = pad_sequence(
+ prompt_only_masks,
+ batch_first=True,
+ padding_value=0,
+ )
+
+ return {
+ "ids": [example["id"] for example in batch],
+ "datasets": [example["dataset"] for example in batch],
+ "task_types": [example["task_type"] for example in batch],
+ "prompts": [example["prompt"] for example in batch],
+ "targets": [example["target_text"] for example in batch],
+ "input_ids": batch_input_ids,
+ "attention_mask": batch_attention_mask,
+ "labels": batch_labels,
+ "prompt_only_input_ids": batch_prompt_ids,
+ "prompt_only_attention_mask": batch_prompt_masks,
+ "video_messages": video_messages,
+ "teacher_prompt_messages": teacher_prompt_messages,
+ "teacher_full_messages": teacher_full_messages,
+ "video_fps": self.video_fps,
+ "max_frames": self.max_frames,
+ }
+
+
+def move_text_batch_to_device(batch: dict[str, Any], device: torch.device) -> dict[str, Any]:
+ out = {}
+ for key, value in batch.items():
+ if isinstance(value, torch.Tensor):
+ out[key] = value.to(device)
+ else:
+ out[key] = value
+ return out
+
+
+def build_stage1_model(args: TrainArgs, device: torch.device | None = None):
+ processor = AutoProcessor.from_pretrained(
+ args.smolvlm_name_or_path,
+ trust_remote_code=True,
+ )
+ tokenizer = processor.tokenizer
+ if tokenizer.pad_token_id is None:
+ tokenizer.pad_token_id = tokenizer.eos_token_id
+
+ raw_model = AutoModelForImageTextToText.from_pretrained(
+ args.smolvlm_name_or_path,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True,
+ )
+ if device is not None:
+ raw_model = raw_model.to(device)
+
+ peft_config = get_lora_config(
+ args.smolvlm_name_or_path,
+ lora_r=args.lora_r,
+ lora_dropout=args.lora_dropout,
+ target_modules=args.target_modules,
+ )
+ peft_model = PeftModel(raw_model, peft_config)
+ for param in peft_model.parameters():
+ param.requires_grad = False
+
+ raw_model.config.pad_token_id = tokenizer.pad_token_id
+ if getattr(raw_model, "generation_config", None):
+ raw_model.generation_config.pad_token_id = tokenizer.pad_token_id
+
+ hypernet_args = HypernetArguments(
+ latent_size=args.latent_size,
+ dropout_rate=args.dropout_rate,
+ per_rank_gen=True,
+ )
+ aggregator_args = AggregatorArguments(
+ aggregator_type="perceiver",
+ n_latent_queries=args.n_latent_queries,
+ num_blocks=args.num_blocks,
+ num_self_attn_per_block=args.num_self_attn_per_block,
+ )
+ ctx_encoder_args = CtxEncoderArguments(
+ ctx_encoder_model_name_or_path="precomputed",
+ ctx_encoder_type="per_layer_activations",
+ )
+ ctx_config = PretrainedConfig(hidden_size=raw_model.config.text_config.hidden_size)
+ hypernet_config = get_hypernet_config(
+ peft_model,
+ ctx_config,
+ hypernet_args,
+ aggregator_args,
+ ctx_encoder_args,
+ )
+ model = ModulatedPretrainedModel(
+ peft_model,
+ hypernet_config,
+ ctx_encoder_args,
+ use_sequence_packing=False,
+ )
+ model.train()
+ model.base_model.eval()
+ raw_model.eval()
+ return model, raw_model, processor, tokenizer
+
+
+def compute_ce_loss(logits: torch.Tensor, labels: torch.Tensor):
+ shift_labels = nn.functional.pad(labels, (0, 1), value=-100)[..., 1:].contiguous()
+ vocab_size = logits.shape[-1]
+ token_losses = nn.functional.cross_entropy(
+ logits.float().view(-1, vocab_size),
+ shift_labels.view(-1),
+ reduction="none",
+ ).view(labels.shape[0], -1)
+ token_mask = shift_labels.ne(-100)
+ per_sample_loss = (token_losses * token_mask).sum(dim=1) / token_mask.sum(dim=1).clamp_min(1)
+ return per_sample_loss.mean(), per_sample_loss, token_mask.sum(dim=1)
+
+
+def compute_teacher_kl_loss(
+ student_logits: torch.Tensor,
+ student_labels: torch.Tensor,
+ teacher_logits: torch.Tensor,
+ teacher_labels: torch.Tensor,
+ *,
+ temperature: float,
+):
+ shared_vocab_size = min(student_logits.shape[-1], teacher_logits.shape[-1])
+ student_shift_labels = nn.functional.pad(student_labels, (0, 1), value=-100)[
+ ..., 1:
+ ].contiguous()
+ teacher_shift_labels = nn.functional.pad(teacher_labels, (0, 1), value=-100)[
+ ..., 1:
+ ].contiguous()
+ student_shift_logits = student_logits[..., :shared_vocab_size]
+ teacher_shift_logits = teacher_logits[..., :shared_vocab_size]
+
+ per_sample_kl = []
+ for sample_idx in range(student_shift_logits.shape[0]):
+ student_mask = student_shift_labels[sample_idx].ne(-100)
+ teacher_mask = teacher_shift_labels[sample_idx].ne(-100)
+ student_sample_logits = student_shift_logits[sample_idx][student_mask]
+ teacher_sample_logits = teacher_shift_logits[sample_idx][teacher_mask]
+ if student_sample_logits.shape[0] == 0 or teacher_sample_logits.shape[0] == 0:
+ per_sample_kl.append(student_logits.new_zeros(()))
+ continue
+ if student_sample_logits.shape[0] != teacher_sample_logits.shape[0]:
+ min_len = min(student_sample_logits.shape[0], teacher_sample_logits.shape[0])
+ student_sample_logits = student_sample_logits[:min_len]
+ teacher_sample_logits = teacher_sample_logits[:min_len]
+ teacher_logp = nn.functional.log_softmax(
+ teacher_sample_logits.float() / temperature,
+ dim=-1,
+ )
+ student_logp = nn.functional.log_softmax(
+ student_sample_logits.float() / temperature,
+ dim=-1,
+ )
+ teacher_p = teacher_logp.exp()
+ kl_tokens = (teacher_p * (teacher_logp - student_logp)).sum(dim=-1)
+ per_sample_kl.append(kl_tokens.mean())
+
+ per_sample_kl = torch.stack(per_sample_kl)
+ return per_sample_kl.mean() * (temperature**2), per_sample_kl * (temperature**2)
+
+
+def extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ batch,
+ device,
+ num_target_layers: int,
+ video_size_longest_edge: int | None = None,
+ video_load_backend: str = "auto",
+):
+ vlm_inputs = prepare_smolvlm_inputs(
+ processor,
+ batch["video_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=video_size_longest_edge,
+ video_load_backend=video_load_backend,
+ )
+ return extract_l2l_fused_text_features(
+ raw_model,
+ vlm_inputs,
+ num_target_layers=num_target_layers,
+ )
+
+
+def set_model_train_state(model, raw_model) -> None:
+ model.train()
+ model.base_model.eval()
+ raw_model.eval()
+
+
+def disable_lora_hooks(model) -> None:
+ model.reset()
+
+
+def enable_lora_hooks(model) -> None:
+ model.patch_lora_forward()
+
+
+def _iter_exception_chain(exc: BaseException):
+ seen = set()
+ cur: BaseException | None = exc
+ while cur is not None and id(cur) not in seen:
+ seen.add(id(cur))
+ yield cur
+ cur = cur.__cause__ or cur.__context__
+
+
+def _is_skippable_video_error(exc: BaseException) -> bool:
+ for err in _iter_exception_chain(exc):
+ if isinstance(err, UnicodeDecodeError):
+ return True
+ # PyAV/transformers occasionally raises bare IndexError for malformed videos.
+ if isinstance(err, IndexError) and "tuple index out of range" in str(err).lower():
+ return True
+ # PyAV can also surface empty-decode failures as a plain ValueError.
+ if isinstance(err, ValueError) and "need at least one array to stack" in str(err).lower():
+ return True
+ err_name = err.__class__.__name__.lower()
+ err_mod = err.__class__.__module__.lower()
+ msg = str(err).lower()
+ if "decord" in err_mod or "decorderror" in err_name:
+ return True
+ if "av." in err_mod or "ffmpeg" in err_mod or "pyav" in err_mod:
+ return True
+ if "invalid total_num_frames" in msg:
+ return True
+ if "cannot open" in msg and "video" in msg:
+ return True
+ if "error opening input file" in msg:
+ return True
+ if "unable to handle eof" in msg:
+ return True
+ if "thread worker: error sending packet" in msg:
+ return True
+ if "cannot find video stream" in msg:
+ return True
+ if "moov atom not found" in msg:
+ return True
+ if "invalid data found when processing input" in msg:
+ return True
+ if "unsupported codec" in msg:
+ return True
+ if "decode" in err_name and "error" in err_name:
+ return True
+ return False
+
+
+@torch.no_grad()
+def evaluate_loss(
+ accelerator: Accelerator,
+ model,
+ raw_model,
+ processor,
+ data_loader: DataLoader,
+ device: torch.device,
+ args: TrainArgs,
+) -> dict[str, float]:
+ unwrapped = accelerator.unwrap_model(model)
+ disable_lora_hooks(unwrapped)
+ unwrapped.eval()
+ unwrapped.base_model.eval()
+ raw_model.eval()
+
+ total_samples = torch.zeros(1, device=device)
+ total_ce = torch.zeros(1, device=device)
+ total_kl = torch.zeros(1, device=device)
+ total_combined = torch.zeros(1, device=device)
+ total_answer_tokens = torch.zeros(1, device=device)
+ total_context_tokens = torch.zeros(1, device=device)
+ skipped_batches = torch.zeros(1, device=device)
+ skipped_samples = torch.zeros(1, device=device)
+
+ for batch in data_loader:
+ local_skip = False
+ text_batch = None
+ ctx_features = None
+ ctx_attn_mask = None
+ ctx_position_ids = None
+ outputs = None
+ teacher_batch = None
+ teacher_outputs = None
+ local_error = ""
+ try:
+ text_batch = move_text_batch_to_device(batch, device)
+ disable_lora_hooks(unwrapped)
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ batch,
+ device,
+ num_target_layers=unwrapped.hypernet.n_layers,
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ enable_lora_hooks(unwrapped)
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(ctx_features.shape[0], dtype=torch.int32, device=device),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ disable_lora_hooks(unwrapped)
+ if args.kl_weight > 0:
+ teacher_batch = prepare_smolvlm_teacher_batch(
+ processor,
+ batch["teacher_prompt_messages"],
+ batch["teacher_full_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ teacher_outputs = raw_model(
+ input_ids=teacher_batch["input_ids"],
+ attention_mask=teacher_batch.get("attention_mask"),
+ pixel_values=teacher_batch.get("pixel_values"),
+ pixel_attention_mask=teacher_batch.get("pixel_attention_mask"),
+ labels=None,
+ return_dict=True,
+ use_cache=False,
+ )
+ except Exception as e: # pylint: disable=broad-except
+ disable_lora_hooks(unwrapped)
+ if _is_skippable_video_error(e):
+ local_skip = True
+ local_error = f"{type(e).__name__}: {e}"
+ else:
+ raise
+
+ local_skip_tensor = torch.tensor([1 if local_skip else 0], device=device, dtype=torch.int32)
+ global_skip_tensor = accelerator.reduce(local_skip_tensor, reduction="sum")
+ if global_skip_tensor.item() > 0:
+ skipped_batches += 1
+ skipped_samples += torch.tensor([len(batch["ids"])], device=device, dtype=torch.float32)
+ if local_skip and accelerator.is_local_main_process:
+ print(f"[eval] skipped batch due to video decode error: {local_error}", flush=True)
+ continue
+
+ assert text_batch is not None
+ assert outputs is not None
+ assert ctx_attn_mask is not None
+ ce_loss, per_sample_ce, answer_tokens = compute_ce_loss(outputs.logits, text_batch["labels"])
+ if args.kl_weight > 0:
+ assert teacher_batch is not None
+ assert teacher_outputs is not None
+ _, per_sample_kl = compute_teacher_kl_loss(
+ outputs.logits,
+ text_batch["labels"],
+ teacher_outputs.logits,
+ teacher_batch["labels"],
+ temperature=args.kl_temperature,
+ )
+ else:
+ per_sample_kl = torch.zeros_like(per_sample_ce)
+
+ batch_size = torch.tensor([per_sample_ce.shape[0]], device=device, dtype=torch.float32)
+ total_samples += batch_size
+ total_ce += per_sample_ce.sum()
+ total_kl += per_sample_kl.sum()
+ total_combined += (per_sample_ce + args.kl_weight * per_sample_kl).sum()
+ total_answer_tokens += answer_tokens.sum().to(dtype=torch.float32)
+ total_context_tokens += ctx_attn_mask.sum().to(dtype=torch.float32)
+ disable_lora_hooks(unwrapped)
+
+ total_samples = accelerator.reduce(total_samples, reduction="sum")
+ total_ce = accelerator.reduce(total_ce, reduction="sum")
+ total_kl = accelerator.reduce(total_kl, reduction="sum")
+ total_combined = accelerator.reduce(total_combined, reduction="sum")
+ total_answer_tokens = accelerator.reduce(total_answer_tokens, reduction="sum")
+ total_context_tokens = accelerator.reduce(total_context_tokens, reduction="sum")
+ skipped_batches = accelerator.reduce(skipped_batches, reduction="sum")
+ skipped_samples = accelerator.reduce(skipped_samples, reduction="sum")
+
+ metrics = {
+ "ce_loss": (total_ce / total_samples.clamp_min(1)).item(),
+ "kl_loss": (total_kl / total_samples.clamp_min(1)).item(),
+ "combined_loss": (total_combined / total_samples.clamp_min(1)).item(),
+ "samples": total_samples.item(),
+ "skipped_batches": skipped_batches.item(),
+ "skipped_samples": skipped_samples.item(),
+ "answer_tokens_per_sample": (total_answer_tokens / total_samples.clamp_min(1)).item(),
+ "context_tokens_per_sample": (total_context_tokens / total_samples.clamp_min(1)).item(),
+ }
+ set_model_train_state(unwrapped, raw_model)
+ return metrics
+
+
+def save_checkpoint(accelerator: Accelerator, model, output_dir: Path, filename: str) -> Path:
+ ckpt_path = output_dir / "checkpoints" / filename
+ accelerator.wait_for_everyone()
+ if accelerator.is_main_process:
+ state_dict = accelerator.unwrap_model(model).state_dict()
+ torch.save(state_dict, ckpt_path)
+ accelerator.wait_for_everyone()
+ return ckpt_path
+
+
+def trainer_state_path(output_dir: Path, filename: str) -> Path:
+ stem = Path(filename).stem
+ return output_dir / "checkpoints" / f"{stem}.trainer.pt"
+
+
+def capture_rng_state() -> dict[str, Any]:
+ state: dict[str, Any] = {
+ "python_random_state": random.getstate(),
+ "torch_rng_state": torch.get_rng_state(),
+ }
+ if torch.cuda.is_available():
+ state["cuda_rng_state_all"] = torch.cuda.get_rng_state_all()
+ return state
+
+
+def restore_rng_state(state: dict[str, Any] | None) -> None:
+ if not state:
+ return
+ python_state = state.get("python_random_state")
+ if python_state is not None:
+ random.setstate(python_state)
+ torch_state = state.get("torch_rng_state")
+ if torch_state is not None:
+ torch.set_rng_state(torch_state)
+ cuda_state = state.get("cuda_rng_state_all")
+ if cuda_state is not None and torch.cuda.is_available():
+ torch.cuda.set_rng_state_all(cuda_state)
+
+
+def save_trainer_state(
+ accelerator: Accelerator,
+ optimizer,
+ scheduler,
+ output_dir: Path,
+ filename: str,
+ *,
+ global_step: int,
+ best_ce: float,
+ best_combined: float,
+ train_batches_seen: int,
+) -> Path:
+ state_path = trainer_state_path(output_dir, filename)
+ accelerator.wait_for_everyone()
+ if accelerator.is_main_process:
+ state = {
+ "optimizer": optimizer.state_dict(),
+ "scheduler": scheduler.state_dict(),
+ "global_step": global_step,
+ "best_ce": best_ce,
+ "best_combined": best_combined,
+ "train_batches_seen": train_batches_seen,
+ "rng_state": capture_rng_state(),
+ "model_checkpoint_filename": filename,
+ }
+ torch.save(state, state_path)
+ accelerator.wait_for_everyone()
+ return state_path
+
+
+def write_generation_artifacts(output_dir: Path, step: int, rows: list[dict[str, Any]]) -> None:
+ jsonl_path = output_dir / "generations" / f"step-{step:06d}.jsonl"
+ dump_jsonl(jsonl_path, rows)
+ md_path = output_dir / "generations" / f"step-{step:06d}.md"
+ with open(md_path, "w") as f:
+ f.write(f"# Fixed Generations Step {step}\n\n")
+ for row in rows:
+ f.write(f"## {row['id']}\n")
+ f.write(f"- Dataset: {row['dataset']}\n")
+ f.write(f"- Prompt: {row['prompt']}\n")
+ f.write(f"- Target: {row['target_text']}\n")
+ f.write(f"- Prediction: {row['prediction']}\n\n")
+
+
+@torch.no_grad()
+def generate_fixed_examples(
+ accelerator: Accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ rows: list[dict[str, Any]],
+ args: TrainArgs,
+) -> list[dict[str, Any]]:
+ if not accelerator.is_main_process:
+ return []
+
+ device = accelerator.device
+ unwrapped = accelerator.unwrap_model(model)
+ disable_lora_hooks(unwrapped)
+ unwrapped.eval()
+ raw_model.eval()
+
+ outputs: list[dict[str, Any]] = []
+ for row in rows:
+ normalized = normalize_manifest_row(row)
+ internalize_messages = [
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": resolve_video_path(normalized["video_path"])},
+ {"type": "text", "text": args.internalization_prompt},
+ ],
+ }
+ ]
+ ]
+ try:
+ vlm_inputs = prepare_smolvlm_inputs(
+ processor,
+ internalize_messages,
+ device,
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_l2l_fused_text_features(
+ raw_model,
+ vlm_inputs,
+ num_target_layers=unwrapped.hypernet.n_layers,
+ )
+ enable_lora_hooks(unwrapped)
+ generated_loras, _ = unwrapped.generate_weights(
+ ctx_ids=None,
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ )
+ generated_loras = combine_lora(
+ generated_loras,
+ torch.ones(1, dtype=torch.int32, device=device),
+ lora_bias=unwrapped.hypernet.get_head_bias()
+ if unwrapped.hypernet.config.use_bias
+ else None,
+ )
+ prompt_ids = tokenizer.apply_chat_template(
+ [
+ {
+ "role": "user",
+ "content": [{"type": "text", "text": normalized["prompt"]}],
+ }
+ ],
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors="pt",
+ ).to(device)
+ apply_lora_to_layers(
+ unwrapped.base_model,
+ unwrapped.hypernet.layer_indices,
+ generated_loras,
+ torch.ones(1, dtype=torch.int32, device=device),
+ position_ids=None,
+ )
+ generated = unwrapped.base_model.generate(
+ input_ids=prompt_ids,
+ attention_mask=torch.ones_like(prompt_ids),
+ max_new_tokens=args.generation_max_new_tokens,
+ do_sample=False,
+ pad_token_id=tokenizer.pad_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ )
+ completion = tokenizer.decode(
+ generated[0][prompt_ids.shape[1] :],
+ skip_special_tokens=True,
+ ).strip()
+ outputs.append(
+ {
+ "id": normalized["id"],
+ "dataset": normalized["dataset"],
+ "prompt": normalized["prompt"],
+ "target_text": normalized["target_text"],
+ "prediction": completion,
+ "video_path": normalized["video_path"],
+ "task_type": normalized["task_type"],
+ }
+ )
+ except Exception as e: # pylint: disable=broad-except
+ disable_lora_hooks(unwrapped)
+ if _is_skippable_video_error(e):
+ print(
+ f"[gen] skipped id={normalized['id']} due to video decode error: {type(e).__name__}: {e}",
+ flush=True,
+ )
+ continue
+ raise
+ disable_lora_hooks(unwrapped)
+
+ set_model_train_state(unwrapped, raw_model)
+ return outputs
+
+
+def load_generation_rows(path: str | None) -> list[dict[str, Any]]:
+ if path is None:
+ return []
+ return load_video_manifest(path)
+
+
+def load_resume_model_weights(
+ args: TrainArgs,
+ model,
+) -> None:
+ if not args.resume_checkpoint:
+ return
+
+ checkpoint_state = torch.load(
+ args.resume_checkpoint,
+ map_location="cpu",
+ weights_only=False,
+ )
+ model.load_state_dict(checkpoint_state)
+
+
+def load_resume_training_state(
+ args: TrainArgs,
+ optimizer,
+ scheduler,
+) -> tuple[int, float, float, int]:
+ global_step = 0
+ best_ce = float("inf")
+ best_combined = float("inf")
+ train_batches_seen = 0
+
+ if args.resume_trainer_state:
+ trainer_state = torch.load(
+ args.resume_trainer_state,
+ map_location="cpu",
+ weights_only=False,
+ )
+ optimizer_state = trainer_state.get("optimizer")
+ if optimizer_state is not None:
+ optimizer.load_state_dict(optimizer_state)
+ scheduler_state = trainer_state.get("scheduler")
+ if scheduler_state is not None and not args.resume_ignore_scheduler_state:
+ scheduler.load_state_dict(scheduler_state)
+ global_step = int(trainer_state.get("global_step", global_step))
+ best_ce = float(trainer_state.get("best_ce", best_ce))
+ best_combined = float(trainer_state.get("best_combined", best_combined))
+ train_batches_seen = int(trainer_state.get("train_batches_seen", train_batches_seen))
+ restore_rng_state(trainer_state.get("rng_state"))
+ if args.resume_ignore_scheduler_state and global_step > 0:
+ for _ in range(global_step):
+ scheduler.step()
+ elif args.resume_global_step:
+ global_step = int(args.resume_global_step)
+ for _ in range(global_step):
+ scheduler.step()
+
+ return global_step, best_ce, best_combined, train_batches_seen
+
+
+def main():
+ try:
+ with open(f"/tmp/video2lora_main_enter_pid_{os.getpid()}.log", "a") as f:
+ f.write("entered main\n")
+ except OSError:
+ pass
+ args = parse_args()
+ output_dir = Path(args.output_dir)
+ ensure_layout(output_dir)
+
+ accelerator = Accelerator(
+ gradient_accumulation_steps=args.gradient_accumulation_steps,
+ mixed_precision="bf16",
+ )
+ debug_rank_log(accelerator, "accelerator initialized")
+ set_seed(args.seed)
+
+ train_rows = load_video_manifest(
+ args.train_manifest,
+ max_samples=args.max_train_samples,
+ default_split="train",
+ )
+ val_rows = load_video_manifest(
+ args.val_manifest,
+ max_samples=args.max_val_samples,
+ default_split="val",
+ )
+ val_core_rows = (
+ load_video_manifest(args.val_core_manifest, default_split="val")
+ if args.val_core_manifest
+ else val_rows
+ )
+ val_gen_rows = (
+ load_generation_rows(args.val_gen_manifest)
+ if args.val_gen_manifest
+ else []
+ )
+ legacy_rows: list[dict[str, Any]] = []
+ for manifest_path in args.legacy_sanity_manifests:
+ legacy_rows.extend(
+ load_video_manifest(
+ manifest_path,
+ max_samples=args.legacy_sanity_max_samples_per_manifest,
+ )
+ )
+
+ debug_rank_log(
+ accelerator,
+ f"loaded manifests train={len(train_rows)} val={len(val_rows)} val_core={len(val_core_rows)} legacy={len(legacy_rows)}",
+ )
+ model, raw_model, processor, tokenizer = build_stage1_model(
+ args,
+ device=accelerator.device,
+ )
+ debug_rank_log(accelerator, "built stage1 model")
+ collator = Stage1Collator(
+ tokenizer=tokenizer,
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ )
+
+ train_loader = DataLoader(
+ VideoCaptionDataset(train_rows, internalization_prompt=args.internalization_prompt),
+ batch_size=args.per_device_batch_size,
+ shuffle=True,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ )
+ val_core_loader = DataLoader(
+ VideoCaptionDataset(val_core_rows, internalization_prompt=args.internalization_prompt),
+ batch_size=args.eval_batch_size,
+ shuffle=False,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ )
+ legacy_loader = None
+ if legacy_rows:
+ legacy_loader = DataLoader(
+ VideoCaptionDataset(legacy_rows, internalization_prompt=args.internalization_prompt),
+ batch_size=args.eval_batch_size,
+ shuffle=False,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ )
+
+ # Load model weights before creating the optimizer. This model's custom
+ # load_state_dict() rebuilds the hypernetwork modules, so constructing the
+ # optimizer first would leave it pointing at stale Parameter objects.
+ load_resume_model_weights(args, model)
+
+ trainable_params = [p for p in model.parameters() if p.requires_grad]
+ optimizer = torch.optim.AdamW(
+ trainable_params,
+ lr=args.learning_rate,
+ weight_decay=args.weight_decay,
+ )
+ warmup_steps = int(args.max_steps * args.warmup_ratio)
+ scheduler = get_cosine_schedule_with_warmup(
+ optimizer,
+ num_warmup_steps=warmup_steps,
+ num_training_steps=args.max_steps,
+ )
+
+ resume_global_step, resume_best_ce, resume_best_combined, resume_train_batches_seen = (
+ load_resume_training_state(args, optimizer, scheduler)
+ )
+ debug_rank_log(accelerator, "loaded resume/trainer state")
+ model, optimizer, train_loader, val_core_loader = accelerator.prepare(
+ model,
+ optimizer,
+ train_loader,
+ val_core_loader,
+ )
+ debug_rank_log(accelerator, "accelerator.prepare completed")
+ if legacy_loader is not None:
+ legacy_loader = accelerator.prepare(legacy_loader)
+ debug_rank_log(accelerator, "legacy loader prepared")
+
+ set_model_train_state(accelerator.unwrap_model(model), raw_model)
+ disable_lora_hooks(accelerator.unwrap_model(model))
+
+ if accelerator.is_main_process:
+ with open(output_dir / "train_args.json", "w") as f:
+ json.dump(asdict(args), f, indent=2)
+
+ wandb_mode = resolve_wandb_mode(args.wandb_mode)
+ if wandb_mode == "disabled":
+ os.environ["WANDB_DISABLED"] = "true"
+ wandb.init(
+ project=args.wandb_project,
+ name=args.wandb_run_name or output_dir.name,
+ group=args.wandb_group,
+ notes=args.wandb_notes,
+ config=asdict(args),
+ mode=wandb_mode,
+ )
+ wandb.log(
+ {
+ "data/train_samples": len(train_rows),
+ "data/val_samples": len(val_rows),
+ "data/val_core_samples": len(val_core_rows),
+ "data/val_gen_samples": len(val_gen_rows),
+ "data/legacy_sanity_samples": len(legacy_rows),
+ "data/effective_batch_size": (
+ args.per_device_batch_size
+ * args.gradient_accumulation_steps
+ * accelerator.num_processes
+ ),
+ },
+ step=0,
+ )
+
+ global_step = resume_global_step
+ best_ce = resume_best_ce
+ best_combined = resume_best_combined
+ total_ce_running = 0.0
+ total_kl_running = 0.0
+ total_combined_running = 0.0
+ total_answer_tokens = 0.0
+ total_context_tokens = 0.0
+ running_microbatches = 0
+ skipped_train_batches = 0
+ skipped_train_samples = 0
+ train_batches_seen = resume_train_batches_seen
+
+ train_iterator = iter(train_loader)
+ if resume_train_batches_seen and len(train_loader) > 0:
+ skip_batches = resume_train_batches_seen % len(train_loader)
+ for _ in range(skip_batches):
+ try:
+ next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+ next(train_iterator)
+ while global_step < args.max_steps:
+ try:
+ batch = next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+ batch = next(train_iterator)
+ train_batches_seen += 1
+ if global_step == resume_global_step:
+ debug_rank_log(
+ accelerator,
+ f"fetched first batch ids={batch['ids'][:2]} batch_size={len(batch['ids'])}",
+ )
+
+ with accelerator.accumulate(model):
+ local_skip = False
+ local_error = ""
+ text_batch = None
+ ctx_features = None
+ ctx_attn_mask = None
+ ctx_position_ids = None
+ outputs = None
+ teacher_batch = None
+ teacher_outputs = None
+ try:
+ text_batch = move_text_batch_to_device(batch, accelerator.device)
+ if global_step == resume_global_step:
+ debug_rank_log(accelerator, "moved text batch to device")
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ batch,
+ accelerator.device,
+ num_target_layers=accelerator.unwrap_model(model).hypernet.n_layers,
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ if global_step == resume_global_step:
+ debug_rank_log(
+ accelerator,
+ f"extracted ctx features shape={tuple(ctx_features.shape)}",
+ )
+ enable_lora_hooks(accelerator.unwrap_model(model))
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(
+ ctx_features.shape[0],
+ dtype=torch.int32,
+ device=accelerator.device,
+ ),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ if global_step == resume_global_step:
+ debug_rank_log(accelerator, "completed first model forward")
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ if args.kl_weight > 0:
+ with torch.no_grad():
+ teacher_batch = prepare_smolvlm_teacher_batch(
+ processor,
+ batch["teacher_prompt_messages"],
+ batch["teacher_full_messages"],
+ accelerator.device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ video_load_backend=args.video_load_backend,
+ )
+ teacher_outputs = raw_model(
+ input_ids=teacher_batch["input_ids"],
+ attention_mask=teacher_batch.get("attention_mask"),
+ pixel_values=teacher_batch.get("pixel_values"),
+ pixel_attention_mask=teacher_batch.get("pixel_attention_mask"),
+ labels=None,
+ return_dict=True,
+ use_cache=False,
+ )
+ except Exception as e: # pylint: disable=broad-except
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ if _is_skippable_video_error(e):
+ local_skip = True
+ local_error = f"{type(e).__name__}: {e}"
+ else:
+ raise
+
+ local_skip_tensor = torch.tensor(
+ [1 if local_skip else 0],
+ device=accelerator.device,
+ dtype=torch.int32,
+ )
+ global_skip_tensor = accelerator.reduce(local_skip_tensor, reduction="sum")
+ if global_skip_tensor.item() > 0:
+ skipped_train_batches += 1
+ skipped_train_samples += len(batch["ids"])
+ optimizer.zero_grad(set_to_none=True)
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ if local_skip and accelerator.is_local_main_process:
+ print(
+ f"[train] skipped batch due to video decode error: {local_error}",
+ flush=True,
+ )
+ continue
+
+ assert text_batch is not None
+ assert ctx_features is not None
+ assert ctx_attn_mask is not None
+ assert outputs is not None
+ ce_loss, _, answer_tokens = compute_ce_loss(outputs.logits, text_batch["labels"])
+ if args.kl_weight > 0:
+ assert teacher_batch is not None
+ assert teacher_outputs is not None
+ kl_loss, _ = compute_teacher_kl_loss(
+ outputs.logits,
+ text_batch["labels"],
+ teacher_outputs.logits,
+ teacher_batch["labels"],
+ temperature=args.kl_temperature,
+ )
+ else:
+ kl_loss = outputs.logits.new_zeros(())
+ combined_loss = ce_loss + args.kl_weight * kl_loss
+ accelerator.backward(combined_loss)
+ if global_step == resume_global_step:
+ debug_rank_log(accelerator, "completed first backward")
+
+ total_ce_running += ce_loss.item()
+ total_kl_running += kl_loss.item()
+ total_combined_running += combined_loss.item()
+ total_answer_tokens += float(answer_tokens.sum().item())
+ total_context_tokens += float(ctx_attn_mask.sum().item())
+ running_microbatches += 1
+ disable_lora_hooks(accelerator.unwrap_model(model))
+
+ if accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
+ optimizer.step()
+ scheduler.step()
+ optimizer.zero_grad(set_to_none=True)
+ global_step += 1
+ if global_step == resume_global_step + 1:
+ debug_rank_log(accelerator, "completed first optimizer step")
+
+ if accelerator.is_main_process and global_step % args.log_every == 0:
+ mean_den = max(running_microbatches, 1)
+ wandb.log(
+ {
+ "train/ce_loss": total_ce_running / mean_den,
+ "train/kl_loss": total_kl_running / mean_den,
+ "train/combined_loss": total_combined_running / mean_den,
+ "train/answer_tokens_per_sample": total_answer_tokens
+ / max(mean_den * args.per_device_batch_size, 1),
+ "train/context_tokens_per_sample": total_context_tokens
+ / max(mean_den * args.per_device_batch_size, 1),
+ "train/lr": scheduler.get_last_lr()[0],
+ "train/skipped_batches": skipped_train_batches,
+ "train/skipped_samples_local": skipped_train_samples,
+ },
+ step=global_step,
+ )
+
+ total_ce_running = 0.0
+ total_kl_running = 0.0
+ total_combined_running = 0.0
+ total_answer_tokens = 0.0
+ total_context_tokens = 0.0
+ running_microbatches = 0
+
+ if global_step % args.eval_every == 0:
+ val_metrics = evaluate_loss(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ val_core_loader,
+ accelerator.device,
+ args,
+ )
+ legacy_metrics = None
+ if legacy_loader is not None:
+ legacy_metrics = evaluate_loss(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ legacy_loader,
+ accelerator.device,
+ args,
+ )
+
+ if accelerator.is_main_process:
+ log_payload = {
+ "val_core/ce_loss": val_metrics["ce_loss"],
+ "val_core/kl_loss": val_metrics["kl_loss"],
+ "val_core/combined_loss": val_metrics["combined_loss"],
+ "val_core/skipped_batches": val_metrics["skipped_batches"],
+ "val_core/skipped_samples": val_metrics["skipped_samples"],
+ "val_core/answer_tokens_per_sample": val_metrics[
+ "answer_tokens_per_sample"
+ ],
+ "val_core/context_tokens_per_sample": val_metrics[
+ "context_tokens_per_sample"
+ ],
+ }
+ if legacy_metrics is not None:
+ log_payload.update(
+ {
+ "legacy_sanity/ce_loss": legacy_metrics["ce_loss"],
+ "legacy_sanity/kl_loss": legacy_metrics["kl_loss"],
+ "legacy_sanity/combined_loss": legacy_metrics["combined_loss"],
+ }
+ )
+ wandb.log(log_payload, step=global_step)
+
+ if val_gen_rows:
+ fixed_generations = generate_fixed_examples(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ val_gen_rows,
+ args,
+ )
+ write_generation_artifacts(
+ output_dir,
+ global_step,
+ fixed_generations,
+ )
+
+ # Ensure all ranks finish before checkpoint selection so
+ # that non-main ranks don't race ahead while main is still
+ # doing inference inside generate_fixed_examples.
+ accelerator.wait_for_everyone()
+
+ if val_metrics["ce_loss"] < best_ce:
+ best_ce = val_metrics["ce_loss"]
+ save_checkpoint(accelerator, model, output_dir, "best-ce.pt")
+ save_trainer_state(
+ accelerator,
+ optimizer,
+ scheduler,
+ output_dir,
+ "best-ce.pt",
+ global_step=global_step,
+ best_ce=best_ce,
+ best_combined=best_combined,
+ train_batches_seen=train_batches_seen,
+ )
+ if val_metrics["combined_loss"] < best_combined:
+ best_combined = val_metrics["combined_loss"]
+ save_checkpoint(accelerator, model, output_dir, "best-combined.pt")
+ save_trainer_state(
+ accelerator,
+ optimizer,
+ scheduler,
+ output_dir,
+ "best-combined.pt",
+ global_step=global_step,
+ best_ce=best_ce,
+ best_combined=best_combined,
+ train_batches_seen=train_batches_seen,
+ )
+
+ if global_step % args.save_every == 0:
+ save_checkpoint(
+ accelerator,
+ model,
+ output_dir,
+ f"step-{global_step:06d}.pt",
+ )
+ save_trainer_state(
+ accelerator,
+ optimizer,
+ scheduler,
+ output_dir,
+ f"step-{global_step:06d}.pt",
+ global_step=global_step,
+ best_ce=best_ce,
+ best_combined=best_combined,
+ train_batches_seen=train_batches_seen,
+ )
+
+ save_checkpoint(accelerator, model, output_dir, f"last-step-{global_step:06d}.pt")
+ save_trainer_state(
+ accelerator,
+ optimizer,
+ scheduler,
+ output_dir,
+ f"last-step-{global_step:06d}.pt",
+ global_step=global_step,
+ best_ce=best_ce,
+ best_combined=best_combined,
+ train_batches_seen=train_batches_seen,
+ )
+ if accelerator.is_main_process:
+ wandb.finish()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/video2lora/train_smolvlm_stage1_selfdistill.py b/scripts/video2lora/train_smolvlm_stage1_selfdistill.py
new file mode 100644
index 0000000..47e0495
--- /dev/null
+++ b/scripts/video2lora/train_smolvlm_stage1_selfdistill.py
@@ -0,0 +1,1235 @@
+import argparse
+import hashlib
+import json
+import os
+import random
+import time
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any
+
+import torch
+try:
+ import wandb
+except ModuleNotFoundError:
+ class _MissingWandb:
+ def init(self, *args, **kwargs):
+ if kwargs.get("mode") != "disabled":
+ raise ModuleNotFoundError(
+ "wandb is not installed. Install it or pass --wandb-mode disabled."
+ )
+
+ def log(self, *args, **kwargs):
+ return None
+
+ def finish(self, *args, **kwargs):
+ return None
+
+ wandb = _MissingWandb()
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, Dataset
+from transformers import get_cosine_schedule_with_warmup
+
+from ctx_to_lora.data.video_manifest import (
+ DATA_ROOT,
+ dump_jsonl,
+ load_video_manifest,
+ normalize_manifest_row,
+ resolve_video_path,
+)
+from scripts.video2lora.train_smolvlm_online import (
+ prepare_smolvlm_inputs,
+ prepare_smolvlm_teacher_batch,
+)
+from scripts.video2lora.train_smolvlm_stage1 import (
+ build_stage1_model,
+ compute_ce_loss,
+ compute_teacher_kl_loss,
+ disable_lora_hooks,
+ enable_lora_hooks,
+ extract_stage1_ctx_features,
+ resolve_wandb_mode,
+ save_checkpoint,
+ set_model_train_state,
+ _is_skippable_video_error,
+)
+from ctx_to_lora.modeling.lora_layer import apply_lora_to_layers
+from ctx_to_lora.modeling.lora_merger import combine_lora
+
+
+TASK_BANK: tuple[dict[str, str], ...] = (
+ {
+ "task_type": "caption",
+ "prompt": "Describe what is happening in this scene in one concise sentence.",
+ },
+ {
+ "task_type": "caption",
+ "prompt": "Caption this video scene briefly and concretely.",
+ },
+ {
+ "task_type": "summary",
+ "prompt": "Summarize this scene in 2 concise sentences.",
+ },
+ {
+ "task_type": "summary",
+ "prompt": "Give a short summary of the key events in this video scene.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What is the main action in this scene? Answer briefly.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What is the focus of this clip? Answer briefly.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What is happening in this video? Answer in one short sentence.",
+ },
+)
+
+SCENE_TASK_BANK: tuple[dict[str, str], ...] = (
+ {
+ "task_type": "caption",
+ "prompt": "Describe the visible action in this scene in one concise sentence.",
+ },
+ {
+ "task_type": "caption",
+ "prompt": "Caption this short scene briefly and concretely.",
+ },
+ {
+ "task_type": "summary",
+ "prompt": "Summarize the key visible event in this scene.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What is the main visible action in this scene? Answer briefly.",
+ },
+)
+
+ADJACENT_TASK_BANK: tuple[dict[str, str], ...] = (
+ {
+ "task_type": "summary",
+ "prompt": "Summarize the sequence of visible events in this clip.",
+ },
+ {
+ "task_type": "summary",
+ "prompt": "Describe how the action changes across this short video segment.",
+ },
+ {
+ "task_type": "caption",
+ "prompt": "Describe the main sequence of actions in this clip.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What changes or progression are visible across this clip? Answer briefly.",
+ },
+)
+
+FULL_TASK_BANK: tuple[dict[str, str], ...] = (
+ {
+ "task_type": "summary",
+ "prompt": "Summarize the full video in 2 concise sentences, focusing only on visible content.",
+ },
+ {
+ "task_type": "summary",
+ "prompt": "Give a brief visual summary of the full video.",
+ },
+ {
+ "task_type": "caption",
+ "prompt": "Describe the main visible topic and activity of this full video.",
+ },
+ {
+ "task_type": "qa",
+ "prompt": "Question: What is the overall visible focus of this full video? Answer briefly.",
+ },
+)
+
+
+@dataclass
+class TrainArgs:
+ smolvlm_name_or_path: str
+ train_manifest: str
+ val_manifest: str
+ val_gen_manifest: str | None
+ output_dir: str
+ per_device_batch_size: int
+ eval_batch_size: int
+ gradient_accumulation_steps: int
+ max_steps: int
+ learning_rate: float
+ weight_decay: float
+ warmup_ratio: float
+ max_grad_norm: float
+ seed: int
+ log_every: int
+ eval_every: int
+ save_every: int
+ max_train_samples: int | None
+ max_val_samples: int | None
+ num_workers: int
+ lora_r: int
+ lora_dropout: float
+ target_modules: list[str]
+ latent_size: int
+ dropout_rate: float
+ n_latent_queries: int
+ num_blocks: int
+ num_self_attn_per_block: int
+ video_fps: float | None
+ max_frames: int
+ video_size_longest_edge: int | None
+ internalization_prompt: str
+ kl_temperature: float
+ teacher_max_new_tokens: int
+ student_generation_max_new_tokens: int
+ fixed_prompt_mode: str
+ wandb_project: str
+ wandb_mode: str
+ wandb_group: str | None
+ wandb_run_name: str | None
+ wandb_notes: str | None
+
+
+def parse_args() -> TrainArgs:
+ parser = argparse.ArgumentParser(
+ description="Stage 1 self-distillation for Video2LoRA using teacher-generated tasks."
+ )
+ parser.add_argument(
+ "--smolvlm-name-or-path",
+ default="HuggingFaceTB/SmolVLM2-2.2B-Instruct",
+ )
+ parser.add_argument(
+ "--train-manifest",
+ default=str(DATA_ROOT / "processed" / "finevideo" / "train.readable.jsonl"),
+ )
+ parser.add_argument(
+ "--val-manifest",
+ default=str(DATA_ROOT / "processed" / "finevideo" / "val.readable.jsonl"),
+ )
+ parser.add_argument(
+ "--val-gen-manifest",
+ default=str(DATA_ROOT / "processed" / "finevideo" / "val_gen_100.readable.jsonl"),
+ )
+ parser.add_argument("--output-dir", default="")
+ parser.add_argument("--per-device-batch-size", type=int, default=4)
+ parser.add_argument("--eval-batch-size", type=int, default=4)
+ parser.add_argument("--gradient-accumulation-steps", type=int, default=4)
+ parser.add_argument("--max-steps", type=int, default=4000)
+ parser.add_argument("--learning-rate", type=float, default=1e-4)
+ parser.add_argument("--weight-decay", type=float, default=0.01)
+ parser.add_argument("--warmup-ratio", type=float, default=0.03)
+ parser.add_argument("--max-grad-norm", type=float, default=1.0)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--log-every", type=int, default=10)
+ parser.add_argument("--eval-every", type=int, default=200)
+ parser.add_argument("--save-every", type=int, default=1000)
+ parser.add_argument("--max-train-samples", type=int, default=None)
+ parser.add_argument("--max-val-samples", type=int, default=None)
+ parser.add_argument("--num-workers", type=int, default=0)
+ parser.add_argument("--lora-r", type=int, default=16)
+ parser.add_argument("--lora-dropout", type=float, default=0.0)
+ parser.add_argument("--target-modules", default="down_proj")
+ parser.add_argument("--latent-size", type=int, default=512)
+ parser.add_argument("--dropout-rate", type=float, default=0.0)
+ parser.add_argument("--n-latent-queries", type=int, default=8)
+ parser.add_argument("--num-blocks", type=int, default=9)
+ parser.add_argument("--num-self-attn-per-block", type=int, default=0)
+ parser.add_argument("--video-fps", type=float, default=None)
+ parser.add_argument("--max-frames", type=int, default=12)
+ parser.add_argument("--video-size-longest-edge", type=int, default=384)
+ parser.add_argument(
+ "--internalization-prompt",
+ default="Internalize this video for later captioning, summarization, and question answering.",
+ )
+ parser.add_argument("--kl-temperature", type=float, default=1.0)
+ parser.add_argument("--teacher-max-new-tokens", type=int, default=32)
+ parser.add_argument("--student-generation-max-new-tokens", type=int, default=48)
+ parser.add_argument(
+ "--fixed-prompt-mode",
+ default="cycle",
+ choices=("cycle", "caption_only", "summary_only", "qa_only"),
+ )
+ parser.add_argument("--wandb-project", default="video2lora-finevideo-selfdistill")
+ parser.add_argument(
+ "--wandb-mode",
+ default="auto",
+ choices=("auto", "online", "offline", "disabled"),
+ )
+ parser.add_argument("--wandb-group", default="finevideo-selfdistill")
+ parser.add_argument("--wandb-run-name", default=None)
+ parser.add_argument("--wandb-notes", default=None)
+ parsed = parser.parse_args()
+
+ output_dir = parsed.output_dir
+ if not output_dir:
+ timestamp = time.strftime("%Y%m%d-%H%M%S", time.gmtime())
+ output_dir = str(DATA_ROOT / "runs" / f"{timestamp}-smolvlm-stage1-selfdistill")
+
+ def maybe_existing(path: str | None) -> str | None:
+ if path and os.path.exists(path):
+ return path
+ return None
+
+ return TrainArgs(
+ smolvlm_name_or_path=parsed.smolvlm_name_or_path,
+ train_manifest=parsed.train_manifest,
+ val_manifest=parsed.val_manifest,
+ val_gen_manifest=maybe_existing(parsed.val_gen_manifest),
+ output_dir=output_dir,
+ per_device_batch_size=parsed.per_device_batch_size,
+ eval_batch_size=parsed.eval_batch_size,
+ gradient_accumulation_steps=parsed.gradient_accumulation_steps,
+ max_steps=parsed.max_steps,
+ learning_rate=parsed.learning_rate,
+ weight_decay=parsed.weight_decay,
+ warmup_ratio=parsed.warmup_ratio,
+ max_grad_norm=parsed.max_grad_norm,
+ seed=parsed.seed,
+ log_every=parsed.log_every,
+ eval_every=parsed.eval_every,
+ save_every=parsed.save_every,
+ max_train_samples=parsed.max_train_samples,
+ max_val_samples=parsed.max_val_samples,
+ num_workers=parsed.num_workers,
+ lora_r=parsed.lora_r,
+ lora_dropout=parsed.lora_dropout,
+ target_modules=[x.strip() for x in parsed.target_modules.split(",") if x.strip()],
+ latent_size=parsed.latent_size,
+ dropout_rate=parsed.dropout_rate,
+ n_latent_queries=parsed.n_latent_queries,
+ num_blocks=parsed.num_blocks,
+ num_self_attn_per_block=parsed.num_self_attn_per_block,
+ video_fps=parsed.video_fps,
+ max_frames=parsed.max_frames,
+ video_size_longest_edge=parsed.video_size_longest_edge,
+ internalization_prompt=parsed.internalization_prompt,
+ kl_temperature=parsed.kl_temperature,
+ teacher_max_new_tokens=parsed.teacher_max_new_tokens,
+ student_generation_max_new_tokens=parsed.student_generation_max_new_tokens,
+ fixed_prompt_mode=parsed.fixed_prompt_mode,
+ wandb_project=parsed.wandb_project,
+ wandb_mode=parsed.wandb_mode,
+ wandb_group=parsed.wandb_group,
+ wandb_run_name=parsed.wandb_run_name,
+ wandb_notes=parsed.wandb_notes,
+ )
+
+
+def ensure_layout(output_dir: Path) -> None:
+ for path in (
+ DATA_ROOT,
+ DATA_ROOT / "runs",
+ output_dir,
+ output_dir / "checkpoints",
+ output_dir / "generations",
+ ):
+ path.mkdir(parents=True, exist_ok=True)
+
+
+class DistillVideoDataset(Dataset):
+ def __init__(self, rows: list[dict[str, Any]], *, internalization_prompt: str):
+ self.rows = rows
+ self.internalization_prompt = internalization_prompt
+
+ def __len__(self) -> int:
+ return len(self.rows)
+
+ def __getitem__(self, idx: int) -> dict[str, Any]:
+ row = self.rows[idx]
+ return {
+ "id": row["id"],
+ "video_path": resolve_video_path(row["video_path"]),
+ "dataset": row["dataset"],
+ "task_type": row.get("task_type", "caption"),
+ "split": row.get("split"),
+ "metadata": row.get("metadata", {}),
+ "span_type": (row.get("metadata") or {}).get("span_type", "scene"),
+ "internalization_prompt": self.internalization_prompt,
+ }
+
+
+def _stable_index(key: str, modulo: int) -> int:
+ digest = hashlib.sha256(key.encode()).digest()
+ return int.from_bytes(digest[:8], "big") % modulo
+
+
+def _bank_for_span(span_type: str) -> tuple[dict[str, str], ...]:
+ if span_type == "scene":
+ return SCENE_TASK_BANK
+ if span_type == "adjacent":
+ return ADJACENT_TASK_BANK
+ if span_type == "full":
+ return FULL_TASK_BANK
+ return TASK_BANK
+
+
+def _select_task(example_id: str, span_type: str, mode: str, *, train: bool) -> dict[str, str]:
+ if mode == "caption_only":
+ bank = [task for task in _bank_for_span(span_type) if task["task_type"] == "caption"]
+ elif mode == "summary_only":
+ bank = [task for task in _bank_for_span(span_type) if task["task_type"] == "summary"]
+ elif mode == "qa_only":
+ bank = [task for task in _bank_for_span(span_type) if task["task_type"] == "qa"]
+ else:
+ bank = list(_bank_for_span(span_type))
+ if train:
+ return random.choice(bank)
+ return bank[_stable_index(example_id, len(bank))]
+
+
+class SelfDistillCollator:
+ def __init__(self, *, video_fps: float | None, max_frames: int, prompt_mode: str, train: bool):
+ self.video_fps = video_fps
+ self.max_frames = max_frames
+ self.prompt_mode = prompt_mode
+ self.train = train
+
+ def __call__(self, batch: list[dict[str, Any]]) -> dict[str, Any]:
+ video_messages = []
+ teacher_prompt_messages = []
+ prompts = []
+ distill_task_types = []
+ span_types = []
+
+ for example in batch:
+ task = _select_task(
+ example["id"],
+ example["span_type"],
+ self.prompt_mode,
+ train=self.train,
+ )
+ prompts.append(task["prompt"])
+ distill_task_types.append(task["task_type"])
+ span_types.append(example["span_type"])
+ video_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {
+ "type": "text",
+ "text": example["internalization_prompt"],
+ },
+ ],
+ }
+ ]
+ )
+ teacher_prompt_messages.append(
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": example["video_path"]},
+ {"type": "text", "text": task["prompt"]},
+ ],
+ }
+ ]
+ )
+
+ return {
+ "ids": [example["id"] for example in batch],
+ "video_paths": [example["video_path"] for example in batch],
+ "datasets": [example["dataset"] for example in batch],
+ "metadata": [example["metadata"] for example in batch],
+ "prompts": prompts,
+ "distill_task_types": distill_task_types,
+ "span_types": span_types,
+ "video_messages": video_messages,
+ "teacher_prompt_messages": teacher_prompt_messages,
+ "video_fps": self.video_fps,
+ "max_frames": self.max_frames,
+ }
+
+
+def build_labels(tokenizer, prompt: str, target_text: str):
+ prompt_messages = [
+ {"role": "user", "content": [{"type": "text", "text": prompt}]}
+ ]
+ full_messages = [
+ {"role": "user", "content": [{"type": "text", "text": prompt}]},
+ {"role": "assistant", "content": [{"type": "text", "text": target_text}]},
+ ]
+ prompt_ids = tokenizer.apply_chat_template(
+ prompt_messages,
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors=None,
+ )
+ full_ids = tokenizer.apply_chat_template(
+ full_messages,
+ tokenize=True,
+ add_generation_prompt=False,
+ return_tensors=None,
+ )
+ input_ids = torch.tensor(full_ids, dtype=torch.long)
+ attention_mask = torch.ones_like(input_ids)
+ labels = input_ids.clone()
+ labels[: len(prompt_ids)] = -100
+ return input_ids, attention_mask, labels
+
+
+def build_student_text_batch(tokenizer, prompts: list[str], targets: list[str]) -> dict[str, torch.Tensor]:
+ input_ids, attention_masks, labels = [], [], []
+ for prompt, target in zip(prompts, targets, strict=True):
+ inp_ids, attn_mask, lbl = build_labels(tokenizer, prompt, target)
+ input_ids.append(inp_ids)
+ attention_masks.append(attn_mask)
+ labels.append(lbl)
+ batch_input_ids = pad_sequence(
+ input_ids,
+ batch_first=True,
+ padding_value=tokenizer.pad_token_id,
+ )
+ batch_attention_mask = pad_sequence(
+ attention_masks,
+ batch_first=True,
+ padding_value=0,
+ )
+ batch_labels = pad_sequence(
+ labels,
+ batch_first=True,
+ padding_value=-100,
+ )
+ return {
+ "input_ids": batch_input_ids,
+ "attention_mask": batch_attention_mask,
+ "labels": batch_labels,
+ }
+
+
+def move_text_batch_to_device(batch: dict[str, Any], device: torch.device) -> dict[str, Any]:
+ out = {}
+ for key, value in batch.items():
+ if isinstance(value, torch.Tensor):
+ out[key] = value.to(device)
+ else:
+ out[key] = value
+ return out
+
+
+def _prepare_generation_inputs(
+ processor,
+ prompt_messages,
+ device,
+ *,
+ video_fps: float | None,
+ max_frames: int,
+ video_size_longest_edge: int | None,
+):
+ chat_template_kwargs = dict(max_frames=max_frames, padding=True)
+ # Batched generation on decoder-only text backbones should left-pad prompts.
+ processor.tokenizer.padding_side = "left"
+ if video_fps is not None:
+ chat_template_kwargs["target_fps"] = video_fps
+ if video_size_longest_edge is not None:
+ video_size = {"longest_edge": video_size_longest_edge}
+ processor.video_size = video_size
+ processor.image_processor.size = video_size
+ prompt_inputs = processor.apply_chat_template(
+ prompt_messages,
+ add_generation_prompt=True,
+ tokenize=True,
+ return_dict=True,
+ return_tensors="pt",
+ **chat_template_kwargs,
+ )
+ moved = {}
+ for key, value in prompt_inputs.items():
+ if isinstance(value, torch.Tensor):
+ if value.is_floating_point():
+ moved[key] = value.to(device=device, dtype=torch.bfloat16)
+ else:
+ moved[key] = value.to(device=device)
+ else:
+ moved[key] = value
+ return moved
+
+
+@torch.no_grad()
+def generate_teacher_targets(
+ raw_model,
+ processor,
+ tokenizer,
+ prompt_messages,
+ device,
+ *,
+ video_fps: float | None,
+ max_frames: int,
+ video_size_longest_edge: int | None,
+ max_new_tokens: int,
+) -> list[str]:
+ prompt_inputs = _prepare_generation_inputs(
+ processor,
+ prompt_messages,
+ device,
+ video_fps=video_fps,
+ max_frames=max_frames,
+ video_size_longest_edge=video_size_longest_edge,
+ )
+ generated = raw_model.generate(
+ **prompt_inputs,
+ max_new_tokens=max_new_tokens,
+ do_sample=False,
+ pad_token_id=tokenizer.pad_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ )
+ prompt_lens = prompt_inputs["attention_mask"].sum(dim=1).tolist()
+ outputs = []
+ for row_idx, prompt_len in enumerate(prompt_lens):
+ text = tokenizer.decode(
+ generated[row_idx][prompt_len:],
+ skip_special_tokens=True,
+ ).strip()
+ if not text:
+ text = "Unable to determine."
+ outputs.append(text)
+ return outputs
+
+
+def build_teacher_full_messages(prompt_messages, targets: list[str]):
+ full_messages = []
+ for prompt_message, target in zip(prompt_messages, targets, strict=True):
+ full_messages.append(
+ [
+ prompt_message[0],
+ {
+ "role": "assistant",
+ "content": [{"type": "text", "text": target}],
+ },
+ ]
+ )
+ return full_messages
+
+
+@torch.no_grad()
+def evaluate_loss(
+ accelerator: Accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ data_loader: DataLoader,
+ device: torch.device,
+ args: TrainArgs,
+) -> dict[str, float]:
+ unwrapped = accelerator.unwrap_model(model)
+ disable_lora_hooks(unwrapped)
+ unwrapped.eval()
+ unwrapped.base_model.eval()
+ raw_model.eval()
+
+ total_samples = torch.zeros(1, device=device)
+ total_teacher_ce = torch.zeros(1, device=device)
+ total_kl = torch.zeros(1, device=device)
+ total_answer_tokens = torch.zeros(1, device=device)
+ total_context_tokens = torch.zeros(1, device=device)
+ skipped_batches = torch.zeros(1, device=device)
+ skipped_samples = torch.zeros(1, device=device)
+
+ for batch in data_loader:
+ local_skip = False
+ local_error = ""
+ text_batch = None
+ teacher_batch = None
+ teacher_outputs = None
+ outputs = None
+ ctx_attn_mask = None
+ try:
+ teacher_targets = generate_teacher_targets(
+ raw_model,
+ processor,
+ tokenizer,
+ batch["teacher_prompt_messages"],
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ max_new_tokens=args.teacher_max_new_tokens,
+ )
+ student_text_batch = build_student_text_batch(tokenizer, batch["prompts"], teacher_targets)
+ text_batch = move_text_batch_to_device(student_text_batch, device)
+ disable_lora_hooks(unwrapped)
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ batch,
+ device,
+ num_target_layers=unwrapped.hypernet.n_layers,
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+ enable_lora_hooks(unwrapped)
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(ctx_features.shape[0], dtype=torch.int32, device=device),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ disable_lora_hooks(unwrapped)
+ teacher_batch = prepare_smolvlm_teacher_batch(
+ processor,
+ batch["teacher_prompt_messages"],
+ build_teacher_full_messages(batch["teacher_prompt_messages"], teacher_targets),
+ device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+ teacher_outputs = raw_model(
+ input_ids=teacher_batch["input_ids"],
+ attention_mask=teacher_batch.get("attention_mask"),
+ pixel_values=teacher_batch.get("pixel_values"),
+ pixel_attention_mask=teacher_batch.get("pixel_attention_mask"),
+ labels=None,
+ return_dict=True,
+ use_cache=False,
+ )
+ except Exception as exc: # pylint: disable=broad-except
+ disable_lora_hooks(unwrapped)
+ if _is_skippable_video_error(exc):
+ local_skip = True
+ local_error = f"{type(exc).__name__}: {exc}"
+ else:
+ raise
+
+ local_skip_tensor = torch.tensor([1 if local_skip else 0], device=device, dtype=torch.int32)
+ global_skip_tensor = accelerator.reduce(local_skip_tensor, reduction="sum")
+ if global_skip_tensor.item() > 0:
+ skipped_batches += 1
+ skipped_samples += torch.tensor([len(batch["ids"])], device=device, dtype=torch.float32)
+ if local_skip and accelerator.is_local_main_process:
+ print(f"[eval] skipped batch due to video decode error: {local_error}", flush=True)
+ continue
+
+ assert text_batch is not None
+ assert teacher_batch is not None
+ assert teacher_outputs is not None
+ assert outputs is not None
+ assert ctx_attn_mask is not None
+ teacher_ce, per_sample_ce, answer_tokens = compute_ce_loss(outputs.logits, text_batch["labels"])
+ _, per_sample_kl = compute_teacher_kl_loss(
+ outputs.logits,
+ text_batch["labels"],
+ teacher_outputs.logits,
+ teacher_batch["labels"],
+ temperature=args.kl_temperature,
+ )
+ batch_size = torch.tensor([per_sample_ce.shape[0]], device=device, dtype=torch.float32)
+ total_samples += batch_size
+ total_teacher_ce += per_sample_ce.sum()
+ total_kl += per_sample_kl.sum()
+ total_answer_tokens += answer_tokens.sum().to(dtype=torch.float32)
+ total_context_tokens += ctx_attn_mask.sum().to(dtype=torch.float32)
+ disable_lora_hooks(unwrapped)
+
+ total_samples = accelerator.reduce(total_samples, reduction="sum")
+ total_teacher_ce = accelerator.reduce(total_teacher_ce, reduction="sum")
+ total_kl = accelerator.reduce(total_kl, reduction="sum")
+ total_answer_tokens = accelerator.reduce(total_answer_tokens, reduction="sum")
+ total_context_tokens = accelerator.reduce(total_context_tokens, reduction="sum")
+ skipped_batches = accelerator.reduce(skipped_batches, reduction="sum")
+ skipped_samples = accelerator.reduce(skipped_samples, reduction="sum")
+
+ metrics = {
+ "teacher_ce_loss": (total_teacher_ce / total_samples.clamp_min(1)).item(),
+ "kl_loss": (total_kl / total_samples.clamp_min(1)).item(),
+ "combined_loss": (total_kl / total_samples.clamp_min(1)).item(),
+ "samples": total_samples.item(),
+ "skipped_batches": skipped_batches.item(),
+ "skipped_samples": skipped_samples.item(),
+ "answer_tokens_per_sample": (total_answer_tokens / total_samples.clamp_min(1)).item(),
+ "context_tokens_per_sample": (total_context_tokens / total_samples.clamp_min(1)).item(),
+ }
+ set_model_train_state(unwrapped, raw_model)
+ return metrics
+
+
+def write_generation_artifacts(output_dir: Path, step: int, rows: list[dict[str, Any]]) -> None:
+ jsonl_path = output_dir / "generations" / f"step-{step:06d}.jsonl"
+ dump_jsonl(jsonl_path, rows)
+ md_path = output_dir / "generations" / f"step-{step:06d}.md"
+ with open(md_path, "w") as f:
+ f.write(f"# Fixed Generations Step {step}\n\n")
+ for row in rows:
+ f.write(f"## {row['id']}\n")
+ f.write(f"- Dataset: {row['dataset']}\n")
+ f.write(f"- Task: {row['task_type']}\n")
+ f.write(f"- Prompt: {row['prompt']}\n")
+ f.write(f"- Teacher: {row['teacher_prediction']}\n")
+ f.write(f"- Student: {row['student_prediction']}\n\n")
+
+
+@torch.no_grad()
+def generate_fixed_examples(
+ accelerator: Accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ rows: list[dict[str, Any]],
+ args: TrainArgs,
+) -> list[dict[str, Any]]:
+ if not accelerator.is_main_process:
+ return []
+
+ device = accelerator.device
+ unwrapped = accelerator.unwrap_model(model)
+ disable_lora_hooks(unwrapped)
+ unwrapped.eval()
+ raw_model.eval()
+
+ outputs: list[dict[str, Any]] = []
+ for row in rows:
+ normalized = normalize_manifest_row(row)
+ span_type = (normalized.get("metadata") or {}).get("span_type", "scene")
+ task = _select_task(normalized["id"], span_type, args.fixed_prompt_mode, train=False)
+ prompt = task["prompt"]
+ internalize_messages = [
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": resolve_video_path(normalized["video_path"])},
+ {"type": "text", "text": args.internalization_prompt},
+ ],
+ }
+ ]
+ ]
+ teacher_prompt_messages = [
+ [
+ {
+ "role": "user",
+ "content": [
+ {"type": "video", "path": resolve_video_path(normalized["video_path"])},
+ {"type": "text", "text": prompt},
+ ],
+ }
+ ]
+ ]
+ try:
+ teacher_prediction = generate_teacher_targets(
+ raw_model,
+ processor,
+ tokenizer,
+ teacher_prompt_messages,
+ device,
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ video_size_longest_edge=args.video_size_longest_edge,
+ max_new_tokens=args.teacher_max_new_tokens,
+ )[0]
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ {
+ "video_messages": internalize_messages,
+ "video_fps": args.video_fps,
+ "max_frames": args.max_frames,
+ },
+ device,
+ num_target_layers=unwrapped.hypernet.n_layers,
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+ enable_lora_hooks(unwrapped)
+ generated_loras, _ = unwrapped.generate_weights(
+ ctx_ids=None,
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ )
+ generated_loras = combine_lora(
+ generated_loras,
+ torch.ones(1, dtype=torch.int32, device=device),
+ lora_bias=unwrapped.hypernet.get_head_bias()
+ if unwrapped.hypernet.config.use_bias
+ else None,
+ )
+ prompt_ids = tokenizer.apply_chat_template(
+ [
+ {
+ "role": "user",
+ "content": [{"type": "text", "text": prompt}],
+ }
+ ],
+ tokenize=True,
+ add_generation_prompt=True,
+ return_tensors="pt",
+ ).to(device)
+ apply_lora_to_layers(
+ unwrapped.base_model,
+ unwrapped.hypernet.layer_indices,
+ generated_loras,
+ torch.ones(1, dtype=torch.int32, device=device),
+ position_ids=None,
+ )
+ generated = unwrapped.base_model.generate(
+ input_ids=prompt_ids,
+ attention_mask=torch.ones_like(prompt_ids),
+ max_new_tokens=args.student_generation_max_new_tokens,
+ do_sample=False,
+ pad_token_id=tokenizer.pad_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ )
+ student_prediction = tokenizer.decode(
+ generated[0][prompt_ids.shape[1] :],
+ skip_special_tokens=True,
+ ).strip()
+ outputs.append(
+ {
+ "id": normalized["id"],
+ "dataset": normalized["dataset"],
+ "task_type": task["task_type"],
+ "span_type": span_type,
+ "prompt": prompt,
+ "teacher_prediction": teacher_prediction,
+ "student_prediction": student_prediction,
+ "video_path": normalized["video_path"],
+ }
+ )
+ except Exception as exc: # pylint: disable=broad-except
+ disable_lora_hooks(unwrapped)
+ if _is_skippable_video_error(exc):
+ print(
+ f"[gen] skipped id={normalized['id']} due to video decode error: {type(exc).__name__}: {exc}",
+ flush=True,
+ )
+ continue
+ raise
+ disable_lora_hooks(unwrapped)
+
+ set_model_train_state(unwrapped, raw_model)
+ return outputs
+
+
+def main():
+ args = parse_args()
+ output_dir = Path(args.output_dir)
+ ensure_layout(output_dir)
+ set_seed(args.seed)
+
+ accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps)
+
+ train_rows = load_video_manifest(args.train_manifest, max_samples=args.max_train_samples)
+ val_rows = load_video_manifest(args.val_manifest, max_samples=args.max_val_samples)
+ val_gen_rows = load_video_manifest(args.val_gen_manifest) if args.val_gen_manifest else []
+
+ train_dataset = DistillVideoDataset(train_rows, internalization_prompt=args.internalization_prompt)
+ val_dataset = DistillVideoDataset(val_rows, internalization_prompt=args.internalization_prompt)
+
+ collator = SelfDistillCollator(
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ prompt_mode="cycle",
+ train=True,
+ )
+ val_collator = SelfDistillCollator(
+ video_fps=args.video_fps,
+ max_frames=args.max_frames,
+ prompt_mode=args.fixed_prompt_mode,
+ train=False,
+ )
+
+ train_loader = DataLoader(
+ train_dataset,
+ batch_size=args.per_device_batch_size,
+ shuffle=True,
+ num_workers=args.num_workers,
+ collate_fn=collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ )
+ val_loader = DataLoader(
+ val_dataset,
+ batch_size=args.eval_batch_size,
+ shuffle=False,
+ num_workers=args.num_workers,
+ collate_fn=val_collator,
+ pin_memory=True,
+ persistent_workers=args.num_workers > 0,
+ )
+
+ model, raw_model, processor, tokenizer = build_stage1_model(args)
+ trainable_params = [p for p in model.parameters() if p.requires_grad]
+ optimizer = torch.optim.AdamW(
+ trainable_params,
+ lr=args.learning_rate,
+ weight_decay=args.weight_decay,
+ )
+ warmup_steps = int(args.max_steps * args.warmup_ratio)
+ scheduler = get_cosine_schedule_with_warmup(
+ optimizer,
+ num_warmup_steps=warmup_steps,
+ num_training_steps=args.max_steps,
+ )
+
+ model, optimizer, train_loader, val_loader, scheduler = accelerator.prepare(
+ model,
+ optimizer,
+ train_loader,
+ val_loader,
+ scheduler,
+ )
+
+ set_model_train_state(accelerator.unwrap_model(model), raw_model)
+ disable_lora_hooks(accelerator.unwrap_model(model))
+
+ if accelerator.is_main_process:
+ with open(output_dir / "train_args.json", "w") as f:
+ json.dump(asdict(args), f, indent=2)
+ wandb_mode = resolve_wandb_mode(args.wandb_mode)
+ if wandb_mode == "disabled":
+ os.environ["WANDB_DISABLED"] = "true"
+ wandb.init(
+ project=args.wandb_project,
+ name=args.wandb_run_name or output_dir.name,
+ group=args.wandb_group,
+ notes=args.wandb_notes,
+ config=asdict(args),
+ mode=wandb_mode,
+ )
+ wandb.log(
+ {
+ "data/train_samples": len(train_rows),
+ "data/val_samples": len(val_rows),
+ "data/val_gen_samples": len(val_gen_rows),
+ "data/effective_batch_size": (
+ args.per_device_batch_size
+ * args.gradient_accumulation_steps
+ * accelerator.num_processes
+ ),
+ },
+ step=0,
+ )
+
+ global_step = 0
+ best_kl = float("inf")
+ total_teacher_ce_running = 0.0
+ total_kl_running = 0.0
+ total_answer_tokens = 0.0
+ total_context_tokens = 0.0
+ running_microbatches = 0
+ skipped_train_batches = 0
+ skipped_train_samples = 0
+
+ train_iterator = iter(train_loader)
+ while global_step < args.max_steps:
+ try:
+ batch = next(train_iterator)
+ except StopIteration:
+ train_iterator = iter(train_loader)
+ batch = next(train_iterator)
+
+ with accelerator.accumulate(model):
+ local_skip = False
+ local_error = ""
+ text_batch = None
+ teacher_batch = None
+ teacher_outputs = None
+ outputs = None
+ ctx_attn_mask = None
+ try:
+ teacher_targets = generate_teacher_targets(
+ raw_model,
+ processor,
+ tokenizer,
+ batch["teacher_prompt_messages"],
+ accelerator.device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ max_new_tokens=args.teacher_max_new_tokens,
+ )
+ student_text_batch = build_student_text_batch(
+ tokenizer,
+ batch["prompts"],
+ teacher_targets,
+ )
+ text_batch = move_text_batch_to_device(student_text_batch, accelerator.device)
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ ctx_features, ctx_attn_mask, ctx_position_ids = extract_stage1_ctx_features(
+ raw_model,
+ processor,
+ batch,
+ accelerator.device,
+ num_target_layers=accelerator.unwrap_model(model).hypernet.n_layers,
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+ enable_lora_hooks(accelerator.unwrap_model(model))
+ outputs = model(
+ ctx_features=ctx_features,
+ ctx_attn_mask=ctx_attn_mask,
+ ctx_position_ids=ctx_position_ids,
+ n_ctx_chunks=torch.ones(
+ ctx_features.shape[0],
+ dtype=torch.int32,
+ device=accelerator.device,
+ ),
+ input_ids=text_batch["input_ids"],
+ attention_mask=text_batch["attention_mask"],
+ labels=text_batch["labels"],
+ )
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ with torch.no_grad():
+ teacher_batch = prepare_smolvlm_teacher_batch(
+ processor,
+ batch["teacher_prompt_messages"],
+ build_teacher_full_messages(batch["teacher_prompt_messages"], teacher_targets),
+ accelerator.device,
+ video_fps=batch["video_fps"],
+ max_frames=batch["max_frames"],
+ video_size_longest_edge=args.video_size_longest_edge,
+ )
+ teacher_outputs = raw_model(
+ input_ids=teacher_batch["input_ids"],
+ attention_mask=teacher_batch.get("attention_mask"),
+ pixel_values=teacher_batch.get("pixel_values"),
+ pixel_attention_mask=teacher_batch.get("pixel_attention_mask"),
+ labels=None,
+ return_dict=True,
+ use_cache=False,
+ )
+ except Exception as exc: # pylint: disable=broad-except
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ if _is_skippable_video_error(exc):
+ local_skip = True
+ local_error = f"{type(exc).__name__}: {exc}"
+ else:
+ raise
+
+ local_skip_tensor = torch.tensor(
+ [1 if local_skip else 0],
+ device=accelerator.device,
+ dtype=torch.int32,
+ )
+ global_skip_tensor = accelerator.reduce(local_skip_tensor, reduction="sum")
+ if global_skip_tensor.item() > 0:
+ skipped_train_batches += 1
+ skipped_train_samples += len(batch["ids"])
+ optimizer.zero_grad(set_to_none=True)
+ disable_lora_hooks(accelerator.unwrap_model(model))
+ if local_skip and accelerator.is_local_main_process:
+ print(
+ f"[train] skipped batch due to video decode error: {local_error}",
+ flush=True,
+ )
+ continue
+
+ assert text_batch is not None
+ assert teacher_batch is not None
+ assert teacher_outputs is not None
+ assert outputs is not None
+ assert ctx_attn_mask is not None
+ teacher_ce_loss, _, answer_tokens = compute_ce_loss(outputs.logits, text_batch["labels"])
+ kl_loss, _ = compute_teacher_kl_loss(
+ outputs.logits,
+ text_batch["labels"],
+ teacher_outputs.logits,
+ teacher_batch["labels"],
+ temperature=args.kl_temperature,
+ )
+ accelerator.backward(kl_loss)
+
+ total_teacher_ce_running += teacher_ce_loss.item()
+ total_kl_running += kl_loss.item()
+ total_answer_tokens += float(answer_tokens.sum().item())
+ total_context_tokens += float(ctx_attn_mask.sum().item())
+ running_microbatches += 1
+ disable_lora_hooks(accelerator.unwrap_model(model))
+
+ if accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
+ optimizer.step()
+ scheduler.step()
+ optimizer.zero_grad(set_to_none=True)
+ global_step += 1
+
+ if accelerator.is_main_process and global_step % args.log_every == 0:
+ mean_den = max(running_microbatches, 1)
+ wandb.log(
+ {
+ "train/teacher_ce_loss": total_teacher_ce_running / mean_den,
+ "train/kl_loss": total_kl_running / mean_den,
+ "train/answer_tokens_per_sample": total_answer_tokens
+ / max(mean_den * args.per_device_batch_size, 1),
+ "train/context_tokens_per_sample": total_context_tokens
+ / max(mean_den * args.per_device_batch_size, 1),
+ "train/lr": scheduler.get_last_lr()[0],
+ "train/skipped_batches": skipped_train_batches,
+ "train/skipped_samples_local": skipped_train_samples,
+ },
+ step=global_step,
+ )
+
+ total_teacher_ce_running = 0.0
+ total_kl_running = 0.0
+ total_answer_tokens = 0.0
+ total_context_tokens = 0.0
+ running_microbatches = 0
+
+ if global_step % args.eval_every == 0:
+ val_metrics = evaluate_loss(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ val_loader,
+ accelerator.device,
+ args,
+ )
+ if accelerator.is_main_process:
+ wandb.log(
+ {
+ "val/teacher_ce_loss": val_metrics["teacher_ce_loss"],
+ "val/kl_loss": val_metrics["kl_loss"],
+ "val/skipped_batches": val_metrics["skipped_batches"],
+ "val/skipped_samples": val_metrics["skipped_samples"],
+ "val/answer_tokens_per_sample": val_metrics[
+ "answer_tokens_per_sample"
+ ],
+ "val/context_tokens_per_sample": val_metrics[
+ "context_tokens_per_sample"
+ ],
+ },
+ step=global_step,
+ )
+ if val_gen_rows:
+ fixed_generations = generate_fixed_examples(
+ accelerator,
+ model,
+ raw_model,
+ processor,
+ tokenizer,
+ val_gen_rows,
+ args,
+ )
+ write_generation_artifacts(output_dir, global_step, fixed_generations)
+
+ accelerator.wait_for_everyone()
+
+ if val_metrics["kl_loss"] < best_kl:
+ best_kl = val_metrics["kl_loss"]
+ save_checkpoint(accelerator, model, output_dir, "best-kl.pt")
+
+ if global_step % args.save_every == 0:
+ save_checkpoint(
+ accelerator,
+ model,
+ output_dir,
+ f"step-{global_step:06d}.pt",
+ )
+
+ save_checkpoint(accelerator, model, output_dir, f"last-step-{global_step:06d}.pt")
+ if accelerator.is_main_process:
+ wandb.finish()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 6218bb5..0000000
--- a/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# read the contents of the README file
-from pathlib import Path
-
-from setuptools import setup
-
-this_directory = Path(__file__).parent
-long_description = (this_directory / "README.md").read_text()
-
-setup(
- long_description=long_description,
- long_description_content_type="text/markdown",
-)
diff --git a/src/ctx_to_lora/data/finevideo.py b/src/ctx_to_lora/data/finevideo.py
new file mode 100644
index 0000000..5741564
--- /dev/null
+++ b/src/ctx_to_lora/data/finevideo.py
@@ -0,0 +1,617 @@
+import hashlib
+import json
+import math
+import random
+import re
+import subprocess
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ctx_to_lora.data.video_manifest import (
+ DATA_ROOT,
+ dump_jsonl,
+ normalize_manifest_row,
+ relativize_video_path,
+)
+
+
+SCENE_PROMPTS = (
+ "Describe what is happening in this scene.",
+ "Write a short summary of this scene.",
+)
+
+ADJACENT_PROMPTS = (
+ "Summarize this video segment.",
+ "Describe the sequence of events in this segment.",
+)
+
+FULL_PROMPTS = (
+ "Summarize this video in 1-3 sentences.",
+)
+
+_WHITESPACE_RE = re.compile(r"\s+")
+
+
+@dataclass(frozen=True)
+class SpanCandidate:
+ sample_id: str
+ split: str
+ dataset: str
+ span_type: str
+ source_video_abs: str
+ output_video_abs: str
+ clip_start_sec: float | None
+ clip_end_sec: float | None
+ prompt: str
+ target_text: str
+ metadata: dict[str, Any]
+
+
+def _read_json(path: Path) -> dict[str, Any]:
+ with open(path) as f:
+ return json.load(f)
+
+
+def _clean_text(text: str) -> str:
+ cleaned = _WHITESPACE_RE.sub(" ", text.strip())
+ cleaned = cleaned.strip(" \t\r\n")
+ return cleaned
+
+
+def _ensure_sentence(text: str) -> str:
+ text = _clean_text(text)
+ if not text:
+ return ""
+ if text[-1] not in {".", "!", "?"}:
+ text += "."
+ return text
+
+
+def _choose_prompt(sample_id: str, *, span_type: str) -> str:
+ if span_type == "scene":
+ choices = SCENE_PROMPTS
+ elif span_type == "adjacent":
+ choices = ADJACENT_PROMPTS
+ elif span_type == "full":
+ choices = FULL_PROMPTS
+ else:
+ raise ValueError(f"Unknown span type: {span_type}")
+ digest = hashlib.sha256(sample_id.encode()).digest()
+ return choices[digest[0] % len(choices)]
+
+
+def parse_hhmmss_to_seconds(value: str | None) -> float | None:
+ if not value:
+ return None
+ value = str(value).strip()
+ parts = value.split(":")
+ if len(parts) != 3:
+ return None
+ try:
+ hours = int(parts[0])
+ minutes = int(parts[1])
+ seconds = float(parts[2])
+ except ValueError:
+ return None
+ return hours * 3600.0 + minutes * 60.0 + seconds
+
+
+def _scene_bounds(scene: dict[str, Any]) -> tuple[float | None, float | None]:
+ timestamps = scene.get("timestamps") or {}
+ if isinstance(timestamps, dict):
+ start = parse_hhmmss_to_seconds(timestamps.get("start_timestamp"))
+ end = parse_hhmmss_to_seconds(timestamps.get("end_timestamp"))
+ if start is not None and end is not None and end > start:
+ return start, end
+ return None, None
+
+
+def _scene_activity_descriptions(scene: dict[str, Any]) -> list[str]:
+ activities = scene.get("activities") or []
+ out: list[str] = []
+ for activity in activities:
+ if not isinstance(activity, dict):
+ continue
+ description = _ensure_sentence(str(activity.get("description") or ""))
+ if description:
+ out.append(description)
+ return out
+
+
+def _scene_target(scene: dict[str, Any]) -> str:
+ activities = _scene_activity_descriptions(scene)
+ if activities:
+ return " ".join(activities[:2])
+ scene_title = _ensure_sentence(str(scene.get("title") or ""))
+ if scene_title:
+ return scene_title
+ mood = ((scene.get("mood") or {}).get("description") or "").strip()
+ mood_text = _ensure_sentence(f"The scene mood is {mood.lower()}." if mood else "")
+ return mood_text
+
+
+def _adjacent_target(scenes: list[dict[str, Any]]) -> str:
+ activities: list[str] = []
+ for scene in scenes:
+ activities.extend(_scene_activity_descriptions(scene))
+ if activities:
+ return " ".join(activities[:4])
+ titles = [
+ _ensure_sentence(str(scene.get("title") or ""))
+ for scene in scenes
+ ]
+ titles = [title for title in titles if title]
+ return " ".join(titles[:3])
+
+
+def _full_target_from_description(meta: dict[str, Any]) -> str:
+ description = _ensure_sentence(str((meta.get("description") or "")).strip())
+ return description
+
+
+def _span_stem(split: str, source_stem: str, span_type: str, span_key: str) -> str:
+ return f"{split}-finevideo-{source_stem}-{span_type}-{span_key}"
+
+
+def _clip_output_path(clips_root: Path, split: str, span_type: str, stem: str) -> Path:
+ digest = hashlib.sha256(stem.encode()).hexdigest()[:6]
+ return clips_root / split / span_type / digest / f"{stem}.mp4"
+
+
+def _ffmpeg_extract_clip(
+ source_video: Path,
+ output_video: Path,
+ start_sec: float,
+ end_sec: float,
+ overwrite: bool,
+ *,
+ ffmpeg_threads: int = 1,
+ ffmpeg_preset: str = "ultrafast",
+ ffmpeg_crf: int = 30,
+ include_audio: bool = False,
+) -> None:
+ if output_video.exists() and not overwrite:
+ return
+ output_video.parent.mkdir(parents=True, exist_ok=True)
+ duration = end_sec - start_sec
+ if duration <= 0:
+ raise ValueError(f"Invalid clip duration for {source_video}: {start_sec} -> {end_sec}")
+ command = [
+ "ffmpeg",
+ "-hide_banner",
+ "-loglevel",
+ "error",
+ "-threads",
+ str(max(1, int(ffmpeg_threads))),
+ "-ss",
+ f"{start_sec:.3f}",
+ "-t",
+ f"{duration:.3f}",
+ "-i",
+ str(source_video),
+ "-c:v",
+ "libx264",
+ "-preset",
+ ffmpeg_preset,
+ "-crf",
+ str(int(ffmpeg_crf)),
+ "-movflags",
+ "+faststart",
+ ]
+ if include_audio:
+ command.extend(["-c:a", "aac"])
+ else:
+ command.append("-an")
+ command.extend(
+ [
+ "-y" if overwrite else "-n",
+ str(output_video),
+ ]
+ )
+ subprocess.run(command, check=True)
+
+
+def _build_scene_candidate(
+ *,
+ split: str,
+ source_video: Path,
+ source_stem: str,
+ scene: dict[str, Any],
+ scene_index: int,
+ clips_root: Path,
+) -> SpanCandidate | None:
+ start, end = _scene_bounds(scene)
+ if start is None or end is None:
+ return None
+ target = _scene_target(scene)
+ if not target:
+ return None
+ span_key = f"s{scene_index:02d}"
+ stem = _span_stem(split, source_stem, "scene", span_key)
+ output_path = _clip_output_path(clips_root, split, "scene", stem)
+ prompt = _choose_prompt(stem, span_type="scene")
+ metadata = {
+ "dataset": "finevideo",
+ "span_type": "scene",
+ "source_video_path": relativize_video_path(str(source_video)),
+ "scene_index": scene_index,
+ "scene_id": scene.get("sceneId"),
+ "scene_title": scene.get("title"),
+ }
+ return SpanCandidate(
+ sample_id=stem,
+ split=split,
+ dataset="finevideo",
+ span_type="scene",
+ source_video_abs=str(source_video),
+ output_video_abs=str(output_path),
+ clip_start_sec=start,
+ clip_end_sec=end,
+ prompt=prompt,
+ target_text=target,
+ metadata=metadata,
+ )
+
+
+def _build_adjacent_candidate(
+ *,
+ split: str,
+ source_video: Path,
+ source_stem: str,
+ scenes: list[dict[str, Any]],
+ start_scene_index: int,
+ window_size: int,
+ clips_root: Path,
+) -> SpanCandidate | None:
+ window = scenes[start_scene_index : start_scene_index + window_size]
+ if len(window) != window_size:
+ return None
+ first_start, _ = _scene_bounds(window[0])
+ _, last_end = _scene_bounds(window[-1])
+ if first_start is None or last_end is None or last_end <= first_start:
+ return None
+ target = _adjacent_target(window)
+ if not target:
+ return None
+ span_key = f"a{start_scene_index:02d}w{window_size}"
+ stem = _span_stem(split, source_stem, "adjacent", span_key)
+ output_path = _clip_output_path(clips_root, split, "adjacent", stem)
+ prompt = _choose_prompt(stem, span_type="adjacent")
+ metadata = {
+ "dataset": "finevideo",
+ "span_type": "adjacent",
+ "source_video_path": relativize_video_path(str(source_video)),
+ "scene_index_start": start_scene_index,
+ "scene_index_end": start_scene_index + window_size - 1,
+ "scene_window_size": window_size,
+ "scene_ids": [scene.get("sceneId") for scene in window],
+ }
+ return SpanCandidate(
+ sample_id=stem,
+ split=split,
+ dataset="finevideo",
+ span_type="adjacent",
+ source_video_abs=str(source_video),
+ output_video_abs=str(output_path),
+ clip_start_sec=first_start,
+ clip_end_sec=last_end,
+ prompt=prompt,
+ target_text=target,
+ metadata=metadata,
+ )
+
+
+def _build_full_candidate(
+ *,
+ split: str,
+ source_video: Path,
+ source_stem: str,
+ content_metadata: dict[str, Any],
+ youtube_title: str,
+) -> SpanCandidate | None:
+ target = _full_target_from_description(content_metadata)
+ if not target:
+ return None
+ stem = _span_stem(split, source_stem, "full", "full")
+ prompt = _choose_prompt(stem, span_type="full")
+ metadata = {
+ "dataset": "finevideo",
+ "span_type": "full",
+ "source_video_path": relativize_video_path(str(source_video)),
+ "youtube_title": youtube_title,
+ }
+ return SpanCandidate(
+ sample_id=stem,
+ split=split,
+ dataset="finevideo",
+ span_type="full",
+ source_video_abs=str(source_video),
+ output_video_abs=str(source_video),
+ clip_start_sec=None,
+ clip_end_sec=None,
+ prompt=prompt,
+ target_text=target,
+ metadata=metadata,
+ )
+
+
+def collect_finevideo_span_pools(
+ split_root: str | Path,
+ *,
+ split: str,
+ clips_root: str | Path,
+) -> dict[str, list[SpanCandidate]]:
+ split_root = Path(split_root)
+ clips_root = Path(clips_root)
+ scene_pool: list[SpanCandidate] = []
+ adjacent_pool: list[SpanCandidate] = []
+ full_pool: list[SpanCandidate] = []
+
+ json_paths = sorted(split_root.rglob("*.json"))
+ for metadata_path in json_paths:
+ video_path = metadata_path.with_suffix(".mp4")
+ if not video_path.exists():
+ continue
+ metadata = _read_json(metadata_path)
+ content_metadata = metadata.get("content_metadata") or {}
+ scenes = content_metadata.get("scenes") or []
+ if not isinstance(scenes, list) or not scenes:
+ continue
+
+ source_stem = metadata_path.stem
+ youtube_title = str(metadata.get("youtube_title") or "")
+
+ for scene_index, scene in enumerate(scenes):
+ if not isinstance(scene, dict):
+ continue
+ candidate = _build_scene_candidate(
+ split=split,
+ source_video=video_path,
+ source_stem=source_stem,
+ scene=scene,
+ scene_index=scene_index,
+ clips_root=clips_root,
+ )
+ if candidate is not None:
+ scene_pool.append(candidate)
+
+ for window_size in (2, 3):
+ for start_scene_index in range(0, len(scenes) - window_size + 1):
+ candidate = _build_adjacent_candidate(
+ split=split,
+ source_video=video_path,
+ source_stem=source_stem,
+ scenes=scenes,
+ start_scene_index=start_scene_index,
+ window_size=window_size,
+ clips_root=clips_root,
+ )
+ if candidate is not None:
+ adjacent_pool.append(candidate)
+
+ full_candidate = _build_full_candidate(
+ split=split,
+ source_video=video_path,
+ source_stem=source_stem,
+ content_metadata=content_metadata,
+ youtube_title=youtube_title,
+ )
+ if full_candidate is not None:
+ full_pool.append(full_candidate)
+
+ return {
+ "scene": scene_pool,
+ "adjacent": adjacent_pool,
+ "full": full_pool,
+ }
+
+
+def _counts_from_target(
+ target_total: int,
+ *,
+ scene_ratio: float,
+ adjacent_ratio: float,
+ full_ratio: float,
+) -> tuple[int, int, int]:
+ scene_count = int(round(target_total * scene_ratio))
+ adjacent_count = int(round(target_total * adjacent_ratio))
+ full_count = target_total - scene_count - adjacent_count
+ return scene_count, adjacent_count, full_count
+
+
+def max_unique_mixture_total(
+ *,
+ scene_pool_size: int,
+ adjacent_pool_size: int,
+ full_pool_size: int,
+ scene_ratio: float,
+ adjacent_ratio: float,
+ full_ratio: float,
+) -> int:
+ by_scene = math.floor(scene_pool_size / scene_ratio) if scene_ratio > 0 else math.inf
+ by_adjacent = (
+ math.floor(adjacent_pool_size / adjacent_ratio) if adjacent_ratio > 0 else math.inf
+ )
+ by_full = math.floor(full_pool_size / full_ratio) if full_ratio > 0 else math.inf
+ return int(min(by_scene, by_adjacent, by_full))
+
+
+def sample_mixture(
+ pools: dict[str, list[SpanCandidate]],
+ *,
+ target_total: int,
+ scene_ratio: float = 0.60,
+ adjacent_ratio: float = 0.30,
+ full_ratio: float = 0.10,
+ seed: int = 42,
+) -> list[SpanCandidate]:
+ if target_total <= 0:
+ target_total = max_unique_mixture_total(
+ scene_pool_size=len(pools["scene"]),
+ adjacent_pool_size=len(pools["adjacent"]),
+ full_pool_size=len(pools["full"]),
+ scene_ratio=scene_ratio,
+ adjacent_ratio=adjacent_ratio,
+ full_ratio=full_ratio,
+ )
+ scene_count, adjacent_count, full_count = _counts_from_target(
+ target_total,
+ scene_ratio=scene_ratio,
+ adjacent_ratio=adjacent_ratio,
+ full_ratio=full_ratio,
+ )
+ if scene_count > len(pools["scene"]):
+ raise ValueError(
+ f"scene pool too small for target: need {scene_count}, have {len(pools['scene'])}"
+ )
+ if adjacent_count > len(pools["adjacent"]):
+ raise ValueError(
+ f"adjacent pool too small for target: need {adjacent_count}, have {len(pools['adjacent'])}"
+ )
+ if full_count > len(pools["full"]):
+ raise ValueError(
+ f"full pool too small for target: need {full_count}, have {len(pools['full'])}"
+ )
+
+ rng = random.Random(seed)
+ out: list[SpanCandidate] = []
+ out.extend(rng.sample(pools["scene"], scene_count))
+ out.extend(rng.sample(pools["adjacent"], adjacent_count))
+ out.extend(rng.sample(pools["full"], full_count))
+ rng.shuffle(out)
+ return out
+
+
+def sample_fixed_count(pool: list[SpanCandidate], *, count: int, seed: int) -> list[SpanCandidate]:
+ if count <= 0:
+ return []
+ if count >= len(pool):
+ return list(pool)
+ rng = random.Random(seed)
+ return rng.sample(pool, count)
+
+
+def _extract_one(
+ candidate: SpanCandidate,
+ overwrite: bool,
+ ffmpeg_threads: int,
+ ffmpeg_preset: str,
+ ffmpeg_crf: int,
+ include_audio: bool,
+) -> tuple[str, str | None]:
+ if candidate.span_type == "full":
+ return candidate.sample_id, None
+ if candidate.clip_start_sec is None or candidate.clip_end_sec is None:
+ return candidate.sample_id, "missing_clip_bounds"
+ try:
+ _ffmpeg_extract_clip(
+ Path(candidate.source_video_abs),
+ Path(candidate.output_video_abs),
+ candidate.clip_start_sec,
+ candidate.clip_end_sec,
+ overwrite=overwrite,
+ ffmpeg_threads=ffmpeg_threads,
+ ffmpeg_preset=ffmpeg_preset,
+ ffmpeg_crf=ffmpeg_crf,
+ include_audio=include_audio,
+ )
+ return candidate.sample_id, None
+ except Exception as e: # pylint: disable=broad-except
+ return candidate.sample_id, str(e)
+
+
+def materialize_span_clips(
+ candidates: list[SpanCandidate],
+ *,
+ num_workers: int = 16,
+ overwrite: bool = False,
+ ffmpeg_threads: int = 1,
+ ffmpeg_preset: str = "ultrafast",
+ ffmpeg_crf: int = 30,
+ include_audio: bool = False,
+) -> list[tuple[str, str]]:
+ clip_candidates = [candidate for candidate in candidates if candidate.span_type != "full"]
+ failures: list[tuple[str, str]] = []
+ if not clip_candidates:
+ return failures
+ with ThreadPoolExecutor(max_workers=num_workers) as executor:
+ futures = [
+ executor.submit(
+ _extract_one,
+ candidate,
+ overwrite,
+ ffmpeg_threads,
+ ffmpeg_preset,
+ ffmpeg_crf,
+ include_audio,
+ )
+ for candidate in clip_candidates
+ ]
+ done = 0
+ for future in as_completed(futures):
+ sample_id, error = future.result()
+ done += 1
+ if error is not None:
+ failures.append((sample_id, error))
+ if done % 500 == 0:
+ print(
+ f"[clip] done={done}/{len(clip_candidates)} failures={len(failures)}",
+ flush=True,
+ )
+ return failures
+
+
+def candidates_to_rows(candidates: list[SpanCandidate]) -> list[dict[str, Any]]:
+ rows: list[dict[str, Any]] = []
+ for candidate in candidates:
+ video_rel = relativize_video_path(candidate.output_video_abs)
+ row = {
+ "id": candidate.sample_id,
+ "video_path": video_rel,
+ "task_type": "caption",
+ "prompt": candidate.prompt,
+ "target_text": candidate.target_text,
+ "dataset": candidate.dataset,
+ "split": candidate.split,
+ "metadata": dict(candidate.metadata),
+ }
+ if candidate.clip_start_sec is not None:
+ row["clip_start_sec"] = candidate.clip_start_sec
+ if candidate.clip_end_sec is not None:
+ row["clip_end_sec"] = candidate.clip_end_sec
+ rows.append(normalize_manifest_row(row, default_split=candidate.split))
+ return rows
+
+
+def write_finevideo_manifests(
+ *,
+ train_rows: list[dict[str, Any]],
+ val_rows: list[dict[str, Any]],
+ val_scene_rows: list[dict[str, Any]],
+ val_adjacent_rows: list[dict[str, Any]],
+ val_full_rows: list[dict[str, Any]],
+ val_gen_rows: list[dict[str, Any]],
+ train_out: str | Path,
+ val_out: str | Path,
+ val_scene_out: str | Path,
+ val_adjacent_out: str | Path,
+ val_full_out: str | Path,
+ val_core_out: str | Path,
+ val_gen_out: str | Path,
+) -> None:
+ dump_jsonl(train_out, train_rows)
+ dump_jsonl(val_out, val_rows)
+ dump_jsonl(val_scene_out, val_scene_rows)
+ dump_jsonl(val_adjacent_out, val_adjacent_rows)
+ dump_jsonl(val_full_out, val_full_rows)
+ dump_jsonl(val_core_out, val_rows[:1024])
+ dump_jsonl(val_gen_out, val_gen_rows)
+
+
+def ensure_relative_to_data_root(path: str | Path) -> str:
+ path = Path(path)
+ if not path.is_absolute():
+ return str(path)
+ return str(path.relative_to(DATA_ROOT))
diff --git a/src/ctx_to_lora/data/video_manifest.py b/src/ctx_to_lora/data/video_manifest.py
new file mode 100644
index 0000000..29a57de
--- /dev/null
+++ b/src/ctx_to_lora/data/video_manifest.py
@@ -0,0 +1,129 @@
+import os
+import json
+from pathlib import Path
+from typing import Any
+
+
+DATA_ROOT = Path(os.environ.get("VIDEO2LORA_DATA_ROOT", "data/video2lora"))
+
+CAPTION_PROMPTS = (
+ "Describe what is happening in this video.",
+ "Write a short caption for this clip.",
+ "Summarize this video in one sentence.",
+ "Describe the main event in this clip.",
+)
+
+DEFAULT_INTERNALIZATION_PROMPT = "Internalize this video for later captioning."
+
+
+def resolve_video_path(video_path_str: str) -> str:
+ video_path = Path(video_path_str)
+ if not video_path.is_absolute():
+ video_path = DATA_ROOT / video_path
+ return str(video_path)
+
+
+def relativize_video_path(video_path_str: str) -> str:
+ video_path = Path(video_path_str)
+ if not video_path.is_absolute():
+ return str(video_path)
+ try:
+ return str(video_path.relative_to(DATA_ROOT))
+ except ValueError:
+ return str(video_path)
+
+
+def dump_jsonl(path: str | Path, rows: list[dict[str, Any]]) -> None:
+ output_path = Path(path)
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ with open(output_path, "w") as f:
+ for row in rows:
+ f.write(json.dumps(row) + "\n")
+
+
+def infer_split(row: dict[str, Any], default_split: str | None = None) -> str | None:
+ if row.get("split") is not None:
+ return str(row["split"])
+ sample_id = str(row.get("id", ""))
+ for candidate in ("train", "val", "test"):
+ if sample_id.startswith(f"{candidate}-") or sample_id.startswith(f"{candidate}_"):
+ return candidate
+ return default_split
+
+
+def infer_dataset(row: dict[str, Any]) -> str:
+ metadata = row.get("metadata") or {}
+ if row.get("dataset") is not None:
+ return str(row["dataset"])
+ if metadata.get("dataset") is not None:
+ return str(metadata["dataset"])
+ return "unknown"
+
+
+def normalize_manifest_row(
+ row: dict[str, Any],
+ *,
+ default_split: str | None = None,
+) -> dict[str, Any]:
+ prompt = row.get("prompt")
+ target_text = row.get("target_text")
+ task_type = row.get("task_type")
+
+ if prompt is None and "question" in row:
+ prompt = row["question"]
+ if target_text is None and "answer" in row:
+ target_text = row["answer"]
+ if task_type is None:
+ task_type = "qa" if "question" in row or "answer" in row else "caption"
+
+ if prompt is None:
+ raise ValueError(f"Missing prompt/question in row: {row}")
+ if target_text is None:
+ raise ValueError(f"Missing target_text/answer in row: {row}")
+ if "video_path" not in row:
+ raise ValueError(f"Missing video_path in row: {row}")
+
+ metadata = dict(row.get("metadata") or {})
+ dataset = infer_dataset(row)
+ if "dataset" not in metadata:
+ metadata["dataset"] = dataset
+
+ normalized = {
+ "id": str(row.get("id", Path(str(row["video_path"])).stem)),
+ "video_path": relativize_video_path(str(row["video_path"])),
+ "task_type": str(task_type),
+ "prompt": str(prompt),
+ "target_text": str(target_text),
+ "dataset": dataset,
+ "split": infer_split(row, default_split=default_split),
+ "metadata": metadata,
+ }
+
+ for key in ("clip_start_sec", "clip_end_sec"):
+ if row.get(key) is not None:
+ normalized[key] = float(row[key])
+
+ return normalized
+
+
+def load_video_manifest(
+ path: str | Path,
+ *,
+ max_samples: int | None = None,
+ default_split: str | None = None,
+) -> list[dict[str, Any]]:
+ rows: list[dict[str, Any]] = []
+ with open(path) as f:
+ for line in f:
+ line = line.strip()
+ if not line:
+ continue
+ rows.append(
+ normalize_manifest_row(
+ json.loads(line),
+ default_split=default_split,
+ )
+ )
+ if max_samples is not None and len(rows) >= max_samples:
+ break
+ return rows
diff --git a/src/ctx_to_lora/eval_utils.py b/src/ctx_to_lora/eval_utils.py
deleted file mode 100644
index a338acb..0000000
--- a/src/ctx_to_lora/eval_utils.py
+++ /dev/null
@@ -1,1149 +0,0 @@
-import json
-import logging
-import os
-import re
-import string
-import time
-from argparse import Namespace
-from collections import Counter, defaultdict
-from dataclasses import fields
-from functools import partial
-
-import numpy as np
-import pandas as pd
-import torch
-import yaml
-from datasets import disable_caching
-from peft import get_peft_model
-from transformers import (
- PreTrainedModel,
- Seq2SeqTrainer,
- Seq2SeqTrainingArguments,
- Trainer,
- set_seed,
-)
-
-from ctx_to_lora.data.collator import eval_collator, generation_collator
-from ctx_to_lora.data.definitions import (
- CLOSED_QA_DATASETS,
- CTX_AFFIXES,
- LONGBENCH_E_TASKS,
- LONGBENCH_TASKS,
- MULTI_ANSWER_DATASETS,
-)
-from ctx_to_lora.data.processing import (
- get_tokenized_dataset,
- load_answers,
-)
-from ctx_to_lora.data.self_gen_template import SELF_QA_INTX
-from ctx_to_lora.metrics import (
- LENGTH_BINS,
- Evaluator,
- compute_metrics,
- compute_per_token_acc,
- compute_perplexity,
- compute_prefix_matching,
- compute_rouge,
-)
-from ctx_to_lora.model_loading import (
- get_lora_config,
- get_model,
- get_model_and_tokenizer,
- get_tokenizer,
-)
-from ctx_to_lora.modeling.context_distillation import CtxDistillModel
-from ctx_to_lora.modeling.generative_adapter import GenerativeAdapter
-from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-from ctx_to_lora.modeling.llm_lingua import LLMLinguaModel
-from ctx_to_lora.modeling.text_to_lora import TextToLoRA
-from ctx_to_lora.tracker.tracker import (
- add_tracker,
- print_global_tracker_stats,
- print_tracker_stats,
- reset_trackers,
- save_tracker_stats_csv,
-)
-from ctx_to_lora.utils import clear_gpu, concat_list, get_run_name, setup_logging
-
-logger = logging.getLogger()
-
-
-# from https://gist.github.com/cloneofsimo/8abd0284d4738f28f04200628f9a83f5
-# https://github.com/Nordth/humanize-ai-lib/blob/main/src/humanize-string.ts
-
-_HIDDEN_CHARS = re.compile(
- r"[\u00AD\u180E\u200B-\u200F\u202A-\u202E\u2060\u2066-\u2069\uFEFF]"
-)
-_TRAILING_WS = re.compile(r"[ \t\x0B\f]+$", re.MULTILINE)
-_NBSP = re.compile(r"\u00A0")
-_DASHES = re.compile(r"[โโ]+") # em- & en-dashes โ ASCII hyphen
-_DQUOTES = re.compile(r"[โโยซยปโ]") # curly / guillemets โ "
-_SQUOTES = re.compile(r"[โโสผ]") # curly apostrophes โ '
-_ELLIPSIS = re.compile(r"โฆ") # singleโchar ellipsis โ "..."
-_ENDASH = re.compile(r"\u2013")
-_EMDASH = re.compile(r"\u2014")
-
-
-def humanize_str(text: str) -> str:
- text = _HIDDEN_CHARS.sub("", text)
- text = _TRAILING_WS.sub("", text)
- text = _NBSP.sub(" ", text)
- text = _DASHES.sub("-", text)
- text = _ENDASH.sub("-", text)
- text = _EMDASH.sub("-", text)
- text = _DQUOTES.sub('"', text)
- text = _SQUOTES.sub("'", text)
- text = _ELLIPSIS.sub("...", text)
- return text
-
-
-def normalize_answer(s: str) -> str:
- """Lower text and remove punctuation, articles and extra whitespace."""
-
- def remove_articles(text: str) -> str:
- return re.sub(r"\b(a|an|the)\b", " ", text)
-
- def white_space_fix(text: str) -> str:
- return " ".join(text.split())
-
- def remove_punc(text: str) -> str:
- exclude = set(string.punctuation)
- # return "".join(ch for ch in text if ch not in exclude)
- return " ".join(re.split(f"[{string.punctuation}]", text))
-
- def lower(text: str) -> str:
- return text.lower()
-
- return white_space_fix(remove_articles(remove_punc(lower(humanize_str(s)))))
-
-
-def split_string(s: str) -> list[str]:
- out = re.split(r"[- \s]", s) # split by hyphen, space, or whitespace
- return [x for x in out if x] # remove empty spaces
-
-
-def f1_score(
- prediction: list[str], ground_truth: list[str]
-) -> tuple[float, float, float]:
- """Compute F1 score, precision, and recall between prediction and ground truth strings."""
- common = Counter(prediction) & Counter(ground_truth)
- num_same = sum(common.values())
- if num_same == 0:
- return 0, 0, 0
- precision = 1.0 * num_same / len(prediction) if len(prediction) > 0 else 0
- recall = 1.0 * num_same / len(ground_truth) if len(ground_truth) > 0 else 0
- f1 = (
- (2 * precision * recall) / (precision + recall)
- if (precision + recall) > 0
- else 0
- )
- return f1, precision, recall
-
-
-def compute_qa_f1_score(
- pred_texts: list[str], answers_list: list[list[str]]
-) -> dict[str, float]:
- """
- Word-level F1 score for evaluating question answering systems.
- Order of the words does not matter.
- """
- f1_scores = []
- precisions = []
- recalls = []
-
- for prediction, answers in zip(pred_texts, answers_list):
- normalized_prediction = normalize_answer(prediction)
- prediction_words = split_string(normalized_prediction)
- best_f1 = 0
- best_precision = 0
- best_recall = 0
-
- for answer in answers:
- normalized_label = normalize_answer(answer)
- label_words = split_string(normalized_label)
- f1, precision, recall = f1_score(prediction_words, label_words)
- if f1 > best_f1:
- best_f1 = f1
- best_precision = precision
- best_recall = recall
-
- f1_scores.append(best_f1)
- precisions.append(best_precision)
- recalls.append(best_recall)
-
- return dict(
- qa_f1_score=np.mean(f1_scores),
- qa_precision=np.mean(precisions),
- qa_recall=np.mean(recalls),
- ), dict(qa_f1_score=f1_scores, qa_precision=precisions, qa_recall=recalls)
-
-
-def add_longbench_tasks(ds_names: list[str]) -> None:
- """Add longbench tasks to dataset names list."""
- if "longbench" in ds_names:
- ds_names.remove("longbench")
- ds_names += LONGBENCH_TASKS
- if "longbench_e" in ds_names:
- ds_names.remove("longbench_e")
- ds_names += LONGBENCH_E_TASKS
-
-
-def save_generated_text(
- samples: list[dict],
- per_sample_metric: dict[str, list[float]],
- output_dir: str,
- split: str,
-) -> None:
- """Save generated text samples to JSONL file."""
- os.makedirs(output_dir, exist_ok=True)
- # Create any necessary subdirectories if split contains path separators
- if "/" in split:
- split_dir = os.path.join(output_dir, os.path.dirname(split))
- os.makedirs(split_dir, exist_ok=True)
-
- metric_keys = list(per_sample_metric.keys())
- # assert len(metric_keys) == 1
-
- with open(f"{output_dir}/{split}_generated_text.jsonl", "w") as f:
- for i, sample in enumerate(samples):
- for metric_name in metric_keys:
- sample[f"{metric_name}"] = per_sample_metric[metric_name][i]
- f.write(json.dumps(sample) + "\n")
-
-
-# ============================================================================
-# CSV Export Utilities
-# ============================================================================
-
-
-def _extract_model_info(eval_trainer) -> tuple[str, bool]:
- """Extract model name and type information from the trainer."""
- model_name = "unknown_model"
- is_hypernet = False
-
- if hasattr(eval_trainer.model, "base_model"):
- if hasattr(eval_trainer.model.base_model, "config"):
- model_name = getattr(
- eval_trainer.model.base_model.config,
- "name_or_path",
- getattr(
- eval_trainer.model.base_model.config, "_name_or_path", "unknown"
- ),
- )
- is_hypernet = hasattr(eval_trainer.model, "ctx_encoder")
- elif hasattr(eval_trainer.model, "config"):
- model_name = getattr(
- eval_trainer.model.config,
- "name_or_path",
- getattr(eval_trainer.model.config, "_name_or_path", "unknown"),
- )
-
- # Clean up model name for display
- if "/" in model_name:
- model_name = model_name.split("/")[-1]
-
- if getattr(eval_trainer.args, "run_name", None):
- model_name += f"_{eval_trainer.args.run_name}"
-
- return model_name, is_hypernet
-
-
-def _parse_metrics_for_csv(
- metrics_dict: dict[str, dict[str, any]],
-) -> tuple[set, set, set]:
- """Parse metrics dictionary to extract unique metrics, length groups, and splits."""
- all_metrics = set()
- all_length_groups = set()
- all_splits = set()
-
- for split_name, metrics in metrics_dict.items():
- all_splits.add(split_name)
-
- for metric_key in metrics.keys():
- if not metric_key.startswith(split_name):
- continue
-
- # Skip timing and performance metrics that aren't evaluation results
- if any(
- skip_term in metric_key
- for skip_term in [
- "model_preparation_time",
- "steps_per_second",
- "samples_per_second",
- "runtime",
- ]
- ):
- continue
-
- # Remove the split prefix to get the actual metric name
- metric_name = metric_key[len(split_name) + 1 :]
-
- # Check if this is a length-specific metric
- if "_len_" in metric_name:
- base_metric, length_part = metric_name.split("_len_", 1)
- all_metrics.add(base_metric)
- all_length_groups.add(length_part)
- else:
- all_metrics.add(metric_name)
-
- # Add overall metric (no length grouping)
- all_length_groups.add("overall")
- return all_metrics, all_length_groups, all_splits
-
-
-def _sort_length_groups(length_groups: set[str]) -> list[str]:
- """Sort length groups with custom ordering for proper numerical ranges."""
-
- def sort_key(length_group: str) -> tuple:
- if length_group == "overall":
- return (1, 0, 0) # Put "overall" after numerical ranges
- try:
- # Parse "low-high" format
- low, high = map(float, length_group.split("-"))
- return (0, low, high)
- except (ValueError, IndexError):
- return (2, 0, 0) # Put any malformed strings last
-
- return sorted(list(length_groups), key=sort_key)
-
-
-def create_metrics_csv(
- metrics_dict: dict[str, dict[str, any]],
- output_dir: str,
- model_name: str,
- is_hypernet_model: bool = False,
- remove_context: bool = False,
- csv_suffix: str = "",
-) -> None:
- """
- Create a CSV file with columns: model_name, group_len, tasks, num_samples, and all available metrics.
- One row per model-length-task combination.
-
- Args:
- metrics_dict: Dictionary containing evaluation metrics for each dataset/split
- output_dir: Directory to save the CSV file
- model_name: Name of the model being evaluated
- is_hypernet_model: Whether this is a hypernet/modulated model or base model
- remove_context: Whether context was removed during evaluation
- csv_suffix: Additional suffix for the CSV filename
- """
- os.makedirs(output_dir, exist_ok=True)
-
- # Parse metrics to extract components
- all_metrics, all_length_groups, all_splits = _parse_metrics_for_csv(metrics_dict)
-
- # Sort for consistent ordering
- all_length_groups = _sort_length_groups(all_length_groups)
- all_splits = sorted(all_splits)
-
- # Create rows for each model-length-task combination
- rows = []
-
- for task in all_splits:
- metrics = metrics_dict[task]
-
- for length_group in all_length_groups:
- # Initialize row with basic info
- row = {
- "model_name": model_name,
- "group_len": length_group,
- "tasks": task,
- "num_samples": 0,
- }
-
- # Look for all metrics for this task and length group
- if length_group == "overall":
- # Look for overall metrics (no length suffix)
- for metric_key in metrics:
- if not metric_key.startswith(f"{task}_"):
- continue
-
- # Skip length-specific metrics
- if "_len_" in metric_key:
- continue
-
- # Extract metric name after task prefix
- metric_name = metric_key[len(task) + 1 :]
- if metric_name in [
- "samples_per_second",
- "steps_per_second",
- "model_preparation_time",
- "runtime",
- ]:
- # Skip timing and performance metrics
- continue
-
- # Handle num_samples specially
- if metric_name.startswith("num_samples_"):
- row["num_samples"] = metrics[metric_key]
- else:
- # Add all other metrics as columns
- row[metric_name] = metrics[metric_key]
- else:
- # Look for length-specific metrics
- for metric_key in metrics:
- if not metric_key.startswith(f"{task}_"):
- continue
-
- # Only process metrics for this specific length group
- if f"_len_{length_group}" not in metric_key:
- continue
-
- # Extract metric name (remove task prefix and length suffix)
- metric_part = metric_key[len(task) + 1 :]
- metric_name = metric_part.replace(f"_len_{length_group}", "")
- if metric_name in [
- "samples_per_second",
- "steps_per_second",
- "model_preparation_time",
- "runtime",
- ]:
- # Skip timing and performance metrics
- continue
-
- # Handle num_samples specially
- if metric_name.startswith("num_samples_"):
- row["num_samples"] = metrics[metric_key]
- else:
- # Add all other metrics as columns
- row[metric_name] = metrics[metric_key]
-
- # Fill missing metrics with N/A for consistent columns
- for metric in all_metrics:
- if metric not in row and not metric.startswith("num_samples"):
- row[metric] = "N/A"
-
- rows.append(row)
-
- # Create DataFrame
- if rows:
- new_df = pd.DataFrame(rows)
- # define categories so that they're sorted properly
- new_df["group_len"] = pd.Categorical(
- new_df["group_len"],
- categories=[f"{i}-{j}" for (i, j) in LENGTH_BINS] + ["overall"],
- )
-
- # Sort by tasks, then by length group
- new_df = new_df.sort_values(["tasks", "group_len"]).reset_index(drop=True)
-
- # Construct filename
- csv_filename = "evaluation_results"
- if csv_suffix:
- csv_filename += f"_{csv_suffix}"
- if remove_context:
- csv_filename += "_no_context"
- csv_filename += ".csv"
-
- csv_path = os.path.join(output_dir, csv_filename)
- os.makedirs(os.path.dirname(csv_path), exist_ok=True)
-
- # Check if CSV already exists and merge if it does
- if os.path.exists(csv_path):
- try:
- existing_df = pd.read_csv(csv_path)
-
- # Remove existing rows with the same model_name, group_len, and tasks
- # to avoid duplicates when updating
- mask = ~existing_df["tasks"].isin(new_df["tasks"])
- existing_df = existing_df[mask]
-
- # Concatenate existing and new data
- df = pd.concat([existing_df, new_df], ignore_index=True)
-
- # Sort by tasks, then by length group
- df = df.sort_values(["tasks"]).reset_index(drop=True)
-
- print(f"Updated existing CSV with {len(new_df)} new rows")
- except Exception as e:
- print(f"Warning: Could not read existing CSV ({e}), creating new file")
- df = new_df
- else:
- df = new_df
- print(f"Created new CSV with {len(new_df)} rows")
-
- df.to_csv(csv_path, index=False)
-
- print(f"Evaluation results saved to: {csv_path}")
- else:
- print("No evaluation data found to save to CSV")
-
-
-# ============================================================================
-# Evaluation Functions
-# ============================================================================
-
-
-def decode_test_result(
- test_dataset, test_result, tokenizer, ctx_tokenizer
-) -> list[dict]:
- """Decode test results into human-readable format."""
- out = []
- for sample, pred_toks in zip(test_dataset, test_result.predictions):
- d = dict()
- if "labels" in sample:
- start_idx = np.argmax(sample["labels"] != -100)
- label_toks = sample["labels"][start_idx:]
- # labels are padded with -100, so we need to
- # replace them with the pad token id
- label_toks = np.where(
- label_toks == -100, tokenizer.pad_token_id, label_toks
- )
- label_text = tokenizer.decode(label_toks, skip_special_tokens=True)
- d["label"] = label_text.strip()
-
- # remove the label part
- input_toks = sample["input_ids"][:start_idx]
-
- gen_toks = pred_toks[np.argmax(pred_toks != tokenizer.pad_token_id) :]
- # gen_toks = gen_toks[start_idx:]
- suffix = np.array(CTX_AFFIXES[tokenizer.name_or_path]["suffix"])
- # iterate over gen_toks and take the answer after the suffix
- for i in range(len(gen_toks) - len(suffix), -1, -1):
- if all(gen_toks[i : i + len(suffix)] == suffix):
- gen_toks = gen_toks[i + len(suffix) :]
- break
- gen_toks = np.where(gen_toks == -100, tokenizer.pad_token_id, gen_toks)
-
- d["input"] = tokenizer.decode(input_toks, skip_special_tokens=False)
- d["generated"] = tokenizer.decode(gen_toks, skip_special_tokens=True).strip()
- if "ctx_ids" in sample:
- d["context"] = ctx_tokenizer.decode(
- concat_list(sample["ctx_ids"]), skip_special_tokens=False
- )
- for k in sample:
- if k.endswith("_len"):
- d[k] = sample[k].item()
- out.append(d)
-
- # sort samples by length if possible
- len_key = "ctx_ids_len" if "ctx_ids_len" in sample else "input_ids_len"
- sorted(out, key=lambda x: x[len_key])
-
- return out
-
-
-def eval_generation(
- eval_trainer,
- tokenizer,
- ctx_tokenizer,
- datasets,
- original_datasets,
- answers,
- split,
- remove_context,
- gen_kwargs,
-) -> dict[str, dict]:
- """Evaluate model using generation and save metrics to CSV."""
- if not isinstance(datasets, dict):
- datasets = {"": datasets}
-
- out = {}
- for ds_name, ds in datasets.items():
- print(f"Evaluating: {ds_name}")
- split_name = f"{split}_{ds_name}" if ds_name else split
- if remove_context:
- split_name += "_no_context"
-
- clear_gpu()
- eval_result = eval_trainer.predict(
- ds,
- metric_key_prefix=split_name,
- **gen_kwargs,
- )
- decoded_txts = decode_test_result(ds, eval_result, tokenizer, ctx_tokenizer)
-
- pred_texts = [txt["generated"] for txt in decoded_txts]
- label_texts = [txt["label"] for txt in decoded_txts]
- if ds_name in answers:
- answers_list = answers[ds_name]["answers"]
- else:
- answers_list = [[txt] for txt in label_texts]
- n = len(pred_texts)
-
- if ds_name in CLOSED_QA_DATASETS:
- print("Computing QA F1 Score")
- qa_f1_metric, per_sample_metric = compute_qa_f1_score(
- pred_texts, answers_list
- )
- for k, v in qa_f1_metric.items():
- eval_result.metrics[f"{split_name}_{k}"] = v
- eval_result.metrics[f"{split_name}_num_samples_{k}"] = n
- else:
- rouge_metrics, per_sample_metric = compute_rouge(pred_texts, label_texts)
- for k, v in rouge_metrics.items():
- eval_result.metrics[f"{split_name}_{k}"] = v
- eval_result.metrics[f"{split_name}_num_samples_{k}"] = n
-
- # Ensure all keys for length metrics are present, even if a bin is empty
- for low, high in LENGTH_BINS:
- if ds_name in CLOSED_QA_DATASETS:
- eval_result.metrics[f"{split_name}_qa_f1_len_{low}-{high}"] = "None"
- else:
- eval_result.metrics[f"{split_name}_rougeL.f1_len_{low}-{high}"] = "None"
-
- grouped_texts = defaultdict(lambda: {"generated": [], "label": [], "count": 0})
- for txt in decoded_txts:
- len_key = "ctx_ids_len" if "ctx_ids_len" in txt else "input_ids_len"
- input_len = txt[len_key]
- for low, high in LENGTH_BINS:
- if low <= input_len <= high:
- group_key = f"{low}-{high}"
- grouped_texts[group_key]["generated"].append(txt["generated"])
- grouped_texts[group_key]["label"].append(txt["label"])
- grouped_texts[group_key]["count"] += 1
- # break
-
- for group_key, data in grouped_texts.items():
- if data["count"] > 0:
- if ds_name in CLOSED_QA_DATASETS:
- # Get corresponding answers for this group
- group_answers = []
- group_idx = 0
- for i, txt in enumerate(decoded_txts):
- len_key = (
- "ctx_ids_len" if "ctx_ids_len" in txt else "input_ids_len"
- )
- input_len = txt[len_key]
- for low, high in LENGTH_BINS:
- if low <= input_len <= high:
- if f"{low}-{high}" == group_key:
- if ds_name in answers:
- group_answers.append(
- answers[ds_name]["answers"][i]
- )
- else:
- group_answers.append([txt["label"]])
- # break
-
- group_qa_f1_metric, _ = compute_qa_f1_score(
- data["generated"], group_answers
- )
- for k, v in group_qa_f1_metric.items():
- eval_result.metrics[f"{split_name}_{k}_len_{group_key}"] = v
- eval_result.metrics[
- f"{split_name}_num_samples_{k}_len_{group_key}"
- ] = data["count"]
- else:
- group_rouge_metrics, _ = compute_rouge(
- data["generated"], data["label"]
- )
- for k, v in group_rouge_metrics.items():
- eval_result.metrics[f"{split_name}_{k}_len_{group_key}"] = v
- # also add count data
- eval_result.metrics[
- f"{split_name}_num_samples_{k}_len_{group_key}"
- ] = data["count"]
-
- save_generated_text(
- decoded_txts,
- per_sample_metric,
- split=split_name,
- output_dir=eval_trainer.args.output_dir,
- )
- out[split_name] = eval_result.metrics
- eval_trainer.log_metrics(split_name, eval_result.metrics)
- eval_trainer.save_metrics(split_name, eval_result.metrics)
- clear_gpu()
-
- # Create CSV summary of all evaluation results
- if out:
- model_name, is_hypernet = _extract_model_info(eval_trainer)
- create_metrics_csv(
- out,
- output_dir=eval_trainer.args.output_dir,
- model_name=model_name,
- is_hypernet_model=is_hypernet,
- remove_context=remove_context,
- csv_suffix="generation",
- )
-
- return out
-
-
-def eval_teacher_forcing(
- eval_trainer, datasets, split, remove_context
-) -> dict[str, dict]:
- """Evaluate using teacher forcing and save metrics to CSV."""
- if not isinstance(datasets, dict):
- datasets = {"": datasets}
-
- out = {}
- for ds_name, ds in datasets.items():
- split_name = f"{split}_{ds_name}" if ds_name else split
- if "/" in split_name:
- split_dir = os.path.join(
- eval_trainer.args.output_dir, os.path.dirname(split_name)
- )
- os.makedirs(split_dir, exist_ok=True)
- if remove_context:
- split_name += "_no_context"
-
- metrics = eval_trainer.evaluate(ds, metric_key_prefix=split_name)
- out[split_name] = metrics
- eval_trainer.log_metrics(split_name, metrics)
- eval_trainer.save_metrics(split_name, metrics)
- clear_gpu()
-
- # Create CSV for teacher forcing metrics
- if out:
- model_name, is_hypernet = _extract_model_info(eval_trainer)
- create_metrics_csv(
- out,
- output_dir=eval_trainer.args.output_dir,
- model_name=model_name,
- is_hypernet_model=is_hypernet,
- remove_context=remove_context,
- csv_suffix="teacher_forcing",
- )
-
- return out
-
-
-def evaluate(
- checkpoint_path: str,
- model_name_or_path: str,
- eval_batch_size: int,
- args: Namespace,
- split: str,
- max_ctx_chunk_len: int,
- max_new_tokens: int,
- generative: bool,
-) -> dict[str, dict]:
- """Main evaluation function."""
- assert split in ["validation", "test"]
- ctx_name = None
- model_kwargs = dict(attn_implementation="flash_attention_2")
-
- tokenizer = get_tokenizer(args.model_name_or_path, train=False)
- if tokenizer.pad_token_id is None:
- tokenizer.pad_token_id = tokenizer.eos_token_id
-
- use_cd = False
- ctx_model_max_len = None
- base_model = None
-
- if model_name_or_path is None:
- try:
- state_dict = torch.load(checkpoint_path, weights_only=False)
- except FileNotFoundError:
- raise FileNotFoundError(f"Checkpoint {checkpoint_path} not found. ")
- ctx_name = state_dict["ctx_encoder_args"].ctx_encoder_model_name_or_path
-
- model = ModulatedPretrainedModel.from_state_dict(
- state_dict,
- train=False,
- base_model_kwargs=model_kwargs,
- use_flash_attn=True,
- use_sequence_packing=False, # for generation
- user_defined_scaling=args.gen_lora_scaling,
- )
- if getattr(args, "use_llmlingua", False):
- print("Using LLMLingua-2 for compressing inp")
- inp_compressor = LLMLinguaModel(
- model.base_model, tokenizer, args.llmlingua_compression_rate
- )
- model.inp_compressor = inp_compressor
- ctx_model_max_len = model.ctx_encoder.config.max_position_embeddings
- model.enable_iterative_mode(args.use_iterative_mode)
- add_tracker(model.base_model.generate, "generate")
- add_tracker(model.generate_weights, "generate_weights")
- add_tracker(model.combine_lora, "combine_lora")
- add_tracker(model.apply_lora_to_layers, "apply_lora_to_layers")
- else:
- model = base_model = get_model(
- model_name_or_path,
- train=False,
- requires_grad=False,
- model_kwargs=model_kwargs,
- use_flash_attn=True,
- )
- add_tracker(base_model.generate, "generate")
- if use_cd := getattr(args, "use_cd", False):
- peft_config = get_lora_config(
- model_name_or_path,
- lora_r=8,
- lora_dropout=0,
- target_modules=["down_proj"],
- )
- peft_config.lora_alpha = 16
- peft_model = get_peft_model(base_model, peft_config)
- sep_seq = (
- tokenizer(
- SELF_QA_INTX.strip("\n"),
- add_special_tokens=False,
- return_tensors="pt",
- )
- .input_ids[0][1:]
- .to(base_model.device)
- )
- ctx_distill_kwargs = dict(
- prefix_tokens=torch.tensor(
- CTX_AFFIXES[model_name_or_path]["prefix"], device=base_model.device
- ),
- ctx_inp_sep_seq=sep_seq,
- pad_token_id=tokenizer.pad_token_id,
- update_iterations=args.cd_update_iterations,
- tokenizer=tokenizer,
- reprompt_ctx=args.add_ctx_to_input,
- )
- if args.cd_use_gen_q:
- q_model, q_tokenizer = get_model_and_tokenizer(
- "google/gemma-3-4b-it",
- train=False,
- requires_grad=False,
- )
- ctx_distill_kwargs["q_model"] = q_model
- ctx_distill_kwargs["q_tokenizer"] = q_tokenizer
- ctx_distill_kwargs["q_gen_rounds"] = args.q_gen_rounds
- ctx_distill_kwargs["batch_size"] = args.cd_batch_size
- model = CtxDistillModel(peft_model, **ctx_distill_kwargs)
-
- add_tracker(model._distill_context, "distill_context")
- add_tracker(model.generate_questions, "generate_questions")
- add_tracker(model.teacher_generate, "teacher_generate")
- add_tracker(model.student_generate, "student_generate")
- elif use_llmlingua := getattr(args, "use_llmlingua", False):
- model = LLMLinguaModel(
- base_model, tokenizer, args.llmlingua_compression_rate
- )
- ctx_model_max_len = model.base_model.config.max_position_embeddings
- add_tracker(model.base_model.generate, "generate")
- add_tracker(model.compress, "prompt_compress")
-
- elif use_t2l := getattr(args, "use_t2l", False):
- model = TextToLoRA(
- base_model.name_or_path,
- prefix_tokens=torch.tensor(
- CTX_AFFIXES[model_name_or_path]["prefix"], device=base_model.device
- ),
- device=base_model.device,
- )
- ctx_model_max_len = model.base_model.config.max_position_embeddings
- add_tracker(model.base_model.generate, "base_model.generate")
- add_tracker(model.generate_weights, "generate_weights")
-
- elif args.use_generative_adapter:
- model = GenerativeAdapter(model=base_model, tokenizer=tokenizer)
- ctx_model_max_len = base_model.config.max_position_embeddings
-
- if base_model is None:
- base_model = model.base_model
- base_model.config.pad_token_id = tokenizer.pad_token_id
- base_model.generation_config.pad_token_id = tokenizer.pad_token_id
-
- ctx_tokenizer = tokenizer
- if ctx_name:
- ctx_tokenizer = get_tokenizer(ctx_name, train=False)
- if ctx_tokenizer.pad_token_id is None:
- ctx_tokenizer.pad_token_id = ctx_tokenizer.eos_token_id
-
- add_ctx_to_chat = (
- (isinstance(model, PreTrainedModel) and not args.remove_context)
- or isinstance(model, CtxDistillModel)
- or args.add_ctx_to_input
- )
-
- _get_tokenized_dataset = partial(
- get_tokenized_dataset,
- max_qas_len=-1,
- max_qas_per_sample=1,
- max_ctx_chunk_len=max_ctx_chunk_len,
- min_ctx_chunk_len=-1,
- num_chunk_probs=None,
- max_ctx_chunk_num=None,
- base_model_max_len=model.base_model.config.max_position_embeddings,
- tokenizer=tokenizer,
- ctx_model_max_len=ctx_model_max_len,
- ctx_tokenizer=ctx_tokenizer,
- add_ctx_to_chat=add_ctx_to_chat,
- add_negative_prompt=False,
- use_kl_loss=False,
- max_new_tokens=max_new_tokens,
- set_format="pt",
- add_self_distill_template=use_cd, # only for eval
- truncate_if_too_long_inp=args.truncate_if_too_long_inp, # only for eval
- truncate_if_too_long_ctx=args.truncate_if_too_long_ctx, # only for eval
- flip_ctx_inp=args.flip_ctx_inp, # only for eval
- )
-
- datasets = dict()
- original_datasets = dict()
- answers = dict()
- ds_names = args.val_ds_names if split == "validation" else args.test_ds_names
- add_longbench_tasks(ds_names)
- for ds_name in ds_names:
- datasets[ds_name] = _get_tokenized_dataset(ds_name, split)
- # handling cases where there are multiple answers
- if ds_name in MULTI_ANSWER_DATASETS:
- answers[ds_name] = load_answers(ds_name, split)
- print(f"Datasets: {datasets}")
- print(f"Answers: {answers}")
-
- # truncating num val samples
- max_eval_samples_per_ds = getattr(args, "max_val_samples_per_ds", 0)
- if split == "validation" and max_eval_samples_per_ds > 0:
- print(f"Truncating all validation ds to {max_eval_samples_per_ds} samples")
- for ds_name, ds in datasets.items():
- val_indices = np.random.permutation(len(ds))[:max_eval_samples_per_ds]
- datasets[ds_name] = ds.select(val_indices)
- if ds_name in answers:
- answers[ds_name] = answers[ds_name].select(val_indices)
- if ds_name in original_datasets:
- original_datasets[ds_name] = original_datasets[ds_name].select(
- val_indices
- )
-
- max_test_samples_per_ds = getattr(args, "max_test_samples_per_ds", 0)
- if split == "test" and max_test_samples_per_ds > 0:
- print(f"Truncating all test ds to {max_test_samples_per_ds} samples")
- for ds_name, ds in datasets.items():
- test_indices = np.random.permutation(len(ds))[:max_test_samples_per_ds]
- datasets[ds_name] = ds.select(test_indices)
- if ds_name in answers:
- answers[ds_name] = answers[ds_name].select(test_indices)
- if ds_name in original_datasets:
- original_datasets[ds_name] = original_datasets[ds_name].select(
- test_indices
- )
-
- print(f"Datasets: {datasets}")
- print(f"Answers: {answers}")
-
- gen_kwargs = dict(
- do_sample=False,
- max_new_tokens=max_new_tokens,
- )
-
- eval_trainer_args = {}
-
- # Copy only necessary attributes from training_args to eval_trainer_args
- seq2seq_training_args_fields = {f.name for f in fields(Seq2SeqTrainingArguments)}
- for attr, value in dict(**vars(args)).items():
- if attr in seq2seq_training_args_fields and not attr.startswith("_"):
- eval_trainer_args[attr] = value
-
- eval_trainer_args["eval_strategy"] = "no"
- eval_trainer_args["save_strategy"] = "no"
- eval_trainer_args["overwrite_output_dir"] = True
- eval_trainer_args["batch_eval_metrics"] = True
- eval_trainer_args["per_device_eval_batch_size"] = eval_batch_size
- eval_trainer_args["include_for_metrics"] = ["inputs"]
- eval_trainer_args["batch_eval_metrics"] = True
- eval_trainer_args["remove_unused_columns"] = False
- eval_trainer_args["bf16"] = False
- eval_trainer_args["tf32"] = False
- eval_trainer_args["use_liger_kernel"] = False
- eval_trainer_args["dataloader_num_workers"] = 0
- eval_trainer_args["dataloader_prefetch_factor"] = None
-
- eval_trainer_args = Seq2SeqTrainingArguments(
- **eval_trainer_args,
- predict_with_generate=generative,
- # generation_config=GenerationConfig(**gen_kwargs),
- )
-
- print("=" * 80 + "\n" + "Evaluating model..." + "\n" + "=" * 80)
- print(f"checkpoint_path: {checkpoint_path}")
-
- model.eval()
-
- collator = generation_collator if generative else eval_collator
-
- # if isinstance(model, CtxDistillModel):
-
- # model.generate = partial(
- # model.generate,
- # ctx_inp_sep_seq=sep_seq,
- # reset=True,
- # )
-
- trainer_kwargs = {
- "model": model,
- "args": eval_trainer_args,
- "data_collator": partial(collator, tokenizer=tokenizer),
- }
-
- out = {}
- if not generative:
- trainer_kwargs["compute_metrics"] = partial(
- compute_metrics,
- evaluator=Evaluator(
- [compute_per_token_acc, compute_prefix_matching, compute_perplexity]
- ),
- )
- # this is insane
- # but i don't know why calling trainer.evaluate() on different datasets
- # always gives the same numbers across datasets...
- # spents a few hours on this but couldn't find the reason
- for ds_name, ds in datasets.items():
- eval_trainer = Trainer(**trainer_kwargs)
- clear_gpu()
- metrics = eval_teacher_forcing(
- eval_trainer, {ds_name: ds}, split, args.remove_context
- )
- out.update(metrics)
- else:
- eval_trainer = Seq2SeqTrainer(**trainer_kwargs)
- for ds_name, ds in datasets.items():
- metrics = eval_generation(
- eval_trainer,
- tokenizer,
- ctx_tokenizer,
- {ds_name: ds},
- original_datasets,
- answers,
- split,
- args.remove_context,
- gen_kwargs,
- )
- out.update(metrics)
-
- print_tracker_stats()
- print_global_tracker_stats()
- ds_suffix = "_no_context" if args.remove_context else ""
- save_tracker_stats_csv(
- f"{args.logging_dir}/{split}_{ds_name}{ds_suffix}_tracked_stats.csv"
- )
- reset_trackers()
-
- clear_gpu()
- return out
-
-
-def run_eval(
- checkpoint_path: str = None,
- model_name_or_path: str = None,
- datasets: list[str] = None,
- split: str = "validation",
- eval_batch_size: int = 8,
- max_val_samples_per_ds: int = -1,
- max_test_samples_per_ds: int = -1,
- max_ctx_chunk_len: int = -1,
- remove_context: bool = False,
- max_new_tokens: int = 256,
- generative: bool = False,
- use_cd: bool = False,
- cd_update_iterations: int = 10,
- cd_use_gen_q: bool = False,
- q_gen_rounds: int = 4,
- cd_batch_size: int = 16,
- use_iterative_mode: bool = False,
- use_llmlingua: bool = False,
- llmlingua_compression_rate: float = 0.9,
- use_t2l: bool = False,
- add_ctx_to_input: bool = False,
- truncate_if_too_long_inp: bool = False,
- truncate_if_too_long_ctx: bool = False,
- flip_ctx_inp: bool = False,
- gen_lora_scaling: float = 1,
- use_generative_adapter: bool = False,
-) -> None:
- """Run evaluation with the specified parameters."""
- assert bool(model_name_or_path) ^ bool(checkpoint_path), (
- "Either --model_name_or_path or --checkpoint_path must be provided"
- )
- if (use_cd or use_llmlingua or use_t2l) and eval_batch_size != 1:
- raise ValueError("When using a baseline method, eval_batch_size must be 1.")
-
- if use_llmlingua and add_ctx_to_input:
- raise ValueError(
- "LLMLingua always adds compressed context to input by default."
- )
- if use_generative_adapter and (
- model_name_or_path != "mistralai/Mistral-7B-Instruct-v0.2"
- ):
- raise ValueError(
- "Generative adapter is only available for Mistral-7B-Instruct-v0.2."
- )
-
- disable_caching()
- set_seed(42)
-
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
- os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
- os.environ["FLASH_ATTENTION_DETERMINISTIC"] = "1"
- os.environ["OMP_NUM_THREADS"] = "23"
-
- # torch.use_deterministic_algorithms(True, warn_only=True)
- torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
- torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
- torch.backends.cudnn.benchmark = False
- torch.backends.cuda.matmul.allow_tf32 = False
- torch.backends.cudnn.allow_tf32 = False
-
- slurm_job_id = f"_{os.getenv('SLURM_JOB_ID')}" if os.getenv("SLURM_JOB_ID") else ""
- run_name = get_run_name(seed_str=time.strftime("%Y%m%d-%H%M%S") + slurm_job_id)
-
- if checkpoint_path:
- checkpoint_dir = "/".join(checkpoint_path.split("/")[:-1])
- run_dir = "/".join(checkpoint_path.split("/")[:-2])
- cur_it = int(checkpoint_path.split("checkpoint-")[1].split("/")[0])
- try:
- args = Namespace(**yaml.unsafe_load(open(f"{run_dir}/args.yaml")))
- except FileNotFoundError:
- raise FileNotFoundError(f"Could not find args.yaml in {run_dir}. ")
- print(f"checkpoint_path: {checkpoint_path}")
- print(f"run_dir: {run_dir}")
-
- args.output_dir = f"{run_dir}/eval-results-{cur_it}/{run_name}"
- args.logging_dir = f"{run_dir}/eval-results-{cur_it}/{run_name}"
- args.run_name = run_dir.split("/")[-1]
- # modulated model doesn't see ctx by default
- # but remove_context has to be false for correct file naming
- args.remove_context = False
- args.use_iterative_mode = use_iterative_mode
- if use_llmlingua:
- args.use_llmlingua = use_llmlingua
- args.llmlingua_compression_rate = llmlingua_compression_rate
- else:
- args = Namespace(
- model_name_or_path=model_name_or_path,
- output_dir=f"eval_results/{model_name_or_path}/{run_name}",
- logging_dir=f"eval_results/{model_name_or_path}/{run_name}",
- run_name=f"eval_results/{model_name_or_path}/{run_name}",
- val_ds_names=[],
- test_ds_names=[],
- remove_context=remove_context,
- )
- if use_cd:
- args.use_cd = use_cd
- args.cd_update_iterations = cd_update_iterations
- args.cd_use_gen_q = cd_use_gen_q
- args.q_gen_rounds = q_gen_rounds
- args.cd_batch_size = cd_batch_size
- if use_llmlingua:
- args.use_llmlingua = use_llmlingua
- args.llmlingua_compression_rate = llmlingua_compression_rate
- if use_t2l:
- args.use_t2l = use_t2l
- args.use_generative_adapter = use_generative_adapter
- if max_val_samples_per_ds > 0:
- args.max_val_samples_per_ds = max_val_samples_per_ds
- if max_test_samples_per_ds > 0:
- args.max_test_samples_per_ds = max_test_samples_per_ds
- args.add_ctx_to_input = add_ctx_to_input
- args.gen_lora_scaling = gen_lora_scaling
- args.truncate_if_too_long_inp = truncate_if_too_long_inp
- args.truncate_if_too_long_ctx = truncate_if_too_long_ctx
- args.flip_ctx_inp = flip_ctx_inp
- setup_logging(args.logging_dir)
- logger.debug(f"CMD: {' '.join(os.sys.argv)}")
-
- # Override dataset names if provided via CLI
- if datasets:
- if split == "validation":
- args.val_ds_names = datasets
- else:
- args.test_ds_names = datasets
-
- return evaluate(
- checkpoint_path,
- model_name_or_path,
- eval_batch_size,
- args,
- split,
- max_ctx_chunk_len,
- max_new_tokens,
- generative=generative,
- )
diff --git a/src/ctx_to_lora/metrics.py b/src/ctx_to_lora/metrics.py
deleted file mode 100644
index 7258eb0..0000000
--- a/src/ctx_to_lora/metrics.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from collections import defaultdict
-from collections.abc import Callable
-
-import numpy as np
-import torch
-from rouge_score import rouge_scorer
-from transformers import EvalPrediction
-
-LENGTH_BINS = [
- # finegrain bins
- (0, 2**7 - 1),
- (2**7, 2**8 - 1),
- (2**8, 2**9 - 1),
- # coarse bins
- (0, 2**9 - 1),
- (2**9, 2**10 - 1),
- (2**10, 2**11 - 1),
- (2**11, 2**12 - 1),
- (2**12, 2**13 - 1),
- (0, 2**13 - 1),
- (2**13, 2**14 - 1),
- (2**14, 2**15 - 1),
- (2**15, float("inf")),
-]
-
-
-def get_length_bin(length: int):
- """Get the length bin for a given length."""
- for i, (start, end) in enumerate(LENGTH_BINS):
- if start <= length < end:
- return (start, end)
-
-
-def compute_rouge(pred_texts, label_texts):
- out = defaultdict(list)
- scorer = rouge_scorer.RougeScorer(["rougeL"], use_stemmer=True)
- for pred_text, label_text in zip(pred_texts, label_texts):
- scores = scorer.score(pred_text, label_text)
- for k, v in scores.items():
- out[f"{k}.f1"].append(v.fmeasure)
- out_mean = dict()
- for k in out:
- out_mean[k] = np.mean(out[k])
- return out_mean, out
-
-
-@torch.inference_mode()
-def compute_per_token_acc(shift_logits, shift_labels, valid_masks):
- indices = torch.where(valid_masks)
- acc = (shift_logits.argmax(-1) == shift_labels)[indices].float()
- return {
- "per_token_accs": acc.flatten().tolist(),
- "n_per_token_accs": valid_masks.sum().item(),
- }
-
-
-@torch.inference_mode()
-def compute_prefix_matching(shift_logits, shift_labels, valid_masks):
- lengths = valid_masks.sum(dim=1)
-
- is_wrong = (shift_logits.argmax(-1) != shift_labels) * valid_masks
- is_correct = (shift_logits.argmax(-1) == shift_labels) * valid_masks
- # NOTE: not reliable for multi-turn conversations
- # ie, all tokens in the following user's turn will be correct
- # still monotonically correlate with perf though
- wrong_pos = torch.argmax(is_wrong, dim=1) - torch.argmax(valid_masks, dim=1)
- perf = wrong_pos / lengths
-
- # if all tokens are correct, set to 1
- perf = torch.where(is_correct.sum(dim=1) == lengths, 1, perf)
- return {
- "prefix_matchings": perf.tolist(),
- "n_prefix_matchings": valid_masks.shape[0],
- }
-
-
-@torch.inference_mode()
-def compute_perplexity(shift_logits, shift_labels, valid_masks):
- return {"perplexities_ph": [1], "n_perplexities_ph": 1}
-
-
-class Evaluator:
- def __init__(self, metric_fns: list[Callable]):
- self.metric_fns = metric_fns
- self.reset()
-
- def reset(self):
- self.accum_metrics = defaultdict(lambda: list((0,)))
- self.count = defaultdict(lambda: list((0,)))
-
- def update(self, shift_logits, shift_labels, valid_masks, lengths=None):
- for metric_fn in self.metric_fns:
- # overall metric
- metric = metric_fn(shift_logits, shift_labels, valid_masks)
- for k, v in metric.items():
- key = k if not k.startswith("n_") else k[2:]
- if k.startswith("n_"):
- # prefix "n_" indicates the count of the metric
- self.count[key].append(v)
- else:
- self.accum_metrics[key] += v
- for start, end in LENGTH_BINS:
- key_w_len = f"{key}_len_{start}-{end}"
- if key_w_len not in self.accum_metrics:
- # add key here so that it shows up in the output
- self.accum_metrics[key_w_len] = [0]
- self.count[key_w_len] = [0]
- # split samples into length groups, calculate metric for each group
- if lengths is not None:
- for start, end in LENGTH_BINS:
- logits, labels, masks = [], [], []
-
- for logit, label, m, len in zip(
- shift_logits, shift_labels, valid_masks, lengths
- ):
- if isinstance(len, torch.Tensor):
- len = len.item()
- if start <= len < end:
- logits.append(logit)
- labels.append(label)
- masks.append(m)
-
- if not logits:
- continue
-
- metric = metric_fn(
- torch.stack(logits), torch.stack(labels), torch.stack(masks)
- )
- for k, v in metric.items():
- if k.startswith("n_"):
- key = f"{k[2:]}_len_{start}-{end}"
- self.count[key].append(v)
- else:
- key = f"{k}_len_{start}-{end}"
- self.accum_metrics[key] += v
-
- def compute(self):
- # Get result across entire eval set
- result = {
- k: np.sum(v) / np.sum(self.count[k]) if len(v) > 1 else "None"
- for k, v in self.accum_metrics.items()
- }
- # Reset batch statistics
- self.reset()
- return result
-
-
-@torch.no_grad()
-def compute_metrics(
- eval_pred: EvalPrediction,
- compute_result: bool,
- evaluator: Evaluator,
-) -> dict | None:
- inputs = eval_pred.inputs
- len_key = "ctx_ids_len" if "ctx_ids_len" in inputs else "input_ids_len"
- lengths = inputs[len_key]
- logits, labels = eval_pred.predictions, eval_pred.label_ids
- shift_logits = logits[..., :-1, :]
- shift_labels = labels[..., 1:]
- valid_masks = torch.where(shift_labels != -100, 1, 0)
- evaluator.update(shift_logits, shift_labels, valid_masks, lengths)
- if compute_result:
- return evaluator.compute()
diff --git a/src/ctx_to_lora/modeling/aggregator.py b/src/ctx_to_lora/modeling/aggregator.py
index 8080ef4..cc42745 100644
--- a/src/ctx_to_lora/modeling/aggregator.py
+++ b/src/ctx_to_lora/modeling/aggregator.py
@@ -112,7 +112,7 @@ class Perceiver(nn.Module):
n_latents=n_latent_queries,
intermediate_size_factor=4,
hidden_size=output_size,
- attn_implementation="flash_attention_2",
+ attn_implementation="sdpa",
)
self.decoder_config = Idefics2PerceiverConfig(
input_size=output_size,
@@ -122,7 +122,7 @@ class Perceiver(nn.Module):
n_latents=n_output_queries,
intermediate_size_factor=4,
hidden_size=output_size,
- attn_implementation="flash_attention_2",
+ attn_implementation="sdpa",
)
self.perceiver = Idefics2Perceiver(self.config, self.decoder_config)
self.iterative_mode = False
diff --git a/src/ctx_to_lora/modeling/context_distillation.py b/src/ctx_to_lora/modeling/context_distillation.py
deleted file mode 100644
index db2974e..0000000
--- a/src/ctx_to_lora/modeling/context_distillation.py
+++ /dev/null
@@ -1,632 +0,0 @@
-import gc
-import json
-import logging
-import os
-import re
-from math import ceil
-from typing import Any
-
-import torch
-from jaxtyping import Integer
-from peft import PeftModel
-from peft.tuners.tuners_utils import BaseTunerLayer, check_target_module_exists
-from torch import Tensor, nn
-from transformers import PreTrainedModel
-from transformers.modeling_outputs import ModelOutput
-
-from ctx_to_lora.data.definitions import CTX_AFFIXES
-from ctx_to_lora.data.q_generation_template import (
- Q_GEN_PROMPT_TEMPLATE,
- Q_GEN_PROMPT_TEMPLATE_REPEAT,
- Q_GEN_SYSTEM_TEMPLATE,
- STOP_STRINGS,
-)
-from ctx_to_lora.data.self_gen_template import SELF_QA_INTX
-from ctx_to_lora.utils import log_num_train_params
-
-logger = logging.getLogger()
-
-
-def get_q_gen_prompt(context, n_qa_pairs):
- prompt = Q_GEN_PROMPT_TEMPLATE.format(context=context, n_qa_pairs=n_qa_pairs)
- return prompt
-
-
-def get_q_gen_prompt_repeat(context, qa_pairs, n_qa_pairs):
- example_qa_pairs = ""
- for i, (q, a) in enumerate(qa_pairs, 1):
- example_qa_pairs += f"Question {i}: {q}\nAnswer {i}: {a}\n"
- prompt = Q_GEN_PROMPT_TEMPLATE_REPEAT.format(
- context=context,
- qa_pairs=example_qa_pairs,
- n_qa_pairs=n_qa_pairs,
- )
- return prompt
-
-
-def check_should_skip(txt: str, vllm_model: str) -> bool:
- """Check if the response should be skipped based on stop strings."""
- for stop in STOP_STRINGS[vllm_model]:
- if stop in txt[-len(stop) :]:
- return (txt.split(stop)[0], False) # Found a valid stop string
- return (txt, True) # No valid stop string found, skip this response
-
-
-def postprocess_qa_pairs(res_txt: str):
- """
- Postprocesses the QA pairs from the response text.
-
- Args:
- res_txt: The response text.
- n_qa_pairs: The number of QA pairs.
-
- Returns:
- A tuple of two lists, the first containing the questions and the second containing the answers.
- """
- # capture everything after each "Question {number}:" until "Answer"
- q_pattern = r"Question \d+:(.*?)(?=Answer|$)" # thanks chatgpt
- questions = re.findall(q_pattern, res_txt, flags=re.S)
-
- a_pattern = r"Answer \d+:(.*?)(?=Question|$)" # thanks chatgpt
- answers = re.findall(a_pattern, res_txt, flags=re.S)
-
- if len(questions) != len(answers):
- print(f"Warning---number of questions and answers do not match")
- print(f"Number of questions: {len(questions)}")
- print(f"Number of answers: {len(answers)}")
-
- out_q = []
- out_a = []
- n_skips = 0
- if (len(questions) > 0) and (len(answers) > 0):
- n_gen_pairs = min(len(questions), len(answers))
- has_left_over = n_gen_pairs < len(questions) or n_gen_pairs < len(answers)
- for i in range(n_gen_pairs):
- response = answers[i].strip()
- question = questions[i].strip()
- if not response or not question:
- print(f"Skipping empty question or answer at index {i}")
- continue
- if (not has_left_over) and (i == n_gen_pairs - 1):
- response, skip = check_should_skip(response, "google/gemma-3-12b-it")
- if skip:
- print(f"Skipping due to missing stop string")
- n_skips += 1
- continue
- out_q.append(question.strip())
- out_a.append(response.strip())
- print(f"Skipped {n_skips} responses due to missing stop strings")
-
- return out_q, out_a
-
-
-def build_messages(ctx_text: str, level: int, example_qa_pairs: list = None):
- messages = [
- {"role": "system", "content": Q_GEN_SYSTEM_TEMPLATE},
- {
- "role": "user",
- "content": get_q_gen_prompt(ctx_text, 5)
- if level == 0
- else get_q_gen_prompt_repeat(ctx_text, example_qa_pairs, 5),
- },
- ]
- return messages
-
-
-def get_shifted_label_pos(labels):
- pos = torch.where(labels != -100)
- # (batch_idx, token_idx)
- return (pos[0], pos[1] - 1)
-
-
-def logits_at_positions(outputs: ModelOutput, pos) -> Tensor:
- logits = outputs.logits
- return logits[pos[0], pos[1]]
-
-
-def ctx_inp_split(
- ctx_inp_ids, ctx_inp_sep_seq, pad_token_id, prefix_tokens=None, padding_side="right"
-):
- # Split each row in ctx_inp_ids at the first occurrence of ctx_inp_sep_seq
- # Return the part after the separator for each row
- batch_size = ctx_inp_ids.size(0)
- sep_len = ctx_inp_sep_seq.size(0)
- out_inp = []
- out_ctx = []
- for i in range(batch_size):
- row = ctx_inp_ids[i]
- # Find where the separator starts
- for j in range(row.size(0) - sep_len + 1):
- if torch.equal(row[j : j + sep_len], ctx_inp_sep_seq):
- out_ctx.append(row[:j])
- if prefix_tokens is not None:
- out_inp.append(
- torch.cat([prefix_tokens, row[j + sep_len :]], axis=-1)
- )
- else:
- out_inp.append(row[j + sep_len :])
- break
- else:
- # If separator not found
- raise ValueError(f"Separator sequence not found in row {i}")
- out_inp = torch.nn.utils.rnn.pad_sequence(
- out_inp, batch_first=True, padding_value=pad_token_id, padding_side=padding_side
- )
- out_ctx = torch.nn.utils.rnn.pad_sequence(
- out_ctx, batch_first=True, padding_value=pad_token_id, padding_side=padding_side
- )
- return out_ctx, out_inp
-
-
-def get_peft_layers(model, peft_config):
- out = []
- for module_name, module in model.named_modules():
- if not check_target_module_exists(peft_config, module_name):
- continue
- if not isinstance(module, BaseTunerLayer):
- continue
- # support just Linear layer for now
- # all modules should be a leave module that is Linear layer
- assert isinstance(module.base_layer, nn.Linear), (
- "all modules should be a leave module that is Linear layer"
- )
-
- # this should always pass
- name = module_name.split(".")[-1]
- assert name in peft_config.target_modules
- out.append(module)
-
- return out
-
-
-class CtxDistillModel(nn.Module):
- def __init__(
- self,
- base_model: PeftModel,
- prefix_tokens: Integer[Tensor, "n"],
- ctx_inp_sep_seq: Integer[Tensor, "m"],
- pad_token_id: int,
- update_iterations: int,
- reset: bool = True,
- tokenizer=None,
- q_model: PreTrainedModel | None = None,
- q_tokenizer=None,
- reprompt_ctx: bool = False,
- lora_save_dir: str | None = None,
- save_after_distill: bool = True,
- q_gen_rounds: int = 4,
- batch_size: int = 16,
- ):
- super().__init__()
- self.register_module("base_model", base_model)
- self.register_module("q_model", q_model)
- self.register_buffer("prefix_tokens", prefix_tokens)
- self.register_buffer("ctx_inp_sep_seq", ctx_inp_sep_seq)
- self.tokenizer = tokenizer
- self.q_tokenizer = q_tokenizer
- self.pad_token_id = pad_token_id
- self.update_iterations = update_iterations
- self.reprompt_ctx = reprompt_ctx
- self.reset = reset
- self.device = base_model.device
- self.to(self.device)
- self.q_gen_rounds = q_gen_rounds
- # New save options
- self.lora_save_dir = lora_save_dir
- self.save_after_distill = save_after_distill
-
- self.peft_config = base_model.peft_config["default"]
- self.adapter_name = "default"
- self.base_model.set_adapter("default")
- for layer in get_peft_layers(self.base_model, self.peft_config):
- for name, p in layer.named_parameters():
- if "lora_A" in name or "lora_B" in name:
- p.requires_grad = True
- log_num_train_params(self.base_model)
- self._init_optim()
- # Mini-batch size for distillation updates
- self.batch_size = batch_size
-
- @property
- def generation_config(self):
- return self.base_model.generation_config
-
- def _init_optim(self):
- self.optimizer = torch.optim.AdamW(
- [
- p
- for l in get_peft_layers(self.base_model, self.peft_config)
- for p in l.parameters()
- if p.requires_grad
- ],
- lr=1e-4,
- )
-
- def reset_lora(self):
- print("Resetting LoRA")
- for layer in get_peft_layers(self.base_model, self.peft_config):
- layer.reset_lora_parameters(self.adapter_name, init_lora_weights=True)
- self._init_optim()
-
- def save_lora(self):
- """
- Save current LoRA adapter in PEFT format plus a lightweight JSON summary
- for easy human inspection/manipulation.
- """
- if self.lora_save_dir is None:
- return
- os.makedirs(self.lora_save_dir, exist_ok=True)
- # Standard PEFT save (produces adapter_config.json + adapter_model.bin / safetensors)
- self.base_model.save_pretrained(self.lora_save_dir)
- # Human-readable summary of LoRA parameter shapes
- summary = {
- name: list(p.shape)
- for name, p in self.base_model.named_parameters()
- if ("lora_A" in name or "lora_B" in name) and p.requires_grad
- }
- with open(os.path.join(self.lora_save_dir, "lora_summary.json"), "w") as f:
- json.dump(summary, f, indent=2)
- print(f"LoRA adapter saved to {self.lora_save_dir}")
-
- @torch.enable_grad()
- def _distill_context(
- self,
- ctx_inp_res_ids: Integer[Tensor, "bs ctx_inp_length"],
- ctx_inp_res_attention_mask: Integer[Tensor, "bs ctx_inp_length"],
- teacher_labels: Integer[Tensor, "bs ctx_inp_length"],
- inp_res_ids: Integer[Tensor, "bs inp_length"],
- inp_res_attention_mask: Integer[Tensor, "bs inp_length"],
- student_labels: Integer[Tensor, "bs inp_length"],
- ):
- # Implements KD-style loss by computing teacher (with context) and student (no context)
- # log-probs locally, using mini-batches for updates.
-
- was_training = self.training
- self.train()
-
- num_samples = ctx_inp_res_ids.size(0)
- mb = self.batch_size
- num_batches = ceil(num_samples / mb)
-
- total_steps = max(self.update_iterations * num_batches, 1)
- scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
- self.optimizer, T_max=total_steps, eta_min=0.0
- )
-
- print(
- f"Starting context distillation for {self.update_iterations} epochs, "
- f"mini-batch size {mb} ({num_batches} batches/epoch), "
- f"cosine LR schedule over {total_steps} steps"
- )
-
- for epoch in range(self.update_iterations):
- # Shuffle order each epoch for SGD
- perm = torch.randperm(num_samples, device=self.device)
-
- epoch_loss = 0.0
- for b in range(num_batches):
- start = b * mb
- end = min(start + mb, num_samples)
- indices = perm[start:end]
-
- b_ctx_ids = ctx_inp_res_ids[indices]
- b_ctx_am = ctx_inp_res_attention_mask[indices]
- b_teacher_labels = teacher_labels[indices]
-
- b_inp_ids = inp_res_ids[indices]
- b_inp_am = inp_res_attention_mask[indices]
- b_student_labels = student_labels[indices]
-
- # Compute teacher distribution (top-k) for this mini-batch
- with torch.no_grad(), self.base_model.disable_adapter():
- t_pos = get_shifted_label_pos(b_teacher_labels)
- teacher_outputs = self.base_model(
- b_ctx_ids, attention_mask=b_ctx_am
- )
- teacher_logits = logits_at_positions(teacher_outputs, t_pos)
- K = 16
- topk_vals, topk_idx = teacher_logits.topk(K, dim=-1)
- teacher_denom = torch.logsumexp(
- teacher_logits.float(), dim=-1, keepdim=True
- )
- teacher_p = (topk_vals - teacher_denom).exp().detach() # [N, K]
-
- # Student forward and update for this mini-batch
- self.optimizer.zero_grad()
- s_pos = get_shifted_label_pos(b_student_labels)
- student_outputs = self.base_model(b_inp_ids, attention_mask=b_inp_am)
- student_logits = logits_at_positions(student_outputs, s_pos)
- student_denom = torch.logsumexp(
- student_logits.float(), dim=-1, keepdim=True
- )
- selected_student_logits = student_logits.gather(-1, topk_idx)
- student_logq = selected_student_logits - student_denom # [N, K]
- token_losses = -(teacher_p * student_logq).sum(dim=-1) # [N]
- loss = token_losses.mean()
-
- loss.backward()
- self.optimizer.step()
- scheduler.step()
-
- epoch_loss += loss.detach().item()
-
- cur_lr = self.optimizer.param_groups[0]["lr"]
- print(
- f"Epoch {epoch + 1}/{self.update_iterations}, "
- f"batch {b + 1}/{num_batches}: loss={epoch_loss / num_batches:.4f}, lr={cur_lr:.6e}"
- )
-
- if not was_training:
- self.eval()
-
- def generate_questions(self, *args, **kwargs):
- questions = self.q_model.generate(*args, **kwargs)
- return questions
-
- def teacher_generate(self, *args, **kwargs):
- # rename for separate timing
- return self.base_model.generate(*args, **kwargs)
-
- def student_generate(self, *args, **kwargs):
- # rename for separate timing
- return self.base_model.generate(*args, **kwargs)
-
- def get_lora_state(self, clone: bool = True):
- """
- Return a dict of current LoRA parameter tensors.
- clone=True returns detached cloned tensors (safe to store).
- """
- return {
- name: (p.detach().clone() if clone else p)
- for name, p in self.base_model.named_parameters()
- if ("lora_A" in name or "lora_B" in name)
- }
-
- def generate(
- self,
- *model_inputs_args: Any,
- distill_only: bool = False,
- **model_inputs_kwargs: dict[str, Any],
- ):
- if self.reset:
- self.reset_lora()
-
- # teacher tokens
- orig_ctx_inp_ids = model_inputs_kwargs.pop("input_ids")
- ctx_inp_ids = orig_ctx_inp_ids.clone()
- _, orig_inp_ids = ctx_inp_split(
- ctx_inp_ids,
- self.ctx_inp_sep_seq,
- self.pad_token_id,
- self.prefix_tokens,
- padding_side="left",
- )
- ctx_inp_attention_mask = model_inputs_kwargs.pop("attention_mask")
-
- if self.q_model is not None:
- self.q_model.to(self.base_model.device)
- # Extract context-only portion after separator (remove prefix tokens from first row)
- ctx_ids_full, _ = ctx_inp_split(
- ctx_inp_ids, self.ctx_inp_sep_seq, self.pad_token_id
- ) # [bs, var_len]
- ctx_ids = ctx_ids_full[0, len(self.prefix_tokens) :]
- ctx_txt = self.tokenizer.decode(ctx_ids, skip_special_tokens=True)
- questions = []
- answers = []
- # Build multiple instruction variants
- for lvl in range(self.q_gen_rounds):
- messages = build_messages(
- ctx_txt, lvl, zip(questions, answers) if lvl > 0 else None
- )
- q_inputs = self.q_tokenizer.apply_chat_template(
- messages,
- tokenize=True,
- add_special_tokens=False,
- padding=False,
- truncation=False,
- add_generation_prompt=True,
- return_dict=True,
- return_tensors="pt",
- )
- q_inputs = {k: v.to(self.q_model.device) for k, v in q_inputs.items()}
-
- with torch.no_grad():
- question_outputs = self.generate_questions(
- input_ids=q_inputs["input_ids"],
- attention_mask=q_inputs["attention_mask"],
- max_new_tokens=1024,
- do_sample=False,
- temperature=0.0,
- eos_token_id=106, # for gemma-3-12b-it
- )
- # Slice off the prompt portion
- gen_only = question_outputs[:, q_inputs["input_ids"].shape[-1] :]
- res = self.q_tokenizer.batch_decode(gen_only, skip_special_tokens=False)
- gen_q_list, gen_a_list = postprocess_qa_pairs(res[0])
- questions += gen_q_list
- answers += gen_a_list
-
- if len(questions) == 0:
- # when q_model refuses to provide questions
- # only happens with sample 116 in longbench/multifieldqa_en_e
- # in this case cd just doesn't work, we fall back to zero-shot answer
- print(f"Warning---no questions generated, skipping distillation")
- attention_mask = torch.where(
- orig_inp_ids != self.pad_token_id, 1, 0
- ).long()
- return self.student_generate(
- orig_inp_ids, attention_mask=attention_mask, **model_inputs_kwargs
- )
-
- ctx_inp_messages = [
- [{"role": "user", "content": f"{ctx_txt}\n\n{SELF_QA_INTX}\n\n{q}"}]
- for q in questions
- ]
- encoded_ctx_inp = self.tokenizer.apply_chat_template(
- ctx_inp_messages,
- tokenize=True,
- add_special_tokens=False,
- padding=True,
- truncation=False,
- add_generation_prompt=True,
- return_dict=True,
- return_tensors="pt",
- )
- encoded_ctx_inp = {k: v.to(self.device) for k, v in encoded_ctx_inp.items()}
- ctx_inp_ids = encoded_ctx_inp["input_ids"]
- ctx_inp_attention_mask = encoded_ctx_inp["attention_mask"]
- self.q_model.to("cpu")
- gc.collect()
- torch.cuda.empty_cache()
-
- # sample responses first
- ctx_inp_res_ids = self.teacher_generate(
- ctx_inp_ids,
- attention_mask=ctx_inp_attention_mask,
- **model_inputs_kwargs,
- )
- ctx_inp_res_attention_mask = torch.where(
- ctx_inp_res_ids != self.pad_token_id, 1, 0
- ).long()
- ctx_inp_res_txt = self.tokenizer.batch_decode(ctx_inp_res_ids)
-
- bs = ctx_inp_ids.shape[0]
- res_len = ctx_inp_res_ids.shape[-1] - ctx_inp_ids.shape[-1]
- res_ids = ctx_inp_res_ids[:, -res_len:] # correct
-
- pads = torch.full_like(ctx_inp_ids, self.pad_token_id)
- teacher_labels = torch.cat([pads, res_ids], dim=-1)
- teacher_labels = torch.where(
- teacher_labels != self.pad_token_id, teacher_labels, -100
- )
-
- # student tokens
- _, inp_res_ids = ctx_inp_split(
- ctx_inp_res_ids,
- self.ctx_inp_sep_seq,
- self.pad_token_id,
- self.prefix_tokens,
- padding_side="left",
- )
- inp_res_attention_mask = torch.where(
- inp_res_ids != self.pad_token_id, 1, 0
- ).long()
-
- student_labels = inp_res_ids.clone()
- student_labels[:, :-res_len] = -100
- student_labels = torch.where(
- student_labels != self.pad_token_id, student_labels, -100
- )
-
- self._distill_context(
- ctx_inp_res_ids,
- ctx_inp_res_attention_mask,
- teacher_labels,
- inp_res_ids,
- inp_res_attention_mask,
- student_labels,
- )
- # Save LoRA after distillation if requested
- if distill_only:
- return self.get_lora_state()
-
- model_inputs_kwargs.pop("attention_mask", None)
- model_inputs_kwargs.pop("input_ids", None)
- if self.reprompt_ctx:
- attention_mask = torch.where(orig_ctx_inp_ids != self.pad_token_id, 1, 0)
- model_outputs = self.student_generate(
- orig_ctx_inp_ids, attention_mask=attention_mask, **model_inputs_kwargs
- )
- else:
- attention_mask = torch.where(orig_inp_ids != self.pad_token_id, 1, 0).long()
- model_outputs = self.student_generate(
- orig_inp_ids, attention_mask=attention_mask, **model_inputs_kwargs
- )
- return model_outputs
-
-
-if __name__ == "__main__":
- from ctx_to_lora.data.processing import load_and_process_dataset
- from ctx_to_lora.model_loading import get_lora_config, get_model_and_tokenizer
-
- model_name = "google/gemma-2-2b-it"
- q_model_name = "google/gemma-3-12b-it"
- peft_config = get_lora_config(
- model_name, r=8, target_modules=["down_proj"], lora_dropout=0.0
- )
- peft_config.lora_alpha = 16
- model, tokenizer = get_model_and_tokenizer(
- model_name,
- train=False,
- requires_grad=False,
- peft_config=peft_config,
- )
- q_model, q_tokenizer = get_model_and_tokenizer(
- q_model_name,
- train=False,
- requires_grad=False,
- peft_config=peft_config,
- )
-
- ds = load_and_process_dataset("pwc", split="train", num_proc=8)
- ctx = ds[0]["context"]
- inp = ds[1]["prompts"][0]
- # Build a simple context/input pair separated by a unique token sequence
- sep_text = SELF_QA_INTX
- # ctx = "# Provided Information\nMy name is Tan."
- # ctx = "# Provided Info"
- prompt = f"{ctx}\n\n{sep_text}\n\n{inp}"
- messages = [{"role": "user", "content": prompt}]
- encoded = tokenizer.apply_chat_template(
- messages,
- return_tensors="pt",
- return_dict=True,
- add_generation_prompt=True,
- )
- # encoded = tokenizer(prompt, return_tensors="pt")
- encoded = {k: v.to(model.device) for k, v in encoded.items()}
-
- prefix_tokens = CTX_AFFIXES[model_name]["prefix"]
- prefix_tokens = torch.tensor(prefix_tokens, dtype=torch.long)
-
- sep_ids = (
- tokenizer(sep_text.strip("\n"), add_special_tokens=False, return_tensors="pt")
- .input_ids[0]
- .to(model.device)
- )
-
- cd_model = CtxDistillModel(
- base_model=model,
- prefix_tokens=prefix_tokens,
- ctx_inp_sep_seq=sep_ids,
- pad_token_id=tokenizer.pad_token_id,
- update_iterations=100,
- q_model=q_model,
- q_tokenizer=q_tokenizer,
- tokenizer=tokenizer,
- reprompt_ctx=False,
- lora_save_dir="./saved_lora_adapter", # example save path
- save_after_distill=True,
- )
-
- with torch.no_grad():
- for _ in range(1):
- base_model_res = model.generate(
- input_ids=encoded["input_ids"],
- attention_mask=encoded["attention_mask"],
- max_new_tokens=256,
- do_sample=False,
- )
- print(
- f"Base model response:{tokenizer.batch_decode(base_model_res, skip_special_tokens=False)}"
- )
-
- outputs = cd_model.generate(
- input_ids=encoded["input_ids"],
- attention_mask=encoded["attention_mask"],
- max_new_tokens=256,
- do_sample=False,
- )
- print(
- f"Student response: {tokenizer.batch_decode(outputs, skip_special_tokens=False)}"
- )
diff --git a/src/ctx_to_lora/modeling/generative_adapter.py b/src/ctx_to_lora/modeling/generative_adapter.py
deleted file mode 100644
index 91fdd27..0000000
--- a/src/ctx_to_lora/modeling/generative_adapter.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import requests
-import torch
-
-
-def call_generate(
- input_txt: str,
- context_txt: str,
- window_size: int | None = None,
- max_new_tokens: int | None = None,
- host: str = "http://127.0.0.1:8989",
- timeout: int = 120,
-) -> torch.Tensor:
- """Send the prompt to the API server and return the generated token tensor."""
- payload: dict[str, object] = {
- "input_txt": input_txt,
- "context_txt": context_txt,
- }
- if window_size is not None:
- payload["window_size"] = int(window_size)
- if max_new_tokens is not None:
- payload["max_new_tokens"] = int(max_new_tokens)
-
- response = requests.post(f"{host}/generate", json=payload, timeout=timeout)
- response.raise_for_status()
- data = response.json()
- if "output" not in data:
- raise ValueError(f"Unexpected response payload: {data}")
- return torch.tensor([data["output"]])
-
-
-def check_server_health(host: str = "http://127.0.0.1:8989", timeout: int = 60) -> None:
- """Check if the API server is healthy and responding."""
- try:
- response = requests.get(f"{host}/health", timeout=timeout)
- response.raise_for_status()
- data = response.json()
- if data.get("status") != "ok":
- raise RuntimeError(f"Server is not healthy: {data}")
- except requests.exceptions.ConnectionError as e:
- raise RuntimeError(
- f"Cannot connect to server at {host}. Is the server running?"
- ) from e
- except requests.exceptions.Timeout as e:
- raise RuntimeError(f"Server health check timed out after {timeout}s") from e
- except requests.exceptions.RequestException as e:
- raise RuntimeError(f"Server health check failed: {e}") from e
- print("Server is healthy.")
-
-
-class GenerativeAdapter(torch.nn.Module):
- def __init__(self, model, tokenizer):
- super().__init__()
- self.base_model = model # placeholder
- self.tokenizer = tokenizer
- check_server_health()
-
- @property
- def generation_config(self):
- return self.base_model.generation_config
-
- def generate(self, *args, **kwargs):
- ctx_ids = kwargs["ctx_ids"]
- input_ids = kwargs["input_ids"]
- assert ctx_ids.shape[0] == 1
- assert input_ids.shape[0] == 1
-
- context_txt = self.tokenizer.decode(ctx_ids[0])
- input_txt = self.tokenizer.decode(input_ids[0])
- outputs = call_generate(input_txt, context_txt)
- return outputs
diff --git a/src/ctx_to_lora/modeling/hypernet.py b/src/ctx_to_lora/modeling/hypernet.py
index edc49a2..19efbed 100644
--- a/src/ctx_to_lora/modeling/hypernet.py
+++ b/src/ctx_to_lora/modeling/hypernet.py
@@ -87,6 +87,16 @@ def get_hypernet_config(
aggregator_args: AggregatorArguments,
ctx_encoder_args: CtxEncoderArguments,
):
+ base_model_config = model.config
+ if getattr(base_model_config, "hidden_size", None) is not None:
+ base_hidden_size = base_model_config.hidden_size
+ elif getattr(base_model_config, "text_config", None) is not None:
+ base_hidden_size = base_model_config.text_config.hidden_size
+ else:
+ raise AttributeError(
+ f"Could not determine hidden size from config type {type(base_model_config)!r}"
+ )
+
num_modules = 0
lora_config = getattr(model, "peft_config", None)
if lora_config is not None:
@@ -96,7 +106,7 @@ def get_hypernet_config(
indices = torch.arange(get_num_layers(model), device=model.device)
return HypernetConfig(
**vars(hypernet_args),
- base_hidden_size=model.config.hidden_size,
+ base_hidden_size=base_hidden_size,
lora_config=lora_config,
layer_indices=indices,
feature_sizes=get_peft_in_out_features(model, peft_config=lora_config),
@@ -466,6 +476,17 @@ class ModulatedPretrainedModel(nn.Module):
self._init_model()
self._bias_hyper_init()
+ def _runtime_device(
+ self,
+ ctx_ids: Tensor | None = None,
+ ctx_features: Tensor | None = None,
+ input_ids: Tensor | None = None,
+ ) -> torch.device:
+ for tensor in (ctx_features, ctx_ids, input_ids):
+ if tensor is not None:
+ return tensor.device
+ return next(self.base_model.parameters()).device
+
@classmethod
def from_state_dict(
cls,
@@ -533,6 +554,9 @@ class ModulatedPretrainedModel(nn.Module):
self.patch_lora_forward()
ctx_model_name = self.ctx_encoder_args.ctx_encoder_model_name_or_path
+ if ctx_model_name == "precomputed":
+ self.ctx_encoder = None
+ return
if ctx_model_name is None:
ctx_model_name = self.base_model.config.name_or_path
# use an explicit copy of the base model
@@ -588,7 +612,9 @@ class ModulatedPretrainedModel(nn.Module):
def state_dict(self, *args, **kwargs):
# we assume ctx_encoder and base model is frozen here
- if len([p for p in self.ctx_encoder.parameters() if p.requires_grad]):
+ if self.ctx_encoder is not None and len(
+ [p for p in self.ctx_encoder.parameters() if p.requires_grad]
+ ):
raise ValueError("ctx_encoder contains trainable parameters")
if len([p for p in self.base_model.parameters() if p.requires_grad]):
raise ValueError("base model contains trainable parameters")
@@ -630,43 +656,51 @@ class ModulatedPretrainedModel(nn.Module):
ctx_ids: Integer[Tensor, "bs ctx_len"],
ctx_attn_mask: Integer[Tensor, "bs ctx_len"] | None = None,
ctx_position_ids: Integer[Tensor, "bs ctx_len"] | None = None,
+ ctx_features: Float[Tensor, "bs ctx_len hidden_size"] | None = None,
**kwargs: Any,
):
- with torch.no_grad():
- ctx_encoder_kwargs = dict(
- input_ids=ctx_ids,
- attention_mask=ctx_attn_mask,
- position_ids=ctx_position_ids,
- )
- if isinstance(self.ctx_encoder.base_model, ModernBertModel):
- position_ids = ctx_position_ids.flatten()
- indices = torch.arange(
- position_ids.size(0), device=position_ids.device, dtype=torch.int32
- )
- # [bsz + 1]
- cu_seqlens = torch.cat(
- (
- indices[position_ids == 0],
- torch.tensor(
- position_ids.size(),
- device=position_ids.device,
- dtype=torch.int32,
- ),
- )
+ if ctx_features is None:
+ if self.ctx_encoder is None:
+ raise ValueError(
+ "ctx_features must be provided when ctx_encoder_model_name_or_path='precomputed'"
)
+ with torch.no_grad():
ctx_encoder_kwargs = dict(
- input_ids=ctx_ids.squeeze(0),
- cu_seqlens=cu_seqlens,
- max_seqlen=position_ids.max() + 1,
- attention_mask=-1,
- seq_len=-1,
- batch_size=-1,
+ input_ids=ctx_ids,
+ attention_mask=ctx_attn_mask,
+ position_ids=ctx_position_ids,
)
+ if isinstance(self.ctx_encoder.base_model, ModernBertModel):
+ position_ids = ctx_position_ids.flatten()
+ indices = torch.arange(
+ position_ids.size(0),
+ device=position_ids.device,
+ dtype=torch.int32,
+ )
+ # [bsz + 1]
+ cu_seqlens = torch.cat(
+ (
+ indices[position_ids == 0],
+ torch.tensor(
+ position_ids.size(),
+ device=position_ids.device,
+ dtype=torch.int32,
+ ),
+ )
+ )
+ ctx_encoder_kwargs = dict(
+ input_ids=ctx_ids.squeeze(0),
+ cu_seqlens=cu_seqlens,
+ max_seqlen=position_ids.max() + 1,
+ attention_mask=-1,
+ seq_len=-1,
+ batch_size=-1,
+ )
- ctx_features = self.ctx_encoder(**ctx_encoder_kwargs, **kwargs)
+ ctx_features = self.ctx_encoder(**ctx_encoder_kwargs, **kwargs)
- if isinstance(self.ctx_encoder.base_model, ModernBertModel):
- ctx_features = ctx_features.unsqueeze(0)
+ if isinstance(self.ctx_encoder.base_model, ModernBertModel):
+ ctx_features = ctx_features.unsqueeze(0)
if self.user_defined_scaling == 1:
return self.hypernet.generate_weights(
ctx_features, ctx_attn_mask, ctx_position_ids
@@ -688,6 +722,7 @@ class ModulatedPretrainedModel(nn.Module):
ctx_ids: Integer[Tensor, "n_ctx ctx_len"] | None = None,
ctx_attn_mask: Integer[Tensor, "n_ctx ctx_len"] | None = None,
ctx_position_ids: Integer[Tensor, "n_ctx ctx_len"] | None = None,
+ ctx_features: Float[Tensor, "n_ctx ctx_len hidden_size"] | None = None,
n_ctx_chunks: Integer[Tensor, "n_ctx"] | None = None,
n_queries: Integer[Tensor, "n_ctx"] | None = None,
return_generated_lora: bool | None = False,
@@ -697,7 +732,12 @@ class ModulatedPretrainedModel(nn.Module):
"""Forward pass of the modulated model."""
generated_loras = None
generated_layernorms = None
- if ctx_ids is None and not self.use_base_input_as_ctx:
+ runtime_device = self._runtime_device(
+ ctx_ids=ctx_ids,
+ ctx_features=ctx_features,
+ input_ids=model_inputs_kwargs.get("input_ids"),
+ )
+ if ctx_ids is None and ctx_features is None and not self.use_base_input_as_ctx:
logger.warning(
(
"*" * 100,
@@ -724,7 +764,10 @@ class ModulatedPretrainedModel(nn.Module):
else None
)
generated_loras, generated_layernorms = self.generate_weights(
- ctx_ids, ctx_attn_mask, ctx_position_ids
+ ctx_ids,
+ ctx_attn_mask,
+ ctx_position_ids,
+ ctx_features=ctx_features,
)
if generated_loras is not None:
@@ -745,9 +788,19 @@ class ModulatedPretrainedModel(nn.Module):
)
if n_queries is None:
- if ctx_position_ids is None:
+ if not self.use_sequence_packing:
+ ctx_batch_size = (
+ ctx_ids.shape[0] if ctx_ids is not None else ctx_features.shape[0]
+ )
n_queries = torch.ones(
- ctx_ids.shape[0], dtype=torch.int32, device=self.device
+ ctx_batch_size, dtype=torch.int32, device=runtime_device
+ )
+ elif ctx_position_ids is None:
+ ctx_batch_size = (
+ ctx_ids.shape[0] if ctx_ids is not None else ctx_features.shape[0]
+ )
+ n_queries = torch.ones(
+ ctx_batch_size, dtype=torch.int32, device=runtime_device
)
else:
# quite redundant (we do cu_seqlens many places)
@@ -755,7 +808,7 @@ class ModulatedPretrainedModel(nn.Module):
n_queries = torch.ones(
(ctx_position_ids == 0).sum(),
dtype=torch.int32,
- device=self.device,
+ device=runtime_device,
)
apply_lora_to_layers(
@@ -825,6 +878,10 @@ class ModulatedPretrainedModel(nn.Module):
*model_inputs_args: Any,
**model_inputs_kwargs: dict[str, Any],
):
+ runtime_device = self._runtime_device(
+ ctx_ids=ctx_ids,
+ input_ids=model_inputs_kwargs.get("input_ids"),
+ )
generated_loras = None
generated_layernorms = None
if (
@@ -840,7 +897,7 @@ class ModulatedPretrainedModel(nn.Module):
elif ctx_ids is None and self.generated_loras:
generated_loras = self.generated_loras
if n_ctx_chunks is None:
- n_ctx_chunks = torch.tensor((1,), device=self.device)
+ n_ctx_chunks = torch.tensor((1,), device=runtime_device)
print(
"*" * 100
+ "\n\nUsing internalized LoRAs for generation\n\n"
@@ -890,7 +947,7 @@ class ModulatedPretrainedModel(nn.Module):
n_queries = torch.ones(
model_inputs_kwargs["input_ids"].shape[0],
dtype=torch.int32,
- device=self.device,
+ device=runtime_device,
)
else:
# quite redundant (we do cu_seqlens many places)
@@ -898,7 +955,7 @@ class ModulatedPretrainedModel(nn.Module):
n_queries = torch.ones(
(ctx_position_ids == 0).sum(),
dtype=torch.int32,
- device=self.device,
+ device=runtime_device,
)
apply_lora_to_layers(
diff --git a/src/ctx_to_lora/modeling/idefics2.py b/src/ctx_to_lora/modeling/idefics2.py
index 6fcc5c2..bc3ebd4 100644
--- a/src/ctx_to_lora/modeling/idefics2.py
+++ b/src/ctx_to_lora/modeling/idefics2.py
@@ -246,7 +246,8 @@ class Idefics2PerceiverAttention(nn.Module):
def forward(
self,
latents: torch.Tensor,
- context: torch.Tensor,
+ is_cross_attn: bool,
+ context: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_value: tuple[torch.Tensor] | None = None,
@@ -266,9 +267,13 @@ class Idefics2PerceiverAttention(nn.Module):
use_cache (`bool`, *optional*, defaults to `False`): Whether to use past_key_value for caching.
"""
bsz, q_len, _ = latents.size()
- kv_seq_len = q_len + context.size()[1]
-
- hidden_states = torch.concat([context, latents], dim=-2)
+ if is_cross_attn:
+ if context is None:
+ raise ValueError("Cross-attention requires context inputs.")
+ hidden_states = torch.concat([context, latents], dim=-2)
+ else:
+ hidden_states = latents
+ kv_seq_len = hidden_states.size(1)
query_states = self.q_proj(latents)
key_states = self.k_proj(hidden_states)
@@ -448,7 +453,8 @@ class Idefics2PerceiverFlashAttention2(Idefics2PerceiverAttention):
IDEFICS2_PERCEIVER_ATTENTION_CLASSES = {
- # "eager": Idefics2PerceiverAttention,
+ "eager": Idefics2PerceiverAttention,
+ "sdpa": Idefics2PerceiverAttention,
"flash_attention_2": Idefics2PerceiverFlashAttention2,
}
@@ -566,7 +572,7 @@ IDEFICS2_INPUTS_DOCSTRING = r"""
IDEFICS2_START_DOCSTRING,
)
class Idefics2PerceiverResampler(Idefics2PreTrainedModel):
- _supports_sdpa = False
+ _supports_sdpa = True
config_class = Idefics2PerceiverConfig
def __init__(self, config) -> None:
@@ -612,7 +618,6 @@ class Idefics2PerceiverResampler(Idefics2PreTrainedModel):
self.layernorm = Idefics2RMSNorm(self.hidden_size, eps=self.rms_norm_eps)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
- assert self._use_flash_attention_2
def forward(
self,
@@ -621,79 +626,86 @@ class Idefics2PerceiverResampler(Idefics2PreTrainedModel):
position_ids: torch.LongTensor | None = None,
) -> torch.Tensor:
# seq embed -> bsz seq embed
- if position_ids is None:
- bsz = context.shape[0]
- else:
+ if self._use_flash_attention_2 and position_ids is not None:
# flattened packed sequence
bsz = torch.where(position_ids == 0, 1, 0).sum()
+ else:
+ bsz = context.shape[0]
latents = self.latents_q.unsqueeze(0).expand((bsz, *self.latents_q.size()))
- attention_mask = (
- _prepare_4d_attention_mask(
- attention_mask, latents.dtype, tgt_len=self.n_latents
- )
- if not self._use_flash_attention_2
- else attention_mask
- )
-
compressed_context = latents
-
- cu_seq_lens_q = torch.tensor(
- [self.n_latents] * (bsz + 1), device=context.device, dtype=torch.int32
- ) * torch.arange(bsz + 1, device=context.device, dtype=torch.int32)
- max_length_q = self.n_latents
- # cu_seq_lens_k = None
- # max_length_k = None
- if attention_mask is not None:
- logger.warning_once("Using attention mask for resampler")
- context, _, cu_seq_lens_k, max_length_k, _ = unpad_input(
- context, attention_mask
- )
- context = context.unsqueeze(0)
- position_ids = True # goes down flash attn path that uses cu_seq_lens
-
- elif position_ids is not None:
- logger.warning_once("Using position ids for resampler")
-
- position_ids = position_ids.flatten()
- indices = torch.arange(
- position_ids.size(0), device=position_ids.device, dtype=torch.int32
- )
- # [bsz + 1]
- cu_seq_lens_k = torch.cat(
- (
- indices[position_ids == 0],
- torch.tensor(
- position_ids.size(),
- device=position_ids.device,
- dtype=torch.int32,
- ),
+ if self._use_flash_attention_2:
+ cu_seq_lens_q = torch.tensor(
+ [self.n_latents] * (bsz + 1), device=context.device, dtype=torch.int32
+ ) * torch.arange(bsz + 1, device=context.device, dtype=torch.int32)
+ max_length_q = self.n_latents
+ if attention_mask is not None:
+ logger.warning_once("Using attention mask for resampler")
+ context, _, cu_seq_lens_k, max_length_k, _ = unpad_input(
+ context, attention_mask
)
+ context = context.unsqueeze(0)
+ position_ids = True
+ elif position_ids is not None:
+ logger.warning_once("Using position ids for resampler")
+ position_ids = position_ids.flatten()
+ indices = torch.arange(
+ position_ids.size(0), device=position_ids.device, dtype=torch.int32
+ )
+ cu_seq_lens_k = torch.cat(
+ (
+ indices[position_ids == 0],
+ torch.tensor(
+ position_ids.size(),
+ device=position_ids.device,
+ dtype=torch.int32,
+ ),
+ )
+ )
+ max_length_k = position_ids.max() + 1
+ else:
+ raise ValueError("either position_ids or attention_mask is required")
+
+ x_attn_kwargs = dict(
+ position_ids=position_ids,
+ cu_seq_lens_q=cu_seq_lens_q,
+ cu_seq_lens_k=cu_seq_lens_k,
+ max_length_q=max_length_q,
+ max_length_k=max_length_k,
+ )
+ self_attn_position_ids = torch.arange(
+ self.n_latents, device=context.device, dtype=torch.int32
+ ).repeat(1, bsz)
+ self_attn_kwargs = dict(
+ position_ids=self_attn_position_ids,
+ cu_seq_lens_q=cu_seq_lens_q,
+ cu_seq_lens_k=cu_seq_lens_q,
+ max_length_q=max_length_q,
+ max_length_k=max_length_q,
)
-
- max_length_k = position_ids.max() + 1
-
else:
- raise ValueError("either position_ids or attention_mask is required")
- x_attn_kwargs = dict(
- position_ids=position_ids,
- cu_seq_lens_q=cu_seq_lens_q,
- cu_seq_lens_k=cu_seq_lens_k,
- max_length_q=max_length_q,
- max_length_k=max_length_k,
- )
- self_attn_position_ids = torch.arange(
- self.n_latents, device=context.device, dtype=torch.int32
- ).repeat(1, bsz)
- self_attn_kwargs = dict(
- # attention_mask=self_attn_mask,
- position_ids=self_attn_position_ids,
- cu_seq_lens_q=cu_seq_lens_q,
- cu_seq_lens_k=cu_seq_lens_q,
- max_length_q=max_length_q,
- max_length_k=max_length_q,
- )
+ x_attn_mask = None
+ if attention_mask is not None:
+ context_attn_mask = _prepare_4d_attention_mask(
+ attention_mask, latents.dtype, tgt_len=self.n_latents
+ )
+ latent_attn_mask = torch.zeros(
+ (
+ context_attn_mask.shape[0],
+ 1,
+ self.n_latents,
+ self.n_latents,
+ ),
+ dtype=context_attn_mask.dtype,
+ device=context_attn_mask.device,
+ )
+ x_attn_mask = torch.cat(
+ [context_attn_mask, latent_attn_mask], dim=-1
+ )
+ x_attn_kwargs = {"attention_mask": x_attn_mask}
+ self_attn_kwargs = {"attention_mask": None}
+
for i, layer in enumerate(self.layers):
inp_kwargs = dict(
latents=compressed_context,
@@ -738,10 +750,10 @@ class Idefics2Perceiver(Idefics2PreTrainedModel):
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
):
- if position_ids is None:
- bsz = context.shape[0]
- else:
+ if self.encoder._use_flash_attention_2 and position_ids is not None:
bsz = torch.where(position_ids == 0, 1, 0).sum()
+ else:
+ bsz = context.shape[0]
projected_inputs = self.modality_projection(context)
# [bsz, n_latents, dim]
diff --git a/src/ctx_to_lora/modeling/llm_lingua.py b/src/ctx_to_lora/modeling/llm_lingua.py
deleted file mode 100644
index 99e9b17..0000000
--- a/src/ctx_to_lora/modeling/llm_lingua.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import torch
-from llmlingua import PromptCompressor
-from torch import nn
-
-from ctx_to_lora.data.definitions import CTX_AFFIXES
-
-
-class LLMLinguaModel(nn.Module):
- def __init__(self, model, tokenizer, compression_rate):
- super().__init__()
- self.base_model = model
- self.compressor = PromptCompressor(
- model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
- use_llmlingua2=True, # Whether to use llmlingua-2
- )
- model_name = self.base_model.name_or_path
- self.register_buffer("prefix", torch.tensor(CTX_AFFIXES[model_name]["prefix"]))
- self.register_buffer("suffix", torch.tensor(CTX_AFFIXES[model_name]["suffix"]))
- self.len_prefix = len(self.prefix)
- self.len_suffix = len(self.suffix)
- self.tokenizer = tokenizer
- self.compression_rate = compression_rate
-
- @property
- def generation_config(self):
- return self.base_model.generation_config
-
- def compress(self, prompt_txt: str, rate: float):
- return self.compressor.compress_prompt(
- prompt_txt, rate=rate, force_tokens=["\n", "?"]
- )
-
- def compress_tokens(self, input_ids, query_text):
- bs = input_ids.shape[0]
- txt = self.tokenizer.batch_decode(
- input_ids[:, self.len_prefix : -self.len_suffix]
- )
- q_start_idx = txt[0].rfind(query_text)
- ctx_txt = txt[0][:q_start_idx]
- q_txt = txt[0][q_start_idx:]
- compressed_txt = self.compress(ctx_txt, rate=self.compression_rate)
- compressed_ids = self.tokenizer(
- compressed_txt["compressed_prompt"] + "\n\n" + q_txt,
- return_attention_mask=False,
- add_special_tokens=False,
- return_tensors="pt",
- )["input_ids"].to(self.base_model.device)
-
- out = torch.cat(
- [self.prefix.expand(bs, -1), compressed_ids, self.suffix.expand(bs, -1)],
- dim=-1,
- )
- return out
-
- def generate(self, *args, **kwargs):
- # take ctx_ids
- # strip prefix and suffix
- # ctx_ids is left padded
- ctx_ids = kwargs["ctx_ids"][:, self.len_prefix : -self.len_suffix]
- # decode ctx_ids to ctx_txt
- ctx_txt = self.tokenizer.batch_decode(ctx_ids)
- compressed_ctx_txt = self.compress(ctx_txt, rate=self.compression_rate)
- compressed_ctx_ids = self.tokenizer(
- compressed_ctx_txt["compressed_prompt"] + "\n\n",
- return_attention_mask=False,
- add_special_tokens=False,
- return_tensors="pt",
- ).to(self.base_model.device)
-
- bs = ctx_ids.shape[0]
- ctx_inp_ids = torch.cat(
- [
- self.prefix.expand(bs, -1),
- compressed_ctx_ids["input_ids"],
- kwargs["input_ids"][:, self.len_prefix :],
- ],
- dim=-1,
- )
- attn_mask = torch.ones_like(ctx_inp_ids)
- for k in [
- "ctx_ids",
- "ctx_attn_mask",
- "n_ctx_chunks",
- "input_ids",
- "attention_mask",
- ]:
- kwargs.pop(k, None)
- return self.base_model.generate(ctx_inp_ids, attention_mask=attn_mask, **kwargs)
-
-
-if __name__ == "__main__":
- from ctx_to_lora.model_loading import get_model_and_tokenizer
-
- model, tokenizer = get_model_and_tokenizer(
- "google/gemma-2-2b-it",
- train=False,
- requires_grad=False,
- )
-
- # Demo: build wrapper, create a toy context + prompt, run compression + generation.
- device = "cuda"
- llm = LLMLinguaModel(model, tokenizer).to(device)
-
- # Toy context and user prompt
- context_text = (
- "This is a short illustrative context about large language models and compression. "
- "They can reduce prompt length while preserving meaning."
- )
- user_prompt = (
- "Summarize the context in one concise sentence." # what we want model to do
- )
-
- # Tokenize raw context (core) without special tokens
- core_ctx_ids = tokenizer.apply_chat_template(
- [[{"role": "user", "content": context_text}]],
- tokenize=True,
- add_generation_prompt=True,
- return_attention_mask=False,
- padding=False,
- truncation=False,
- return_tensors="pt",
- add_special_tokens=False,
- ).to(device)
-
- # Input prompt tokens (what follows the contextual block)
- input_ids = tokenizer.apply_chat_template(
- [[{"role": "user", "content": user_prompt}]],
- tokenize=True,
- add_generation_prompt=True,
- return_attention_mask=False,
- padding=False,
- truncation=False,
- return_tensors="pt",
- add_special_tokens=False,
- ).to(device)
-
- print("Original context length (chars):", len(context_text))
-
- # Run generation (may vary depending on model capabilities)
- output_ids = llm.generate(ctx_ids=core_ctx_ids, input_ids=input_ids)
- # Decode only the tail beyond supplied input for readability
- generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=False)
- print(f"\nFull generated text:\n{generated_text}")
diff --git a/src/ctx_to_lora/modeling/text_to_lora.py b/src/ctx_to_lora/modeling/text_to_lora.py
deleted file mode 100644
index 6acb8be..0000000
--- a/src/ctx_to_lora/modeling/text_to_lora.py
+++ /dev/null
@@ -1,160 +0,0 @@
-from functools import partial
-
-import torch
-from peft import PeftConfig
-from torch import nn
-
-from ctx_to_lora.modeling.lora_layer import apply_lora_to_layers, lora_forward
-from ctx_to_lora.modeling.text_to_lora_impl import (
- embed_texts,
- get_layers,
- get_peft_config,
- load_hypermod,
-)
-from ctx_to_lora.utils import get_peft_modules
-
-
-class TextToLoRA(nn.Module):
- def __init__(self, model_name_or_path, prefix_tokens, device):
- assert model_name_or_path == "google/gemma-2-2b-it"
- super().__init__()
- hypermod_dir = "trained_t2l/gemma_2b_t2l"
- peft_config = get_peft_config(
- PeftConfig.from_json_file(f"{hypermod_dir}/adapter_config.json")
- )
-
- # ours lora forward pass uses alpha directly
- peft_config.lora_alpha = peft_config.lora_alpha / peft_config.r
- self.prefix_tokens = prefix_tokens
- self.device = device
- (
- _,
- self.t2l_model,
- self.base_model,
- self.tokenizer,
- self.emb_model,
- self.emb_tokenizer,
- self.task_desc_format_fn,
- self.pooling_fn,
- ) = load_hypermod(hypermod_dir, device)
- layer_indices = range(len(get_layers(self.base_model)))
-
- self.layer_indices = torch.tensor(
- layer_indices, dtype=torch.long, device=device
- )
- # patch base model forward pass to use lora
- layers = get_layers(self.base_model)
- lora_forward_fn = lora_forward
-
- for layer_idx in self.layer_indices:
- for module_info in get_peft_modules(layers[layer_idx], peft_config):
- module = module_info["module"]
- module.forward = partial(
- lora_forward_fn,
- self=module,
- lora_dropout_p=peft_config.lora_dropout,
- scaling=peft_config.lora_alpha,
- )
-
- @property
- def generation_config(self):
- return self.base_model.generation_config
-
- def generate_weights(self, ctx_txt: str):
- # generate loras
- ctx_emb = embed_texts(
- [ctx_txt],
- self.emb_model,
- self.emb_tokenizer,
- self.task_desc_format_fn,
- self.pooling_fn,
- self.device,
- )
- encoder_out = self.t2l_model.task_encoder(ctx_emb)
- encoded_task_emb = encoder_out["encoded_task_emb"].detach()
-
- lora_A, lora_B = dict(), dict()
- lora_dict = dict()
- for target_module in self.t2l_model.target_modules:
- factorized_delta_w = self.t2l_model.get_delta_weights(
- self.layer_indices,
- target_module,
- encoded_task_emb.expand(self.layer_indices.shape[0], -1),
- factorized=True,
- )
- # lora_A[target_module]: [n_layers, r, d_in]
- # lora_A[target_module]: [n_layers, d_out, r]
- lora_A[target_module], lora_B[target_module] = factorized_delta_w
-
- # convert to lora format used by lora_forward
- # dict of {module:
- # {A: [bs, n_layers, r, d_inim],
- # B: [bs, n_layers, r, d_outim]}}
- lora_dict[target_module] = dict(
- A=lora_A[target_module].unsqueeze(0),
- B=lora_B[target_module].transpose(-1, -2).unsqueeze(0),
- )
- return lora_dict
-
- def generate(self, *args, **kwargs):
- ctx_ids_full = kwargs["ctx_ids"]
- ctx_txt = self.tokenizer.decode(
- ctx_ids_full[0, len(self.prefix_tokens) :], skip_special_tokens=True
- )
- generated_loras = self.generate_weights(ctx_txt)
- apply_lora_to_layers(
- self.base_model,
- self.layer_indices,
- generated_loras,
- n_qs=torch.tensor([1], device=self.device),
- position_ids=None,
- )
- kwargs.pop("ctx_ids", None)
- kwargs.pop("ctx_attn_mask", None)
- kwargs.pop("n_ctx_chunks", None)
- return self.base_model.generate(*args, **kwargs)
-
-
-if __name__ == "__main__":
- from transformers import AutoTokenizer
-
- from ctx_to_lora.data.definitions import CTX_AFFIXES
- from ctx_to_lora.data.processing import load_and_process_dataset
-
- model_name = "google/gemma-2-2b-it"
- tokenizer = AutoTokenizer.from_pretrained(model_name)
-
- ds = load_and_process_dataset("pwc", split="train", num_proc=8)
- ctx = ds[0]["context"]
- inp = ds[1]["prompts"][0]
- ctx_ids = tokenizer.apply_chat_template(
- [{"role": "user", "content": ctx}], return_tensors="pt", return_dict=True
- )
- input_ids = tokenizer.apply_chat_template(
- [{"role": "user", "content": ctx + "\n\n" + inp}],
- return_tensors="pt",
- return_dict=True,
- )
- ctx_ids = {k: v.to("cuda") for k, v in ctx_ids.items()}
- input_ids = {k: v.to("cuda") for k, v in input_ids.items()}
-
- prefix_tokens = CTX_AFFIXES[model_name]["prefix"]
- prefix_tokens = torch.tensor(prefix_tokens, dtype=torch.long)
-
- t2l_model = TextToLoRA(
- model_name,
- prefix_tokens,
- device="cuda",
- )
-
- with torch.no_grad():
- for _ in range(1):
- outputs = t2l_model.generate(
- **input_ids,
- ctx_ids=ctx_ids["input_ids"],
- max_new_tokens=256,
- do_sample=False,
- )
- print(
- f"Student response: {tokenizer.batch_decode(outputs, skip_special_tokens=False)}"
- )
diff --git a/src/ctx_to_lora/modeling/text_to_lora_impl.py b/src/ctx_to_lora/modeling/text_to_lora_impl.py
deleted file mode 100644
index 1991174..0000000
--- a/src/ctx_to_lora/modeling/text_to_lora_impl.py
+++ /dev/null
@@ -1,1256 +0,0 @@
-# taken from github.com/sakanaai/text-to-lora
-import argparse
-import logging
-import math
-import os
-from copy import deepcopy
-from math import sqrt
-from typing import Literal
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import yaml
-from peft import PeftConfig, PeftModel, get_peft_config, load_peft_weights
-from peft.tuners._buffer_dict import BufferDict
-from peft.tuners.tuners_utils import BaseTunerLayer, check_target_module_exists
-from peft.utils import get_peft_model_state_dict
-from safetensors.torch import save_file
-from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
-from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding
-
-
-def add_full_stop(s):
- s = s.strip()
- # check if s ends with . or .*
- if s[-1].isalpha():
- s += "."
- return s
-
-
-def get_layers(model):
- if hasattr(model, "model"):
- return get_layers(model.model)
- return model.layers
-
-
-def get_pooling_fn(pooling_type: Literal["last_token", "cls"]):
- if pooling_type == "last_token":
- return last_token_pool
- elif pooling_type == "cls":
- return cls_pool
- else:
- raise ValueError(f"Invalid pooling type: {pooling_type}")
-
-
-def cls_pool(
- outputs: dict[str, torch.Tensor], attention_mask: torch.Tensor
-) -> torch.Tensor:
- right_padding = attention_mask[:, 0].sum() == attention_mask.shape[0]
- assert right_padding, f'tokenizer.padding_side should be "right"'
- return outputs["last_hidden_state"][:, 0].detach()
-
-
-def last_token_pool(
- outputs: dict[str, torch.Tensor], attention_mask: torch.Tensor
-) -> torch.Tensor:
- last_hidden_states = (
- outputs["hidden_states"][-1].detach()
- if "hidden_states" in outputs
- else outputs["last_hidden_state"].detach()
- )
- left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]
- if left_padding:
- return last_hidden_states[:, -1]
- else:
- sequence_lengths = attention_mask.sum(dim=1) - 1
- batch_size = last_hidden_states.shape[0]
- return last_hidden_states[
- torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths
- ]
-
-
-def lora_state_dict_to_tensor_dict(lora_sd, target_modules, layer_indices, device):
- A, B = (
- {
- target_module: [None for _ in range(len(layer_indices))]
- for target_module in target_modules
- },
- {
- target_module: [None for _ in range(len(layer_indices))]
- for target_module in target_modules
- },
- )
-
- for k, v in lora_sd.items():
- for target_module in target_modules:
- if target_module in k:
- layer_idx = int(k.split("layers.")[-1].split(".")[0])
- if layer_idx in layer_indices:
- if "lora_A" in k:
- A[target_module][layer_idx] = v.to(device)
- elif "lora_B" in k:
- B[target_module][layer_idx] = v.to(device)
-
- for target_module in target_modules:
- A[target_module] = torch.stack(A[target_module], dim=0)
- B[target_module] = torch.stack(B[target_module], dim=0)
-
- return dict(A=A, B=B)
-
-
-def get_emb_model_and_fns(emb_model_name, device):
- emb_model = AutoModel.from_pretrained(
- emb_model_name,
- device_map=device,
- torch_dtype=torch.float32 if "gte" in emb_model_name else torch.bfloat16,
- trust_remote_code=True,
- ).eval()
- emb_tokenizer = AutoTokenizer.from_pretrained(emb_model_name)
- if emb_tokenizer.pad_token_id is None:
- emb_tokenizer.pad_token_id = emb_tokenizer.eos_token_id
- task_desc_format_fn = add_full_stop
- pooling_fn = get_pooling_fn("cls")
- return emb_model, emb_tokenizer, task_desc_format_fn, pooling_fn
-
-
-def get_model_and_tokenizer(
- model_path,
- train,
- requires_grad,
- use_flash_attn=True,
- peft_config=None,
- model_kwargs=None,
- tokenizer_kwargs=None,
- device="cuda:0",
- dtype=torch.bfloat16,
-):
- model = get_model(
- model_path,
- train,
- requires_grad,
- use_flash_attn,
- peft_config,
- model_kwargs,
- device,
- dtype,
- )
- tokenizer = get_tokenizer(model_path, tokenizer_kwargs, peft_config, train)
- return model, tokenizer
-
-
-def get_tokenizer(model_path, tokenizer_kwargs=None, peft_config=None, train=False):
- # NOTE: lora models don't have tokenizer config in the folder
-
- padding_side = "left" if not train else "right"
- if peft_config:
- model_path = peft_config.base_model_name_or_path
-
- if tokenizer_kwargs is None:
- tokenizer_kwargs = {}
-
- tokenizer = AutoTokenizer.from_pretrained(
- model_path, padding_side=padding_side, **tokenizer_kwargs
- )
-
- if tokenizer.pad_token_id is None:
- tokenizer.pad_token_id = tokenizer.eos_token_id
-
- template_path = f"chat_templates/{model_path}.jinja"
- assert os.path.exists(template_path), (
- f"Chat template not found for {model_path}.\n"
- "We assume a specfic form of chat template for consistency between models. "
- "Please use the templates provided."
- )
- print(f"Loading chat template from {template_path}")
- chat_template = open(template_path).read()
- chat_template = chat_template.replace(" ", "").replace("\n", "")
- tokenizer.chat_template = chat_template
-
- tokenizer.add_eos_token = False
- tokenizer.truncation_side = "left"
- return tokenizer
-
-
-def get_model(
- model_path,
- train,
- requires_grad,
- use_flash_attn=True,
- peft_config=None,
- model_kwargs=None,
- device="cuda:0",
- dtype=torch.bfloat16,
-):
- model_init_kwargs = dict(
- pretrained_model_name_or_path=model_path,
- device_map=device,
- torch_dtype=dtype,
- trust_remote_code=True,
- )
- if model_kwargs is not None:
- model_init_kwargs.update(model_kwargs)
- if use_flash_attn:
- model_init_kwargs["attn_implementation"] = "flash_attention_2"
- if train:
- # for training disable cache
- model_init_kwargs["use_cache"] = False
- logger.debug(f"Model init kwargs: {model_init_kwargs}")
- model = AutoModelForCausalLM.from_pretrained(**model_init_kwargs)
- if peft_config is not None:
- model = PeftModel(model, peft_config)
- model.train(train)
- for param in model.parameters():
- param.requires_grad = requires_grad
- return model
-
-
-def get_lora_module_names(model, target_modules, layer_indices):
- module_names = {
- target_module: [[] for _ in range(len(layer_indices))]
- for target_module in target_modules
- }
- for k in get_peft_model_state_dict(model):
- if ("lora" not in k) and ("vera_lambda" not in k):
- continue
- layer_idx = int(k.split("layers.")[-1].split(".")[0])
- if layer_idx in layer_indices:
- for target_module in target_modules:
- if target_module in k:
- if "vera_lambda" in k:
- # replace the name to match the lora naming convention
- k = k.replace("vera_lambda_d", "lora_A.weight")
- k = k.replace("vera_lambda_b", "lora_B.weight")
- module_names[target_module][layer_idx].append(k)
- break
- return module_names
-
-
-logger = logging.getLogger()
-
-
-# taken from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-def rotate_half(x):
- """Rotates half the hidden dims of the input."""
- x1 = x[..., : x.shape[-1] // 2]
- x2 = x[..., x.shape[-1] // 2 :]
- return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(x, cos, sin):
- x = (x * cos) + (rotate_half(x) * sin)
- return x
-
-
-class LlamaRMSNorm(nn.Module):
- def __init__(self, hidden_size, eps=1e-6):
- """
- LlamaRMSNorm is equivalent to T5LayerNorm
- """
- super().__init__()
- self.weight = nn.Parameter(torch.ones(hidden_size))
- self.variance_epsilon = eps
-
- def forward(self, hidden_states):
- input_dtype = hidden_states.dtype
- hidden_states = hidden_states.to(torch.float32)
- variance = hidden_states.pow(2).mean(-1, keepdim=True)
- hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
- return self.weight * hidden_states.to(input_dtype)
-
- def extra_repr(self):
- return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class TaskEncoder(nn.Module):
- def __init__(self, task_emb_size: int, encoded_task_emb_size: int):
- super().__init__()
- self.encoded_task_emb_size = encoded_task_emb_size
- self.mlp = nn.Sequential(
- nn.Linear(task_emb_size, encoded_task_emb_size),
- nn.LayerNorm(encoded_task_emb_size),
- )
-
- def get_one_hot_task_emb(
- self, num_tasks: int, task_idx: torch.Tensor
- ) -> torch.Tensor:
- return torch.eye(num_tasks, device=task_idx.device)[task_idx]
-
- def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
- return {"encoded_task_emb": self.mlp(x)}
-
-
-class DiscreteOneHotTaskEncoder(nn.Module):
- def __init__(self, task_emb_size: int, n_classes: int, n_embs: int):
- super().__init__()
- self.n_classes = n_classes
- self.n_embs = n_embs
- self.encoded_task_emb_size = n_classes * n_embs
- self.mlp = nn.Sequential(
- nn.Linear(task_emb_size, self.encoded_task_emb_size * 4),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(self.encoded_task_emb_size * 4, self.encoded_task_emb_size),
- )
-
- def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
- logits = self.mlp(x)
- logits = logits.reshape(*logits.shape[:-1], self.n_embs, self.n_classes)
- if self.training:
- dist = torch.distributions.one_hot_categorical.OneHotCategorical(
- logits=logits
- )
- one_hot = dist.sample() + dist.probs - dist.probs.detach()
- else:
- one_hot = torch.eye(self.n_classes, device=x.device)[
- torch.argmax(logits, dim=-1)
- ]
- return {"encoded_task_emb": torch.flatten(one_hot, start_dim=-2, end_dim=-1)}
-
-
-# similar to https://github.com/karpathy/deep-vector-quantization/blob/c3c026a1ccea369bc892ad6dde5e6d6cd5a508a4/dvq/model/quantize.py#L77
-class SoftmaxTaskEncoder(nn.Module):
- def __init__(self, task_emb_size: int, code_dim: int, n_embs: int):
- super().__init__()
- self.n_embs = n_embs
- self.code_dim = code_dim
- self.mlp = nn.Sequential(
- nn.Linear(task_emb_size, code_dim * 4),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(code_dim * 4, n_embs),
- )
- self.embed = nn.Parameter(torch.randn(n_embs, code_dim), requires_grad=True)
- self.ln = nn.LayerNorm(code_dim)
-
- def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
- logits = self.mlp(x).reshape(-1, self.n_embs) # [bs, n_embs]
- probs = F.softmax(logits, dim=-1) # [bs, n_embs]
- emb = probs.unsqueeze(-1) * self.embed.unsqueeze(0) # [bs, n_embs, code_dim]
- emb = emb.sum(1) # [bs, code_dim]
- emb = self.ln(emb)
- return {"encoded_task_emb": emb}
-
-
-# based on https://github.com/rosinality/vq-vae-2-pytorch/blob/master/vqvae.py
-class VQTaskEncoder(nn.Module):
- def __init__(
- self, task_emb_size: int, code_dim: int, n_embs: int, decay: float = 0.99
- ):
- super().__init__()
- self.n_embs = n_embs
- self.code_dim = code_dim
- self.decay = decay
- self.mlp = nn.Sequential(
- nn.Linear(task_emb_size, code_dim * 4),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(code_dim * 4, code_dim),
- )
- self.ln = nn.LayerNorm(code_dim)
- self.register_buffer("embed", torch.randn(code_dim, n_embs))
- self.register_buffer("cluster_size", torch.zeros(n_embs))
- self.register_buffer("embed_avg", self.embed.clone())
-
- def embed_code(self, embed_id):
- return F.embedding(embed_id, self.embed.transpose(0, 1))
-
- def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
- z_e = self.mlp(x).reshape(-1, self.code_dim) # [bs, code_dim]
-
- dist = (
- z_e.pow(2).sum(1, keepdim=True)
- - 2 * z_e @ self.embed
- + self.embed.pow(2).sum(0, keepdim=True)
- )
- indices = torch.argmin(dist, dim=-1) # [bs]
- one_hot = F.one_hot(indices, self.n_embs).to(z_e.dtype) # [bs, n_embs]
- indices = indices.view(*x.shape[:-1]) # [bs]
- quantize = self.embed_code(indices)
- if self.training:
- # exponential moving average update for the codebook
- embed_onehot_sum = one_hot.sum(0) # [n_embs]
- embed_sum = z_e.transpose(0, 1) @ one_hot
-
- self.cluster_size.data.mul_(self.decay).add_(
- embed_onehot_sum, alpha=1 - self.decay
- )
- self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
- n = self.cluster_size.sum()
- cluster_size = (self.cluster_size + 1e-5) / (n + self.n_embs * 1e-5) * n
- embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
- self.embed.data.copy_(embed_normalized)
-
- commitment_cost = 0.01
- diff = commitment_cost * (quantize.detach() - z_e).pow(2).mean()
- quantize = self.ln(z_e + (quantize - z_e).detach())
- return {"encoded_task_emb": quantize, "loss": diff}
-
-
-class MLPResidualBlock(nn.Module):
- def __init__(
- self, input_size, hidden_size, output_size, pre_layer_norm, post_dropout
- ):
- super().__init__()
- layers = []
- if pre_layer_norm:
- layers.append(nn.LayerNorm(input_size))
- layers += [
- nn.Linear(input_size, hidden_size),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(hidden_size, output_size),
- nn.SiLU(),
- ]
- if post_dropout:
- layers.append(nn.Dropout(0.05))
- self.mlp = nn.Sequential(*layers)
-
- def forward(self, x):
- return x + self.mlp(x)
-
-
-def zero_lora_param_dict(target_modules, n_layers, rank, in_features, out_features):
- return nn.ParameterDict(
- {
- "A": nn.ParameterDict(
- {
- m: nn.Parameter(
- torch.zeros(n_layers, rank, in_features[m]), requires_grad=False
- )
- for m in target_modules
- }
- ),
- "B": nn.ParameterDict(
- {
- m: nn.Parameter(
- torch.zeros(n_layers, out_features[m], rank),
- requires_grad=False,
- )
- for m in target_modules
- }
- ),
- }
- )
-
-
-def lora_tensor_dict_to_param_dict(lora_tensor_dict, requires_grad):
- return nn.ParameterDict(
- {
- "A": nn.ParameterDict(
- {
- k: nn.Parameter(v, requires_grad)
- for k, v in lora_tensor_dict["A"].items()
- }
- ),
- "B": nn.ParameterDict(
- {
- k: nn.Parameter(v, requires_grad)
- for k, v in lora_tensor_dict["B"].items()
- }
- ),
- }
- )
-
-
-class HyperModulator(nn.Module):
- def __init__(
- self,
- model: PeftModel,
- output_space: str,
- module_names: list[str],
- training_task: Literal["sft", "recon"] = "sft",
- pred_z_score: bool | None = None,
- mean_recon_target: dict[str, torch.Tensor] | None = None,
- std_recon_target: dict[str, torch.Tensor] | None = None,
- match_lora_init: bool = False,
- task_emb_size: int | None = None,
- shared_AB_head: bool = False,
- autoreg_gen: bool = False,
- learnable_pos_emb: bool = False,
- encoder_type: Literal["linear", "discrete", "vq", "softmax"] = "linear",
- AB_offset: dict[str, dict[str, torch.Tensor]] | None = None,
- learnable_AB_offset: bool = False,
- zero_init_head: bool = False,
- latent_size: int = 128,
- head_in_size: int = 512,
- head_use_bias: bool = False,
- factorized: bool = False,
- delta_w_scaling: float = 10000,
- dtype: torch.dtype = torch.float32,
- ):
- assert output_space == "lora", f"Invalid output space: {output_space}"
- assert (not shared_AB_head) or output_space == "lora", (
- "shared_AB_head is only supported for lora output space"
- )
- assert (not autoreg_gen) or output_space == "lora", (
- "autoreg_gen is only supported for shared_AB_head and lora output space"
- )
- assert (not learnable_pos_emb) or autoreg_gen, (
- "learnable_pos_emb is only supported when autoreg_gen is True"
- )
- assert not (training_task == "recon" and AB_offset), (
- "AB_offset is not supported for recon training"
- )
-
- super().__init__()
- self.model_config = model_config = model.config
- self.peft_config = peft_config = model.peft_config["default"]
- self.training_task = training_task
- self.pred_z_score = pred_z_score
- self.module_names = module_names
- self.shared_AB_head = shared_AB_head
- self.autoreg_gen = autoreg_gen
- self.learnable_pos_emb = learnable_pos_emb
- self.scaling = (
- peft_config.lora_alpha / peft_config.r if output_space == "lora" else 1
- )
- if getattr(peft_config, "use_rslora", False):
- self.scaling *= math.sqrt(peft_config.r)
- self.output_space = output_space
- self.max_num_layers = model_config.num_hidden_layers
-
- self.target_modules = peft_config.target_modules
- self.module_to_int = {m: i for i, m in enumerate(self.target_modules)}
- self.task_to_int = dict()
-
- self.factorized = factorized
- self.delta_w_scaling = delta_w_scaling
- self.device = device = model.device
- self.dtype = dtype
-
- self.in_features, self.out_features = get_in_out_features(model, peft_config)
- n_layers = self.max_num_layers
- self.AB_offset = zero_lora_param_dict(
- self.target_modules,
- n_layers,
- peft_config.r,
- self.in_features,
- self.out_features,
- )
- self.mean_recon_target = zero_lora_param_dict(
- self.target_modules,
- n_layers,
- peft_config.r,
- self.in_features,
- self.out_features,
- )
- self.std_recon_target = zero_lora_param_dict(
- self.target_modules,
- n_layers,
- peft_config.r,
- self.in_features,
- self.out_features,
- )
- if AB_offset is not None:
- self.AB_offset = lora_tensor_dict_to_param_dict(
- AB_offset, requires_grad=learnable_AB_offset
- )
- if mean_recon_target is not None:
- self.mean_recon_target = lora_tensor_dict_to_param_dict(
- mean_recon_target, requires_grad=False
- )
- if std_recon_target is not None:
- self.std_recon_target = lora_tensor_dict_to_param_dict(
- std_recon_target, requires_grad=False
- )
-
- # the input and output features for each target module (i.e. layer type)
-
- encoded_task_emb_size = 0
-
- if task_emb_size is not None:
- encoded_task_emb_size = latent_size // 2
- self.task_encoder = {
- "linear": TaskEncoder(task_emb_size, encoded_task_emb_size),
- "discrete": DiscreteOneHotTaskEncoder(
- task_emb_size, n_classes=64, n_embs=1
- ),
- "vq": VQTaskEncoder(
- task_emb_size, code_dim=encoded_task_emb_size, n_embs=32
- ),
- "softmax": SoftmaxTaskEncoder(
- task_emb_size, code_dim=encoded_task_emb_size, n_embs=32
- ),
- }[encoder_type].to(device)
- if encoder_type == "discrete":
- depth_emb_size = self.max_num_layers
- type_emb_size = len(self.target_modules)
- self.layer_depth_encoder = lambda x: F.one_hot(
- x, num_classes=self.max_num_layers
- ).to(dtype)
- self.layer_type_encoder = lambda x: F.one_hot(
- x, num_classes=len(self.target_modules)
- ).to(dtype)
- else:
- depth_emb_size = latent_size // 4
- type_emb_size = latent_size // 4
- self.layer_depth_encoder = nn.Sequential(
- nn.Embedding(self.max_num_layers, depth_emb_size),
- nn.LayerNorm(depth_emb_size),
- )
- self.layer_type_encoder = nn.Sequential(
- nn.Embedding(len(self.target_modules), type_emb_size),
- nn.LayerNorm(type_emb_size),
- )
-
- mlp_inp_size = depth_emb_size + type_emb_size + encoded_task_emb_size
-
- self.mixer = nn.Sequential(
- nn.Dropout(0.05),
- nn.Linear(mlp_inp_size, mlp_inp_size * 4),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(mlp_inp_size * 4, mlp_inp_size),
- nn.SiLU(),
- nn.Dropout(0.05),
- )
- self.mlp1 = MLPResidualBlock(
- mlp_inp_size,
- mlp_inp_size * 4,
- mlp_inp_size,
- pre_layer_norm=True,
- post_dropout=True,
- )
- self.mlp2 = MLPResidualBlock(
- mlp_inp_size,
- mlp_inp_size * 4,
- mlp_inp_size,
- pre_layer_norm=True,
- post_dropout=True,
- )
-
- self.mlp3 = nn.Sequential(
- nn.LayerNorm(mlp_inp_size),
- nn.Linear(mlp_inp_size, mlp_inp_size * 4),
- nn.SiLU(),
- nn.Dropout(0.05),
- nn.Linear(mlp_inp_size * 4, head_in_size),
- nn.SiLU(),
- )
-
- if autoreg_gen:
- if learnable_pos_emb:
- self.pos_emb = nn.Sequential(
- nn.Embedding(peft_config.r, mlp_inp_size),
- nn.LayerNorm(mlp_inp_size),
- )
- else:
- self.rotary_emb = LlamaRotaryEmbedding(
- dim=mlp_inp_size,
- max_position_embeddings=64, # model_config.hidden_size,
- device=device,
- )
-
- # multiple heads, each for a different target module
- if shared_AB_head:
- self.out_emb = nn.ParameterDict(
- (
- m,
- nn.ParameterDict(
- dict(
- # same init as the embedding layer
- A=nn.Parameter(
- torch.normal(0, 1, size=(mlp_inp_size,)),
- requires_grad=True,
- ),
- B=nn.Parameter(
- torch.normal(0, 1, size=(mlp_inp_size,)),
- requires_grad=True,
- ),
- )
- ),
- )
- for m in self.target_modules
- )
- self.out_emb_norm = nn.LayerNorm(mlp_inp_size)
-
- heads = []
- for module in self.target_modules:
- in_features = self.in_features[module]
- out_features = self.out_features[module]
- if not shared_AB_head:
- output_size = (
- peft_config.r + out_features
- if output_space == "vera"
- else (peft_config.r * (in_features + out_features))
- )
- if autoreg_gen:
- output_size = in_features + out_features
-
- else:
- output_size = peft_config.r * max(in_features, out_features)
- if autoreg_gen:
- output_size = max(in_features, out_features)
-
- logger.debug(f"head_in_size: {head_in_size}, output_size: {output_size}")
- logger.debug(f"head_use_bias: {head_use_bias}")
- layer = nn.Linear(
- head_in_size, output_size, bias=head_use_bias, device=device
- )
- if zero_init_head:
- logger.debug(f"zeroing out head weights for {module}")
- nn.init.zeros_(layer.weight)
-
- heads.append((module, layer))
- self.heads = nn.ModuleDict(heads)
- peft_weights = get_init_peft_weights(model, peft_config)
- logger.debug(f"peft_weights:\n{peft_weights}")
-
- self.split_shapes = dict()
- # we use the trainable peft weights of the base peft model
- # to initialize the bias of the heads
- for module_name, head in self.heads.items():
- if output_space == "lora":
- A = peft_weights[module_name]["lora_A"].weight.clone().flatten()
- B = peft_weights[module_name]["lora_B"].weight.clone().flatten()
- init_bias = [A, B]
-
- elif output_space == "vera":
- # d init with a constant value (0.1) and b with zeros
- # for vera, we're using the outputs of the hypernet as masking + scaling values
- d = peft_weights[module_name]["vera_lambda_d"].clone().flatten() # 0.1
- b = peft_weights[module_name]["vera_lambda_b"].clone().flatten() # 0
- init_bias = [d, b]
-
- # we just need one copy of A and B
- vera_A_B_learnable = True
- self.vera_A = nn.Parameter(
- peft_weights[module_name]["vera_A"],
- requires_grad=vera_A_B_learnable,
- )
- self.vera_B = nn.Parameter(
- peft_weights[module_name]["vera_B"],
- requires_grad=vera_A_B_learnable,
- )
-
- with torch.no_grad():
- if match_lora_init and head.bias is not None:
- nn.init.zeros_(head.weight)
- nn.init.zeros_(head.bias)
- if (not shared_AB_head) and (not autoreg_gen):
- # based on https://proceedings.mlr.press/v205/beck23a/beck23a.pdf (bias-HyperInit)
- # this encourages the hnet to initially use some shared values of the output
- # also an easy way to match initialization of the corresponding method!
- head.bias.copy_(torch.cat(init_bias))
-
- # match the gradient scale to the full architecture
- elif shared_AB_head:
- if not autoreg_gen:
- head.bias.copy_(init_bias[0] / sqrt(2))
- else:
- size = head.bias.shape[0]
- head.bias.copy_(
- init_bias[0][:size] / sqrt(2) / sqrt(peft_config.r)
- )
- else:
- size = head.bias.shape[0]
- head.bias.copy_(init_bias[0][:size] / sqrt(peft_config.r))
-
- # NOTE: how we slice the weights have to be consistent with the forward pass
- self.split_shapes[module_name] = [len(x) for x in init_bias]
- logger.debug(f"split_shapes: {self.split_shapes}")
-
- self.heads.to(device).to(dtype)
- del peft_weights
-
- def _embed_layer_depth(
- self, depth_indices: list[int] | int | torch.Tensor
- ) -> torch.Tensor:
- if isinstance(depth_indices, int):
- depth_indices = torch.tensor(
- [depth_indices], dtype=torch.long, device=self.device
- )
- elif isinstance(depth_indices, list):
- depth_indices = torch.tensor(
- depth_indices, dtype=torch.long, device=self.device
- )
-
- return self.layer_depth_encoder(depth_indices) # [bs (or 1), max_num_layers]
-
- def _embed_layer_type(self, layer_type: str) -> torch.Tensor:
- module_idx = self.module_to_int[layer_type]
- module_idx = torch.tensor([module_idx], dtype=torch.long, device=self.device)
- # we only forward one layer type at at time
- # so the shape is always [1, num_target_modules]
- return self.layer_type_encoder(module_idx)
-
- def get_one_hot_task_emb(
- self, num_tasks: int, task_idx: torch.Tensor
- ) -> torch.Tensor:
- return self.task_encoder.get_one_hot_task_emb(num_tasks, task_idx)
-
- def _hypernet_forward(self, layer_indices, layer_type, encoded_task_emb):
- # forward one layer type at a time
-
- bs = len(layer_indices)
- depth_emb = self._embed_layer_depth(layer_indices) # [bs, depth_emb_size]
- layer_type_emb = self._embed_layer_type(layer_type) # [1, layer_emb_size]
- layer_type_emb = layer_type_emb.expand(bs, -1) # [bs, layer_emb_size]
- if encoded_task_emb is None:
- encoded_task_emb = torch.empty(0, device=self.device)
-
- cat_emb = torch.cat([encoded_task_emb, depth_emb, layer_type_emb], dim=-1)
- mlp_inp = self.mixer(cat_emb)
-
- mlp_out = self.mlp1(mlp_inp)
- head = self.heads[layer_type]
- if not self.shared_AB_head:
- # head outputs both A and B
- if not self.autoreg_gen:
- # head outputs all the ranks at once
- head_out = head(self.mlp3(self.mlp2(mlp_out)))
- splitted_out = torch.split(
- head_out, self.split_shapes[layer_type], dim=-1
- )
- else:
- # head outputs one rank at a time
- head_in = mlp_out.unsqueeze(1).expand(-1, self.peft_config.r, -1)
- if self.learnable_pos_emb:
- head_in = self.mlp2(
- head_in
- + self.pos_emb(
- torch.arange(self.peft_config.r, device=self.device)
- ).unsqueeze(0)
- )
- else:
- cos, sin = self.rotary_emb(
- head_in,
- torch.arange(self.peft_config.r, device=self.device).unsqueeze(
- 0
- ),
- ) # [1, r, 2048]
- head_in = self.mlp2(apply_rotary_pos_emb(head_in, cos, sin))
- head_out = head(self.mlp3(head_in))
-
- # [bs, self.peft_config.r, self.in_features[layer_type]]
- # [bs, self.peft_config.r, self.out_features[layer_type]]
- A, B = torch.split(
- head_out,
- (self.in_features[layer_type], self.out_features[layer_type]),
- dim=-1,
- )
- A = A.reshape(bs, -1)
- B = B.reshape(bs, -1)
- splitted_out = [A, B]
-
- else:
- # head outputs either A or B
- splitted_out = []
- for out_emb, num_features in zip(
- self.out_emb[layer_type].values(),
- [self.in_features[layer_type], self.out_features[layer_type]],
- ):
- head_in = self.mlp2(mlp_out + self.out_emb_norm(out_emb))
- if not self.autoreg_gen:
- # head outputs all the ranks at once
- head_out = head(self.mlp3(head_in))
- head_out = head_out.view(bs, self.peft_config.r, -1)
- head_out = head_out[..., :num_features]
- head_out = head_out.reshape(bs, self.peft_config.r * num_features)
- else:
- # head outputs one rank at a time
- head_in = head_in.unsqueeze(1).expand(-1, self.peft_config.r, -1)
- if self.learnable_pos_emb:
- pos_emb = self.mlp2(
- self.pos_emb(
- torch.arange(self.peft_config.r, device=self.device)
- ).unsqueeze(0)
- )
- head_in = head_in + pos_emb
- else:
- cos, sin = self.rotary_emb(
- head_in,
- torch.arange(
- self.peft_config.r, device=self.device
- ).unsqueeze(0),
- ) # [1, r, 2048]
- head_in = self.mlp2(apply_rotary_pos_emb(head_in, cos, sin))
-
- head_out = head(self.mlp3(head_in))
- head_out = head_out[..., :num_features]
- head_out = head_out.reshape(bs, self.peft_config.r * num_features)
-
- splitted_out.append(head_out)
-
- return splitted_out
-
- def get_delta_weights(
- self,
- layer_indices: torch.Tensor,
- layer_type: str,
- encoded_task_emb: torch.Tensor = None,
- factorized: bool | None = None,
- ) -> torch.Tensor:
- if factorized is None:
- factorized = self.factorized
- bs = len(layer_indices)
-
- splitted_out = self._hypernet_forward(
- layer_indices,
- layer_type,
- encoded_task_emb,
- )
-
- if self.output_space == "lora":
- A, B = splitted_out
- A = A.reshape(
- bs, self.peft_config.r, self.in_features[layer_type]
- ) # .transpose(-1, -2)
- B = B.reshape(
- bs, self.peft_config.r, self.out_features[layer_type]
- ).transpose(-1, -2)
- if self.training_task == "sft":
- A = A + self.AB_offset["A"][layer_type][layer_indices]
- B = B + self.AB_offset["B"][layer_type][layer_indices]
- if factorized:
- return A, B
- deltaW = torch.bmm(B, A)
-
- elif self.output_space == "vera":
- raise NotImplementedError("Vera output space is deprecated")
- # the deltaW should have shape [bs, (len(layer_indices)), out_features, in_features]
- return deltaW
-
- @torch.no_grad()
- def gen_lora(self, layer_indices, encoded_task_emb):
- assert encoded_task_emb.shape[0] == 1, (
- "Only one task at a time is supported for now"
- )
- lora_A, lora_B = dict(), dict()
- for target_module in self.target_modules:
- factorized_delta_w = self.get_delta_weights(
- layer_indices,
- target_module,
- encoded_task_emb.expand(layer_indices.shape[0], -1),
- factorized=True,
- )
- if self.output_space == "lora":
- lora_A[target_module], lora_B[target_module] = factorized_delta_w
-
- # save deltaW to lora state dict format
- lora_state_dict = dict()
- for target_module in self.target_modules:
- for layer_idx in layer_indices:
- for module_name in self.module_names[target_module][layer_idx]:
- if "lora_A" in module_name:
- lora_state_dict[module_name] = (
- lora_A[target_module][layer_idx].cpu().contiguous()
- )
- elif "lora_B" in module_name:
- lora_state_dict[module_name] = (
- lora_B[target_module][layer_idx].cpu().contiguous()
- )
- else:
- raise ValueError(f"Unexpected module name: {module_name}")
- if self.training_task == "recon":
- lora_state_dict = self.convert_to_raw_scale(lora_state_dict, layer_indices)
- return lora_state_dict
-
-
-def get_in_out_features(
- model: PeftModel,
- peft_config: PeftConfig = None,
-) -> tuple[dict[str, int], dict[str, int]]:
- if peft_config is None:
- peft_config = model.peft_config["default"]
- in_features = dict()
- out_features = dict()
- for module_name, module in model.named_modules():
- if not check_target_module_exists(peft_config, module_name):
- continue
- if not isinstance(module, BaseTunerLayer):
- continue
- # support just Linear layer for now
- # all modules should be a leave module that is Linear layer
- assert isinstance(module.base_layer, nn.Linear), (
- "all modules should be a leave module that is Linear layer"
- )
-
- # this should always pass
- name = module_name.split(".")[-1]
- assert name in peft_config.target_modules, (
- f"Module {name} not in target modules"
- )
-
- if name not in in_features:
- in_features[name] = module.in_features
- out_features[name] = module.out_features
- else:
- # assumes each module has the same input and output features
- assert in_features[name] == module.in_features
- assert out_features[name] == module.out_features
-
- return in_features, out_features
-
-
-def get_init_peft_weights(model: PeftModel, peft_config: PeftConfig = None):
- if peft_config is None:
- peft_config = model.peft_config["default"]
- peft_weights = {module_name: dict() for module_name in peft_config.target_modules}
- adapter_name = "default"
- for module_name, module in model.named_modules():
- if not check_target_module_exists(peft_config, module_name):
- continue
- if not isinstance(module, BaseTunerLayer):
- continue
- # support just Linear layer for now
- # all modules should be a leave module that is Linear layer
- assert isinstance(module.base_layer, nn.Linear), (
- "all modules should be a leave module that is Linear layer"
- )
-
- # this should always pass
- name = module_name.split(".")[-1]
- assert name in peft_config.target_modules
-
- for submodule_name, submodule in module.named_modules():
- if not isinstance(submodule, (nn.ModuleDict, nn.ParameterDict, BufferDict)):
- continue
-
- if adapter_name not in submodule:
- continue
-
- if submodule_name not in peft_weights[name]:
- peft_weights[name][submodule_name] = submodule[adapter_name]
- else:
- smod1 = peft_weights[name][submodule_name]
- smod2 = submodule[adapter_name]
- assert type(smod1) == type(smod2)
-
- return peft_weights
-
-
-def save_lora(lora_state_dict, adapter_config, lora_dir):
- logger.debug(f"lora_dir: {lora_dir}")
- os.makedirs(lora_dir, exist_ok=True)
- adapter_config.save_pretrained(lora_dir)
- save_file(lora_state_dict, f"{lora_dir}/adapter_model.safetensors")
-
-
-def create_hypermod(
- args, peft_type, device, model, layer_indices, task_emb_size, from_scratch=True
-):
- assert args.training_task in ["sft", "recon"], (
- f"Invalid training task: {args.training_task}"
- )
- module_names = get_lora_module_names(model, args.target_modules, layer_indices)
-
- mt_lora_sd = mt_lora_td = mean_recon_target = std_recon_target = None
- if from_scratch:
- if args.mt_lora_path:
- mt_lora_sd = load_peft_weights(args.mt_lora_path)
- mt_lora_td = lora_state_dict_to_tensor_dict(
- mt_lora_sd, args.target_modules, layer_indices, device=device
- )
-
- hypermod = HyperModulator(
- model,
- training_task=args.training_task,
- pred_z_score=args.pred_z_score,
- mean_recon_target=mean_recon_target,
- std_recon_target=std_recon_target,
- output_space=peft_type,
- module_names=module_names,
- match_lora_init=args.mt_lora_path is None and args.training_task == "sft",
- task_emb_size=len(args.train_ds_names)
- if args.use_one_hot_task_emb
- else task_emb_size,
- shared_AB_head=args.shared_AB_head,
- autoreg_gen=args.autoreg_gen,
- learnable_pos_emb=args.learnable_pos_emb,
- AB_offset=mt_lora_td,
- learnable_AB_offset=args.learnable_AB_offset,
- zero_init_head=args.training_task == "sft",
- latent_size=args.hypernet_latent_size,
- head_in_size=args.head_in_size,
- head_use_bias=(args.training_task == "sft" and args.mt_lora_path is None)
- or (args.training_task == "recon" and not args.pred_z_score),
- factorized=getattr(args, "factorized", False),
- delta_w_scaling=getattr(args, "delta_w_scaling", 10000),
- ).to(device)
-
- return hypermod
-
-
-def save_hypermod_checkpoint(save_dir, hypermod, curstep):
- save_path = f"{save_dir}/checkpoints/it_{curstep}/hypermod.pt"
- os.makedirs(os.path.dirname(save_path), exist_ok=True)
- torch.save(hypermod.state_dict(), save_path)
- return save_path
-
-
-def load_hypermod_checkpoint(checkpoint_path, device):
- base_hypermod_dir = os.path.dirname(checkpoint_path)
- if "checkpoint" in base_hypermod_dir:
- base_hypermod_dir = base_hypermod_dir.split("checkpoint")[0]
-
- args = argparse.Namespace(**yaml.safe_load(open(f"{base_hypermod_dir}/args.yaml")))
- peft_config = get_peft_config(
- PeftConfig.from_json_file(f"{base_hypermod_dir}/adapter_config.json")
- )
- peft_type = peft_config.peft_type.lower()
- state_dict = torch.load(checkpoint_path, map_location=device)
-
- model, tokenizer = get_model_and_tokenizer(
- args.model_dir,
- train=False,
- requires_grad=False,
- peft_config=peft_config,
- model_kwargs={"output_hidden_states": True, "output_attentions": False},
- device=device,
- )
- # train to output delta_w for all layers
- layer_indices = torch.tensor(
- range(len(get_layers(model))), dtype=torch.long, device=device
- )
-
- task_emb_size = emb_model = emb_tokenizer = task_desc_format_fn = pooling_fn = None
- if not args.use_one_hot_task_emb:
- emb_tokenizer = deepcopy(tokenizer)
- task_desc_format_fn = add_full_stop
- pooling_fn = get_pooling_fn("last_token")
-
- if args.emb_model:
- emb_model, emb_tokenizer, task_desc_format_fn, pooling_fn = (
- get_emb_model_and_fns(args.emb_model, device)
- )
- logger.debug(f"emb_model: {emb_model}")
- emb_model.eval()
- task_emb_size = emb_model.config.hidden_size
-
- hypermod = create_hypermod(
- args, peft_type, device, model, layer_indices, task_emb_size, from_scratch=False
- )
- info = hypermod.load_state_dict(state_dict, strict=False)
- print(f"Loaded hypermod state dict: {info}")
- hypermod.eval().to(device)
- return (
- args,
- hypermod,
- model,
- tokenizer,
- emb_model,
- emb_tokenizer,
- task_desc_format_fn,
- pooling_fn,
- )
-
-
-def load_hypermod(hypermod_dir, device):
- checkpoint_path = f"{hypermod_dir}/hypermod.pt"
- (
- args,
- hypermod,
- model,
- tokenizer,
- emb_model,
- emb_tokenizer,
- task_desc_format_fn,
- pooling_fn,
- ) = load_hypermod_checkpoint(checkpoint_path, device)
- return (
- args,
- hypermod,
- model,
- tokenizer,
- emb_model,
- emb_tokenizer,
- task_desc_format_fn,
- pooling_fn,
- )
-
-
-def embed_texts(
- texts,
- emb_model,
- emb_tokenizer,
- task_desc_format_fn,
- pooling_fn,
- device,
- batch_size=None,
-):
- formatted_descs = list(map(task_desc_format_fn, texts))
- tokenized_ds_descs = emb_tokenizer(
- formatted_descs,
- truncation=True,
- padding=True,
- max_length=2**13,
- return_tensors="pt",
- )
- return embed_tokens(tokenized_ds_descs, emb_model, pooling_fn, device, batch_size)
-
-
-def embed_tokens(tokenized_texts, emb_model, pooling_fn, device, batch_size=None):
- if batch_size is None:
- # Process all at once if no batch size specified
- tokenized_texts = {k: v.to(device) for k, v in tokenized_texts.items()}
- return _embed_tokens_single_batch(tokenized_texts, emb_model, pooling_fn)
-
- # Process in batches
- n_samples = tokenized_texts["input_ids"].shape[0]
- embeddings = []
-
- for start_idx in range(0, n_samples, batch_size):
- end_idx = min(start_idx + batch_size, n_samples)
- batch = {k: v[start_idx:end_idx].to(device) for k, v in tokenized_texts.items()}
- batch_embeddings = _embed_tokens_single_batch(batch, emb_model, pooling_fn)
- embeddings.append(batch_embeddings)
-
- return torch.cat(embeddings, dim=0)
-
-
-def _embed_tokens_single_batch(tokenized_texts, emb_model, pooling_fn):
- outputs = emb_model(**tokenized_texts, output_hidden_states=True)
- task_embs = pooling_fn(outputs, tokenized_texts["attention_mask"]).to(torch.float32)
- return torch.nn.functional.normalize(task_embs) * sqrt(task_embs.shape[-1])
-
-
-if __name__ == "__main__":
- import random
- import string
- import sys
- import time
-
- hypermod_dir = sys.argv[1]
- task_desc = sys.argv[2].strip("\"' ")
-
- print(f"\nGenerating LoRA for description:\n\n{task_desc}")
-
- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
- # load metadata
- args = argparse.Namespace(**yaml.safe_load(open(f"{hypermod_dir}/args.yaml")))
- peft_config = get_peft_config(
- PeftConfig.from_json_file(f"{hypermod_dir}/adapter_config.json")
- )
- curtime = time.strftime("%Y%m%d_%H%M%S", time.localtime())
- uuid = "".join(
- [random.choice(string.ascii_letters + string.digits) for _ in range(8)]
- )
- (
- args,
- hypermod,
- model,
- tokenizer,
- emb_model,
- emb_tokenizer,
- task_desc_format_fn,
- pooling_fn,
- ) = load_hypermod(hypermod_dir, device)
- layer_indices = range(len(get_layers(model)))
- layer_indices = torch.tensor(layer_indices, dtype=torch.long, device=device)
- emb_size = emb_model.config.hidden_size
-
- # generate loras
- task_emb = embed_texts(
- [task_desc], emb_model, emb_tokenizer, task_desc_format_fn, pooling_fn, device
- )
- encoder_out = hypermod.task_encoder(task_emb)
- encoded_task_emb = encoder_out["encoded_task_emb"].detach()
- lora_sd = hypermod.gen_lora(layer_indices, encoded_task_emb)
- lora_dir = f"{hypermod_dir}/extras/user_generated/{curtime}_{uuid}/"
- save_lora(lora_sd, peft_config, lora_dir)
- with open(f"{lora_dir}/task_desc.txt", "w") as f:
- f.write(task_desc)
- print(f"Saved lora to {lora_dir}")
diff --git a/src/ctx_to_lora/tracker/tracker.py b/src/ctx_to_lora/tracker/tracker.py
index 838d004..6150d76 100644
--- a/src/ctx_to_lora/tracker/tracker.py
+++ b/src/ctx_to_lora/tracker/tracker.py
@@ -1,85 +1,25 @@
-"""Unified tracking interface combining timing and CUDA memory usage.
-
-Primary API
------------
-add_tracker(bound_method, name)
- Wraps a bound instance method so that each invocation records:
- - wall-clock duration (seconds) in timer.TIMER_REGISTRY[name]
- - CUDA peak memory increase (bytes) in cuda_memory_tracker.MEMORY_REGISTRY[name]
- (only if CUDA + torch available; otherwise memory list may stay empty / absent)
-
-print_tracker_stats(name=None)
- Convenience printer that delegates to time + memory aggregate printers.
-
-Design
-------
-We implement a single wrapper (instead of nesting the individual timer & memory
-wrappers) to avoid multiple layers of indirection and to ensure the measured
-CUDA memory footprint reflects only the original method's body (excluding the
-separate timing wrapper's slight overhead). The wrapper is idempotent: repeated
-calls to add_tracker on the same method are ignored.
-
-This file depends on sibling modules:
-- tracker.timer
-- tracker.cuda_memory_tracker
-
-Both registries remain the single source of truth; no additional registry is introduced.
-"""
-
from __future__ import annotations
from collections.abc import Callable
from time import perf_counter
from typing import Any
-# Support both package (relative) and direct script execution.
-try: # Package / normal import path
- from .cuda_memory_tracker import ( # type: ignore
- MEMORY_REGISTRY,
- compute_aggregate_memory_stats,
- print_aggregate_memory_stats,
- print_global_memory_stats,
- reset_memory_trackers,
- save_memory_stats_csv,
- )
- from .timer import ( # type: ignore
- TIMER_REGISTRY,
- compute_aggregate_timer_stats,
- print_aggregate_timer_stats,
- print_global_timer_stats,
- reset_timers,
- save_timer_stats_csv,
- )
-except Exception: # pragma: no cover - fallback when executed directly
- import pathlib
- import sys
-
- _this_file = pathlib.Path(__file__).resolve()
- # project root is two levels up from tracker/ (i.e., .../src)
- _src_root = _this_file.parents[2]
- if str(_src_root) not in sys.path:
- sys.path.insert(0, str(_src_root))
- try:
- from ctx_to_lora.tracker.cuda_memory_tracker import ( # type: ignore
- MEMORY_REGISTRY,
- compute_aggregate_memory_stats,
- print_aggregate_memory_stats,
- print_global_memory_stats,
- reset_memory_trackers,
- save_memory_stats_csv,
- )
- from ctx_to_lora.tracker.timer import ( # type: ignore
- TIMER_REGISTRY,
- compute_aggregate_timer_stats,
- print_aggregate_timer_stats,
- print_global_timer_stats,
- reset_timers,
- save_timer_stats_csv,
- )
- except Exception as e: # If still failing, raise a clearer error.
- raise ImportError(
- f"Failed to import tracking dependencies; ensure project root on PYTHONPATH. Original: {e}"
- )
+from .cuda_memory_tracker import ( # type: ignore
+ MEMORY_REGISTRY,
+ compute_aggregate_memory_stats,
+ print_aggregate_memory_stats,
+ print_global_memory_stats,
+ reset_memory_trackers,
+ save_memory_stats_csv,
+)
+from .timer import ( # type: ignore
+ TIMER_REGISTRY,
+ compute_aggregate_timer_stats,
+ print_aggregate_timer_stats,
+ print_global_timer_stats,
+ reset_timers,
+ save_timer_stats_csv,
+)
try: # Optional torch import (lazy fallback if unavailable)
import torch # type: ignore
diff --git a/src/ctx_to_lora/trainer.py b/src/ctx_to_lora/trainer.py
deleted file mode 100644
index be6cf13..0000000
--- a/src/ctx_to_lora/trainer.py
+++ /dev/null
@@ -1,458 +0,0 @@
-import logging
-
-import torch
-from torch import nn
-from transformers import Trainer
-from transformers.trainer_pt_utils import get_parameter_names
-from transformers.trainer_utils import IntervalStrategy
-
-from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
-
-logger = logging.getLogger()
-
-
-def per_ctx_loss_ce(inputs, labels, loss):
- # loss still has masked out elem (0 at labels=-100)
- n_queries_per_ctx = inputs["n_queries"].tolist()
-
- position_ids = inputs["position_ids"].squeeze(0)
- # account only label positions
- label_mask = labels.squeeze(0) != -100
- label_pos_ids = label_mask * position_ids
- label_pos_ids_diff = label_pos_ids.diff(
- append=torch.tensor([0], device=position_ids.device)
- )
-
- # assumes the input starts with non-assistant tokens
- start_label_pos = torch.where((label_pos_ids_diff > 0) * ~label_mask)[0]
- end_label_pos = torch.where((label_pos_ids_diff < 0) * label_mask)[0]
-
- label_seq_lens = end_label_pos - start_label_pos
-
- # these stack and split can be optimized but let's keep it simple
- # mean across tokens of each q
- qa_losses = torch.stack(
- [
- loss[start : start + llen].mean()
- for start, llen in zip(start_label_pos, label_seq_lens)
- ]
- )
-
- # mean across queries of each ctx
- per_ctx_losses = [ql.mean() for ql in torch.split(qa_losses, n_queries_per_ctx)]
-
- # per-ctx loss
- loss = torch.stack(per_ctx_losses)
- return loss
-
-
-def per_ctx_loss_kl(inputs, labels, loss):
- # loss is compact (label indices selected)
- n_queries_per_ctx = inputs["n_queries"].tolist()
-
- position_ids = inputs["position_ids"].squeeze(0)
- # account only label positions
- label_mask = labels.squeeze(0) != -100
- label_pos_ids = label_mask * position_ids
- label_pos_ids_diff = label_pos_ids.diff(
- append=torch.tensor([0], device=position_ids.device)
- )
- # assumes the input starts with non-assistant tokens
- start_label_pos = torch.where((label_pos_ids_diff > 0) * ~label_mask)[0]
- end_label_pos = torch.where((label_pos_ids_diff < 0) * label_mask)[0]
-
- label_seq_lens = end_label_pos - start_label_pos
-
- # find equiv start indices in the already sliced loss vector
- cu_label_seq_lens = torch.cumsum(label_seq_lens, dim=0)
- start_indices = torch.cat(
- (
- torch.tensor([0], device=cu_label_seq_lens.device),
- cu_label_seq_lens[:-1],
- )
- )
-
- # these stack and split can be optimized but let's keep it simple
- # mean across tokens of each q
- qa_losses = torch.stack(
- [loss[start:end].mean() for start, end in zip(start_indices, cu_label_seq_lens)]
- )
-
- # mean across queries of each ctx
- per_ctx_losses = [ql.mean() for ql in torch.split(qa_losses, n_queries_per_ctx)]
-
- # per-ctx loss
- loss = torch.stack(per_ctx_losses)
- return loss
-
-
-class ModulatedModelTrainer(Trainer):
- # modified from the base Trainer to support per-context average loss
- def get_batch_samples(self, epoch_iterator, num_batches, device):
- # only used with `use_per_ctx_average_loss=True`
- batch_samples = []
- num_items_in_batch = None
-
- for _ in range(num_batches):
- try:
- batch_samples.append(next(epoch_iterator))
- except StopIteration:
- break
-
- count_num_items_in_batch = (
- len(batch_samples) > 0
- and "labels" in batch_samples[0]
- and "n_ctx_chunks" in batch_samples[0]
- )
-
- if count_num_items_in_batch:
- num_items_in_batch = dict()
- num_items_in_batch["ctx"] = torch.tensor(
- sum([batch["n_ctx_chunks"].numel() for batch in batch_samples])
- ).to(device)
- # should we avg over num chunks?
- # num_items_in_batch["ctx"] = sum(
- # [(batch["ctx_position_ids"] == 0).sum() for batch in batch_samples]
- # )
- num_items_in_batch["labels"] = sum(
- [(batch["labels"].ne(-100)).sum() for batch in batch_samples]
- ).to(device)
-
- if num_items_in_batch is not None:
- if self.args.average_tokens_across_devices:
- for k in num_items_in_batch:
- num_items_in_batch[k] = self.accelerator.gather(
- num_items_in_batch[k]
- ).sum()
-
- if torch.is_tensor(num_items_in_batch):
- num_items_in_batch = num_items_in_batch.to(device)
-
- if self.args.n_gpu > 1 and num_items_in_batch.dim() == 0:
- # In the DataParallel case, convert the scalar tensor into a 1-dim tensor
- num_items_in_batch = num_items_in_batch.unsqueeze(0)
-
- return batch_samples, num_items_in_batch
-
-
-class DistillationTrainer(ModulatedModelTrainer):
- def __init__(self, *args, **kwargs):
- self.gen_lora_l1_reg_coef = kwargs.pop("gen_lora_l1_reg_coef", 0.0)
- self.use_per_ctx_average_loss = kwargs.pop("use_per_ctx_average_loss", False)
- super().__init__(*args, **kwargs)
-
- def compute_loss(
- self, model, inputs, return_outputs=False, num_items_in_batch=None
- ):
- # NOTE: the loss output from this fn will be ***added***
- # meaning that we should always scale the loss wrt `num_items_in_batch`
- # (average over the number of items in the accumulated batch)
-
- is_train = num_items_in_batch is not None
- labels = inputs.pop("labels", None)
- label_pos = torch.where(labels != -100)
- outputs, (gen_loras, _) = model(**inputs, return_generated_lora=True)
-
- if "logprobs_vals" not in inputs:
- return (torch.tensor(0.0), outputs) if return_outputs else torch.tensor(0.0)
-
- target_logp = inputs.pop("logprobs_vals").squeeze(0)
- indices = inputs.pop("logprobs_indices").squeeze(0)
-
- assert label_pos[0].shape[0] == target_logp.shape[0], (
- "Label positions and target log probabilities should have the same # tokens."
- f"Got : {label_pos[0].shape[0]=} and {target_logp.shape[0]=}"
- )
-
- ##### KL loss
- outputs_logits = outputs.logits[label_pos[0], label_pos[1] - 1] # shift back 1
-
- logq_full_denom = torch.logsumexp(outputs_logits, dim=-1, keepdim=True) # (N,1)
- selected_logits = outputs_logits.gather(1, indices) # (N,K)
- # log softmax at selected indices
- logq_selected = selected_logits - logq_full_denom
- p = target_logp.exp()
- loss = -(p * logq_selected).sum(dim=-1)
-
- # teacher_logp = torch.full_like(outputs_logits, -torch.inf)
- # teacher_logp.scatter_(1, indices, target_logp)
- # # reduction = "batchmean" if num_items_in_batch is None else "sum"
- # p = teacher_logp.exp()
- # logq = nn.functional.log_softmax(outputs_logits, dim=-1)
- # loss = -torch.sum(p * logq, dim=-1)
-
- if self.use_per_ctx_average_loss:
- loss = per_ctx_loss_kl(inputs, labels, loss)
-
- if is_train:
- if self.use_per_ctx_average_loss:
- loss = loss.sum() / num_items_in_batch["ctx"]
- else:
- loss = loss.sum() / num_items_in_batch["labels"]
- else:
- # eval
- loss = loss.mean()
-
- # if reduction == "batchmean":
- # loss = loss.mean()
- # elif reduction == "sum":
- # # loss does not scale with grad acc
- # # num_items_in_batch does
- # # this works for both token-avg and ctx-avg
- # # loss = loss.sum() / num_items_in_batch
-
- # `num_items_in_batch` is # tokens if `args.use_ctx_average_loss=False``
- # loss = loss.sum() / num_items_in_batch
- #####
-
- ##### unpack gen lora dict and compute regularization loss
- l1_norm = 0
- n_modules = len(gen_loras)
- for module, lora in gen_loras.items():
- l1_norm += lora["A"].abs().sum(0).mean() + lora["B"].abs().sum(0).mean()
- l1_norm /= n_modules
- if is_train:
- # during eval `num_items_in_batch` will be None
- l1_norm /= num_items_in_batch["ctx"]
-
- total_loss = loss + self.gen_lora_l1_reg_coef * l1_norm
- #####
-
- scaler = self.args.gradient_accumulation_steps if is_train else 1
- if self.args.average_tokens_across_devices and is_train:
- total_loss *= self.accelerator.num_processes
- scaler *= self.accelerator.num_processes
-
- # rough estimate of the losses (we only log the values from one step)
- if (self.state.global_step == 1 and self.args.logging_first_step) or (
- self.args.logging_strategy == IntervalStrategy.STEPS
- and self.state.global_step % self.state.logging_steps == 0
- ):
- # compensate `num_items_in_batch` division
- self.log(
- {
- "kl_loss": loss.item() * scaler,
- "gen_lora_l1_norm": l1_norm.item() * scaler,
- }
- )
-
- return (total_loss, outputs) if return_outputs else total_loss
-
-
-def causal_lm_ce_loss(
- logits,
- labels,
- vocab_size: int,
- num_items_in_batch: torch.Tensor | None = None,
- ignore_index: int = -100,
- shift_labels: torch.Tensor | None = None,
- **kwargs,
-) -> torch.Tensor:
- # Upcast to float if we need to compute the loss to avoid potential precision issues
- logits = logits.float()
-
- if shift_labels is None:
- # Shift so that tokens < n predict n
- labels = nn.functional.pad(labels, (0, 1), value=ignore_index)
- shift_labels = labels[..., 1:].contiguous()
-
- # Flatten the tokens
- logits = logits.view(-1, vocab_size)
- shift_labels = shift_labels.view(-1)
- # Enable model parallelism
- shift_labels = shift_labels.to(logits.device)
- # loss = fixed_cross_entropy(
- # logits, shift_labels, num_items_in_batch, ignore_index, **kwargs
- # )
- loss = nn.functional.cross_entropy(logits, shift_labels, reduction="none")
-
- return loss
-
-
-class CrossEntropyTrainer(ModulatedModelTrainer):
- def __init__(self, *args, **kwargs):
- self.gen_lora_l1_reg_coef = kwargs.pop("gen_lora_l1_reg_coef", 0.0)
- self.use_per_ctx_average_loss = kwargs.pop("use_per_ctx_average_loss", False)
- super().__init__(*args, **kwargs)
-
- def compute_loss(
- self, model, inputs, return_outputs=False, num_items_in_batch=None
- ):
- """
- How the loss is computed by Trainer.
- By default, all models return the loss in the first element.
- Subclass and override for custom behavior.
- """
-
- is_train = num_items_in_batch is not None
- labels = inputs.pop("labels", None)
- outputs, (gen_loras, _) = model(**inputs, return_generated_lora=True)
- # [1, tot_seq_len]
- logits = outputs.logits
-
- # [tot_seq_len]
- loss = causal_lm_ce_loss(logits, labels, self.model.vocab_size)
-
- if self.use_per_ctx_average_loss:
- loss = per_ctx_loss_ce(inputs, labels, loss)
-
- if is_train:
- if self.use_per_ctx_average_loss:
- loss = loss.sum() / num_items_in_batch["ctx"]
- else:
- loss = loss.sum() / num_items_in_batch["labels"]
- else:
- # eval
- loss = loss.mean()
-
- #####
- # if is_train:
- # if self.use_per_ctx_average_loss:
- # loss_kwargs["num_items_in_batch"] = num_items_in_batch["ctx"]
- # else:
- # loss_kwargs["num_items_in_batch"] = num_items_in_batch["labels"]
- # inputs = {**inputs, **loss_kwargs}
- # outputs, (gen_loras, _) = model(**inputs, return_generated_lora=True)
-
- # # Save past state if it exists
- # if self.args.past_index >= 0:
- # self._past = outputs[self.args.past_index]
-
- # if labels is not None:
- # unwrapped_model = self.accelerator.unwrap_model(model)
- # if _is_peft_model(unwrapped_model):
- # model_name = unwrapped_model.base_model.model._get_name()
- # else:
- # model_name = unwrapped_model._get_name()
- # # User-defined compute_loss function
- # if self.compute_loss_func is not None:
- # loss = self.compute_loss_func(
- # outputs, labels, num_items_in_batch=num_items_in_batch["labels"]
- # )
- # elif model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
- # loss = self.label_smoother(outputs, labels, shift_labels=True)
- # else:
- # loss = self.label_smoother(outputs, labels)
- # else:
- # if isinstance(outputs, dict) and "loss" not in outputs:
- # raise ValueError(
- # "The model did not return a loss from the inputs, "
- # "only the following keys: "
- # f"{','.join(outputs.keys())}. "
- # "For reference, the inputs it received are "
- # f"{','.join(inputs.keys())}."
- # )
- # # We don't use .loss here since the model may return tuples instead of ModelOutput.
- # loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
- #####
-
- ##### unpack gen lora dict and compute regularization loss
- l1_norm = 0
- n_modules = len(gen_loras)
- for module, lora in gen_loras.items():
- l1_norm += lora["A"].abs().sum(0).mean() + lora["B"].abs().sum(0).mean()
- l1_norm /= n_modules
- if is_train:
- # during eval `num_items_in_batch` will be None
- l1_norm /= num_items_in_batch["ctx"]
-
- total_loss = loss + self.gen_lora_l1_reg_coef * l1_norm
- #####
-
- scaler = self.args.gradient_accumulation_steps if is_train else 1
- if self.args.average_tokens_across_devices and is_train:
- total_loss *= self.accelerator.num_processes
- scaler *= self.accelerator.num_processes
-
- # rough estimate of the losses (we only log the values from one step)
- if (self.state.global_step == 1 and self.args.logging_first_step) or (
- self.args.logging_strategy == IntervalStrategy.STEPS
- and self.state.global_step % self.state.logging_steps == 0
- ):
- # compensate `num_items_in_batch` division
- self.log(
- {
- "ce_loss": loss.item() * scaler,
- "gen_lora_l1_norm": l1_norm.item() * scaler,
- }
- )
-
- return (total_loss, outputs) if return_outputs else total_loss
-
-
-def get_decay_parameter_names(model) -> list[str]:
- """
- Get all parameter names that weight decay will be applied to.
-
- This function filters out parameters in two ways:
- 1. By layer type (nn.Embedding)
- 2. By parameter name patterns (containing 'bias', 'layernorm', 'rmsnorm'
- or 'latents_q' [perceiver's latent queries]).
- """
- decay_parameters = get_parameter_names(
- model,
- [nn.Embedding, nn.LayerNorm],
- ["scaler", "bias", "layernorm", "rmsnorm", "latents_q"],
- )
- return decay_parameters
-
-
-def train_model(
- model,
- training_args,
- train_dataset=None,
- val_dataset=None,
- train_collator=None,
- compute_metrics=None,
-):
- checkpoint = None
- if training_args.resume_from_checkpoint is not None:
- checkpoint = training_args.resume_from_checkpoint
- logger.info(f"Resuming from the checkpoint: {checkpoint}")
-
- trainer_kwargs = dict(
- model=model,
- args=training_args,
- train_dataset=train_dataset,
- eval_dataset=val_dataset,
- data_collator=train_collator,
- compute_metrics=compute_metrics,
- )
-
- is_modulated_model = isinstance(model, ModulatedPretrainedModel)
- trainer_cls = Trainer
- if is_modulated_model:
- logger.info("Training with modulated model.")
- trainer_cls = CrossEntropyTrainer
- trainer_kwargs["gen_lora_l1_reg_coef"] = training_args.gen_lora_l1_reg_coef
- trainer_kwargs["use_per_ctx_average_loss"] = (
- training_args.use_per_ctx_average_loss
- )
- del training_args.gen_lora_l1_reg_coef
- del training_args.use_per_ctx_average_loss
-
- if training_args.use_kl_loss:
- logger.info("Training with distillation loss. Using DistillationTrainer.")
- trainer_cls = DistillationTrainer
- del training_args.use_kl_loss
-
- if training_args.auto_find_batch_size:
- # set the batch size to some high number
- # which will be lowered by the Trainer
- training_args.per_device_train_batch_size = 128
-
- trainer = trainer_cls(**trainer_kwargs)
- # if getattr(trainer, "use_per_ctx_average_loss", False):
- # trainer.get_batch_samples = trainer.get_batch_samples_ctx
-
- # MONKEY PATCH: remove embedding layers from weight decay
- trainer.get_decay_parameter_names = get_decay_parameter_names
-
- # Trainer loads the best model after training
- # is done when load_best_model_at_end=True
- train_result = trainer.train(resume_from_checkpoint=checkpoint)
- trainer.log_metrics("train", train_result.metrics)
- trainer.save_model()
-
- # TODO: add benchmark eval?
- # clear_gpu()
diff --git a/src/ctx_to_lora/utils.py b/src/ctx_to_lora/utils.py
index 86ea369..2f0dc6d 100644
--- a/src/ctx_to_lora/utils.py
+++ b/src/ctx_to_lora/utils.py
@@ -38,10 +38,31 @@ def evaluating(*models):
model.train(training)
+def get_decoder_model(model):
+ visited = set()
+ current = model
+ while id(current) not in visited:
+ visited.add(id(current))
+ if hasattr(current, "model") and getattr(current, "model") is not current:
+ current = current.model
+ continue
+ if hasattr(current, "text_model") and getattr(current, "text_model") is not current:
+ current = current.text_model
+ continue
+ if hasattr(current, "language_model") and getattr(current, "language_model") is not current:
+ current = current.language_model
+ continue
+ break
+ return current
+
+
def get_layers(model):
- if hasattr(model, "model"):
- return get_layers(model.model)
- return model.layers
+ model = get_decoder_model(model)
+ if hasattr(model, "layers"):
+ return model.layers
+ if hasattr(model, "decoder") and hasattr(model.decoder, "layers"):
+ return model.decoder.layers
+ raise AttributeError(f"Could not find decoder layers for model type {type(model)!r}")
def get_num_layers(model):
@@ -49,9 +70,7 @@ def get_num_layers(model):
def get_base_model(model):
- if hasattr(model, "model"):
- return get_base_model(model.model)
- return model
+ return get_decoder_model(model)
def get_num_params(model):
diff --git a/train.py b/train.py
deleted file mode 100755
index e25b60b..0000000
--- a/train.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import contextlib
-import logging
-import os
-from copy import deepcopy
-from functools import partial
-
-import numpy as np
-import torch
-import wandb
-from datasets import disable_caching
-from peft import PeftModel
-from transformers import (
- AutoConfig,
- set_seed,
-)
-
-from ctx_to_lora.configs import (
- AggregatorArguments,
- ArgumentParser,
- CtxEncoderArguments,
- CtxTrainingArguments,
- DataArguments,
- ExperimentSetup,
- HypernetArguments,
- LoRAArguments,
- ModelArguments,
- TrainingArguments,
-)
-from ctx_to_lora.data.collator import ( # train_packed_collator,; DefaultDataCollator,
- flatten_if_not_packed,
-)
-from ctx_to_lora.data.processing import get_tokenized_dataset, pack
-from ctx_to_lora.metrics import (
- Evaluator,
- compute_metrics,
- compute_per_token_acc,
- compute_perplexity,
- compute_prefix_matching,
-)
-from ctx_to_lora.model_loading import (
- check_is_vision_model,
- get_lora_config,
- get_model_and_tokenizer,
- get_tokenizer,
-)
-from ctx_to_lora.modeling.hypernet import (
- ModulatedPretrainedModel,
- get_hypernet_config,
-)
-from ctx_to_lora.trainer import train_model
-from ctx_to_lora.utils import (
- compile_linear,
- extract_cli_args,
- get_run_name,
- log_num_train_params,
- save_yaml,
- setup_logging,
- validate_args,
-)
-
-logger = logging.getLogger()
-
-LOCAL_RANK = int(os.getenv("LOCAL_RANK", "0"))
-
-
-def main():
- ############ Argument parsing
- parser = ArgumentParser(
- (
- DataArguments,
- CtxTrainingArguments,
- ModelArguments,
- LoRAArguments,
- TrainingArguments,
- HypernetArguments,
- AggregatorArguments,
- CtxEncoderArguments,
- )
- )
- (
- data_args,
- ctx_args,
- model_args,
- lora_args,
- training_args,
- hypernet_args,
- aggregator_args,
- ctx_encoder_args,
- ) = parser.parse()
-
- # there shouldn't be overlap between args
- validate_args(
- [
- data_args,
- ctx_args,
- model_args,
- lora_args,
- training_args,
- hypernet_args,
- aggregator_args,
- ctx_encoder_args,
- ]
- )
-
- assert ctx_args.use_sequence_packing, (
- f"Please set use_sequence_packing=True in {ctx_args}. It's faster!"
- )
-
- set_seed(training_args.seed)
- checkpoint_dir = training_args.resume_from_checkpoint
-
- # should be the same across processes
- # still possible to have a name crash though
- # logging_dir is just "runs/DATE_TIME_HOSTNAME"
- slurm_job_id = f"_{os.getenv('SLURM_JOB_ID')}" if os.getenv("SLURM_JOB_ID") else ""
- run_name = (
- get_run_name(seed_str=training_args.logging_dir.strip("runs/") + slurm_job_id)
- if not checkpoint_dir
- else checkpoint_dir.strip("/").split("/")[-2]
- )
-
- output_dir = f"train_outputs/runs/{run_name}"
- setup_logging(output_dir, debug=os.getenv("DEBUG", False))
- logger.debug(f"CMD: {' '.join(os.sys.argv)}")
- cli_args = extract_cli_args(os.sys.argv)
- save_yaml(cli_args, f"{output_dir}/cli_args.yaml")
- if "config" in cli_args:
- config_name = os.path.basename(cli_args["config"]).split(".yaml")[0]
- os.environ["WANDB_TAGS"] = config_name
-
- run_name = os.path.basename(output_dir)
- training_args.run_name = run_name
- training_args.output_dir = output_dir
- training_args.logging_dir = output_dir
-
- if (
- training_args.lr_scheduler_type == "cosine_with_min_lr"
- and training_args.lr_scheduler_kwargs is None
- ):
- training_args.lr_scheduler_kwargs = {"min_lr": 1e-7}
- args = {
- **vars(deepcopy(data_args)),
- **vars(deepcopy(ctx_args)),
- **vars(deepcopy(model_args)),
- **vars(deepcopy(lora_args)),
- **vars(deepcopy(training_args)),
- **vars(deepcopy(hypernet_args)),
- **vars(deepcopy(aggregator_args)),
- **vars(deepcopy(ctx_encoder_args)),
- }
- args["deepspeed_plugin"] = None
- logger.debug(f"args: {args}")
- save_yaml(args, f"{output_dir}/args.yaml")
-
- ############ Model setup
- if not ctx_args.from_pretrained_checkpoint:
- model_name = model_args.model_name_or_path
- base_model, tokenizer = get_model_and_tokenizer(
- **vars(model_args),
- train=True,
- requires_grad=False,
- peft_config=get_lora_config(model_name, **vars(lora_args)),
- )
- ctx_name = ctx_encoder_args.ctx_encoder_model_name_or_path
- if ctx_name is not None:
- ctx_encoder_model_config = AutoConfig.from_pretrained(
- ctx_name, trust_remote_code=True
- )
- if ("Llama" in ctx_name and "Vision" in ctx_name) or check_is_vision_model(
- ctx_name
- ):
- ctx_encoder_model_config = ctx_encoder_model_config.text_config
- ctx_tokenizer = get_tokenizer(ctx_name, train=True)
- else:
- ctx_name = base_model.base_model.config.name_or_path
- ctx_encoder_model_config = base_model.config
- ctx_tokenizer = tokenizer
-
- if ctx_args.exp_setup == ExperimentSetup.HYPERLORA:
- logger.info("Using HyperLoRA")
- if not ctx_args.from_pretrained_checkpoint:
- hypernet_config = get_hypernet_config(
- base_model,
- ctx_encoder_model_config,
- hypernet_args,
- aggregator_args,
- ctx_encoder_args,
- )
- if ctx_encoder_args.layer_idx is None:
- ctx_encoder_args.layer_idx = (
- ctx_encoder_model_config.num_hidden_layers // 4
- )
- logger.info(
- f"Using the first {ctx_encoder_args.layer_idx} layers"
- " as the context encoder"
- )
- ctx_name = ctx_encoder_args.ctx_encoder_model_name_or_path
- if ctx_encoder_args.ctx_encoder_last_layer is None and (
- ctx_name is not None and ctx_name != base_model.name_or_path
- ):
- logger.info(
- f"Setting ctx_encoder_last_layer to {base_model.name_or_path} max layers"
- f":{base_model.config.num_hidden_layers}"
- )
- ctx_encoder_args.ctx_encoder_last_layer = (
- base_model.config.num_hidden_layers
- )
-
- model = ModulatedPretrainedModel(
- base_model, hypernet_config, ctx_encoder_args
- )
-
- else:
- if checkpoint_dir:
- ctx_args.from_pretrained_checkpoint = (
- f"{checkpoint_dir}/pytorch_model.bin"
- )
- logger.info(
- f"Loading from checkpoint: {ctx_args.from_pretrained_checkpoint}"
- )
-
- model = ModulatedPretrainedModel.from_state_dict(
- torch.load(ctx_args.from_pretrained_checkpoint, weights_only=False),
- train=True,
- use_flash_attn=model_args.use_flash_attn,
- )
- tokenizer = get_tokenizer(model.base_model.config.name_or_path, train=True)
- ctx_name = model.ctx_encoder_args.ctx_encoder_model_name_or_path
- if ctx_name is None:
- ctx_name = model.base_model.config.name_or_path
- ctx_tokenizer = get_tokenizer(ctx_name, train=True)
-
- training_args.gen_lora_l1_reg_coef = ctx_args.gen_lora_l1_reg_coef
- training_args.use_kl_loss = ctx_args.use_kl_loss
- training_args.use_per_ctx_average_loss = ctx_args.use_per_ctx_average_loss
-
- if len([p for p in model.ctx_encoder.parameters() if p.requires_grad]):
- raise ValueError("ctx_encoder contains trainable parameters")
- if len([p for p in model.base_model.parameters() if p.requires_grad]):
- raise ValueError("base model contains trainable parameters")
-
- model.hypernet.compile(fullgraph=True, mode="max-autotune")
-
- else:
- # activate LoRA
- base_model_config = AutoConfig.from_pretrained(
- model_args.model_name_or_path, trust_remote_code=True
- )
- base_model_config.save_pretrained(output_dir)
- logger.info("Using LoRA")
- model.set_adapter("default")
- model = torch.compile(model)
-
- model.train()
- logger.debug(model)
- log_num_train_params(model)
-
- ############ Dataset setup
- logger.info("Loading dataset...")
-
- add_ctx_to_chat = not isinstance(model, ModulatedPretrainedModel)
- ctx_model_max_len = model.ctx_encoder.config.max_position_embeddings
- if ctx_args.max_ctx_len > 0:
- ctx_model_max_len = ctx_args.max_ctx_len
- if ctx_args.max_ctx_chunk_len <= 0:
- # set default chunk size to max length of the ctx encoder
- ctx_args.max_ctx_chunk_len = ctx_model_max_len
-
- if ctx_args.num_chunk_probs is not None:
- ctx_args.num_chunk_probs = {
- int(k): float(v) for k, v in ctx_args.num_chunk_probs.items()
- }
-
- _get_tokenized_dataset = partial(
- get_tokenized_dataset,
- max_qas_len=ctx_args.max_qas_len,
- max_qas_per_sample=ctx_args.max_qas_per_sample,
- base_model_max_len=model.base_model.config.max_position_embeddings,
- tokenizer=tokenizer,
- ctx_model_max_len=ctx_model_max_len,
- ctx_tokenizer=ctx_tokenizer,
- add_ctx_to_chat=add_ctx_to_chat,
- add_negative_prompt=ctx_args.add_negative_prompt,
- max_ctx_chunk_len=ctx_args.max_ctx_chunk_len,
- min_ctx_chunk_len=ctx_args.min_ctx_chunk_len,
- num_chunk_probs=ctx_args.num_chunk_probs,
- max_ctx_chunk_num=ctx_args.max_ctx_chunk_num,
- use_kl_loss=ctx_args.use_kl_loss,
- )
- splits = ["train"]
- if training_args.eval_strategy != "no":
- splits.append("validation")
- tokenized_ds = {split: {} for split in splits}
- for split, ds_names in zip(
- splits,
- [data_args.train_ds_names, data_args.val_ds_names],
- ):
- if not ds_names:
- continue
- ctx_mgr = (
- training_args.main_process_first()
- if split == "train"
- else contextlib.nullcontext()
- )
- with ctx_mgr:
- # process and tokenize on the main process
- # then other replicas can just load the cached dataset
- # we dont save cache for validation ds
- for ds_name in ds_names:
- ds = _get_tokenized_dataset(ds_name, split)
-
- base_name = os.path.basename(ds_name)
- if ds_name.startswith("self_gen/"):
- ds_name = "self_gen/" + base_name
- else:
- ds_name = base_name
-
- tokenized_ds[split][ds_name] = ds
-
- train_ds = tokenized_ds["train"]
- if data_args.max_train_samples_per_ds is not None:
- for ds_name, ds in train_ds.items():
- if data_args.max_train_samples_per_ds >= len(ds):
- continue
- train_ds[ds_name] = ds.take(data_args.max_train_samples_per_ds)
- logging.info(f"train_ds: {train_ds}")
-
- val_ds = dict()
- if "validation" in tokenized_ds:
- n_val_samples = data_args.max_val_samples_per_ds
- for ds_name, ds in tokenized_ds["validation"].items():
- if ds is None:
- # take some samples from train_ds
- ds = train_ds[ds_name].take(n_val_samples)
- train_ds[ds_name] = train_ds[ds_name].skip(n_val_samples)
-
- val_ds[ds_name] = ds
- val_indices = np.random.permutation(len(ds))[:n_val_samples]
- val_ds[ds_name] = val_ds[ds_name].select(val_indices)
-
- with training_args.main_process_first():
- train_ds = pack(
- train_ds,
- ctx_args.max_packed_inp_len,
- ctx_args.max_packed_ctx_len,
- max_packed_size=-1,
- seed=training_args.seed,
- num_proc=30,
- )
- logger.info("Setting per_device_train_batch_size to 1")
- training_args.per_device_train_batch_size = 1
-
- logger.info(f"train_ds: {train_ds}")
- logger.info(f"val_ds: {val_ds}")
-
- collator = flatten_if_not_packed
-
- if isinstance(model, ModulatedPretrainedModel):
- if isinstance(model.base_model, PeftModel):
- base_model = model.base_model.base_model
- else:
- base_model = model.base_model
-
- if ctx_name is not None:
- logger.info("Compiling ctx_encoder_model")
- ctx_base_model = model.ctx_encoder.base_model
- compile_linear(ctx_base_model)
-
- elif isinstance(model, PeftModel):
- base_model = model.base_model
-
- logger.info("Compiling base_model")
- base_model.compile(fullgraph=True, mode="max-autotune")
-
- if LOCAL_RANK == 0:
- wandb.init(
- project=os.getenv("WANDB_PROJECT"),
- name=run_name,
- group=run_name,
- config=args,
- tags=os.getenv("WANDB_TAGS").split(","),
- notes=ctx_args.notes,
- resume="allow",
- )
- else:
- wandb.init(mode="disabled")
-
- train_model(
- model,
- training_args,
- train_ds,
- val_ds,
- collator,
- compute_metrics=partial(
- compute_metrics,
- evaluator=Evaluator(
- [compute_per_token_acc, compute_prefix_matching, compute_perplexity]
- ),
- ),
- )
- logger.info(f"Training run finished and saved to {output_dir}")
-
-
-if __name__ == "__main__":
- os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
- os.environ["TOKENIZERS_PARALLELISM"] = "true"
- os.environ["WANDB_DIR"] = ".wandb/"
- os.environ["WANDB_PROJECT"] = os.getenv("WANDB_PROJECT") or "ctx_to_lora"
- os.environ["WANDB_WATCH"] = ""
- os.environ["WANDB_CONSOLE"] = "off"
- os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
- os.environ["OMP_NUM_THREADS"] = "23"
- torch._dynamo.config.capture_scalar_outputs = True
-
- torch.backends.cuda.matmul.allow_tf32 = True
- torch.backends.cudnn.allow_tf32 = True
- if os.getenv("DEBUG", False):
- disable_caching()
- main()
diff --git a/uv.lock b/uv.lock
index b687ded..c6f16b4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,31 +2,25 @@ version = 1
revision = 3
requires-python = ">=3.10"
resolution-markers = [
- "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'",
- "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux'",
- "python_full_version >= '3.13' and sys_platform == 'darwin'",
- "python_full_version == '3.12.*' and sys_platform == 'darwin'",
- "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'",
- "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux'",
- "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux'",
- "python_full_version == '3.11.*' and sys_platform == 'darwin'",
- "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux'",
- "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
- "python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux'",
- "python_full_version < '3.11' and sys_platform == 'darwin'",
- "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux'",
+ "python_full_version >= '3.14' and sys_platform == 'win32'",
+ "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+ "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
+ "python_full_version == '3.11.*' and sys_platform == 'win32'",
+ "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+ "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+ "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version < '3.11'",
]
[[package]]
name = "absl-py"
-version = "2.2.2"
+version = "2.4.0"
source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b5/f0/e6342091061ed3a46aadc116b13edd7bb5249c3ab1b3ef07f24b0c248fc3/absl_py-2.2.2.tar.gz", hash = "sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb", size = 119982, upload-time = "2025-04-03T12:41:04.55Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/f6/d4/349f7f4bd5ea92dab34f5bb0fe31775ef6c311427a14d5a5b31ecb442341/absl_py-2.2.2-py3-none-any.whl", hash = "sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092", size = 135565, upload-time = "2025-04-03T12:41:03.172Z" },
+ { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
]
[[package]]
@@ -35,7 +29,8 @@ version = "1.6.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "huggingface-hub" },
- { name = "numpy" },
+ { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+ { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
{ name = "packaging" },
{ name = "psutil" },
{ name = "pyyaml" },
@@ -47,27 +42,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748, upload-time = "2025-04-01T11:53:01.298Z" },
]
-[[package]]
-name = "aiofiles"
-version = "24.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
-]
-
[[package]]
name = "aiohappyeyeballs"
-version = "2.6.1"
+version = "2.6.2"
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@@ -1468,18 +886,19 @@ http = [
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diff --git a/watcher.py b/watcher.py
deleted file mode 100644
index a3e02b3..0000000
--- a/watcher.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import itertools
-import os
-import time
-from argparse import Namespace
-from glob import glob
-
-import wandb
-import yaml
-
-from ctx_to_lora.eval_utils import run_eval
-from ctx_to_lora.utils import clear_gpu
-
-CP_PATTERN = "train_outputs/runs/*/checkpoint*/pytorch_model.bin"
-
-
-def flatten(l):
- return itertools.chain.from_iterable(l)
-
-
-# handmade file watcher using glob
-# not using watchdog because there are too many saved files
-# but we want to just watch CP_PATTERN files
-class Watcher:
- def __init__(self, patterns):
- self.patterns = patterns
- self.files = self.get_files()
- self.last_files = self.files
-
- def get_files(self):
- return set(flatten(glob(pattern) for pattern in self.patterns))
-
- def watch(self):
- self.files = self.get_files()
- new_files = self.files - self.last_files
- return sorted(list(new_files))
-
- def update(self, file):
- if file in self.last_files:
- return
- self.last_files.add(file)
- print(f"Added {file} to evaluated files.")
-
- def save_state(self):
- with open("watcher_state.yaml", "w") as f:
- yaml.dump({"last_files": self.last_files}, f)
-
- def load_state(self):
- if not os.path.exists("watcher_state.yaml"):
- return
- with open("watcher_state.yaml") as f:
- state = yaml.safe_load(f)
- self.last_files = state["last_files"]
-
-
-if __name__ == "__main__":
- os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
- os.environ["FLASH_ATTENTION_DETERMINISTIC"] = "1"
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
- os.environ["WANDB_PROJECT"] = "ctx_to_lora"
-
- watcher = Watcher([CP_PATTERN])
- watcher.load_state()
- print("Watching for new files...")
- while True:
- time.sleep(10)
- new_files = watcher.watch()
- for file in new_files:
- # workaround to prevent loading incomplete checkpoints
- time.sleep(20)
- if not os.path.exists(file):
- # cp is delete before we can read it
- continue
- run_dir = file.split("/checkpoint")[0]
- run_name = run_dir.split("/")[-1]
- print(f"Evaluating {file}")
- args = Namespace(**yaml.unsafe_load(open(f"{run_dir}/args.yaml")))
- curstep = int(file.split("checkpoint-")[1].split("/")[0])
- wandb_kwargs = {
- "project": os.getenv("WANDB_PROJECT"),
- "group": run_name,
- "name": f"{run_name}-eval",
- "id": f"{run_name}-eval",
- "resume": "allow",
- }
- wandb.init(**wandb_kwargs)
-
- # TODO: have to change this for bigger models
- eval_batch_size = 8
- eval_batch_size_gen = 8
- metrics = {}
-
- # try:
- # # metrics = run_eval(
- # # checkpoint_path=file,
- # # eval_batch_size=eval_batch_size,
- # # split="validation",
- # # generative=False,
- # # )
- # except FileNotFoundError as e:
- # print(f"Error evaluating {file}: {e}. The checkpoint might be deleted.")
- # continue
- try:
- gen_metrics = run_eval(
- checkpoint_path=file,
- split="validation",
- eval_batch_size=eval_batch_size_gen,
- max_ctx_chunk_len=args.max_ctx_chunk_len,
- generative=True,
- )
- except FileNotFoundError as e:
- print(f"The checkpoint might be deleted. Error evaluating {file}: {e}.")
- gen_metrics = {}
- file = ""
- metrics.update(gen_metrics)
- for k in metrics:
- wandb.log(metrics[k], step=curstep)
- wandb.finish()
- print(f"Logged metrics: {metrics}")
- print("=" * 80)
- clear_gpu()
- watcher.update(file)
- watcher.save_state()
diff --git a/webui/SELF_GEN_VIEWER.md b/webui/SELF_GEN_VIEWER.md
deleted file mode 100644
index 408b40d..0000000
--- a/webui/SELF_GEN_VIEWER.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Self-Gen Data Viewer
-
-Thanks Claude.
-
-Running the viewer
-```bash
-uv run self_gen_viewer.py
-```
-
-Then open your browser and go to: **http://localhost:5001**
-
-## Usage
-
-1. **Select a Model Folder**: Choose from the dropdown list (e.g., `google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0`)
-2. **Select a Parquet File**: Once a folder is selected, available parquet files will appear
-3. **Set Number of Samples**: Adjust the sample count (default: 100, max: 1000)
-4. **Click "Load Data"**: View the visualized data with context and Q&A pairs
-
-## Data Structure
-
-The viewer expects data in the following structure:
-```
-data/raw_datasets/self_gen/
-โโโ google/
-โ โโโ gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0/
-โ โโโ fw_qa_v2/
-โ โโโ *.parquet
-โโโ mistralai/
- โโโ Mistral-7B-Instruct-v0.2_temp_0.0_closed_qa_prob_1.0/
- โโโ *.parquet
-```
diff --git a/webui/self_gen_viewer.py b/webui/self_gen_viewer.py
deleted file mode 100644
index 6c8cd13..0000000
--- a/webui/self_gen_viewer.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import traceback
-from pathlib import Path
-
-from datasets import load_dataset
-from flask import Flask, jsonify, render_template, request
-from transformers import AutoTokenizer
-
-app = Flask(__name__)
-
-# Base path for self_gen data
-BASE_DATA_PATH = Path(__file__).parent.parent / "data" / "raw_datasets" / "self_gen"
-
-# Cache for tokenizers
-tokenizer_cache = {}
-
-
-def get_tokenizer(model_path):
- """Get or create tokenizer with caching"""
- if model_path not in tokenizer_cache:
- try:
- tokenizer_cache[model_path] = AutoTokenizer.from_pretrained(model_path)
- except Exception as e:
- print(f"Error loading tokenizer for {model_path}: {e}")
- return None
- return tokenizer_cache[model_path]
-
-
-def discover_folders():
- """Discover all model folders in self_gen directory"""
- folders = []
- if not BASE_DATA_PATH.exists():
- return folders
-
- for vendor_dir in BASE_DATA_PATH.iterdir():
- if vendor_dir.is_dir():
- for model_dir in vendor_dir.iterdir():
- if model_dir.is_dir():
- rel_path = model_dir.relative_to(BASE_DATA_PATH)
- folders.append(str(rel_path))
-
- return sorted(folders)
-
-
-def discover_parquet_files(folder_path):
- """Discover all parquet files in a folder"""
- full_path = BASE_DATA_PATH / folder_path
- parquet_files = []
-
- if full_path.exists():
- for parquet_file in full_path.glob("**/*.parquet"):
- rel_path = parquet_file.relative_to(full_path)
- parquet_files.append(str(rel_path))
-
- return sorted(parquet_files)
-
-
-def extract_model_name_from_folder(folder_path):
- """Extract base model name from folder path"""
- # e.g., "google/gemma-2-2b-it_temp_0.0_closed_qa_prob_1.0" -> "google/gemma-2-2b-it"
- parts = folder_path.split("/")
- if len(parts) >= 2:
- vendor = parts[0]
- model_part = parts[1].split("_temp_")[0]
- return f"{vendor}/{model_part}"
- return None
-
-
-@app.route("/")
-def index():
- """Main page"""
- folders = discover_folders()
- return render_template("self_gen_viewer.html", folders=folders)
-
-
-@app.route("/api/folders")
-def api_folders():
- """API endpoint to get available folders"""
- folders = discover_folders()
- return jsonify({"folders": folders})
-
-
-@app.route("/api/parquet_files")
-def api_parquet_files():
- """API endpoint to get parquet files in a folder"""
- folder = request.args.get("folder", "")
- if not folder:
- return jsonify({"error": "No folder specified"}), 400
-
- files = discover_parquet_files(folder)
- return jsonify({"files": files})
-
-
-@app.route("/api/load_data")
-def api_load_data():
- """API endpoint to load and display data from a parquet file"""
- folder = request.args.get("folder", "")
- parquet_file = request.args.get("file", "")
- num_samples = int(request.args.get("num_samples", 100))
-
- if not folder or not parquet_file:
- return jsonify({"error": "Missing parameters"}), 400
-
- try:
- # Construct full path
- full_path = BASE_DATA_PATH / folder / parquet_file
-
- if not full_path.exists():
- return jsonify({"error": f"File not found: {full_path}"}), 404
-
- # Extract model name for tokenizer
- model_name = extract_model_name_from_folder(folder)
- if not model_name:
- return jsonify({"error": "Could not extract model name from folder"}), 400
-
- # Load tokenizer
- tokenizer = get_tokenizer(model_name)
- if tokenizer is None:
- return jsonify({"error": f"Could not load tokenizer for {model_name}"}), 500
-
- # Load dataset
- ds = load_dataset(
- "parquet", data_files=str(full_path), split=f"train[:{num_samples}]"
- )
-
- # Process samples
- samples = []
- for i, sample in enumerate(ds):
- processed_sample = {
- "index": i,
- "ctx": tokenizer.decode(sample["ctx_ids"], skip_special_tokens=False)
- if "ctx_ids" in sample
- else "N/A",
- "questions": [],
- }
-
- # Decode input_ids if present
- if "input_ids" in sample:
- if isinstance(sample["input_ids"][0], list):
- # Multiple Q&A pairs
- processed_sample["questions"] = [
- tokenizer.decode(qa, skip_special_tokens=False)
- for qa in sample["input_ids"]
- ]
- else:
- # Single item
- processed_sample["questions"] = [
- tokenizer.decode(sample["input_ids"], skip_special_tokens=False)
- ]
-
- samples.append(processed_sample)
-
- return jsonify(
- {
- "success": True,
- "num_samples": len(samples),
- "model_name": model_name,
- "file_path": str(parquet_file),
- "samples": samples,
- }
- )
-
- except Exception as e:
- traceback.print_exc()
- return jsonify({"error": str(e), "traceback": traceback.format_exc()}), 500
-
-
-if __name__ == "__main__":
- print(f"Data path: {BASE_DATA_PATH}")
- print(f"Available folders: {discover_folders()}")
- app.run(debug=True, host="0.0.0.0", port=5001)
diff --git a/webui/templates/self_gen_viewer.html b/webui/templates/self_gen_viewer.html
deleted file mode 100644
index 562d31d..0000000
--- a/webui/templates/self_gen_viewer.html
+++ /dev/null
@@ -1,422 +0,0 @@
-
-
-
-
-
-
- Self-Gen Data Viewer
-
-
-
-
-
-
๐ Self-Gen Data Viewer
-
-
-
- Model Folder:
-
- -- Select a folder --
- {% for folder in folders %}
- {{ folder }}
- {% endfor %}
-
-
-
-
- Parquet File:
-
- -- Select a file --
-
-
-
-
-
- Number of Samples:
-
-
-
Load Data
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file