15 run inference on baskerville

.gitignore

@@ -160,6 +160,7 @@ Thumbs.db
 
 
 slurm_scripts/slurm_logs*
+slurm_scripts/experiments*
 # other
 temp
 .vscode

config/RTC_configs/roberta-mt5-zero-shot.yaml

@@ -1,4 +1,4 @@
-OCR:
+ocr:
   specific_task: "image-to-text"
   model: "microsoft/trocr-base-handwritten"
 

config/data_configs/l1_fr_to_en.yaml

@@ -5,3 +5,5 @@ level: 1
 lang_pair:
   source: "fr"
   target: "en"
+
+drop_length: 1000

config/experiment/baskerville_pipeline_inference_test.yaml

@@ -0,0 +1,13 @@
+data_config: l1_fr_to_en
+
+pipeline_config: roberta-mt5-zero-shot
+
+seed:
+  - 42
+
+bask:
+  jobname: "shortened_input_test"
+  walltime: '0-12:0:0'
+  gpu_number: 1
+  node_number: 1
+  hf_cache_dir: "/bask/projects/v/vjgo8416-spice/hf_cache"

config/experiment/full_experiment_zero_shot.yaml

@@ -0,0 +1,15 @@
+data_config: l1_fr_to_en
+
+pipeline_config: roberta-mt5-zero-shot
+
+seed:
+  - 42
+  - 43
+  - 44
+
+bask:
+  jobname: "full_experiment_with_zero_shot"
+  walltime: '0-24:0:0'
+  gpu_number: 1
+  node_number: 1
+  hf_cache_dir: "/bask/projects/v/vjgo8416-spice/hf_cache"

scripts/README.md

@@ -106,3 +106,30 @@ It's called like so e.g. from project root:
 ```bash
 python scripts/pipeline_inference.py [pipeline_config_path] [data_config_path] translator
 ```
+
+## gen_jobscripts.py
+
+Create jobscript `.sh` files for an experiment, which in this case refers to a `data_config` and `pipeline_config` combo.
+It takes a single argument which is `experiment_config_path`. This refers to a file path to a `.yaml` file structured as below:
+
+### eg. Experiment config:
+
+```yaml
+data_config: l1_fr_to_en
+
+pipeline_config: roberta-mt5-zero-shot
+
+seed:
+  - 42
+  - 43
+  - 44
+
+bask:
+  jobname: "full_experiment_with_zero_shot"
+  walltime: '0-24:0:0'
+  gpu_number: 1
+  node_number: 1
+  hf_cache_dir: "/bask/projects/v/vjgo8416-spice/hf_cache"
+
+
+```

scripts/gen_jobscripts.py

@@ -0,0 +1,82 @@
+import os
+from pathlib import Path
+
+from jinja2 import Environment, FileSystemLoader
+from jsonargparse import CLI
+
+from arc_spice.utils import open_yaml_path
+
+PROJECT_DIR = Path(__file__, "..", "..").resolve()
+
+
+def main(experiment_config_path: str):
+    """
+    _summary_
+
+    Args:
+        experiment_config_path: _description_
+    """
+    experiment_name = experiment_config_path.split("/")[-1].split(".")[0]
+    experiment_config = open_yaml_path(experiment_config_path)
+    pipeline_conf_dir = (
+        f"{PROJECT_DIR}/config/RTC_configs/{experiment_config['pipeline_config']}.yaml"
+    )
+    data_conf_dir = (
+        f"{PROJECT_DIR}/config/data_configs/{experiment_config['data_config']}.yaml"
+    )
+    pipeline_config = open_yaml_path(pipeline_conf_dir)
+    # Get jinja template
+    environment = Environment(
+        loader=FileSystemLoader(PROJECT_DIR / "src" / "arc_spice" / "config")
+    )
+    template = environment.get_template("jobscript_template.sh")
+    # We don't want to overwrite results
+
+    for index, seed in enumerate(experiment_config["seed"]):
+        os.makedirs(
+            f"slurm_scripts/experiments/{experiment_name}/run_{index}", exist_ok=False
+        )
+        for model in pipeline_config:
+            model_script_dict: dict = experiment_config["bask"]
+            model_script_dict.update(
+                {
+                    "script_name": (
+                        "scripts/single_component_inference.py "
+                        f"{pipeline_conf_dir} {data_conf_dir} {seed}"
+                        f" {experiment_name} {model}"
+                    ),
+                    "job_name": f"{experiment_name}_{model}",
+                    "seed": seed,
+                }
+            )
+            model_train_script = template.render(model_script_dict)
+
+            with open(
+                f"slurm_scripts/experiments/{experiment_name}/run_{index}/{model}.sh",
+                "w",
+            ) as f:
+                f.write(model_train_script)
+
+        pipeline_script_dict: dict = experiment_config["bask"]
+        pipeline_script_dict.update(
+            {
+                "script_name": (
+                    "scripts/pipeline_inference.py "
+                    f"{pipeline_conf_dir} {data_conf_dir} {seed}"
+                    f" {experiment_name}"
+                ),
+                "job_name": f"{experiment_name}_full_pipeline",
+                "seed": seed,
+            }
+        )
+        pipeline_train_script = template.render(pipeline_script_dict)
+
+        with open(
+            f"slurm_scripts/experiments/{experiment_name}/run_{index}/full_pipeline.sh",
+            "w",
+        ) as f:
+            f.write(pipeline_train_script)
+
+
+if __name__ == "__main__":
+    CLI(main)

scripts/pipeline_inference.py

@@ -3,28 +3,43 @@
 
 from jsonargparse import CLI
 
-from arc_spice.data.multieurlex_utils import load_multieurlex_for_translation
+from arc_spice.data.multieurlex_utils import load_multieurlex_for_pipeline
 from arc_spice.eval.inference_utils import ResultsGetter, run_inference
-from arc_spice.utils import open_yaml_path
+from arc_spice.utils import open_yaml_path, seed_everything
 from arc_spice.variational_pipelines.RTC_variational_pipeline import (
     RTCVariationalPipeline,
 )
 
 OUTPUT_DIR = "outputs"
 
 
-def main(pipeline_config_pth: str, data_config_pth: str):
+def main(
+    pipeline_config_pth: str, data_config_pth: str, seed: int, experiment_name: str
+):
     """
     Run inference on a given pipeline with provided data config
 
     Args:
         pipeline_config_pth: path to pipeline config yaml file
         data_config_pth: path to data config yaml file
+        seed: seed for the the inference pass
+        experiment_name: name of experiment for saving purposes
     """
+    # create save directory -> fail if already exists
+    data_name = data_config_pth.split("/")[-1].split(".")[0]
+    pipeline_name = pipeline_config_pth.split("/")[-1].split(".")[0]
+    save_loc = (
+        f"{OUTPUT_DIR}/inference_results/{data_name}/{pipeline_name}/"
+        f"{experiment_name}/seed_{seed}/"
+    )
+    # This directory needs to exist for all 4 experiments
+    os.makedirs(save_loc, exist_ok=True)
+    # seed experiment
+    seed_everything(seed=seed)
     # initialise pipeline
     data_config = open_yaml_path(data_config_pth)
     pipeline_config = open_yaml_path(pipeline_config_pth)
-    data_sets, meta_data = load_multieurlex_for_translation(**data_config)
+    data_sets, meta_data = load_multieurlex_for_pipeline(**data_config)
     test_loader = data_sets["test"]
     rtc_variational_pipeline = RTCVariationalPipeline(
         model_pars=pipeline_config, data_pars=meta_data
@@ -37,11 +52,6 @@ def main(pipeline_config_pth: str, data_config_pth: str):
         results_getter=results_getter,
     )
 
-    data_name = data_config_pth.split("/")[-1].split(".")[0]
-    pipeline_name = pipeline_config_pth.split("/")[-1].split(".")[0]
-    save_loc = f"{OUTPUT_DIR}/inference_results/{data_name}/{pipeline_name}"
-    os.makedirs(save_loc, exist_ok=True)
-
     with open(f"{save_loc}/full_pipeline.json", "w") as save_file:
         json.dump(test_results, save_file)
 

scripts/single_component_inference.py

@@ -16,9 +16,9 @@
 
 from jsonargparse import CLI
 
-from arc_spice.data.multieurlex_utils import load_multieurlex_for_translation
+from arc_spice.data.multieurlex_utils import load_multieurlex_for_pipeline
 from arc_spice.eval.inference_utils import ResultsGetter, run_inference
-from arc_spice.utils import open_yaml_path
+from arc_spice.utils import open_yaml_path, seed_everything
 from arc_spice.variational_pipelines.RTC_single_component_pipeline import (
     ClassificationVariationalPipeline,
     RecognitionVariationalPipeline,
@@ -28,19 +28,38 @@
 OUTPUT_DIR = "outputs"
 
 
-def main(pipeline_config_pth: str, data_config_pth: str, model_key: str):
+def main(
+    pipeline_config_pth: str,
+    data_config_pth: str,
+    seed: int,
+    experiment_name: str,
+    model_key: str,
+):
     """
     Run inference on a given pipeline component with provided data config and model key.
 
     Args:
         pipeline_config_pth: path to pipeline config yaml file
         data_config_pth: path to data config yaml file
+        seed: seed for the the inference pass
+        experiment_name: name of experiment for saving purposes
         model_key: name of model on which to run inference
     """
+    # create save directory -> fail if already exists
+    data_name = data_config_pth.split("/")[-1].split(".")[0]
+    pipeline_name = pipeline_config_pth.split("/")[-1].split(".")[0]
+    save_loc = (
+        f"{OUTPUT_DIR}/inference_results/{data_name}/{pipeline_name}/"
+        f"{experiment_name}/seed_{seed}/"
+    )
+    # This directory needs to exist for all 4 experiments
+    os.makedirs(save_loc, exist_ok=True)
+    # seed experiment
+    seed_everything(seed=seed)
     # initialise pipeline
     data_config = open_yaml_path(data_config_pth)
     pipeline_config = open_yaml_path(pipeline_config_pth)
-    data_sets, meta_data = load_multieurlex_for_translation(**data_config)
+    data_sets, meta_data = load_multieurlex_for_pipeline(**data_config)
     test_loader = data_sets["test"]
     if model_key == "ocr":
         rtc_single_component_pipeline = RecognitionVariationalPipeline(
@@ -69,14 +88,6 @@ def main(pipeline_config_pth: str, data_config_pth: str, model_key: str):
         results_getter=results_getter,
     )
 
-    data_name = data_config_pth.split("/")[-1].split(".")[0]
-    pipeline_name = pipeline_config_pth.split("/")[-1].split(".")[0]
-    save_loc = (
-        f"{OUTPUT_DIR}/inference_results/{data_name}/{pipeline_name}/"
-        f"single_component"
-    )
-    os.makedirs(save_loc, exist_ok=True)
-
     with open(f"{save_loc}/{model_key}.json", "w") as save_file:
         json.dump(test_results, save_file)
 

src/arc_spice/config/jobscript_template.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+#SBATCH --account vjgo8416-spice
+#SBATCH --qos turing
+#SBATCH --job-name {{ job_name }}
+#SBATCH --time {{ walltime }}
+#SBATCH --nodes {{ node_number }}
+#SBATCH --gpus {{ gpu_number }}
+#SBATCH --output /bask/projects/v/vjgo8416-spice/ARC-SPICE/slurm_scripts/slurm_logs/{{ job_name }}-%j.out
+#SBATCH --cpus-per-gpu 18
+
+
+# Load required modules here
+module purge
+module load baskerville
+module load bask-apps/live/live
+module load Python/3.10.8-GCCcore-12.2.0
+
+
+# change working directory
+cd /bask/projects/v/vjgo8416-spice/ARC-SPICE/
+
+source /bask/projects/v/vjgo8416-spice/ARC-SPICE/env/bin/activate
+
+# change huggingface cache to be in project dir rather than user home
+export HF_HOME="{{ hf_cache_dir }}"
+
+# TODO: script uses relative path to project home so must be run from home, fix
+python {{ script_name }}

src/arc_spice/data/multieurlex_utils.py

@@ -133,6 +133,7 @@ def load_multieurlex(
     level: int,
     languages: list[str],
     drop_empty: bool = True,
+    drop_length: int | None = None,
     split: str | None = None,
 ) -> tuple[datasets.DatasetDict, dict[str, Any]]:
     """
@@ -188,6 +189,11 @@ def load_multieurlex(
                 lambda x: all(x is not None for x in x["text"].values())
             )
 
+    if drop_length:
+        dataset_dict = dataset_dict.filter(
+            lambda x: len(x["text"][languages[0]]) <= drop_length
+        )
+
     # return datasets and metadata
     return dataset_dict, metadata
 
@@ -197,11 +203,16 @@ def load_multieurlex_for_pipeline(
     level: int,
     lang_pair: dict[str, str],
     drop_empty: bool = True,
+    drop_length: int | None = None,
     load_ocr_data: bool = False,
 ) -> tuple[datasets.DatasetDict, dict[str, Any]]:
     langs = [lang_pair["source"], lang_pair["target"]]
     dataset_dict, meta_data = load_multieurlex(
-        data_dir=data_dir, level=level, languages=langs, drop_empty=drop_empty
+        data_dir=data_dir,
+        level=level,
+        languages=langs,
+        drop_empty=drop_empty,
+        drop_length=drop_length,
     )
     # instantiate the preprocessor
     preprocesser = TranslationPreProcesser(lang_pair)