Add

inference.py

@@ -28,7 +28,7 @@ def parse_args():
         required=True,
         help="Ending file number to download. Valid values: 0 - 1023",
     )
-    parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
+    parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
     parser.add_argument(
         "--model_name",
         type=str,
@@ -98,6 +98,11 @@ def batch_inference(data, batch_size=32):
 if __name__ == "__main__":
     args = parse_args()
 
+    tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
+    if os.path.exists(f"javascript/{tasky_commits_path}"):
+        print("Exists:", tasky_commits_path)
+        exit()
+
     tokenizer = AutoTokenizer.from_pretrained(args.model_name)
     model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -106,8 +111,13 @@ if __name__ == "__main__":
 
     path = "javascript_messages.jsonl"
     ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
-    ds = ds[range(args.start, args.end)]
+    ds = ds[range(args.start, min(args.end, len(ds)))]
     df = pd.DataFrame(ds, index=None)
+    #tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
+    #if os.path.exists(f"javascript/{tasky_commits_path}"):
+    #    print("Exists:", tasky_commits_path)
+    #    exit()
+
     texts = df["message"].to_list()
     commits = df["commit"].to_list()
     preds = batch_inference(texts, batch_size=args.batch_size)

inference.sh

@@ -2,7 +2,7 @@
 #SBATCH --job-name=inference
 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
-#SBATCH --cpus-per-task=8           # number of cores per tasks
+#SBATCH --cpus-per-task=24           # number of cores per tasks
 #SBATCH --hint=nomultithread         # we get physical cores not logical
 #SBATCH --gres=gpu:1                 # number of gpus
 #SBATCH --time 12:00:00             # maximum execution time (HH:MM:SS)

inference_javascript.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --job-name=inference
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=24           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --time 12:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=ajs@v100
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+echo "START TIME: $(date)"
+
+NUM_SAMPLES=4766203
+NUM_SAMPLES_PER_JOB=32909
+#NUM_SAMPLES_PER_JOB=128
+
+TASK_START=$((($SLURM_ARRAY_TASK_ID) * $NUM_SAMPLES_PER_JOB))
+TASK_END=$(($TASK_START + $NUM_SAMPLES_PER_JOB))
+
+echo "Starting task $SLURM_ARRAY_TASK_ID with start $TASK_START and end $TASK_END"
+python inference.py --start $TASK_START --end $TASK_END

inference_python.py

@@ -0,0 +1,144 @@
+# !pip install -q transformers datasets sentencepiece
+import argparse
+import gc
+import json
+import os
+
+import datasets
+import pandas as pd
+import torch
+from tqdm import tqdm
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+TOTAL_NUM_FILES_C4_TRAIN = 1024
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--start",
+        type=int,
+        required=True,
+        help="Starting file number to download. Valid values: 0 - 1023",
+    )
+    parser.add_argument(
+        "--end",
+        type=int,
+        required=True,
+        help="Ending file number to download. Valid values: 0 - 1023",
+    )
+    parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
+    parser.add_argument(
+        "--model_name",
+        type=str,
+        default="taskydata/deberta-v3-base_10xp3nirstbbflanseuni_10xc4",
+        help="Model name",
+    )
+    parser.add_argument(
+        "--local_cache_location",
+        type=str,
+        default="c4_download",
+        help="local cache location from where the dataset will be loaded",
+    )
+    parser.add_argument(
+        "--use_local_cache_location",
+        type=bool,
+        default=True,
+        help="Set True if you want to load the dataset from local cache.",
+    )
+    parser.add_argument(
+        "--clear_dataset_cache",
+        type=bool,
+        default=False,
+        help="Set True if you want to delete the dataset files from the cache after inference.",
+    )
+    parser.add_argument(
+        "--release_memory",
+        type=bool,
+        default=True,
+        help="Set True if you want to release the memory of used variables.",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def chunks(l, n):
+    for i in range(0, len(l), n):
+        yield l[i : i + n]
+
+
+def batch_tokenize(data, batch_size):
+    batches = list(chunks(data, batch_size))
+    tokenized_batches = []
+    for batch in batches:
+        # max_length will automatically be set to the max length of the model (512 for deberta)
+        tensor = tokenizer(
+            batch,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=512,
+        )
+        tokenized_batches.append(tensor)
+    return tokenized_batches, batches
+
+
+def batch_inference(data, batch_size=32):
+    preds = []
+    tokenized_batches, batches = batch_tokenize(data, batch_size)
+    for i in tqdm(range(len(batches))):
+        with torch.no_grad():
+            logits = model(**tokenized_batches[i].to(device)).logits.cpu()
+        preds.extend(logits)
+    return preds
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"
+    if os.path.exists(f"python/{tasky_commits_path}"):
+        print("Exists:", tasky_commits_path)
+        exit()
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model.to(device)
+    model.eval()
+
+    path = "python_messages.jsonl"
+    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    ds = ds[range(args.start, min(args.end, len(ds)))]
+    df = pd.DataFrame(ds, index=None)
+
+    texts = df["message"].to_list()
+    commits = df["commit"].to_list()
+    preds = batch_inference(texts, batch_size=args.batch_size)
+
+    assert len(preds) == len(texts)
+
+    # Write two jsonl files:
+    # 1) Probas for all of C4
+    # 2) Probas + texts for samples predicted as tasky
+    tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"
+
+    with open(tasky_commits_path, "w") as f:
+        for i in range(len(preds)):
+            predicted_class_id = preds[i].argmax().item()
+            pred = model.config.id2label[predicted_class_id]
+            tasky_proba = torch.softmax(preds[i], dim=-1)[-1].item()
+
+            f.write(
+                json.dumps(
+                    {
+                        "commit": commits[i],
+                        "message": texts[i],
+                        "proba": tasky_proba,
+                    }
+                )
+                + "\n"
+            )
+

inference_python.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --job-name=inference
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=24           # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:1                 # number of gpus
+#SBATCH --time 12:00:00             # maximum execution time (HH:MM:SS)
+#SBATCH --output=%x-%j.out           # output file name
+#SBATCH --account=ajs@v100
+
+set -x -e
+
+source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
+echo "START TIME: $(date)"
+
+NUM_SAMPLES=5821648
+NUM_SAMPLES_PER_JOB=32909
+#NUM_SAMPLES_PER_JOB=128
+
+TASK_START=$((($SLURM_ARRAY_TASK_ID) * $NUM_SAMPLES_PER_JOB))
+TASK_END=$(($TASK_START + $NUM_SAMPLES_PER_JOB))
+
+echo "Starting task $SLURM_ARRAY_TASK_ID with start $TASK_START and end $TASK_END"
+python inference_python.py --start $TASK_START --end $TASK_END