Trying new run that will rewrite the prompts as it goes

pdelfin/train/dataloader.py

@@ -1,8 +1,8 @@
 import json
 import logging
-import multiprocessing
+import tempfile
 import re
-import random
+import os
 import base64
 import glob
 
@@ -14,6 +14,8 @@ import boto3
 from datasets import Dataset, Features, Value, load_dataset, concatenate_datasets, DatasetDict
 from .core.config import DataConfig, SourceConfig
 
+from pdelfin.prompts.anchor import get_anchor_text
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -124,6 +126,8 @@ def get_png_dimensions_from_base64(base64_data) -> tuple[int, int]:
     return width, height
 
 
+
+
 def extract_openai_batch_query(query: Dict[str, Any]) -> Dict[str, Any]:
     """
     Extracts necessary fields from a query entry passed to openai's batch API for vision LMs
@@ -153,19 +157,42 @@ def extract_openai_batch_query(query: Dict[str, Any]) -> Dict[str, Any]:
                     except IndexError:
                         input_prompt_image_base64 = ""
 
-    # At this point, the input_prompt_text is the raw text that was passed to the OpenAI model
+    # This code builds the finetuning prompt from the original openai prompt by extracting the "pdf_report hint anchor text" from that
-    # to generate our silver data. But, we want to have a simplfied prompt for this here fine tune,
+    # and reusing it
-    # so we're going to extract out just the raw extracted prompt text
+    # # At this point, the input_prompt_text is the raw text that was passed to the OpenAI model
-    pattern = r"RAW_TEXT_START\s*\n(.*?)\nRAW_TEXT_END"
+    # # to generate our silver data. But, we want to have a simplfied prompt for this here fine tune,
+    # # so we're going to extract out just the raw extracted prompt text
+    # pattern = r"RAW_TEXT_START\s*\n(.*?)\nRAW_TEXT_END"
 
-    # Use re.DOTALL to ensure that the dot matches newline characters
+    # # Use re.DOTALL to ensure that the dot matches newline characters
-    match = re.search(pattern, input_prompt_text, re.DOTALL)
+    # match = re.search(pattern, input_prompt_text, re.DOTALL)
 
-    if match:
+    # if match:
-        raw_page_text = match.group(1).strip()
+    #     raw_page_text = match.group(1).strip()
-    else:
+    # else:
-        raw_page_text = ""
+    #     raw_page_text = ""
 
+
+    # This code builds the finetuning prompt by redownloading the PDF and extracting it's report one more time
+    s3_path = custom_id[:custom_id.rindex("-")]
+    page_num = int(custom_id[custom_id.rindex("-") + 1:])
+
+    s3_client = boto3.client(
+        's3',
+        aws_access_key_id=os.getenv('DS_AWS_ACCESS_KEY_ID'),
+        aws_secret_access_key=os.getenv('DS_AWS_SECRET_ACCESS_KEY')
+    )
+
+    # Split the s3_path into bucket and key
+    bucket_name = s3_path.split('s3://')[1].split('/')[0]
+    s3_key = '/'.join(s3_path.split('s3://')[1].split('/')[1:])
+
+
+    with tempfile.NamedTemporaryFile(delete=False) as tf:
+        s3_client.download_fileobj(bucket_name, s3_key, tf)
+
+    raw_page_text = get_anchor_text(tf.name, page_num, pdf_engine="pdfreport")
+        
     return {
         "custom_id": custom_id,
         "input_prompt_text": input_prompt_text,

scripts/qwen2vl-7b-lora-gantry.sh

@@ -41,6 +41,8 @@ gantry run \
     --env BEAKER_USER_ID=$(beaker account whoami --format json | jq '.[0].name' -cr) \
     --env-secret AWS_ACCESS_KEY_ID=S2_AWS_ACCESS_KEY_ID \
     --env-secret AWS_SECRET_ACCESS_KEY=S2_AWS_SECRET_ACCESS_KEY \
+    --env-secret DS_AWS_ACCESS_KEY_ID=S2_AWS_ACCESS_KEY_ID \
+    --env-secret DS_AWS_SECRET_ACCESS_KEY=S2_AWS_SECRET_ACCESS_KEY \
     --env-secret WANDB_API_KEY=JAKE_WANDB_API_KEY \
     --shared-memory 10GiB \
     --yes \

tests/test_dataloader.py

@@ -9,7 +9,8 @@ from pdelfin.train.dataloader import (
     build_batch_query_response_vision_dataset,
     extract_openai_batch_query,
     extract_openai_batch_response,
-    load_jsonl_into_ds
+    load_jsonl_into_ds,
+    list_dataset_files
 )
 
 from pdelfin.train.dataprep import batch_prepare_data_for_qwen2_training, prepare_data_for_qwen2_training
@@ -25,8 +26,8 @@ class TestBatchQueryResponseDataset(unittest.TestCase):
 
     def testCombinedQueryResponse(self):
         ds = build_batch_query_response_vision_dataset(
-            query_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_train/*.jsonl",
+            query_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_eval/*.jsonl",
-            response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_train/*.json",
+            response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_eval/*.json",
         )
 
         print(ds)
@@ -115,16 +116,25 @@ class TestBatchQueryResponseDataset(unittest.TestCase):
         print(response_data)
         print(response_data[0])
 
-    def testIterableDataset(self):
+    def testPyArrowDirectJson(self):
-        dataset = build_batch_query_response_vision_dataset(
+        query_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_train/*.jsonl"
-            query_glob_path="s3://ai2-oe-data/jakep/openai_batch_data_v2/*.jsonl",
+        response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_train/*.json"
-            response_glob_path="s3://ai2-oe-data/jakep/openai_batch_done_v2/*.json",
+        
-        )
+        all_files = list_dataset_files(query_glob_path)
-        processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
 
-        formatted_dataset = dataset.to_iterable_dataset(num_shards=64)
+        import pyarrow as pa
-        formatted_dataset = formatted_dataset.map(partial(prepare_data_for_qwen2_training, processor=processor, add_batch_dim=True), remove_columns=formatted_dataset.column_names).filter(lambda x: x["input_ids"].shape[0] < 4500)
+        import pyarrow.json as paj
+        import pyarrow.compute as pc
+        import pyarrow.fs as fs
+        
+        s3 = fs.S3FileSystem()
+        
+        block_size = 200 * 1024**2
+
+        for file in all_files:
+            with s3.open_input_stream(file.replace("s3://", "")) as f:
+                table = paj.read_json(f, read_options=paj.ReadOptions(use_threads=False, block_size=block_size))
+
+                print(f"file {file} rows {table.num_rows}")
+                print(table.schema)
 
-        for entry in formatted_dataset:
-            print(entry)
-            break