From 89d4ee214541427c19fba19ebc8e9f70de2afc4a Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 16:30:49 +0000 Subject: [PATCH 01/30] Pipeline work --- pdelfin/assemblepipeline.py | 137 +++++++++++++++++++++++++++++------- 1 file changed, 112 insertions(+), 25 deletions(-) diff --git a/pdelfin/assemblepipeline.py b/pdelfin/assemblepipeline.py index 800b505..fd62216 100644 --- a/pdelfin/assemblepipeline.py +++ b/pdelfin/assemblepipeline.py @@ -23,6 +23,24 @@ from pdelfin.prompts.anchor import get_anchor_text s3 = boto3.client('s3') class DatabaseManager: + @dataclass(frozen=True) + class BatchInferenceRecord: + s3_path: str + page_num: int # 1 indexed! + start_index: int + length: int + finish_reason: str + error: Optional[str] + + def is_usable(self): + return self.error is None and self.finish_reason == "stop" + + @dataclass(frozen=True) + class PDFRecord: + s3_path: str + num_pages: int + status: str + def __init__(self, s3_workspace: str): cache_key = hashlib.sha256(s3_workspace.strip().lower().encode('utf-8')).hexdigest() home_cache_dir = os.path.join(os.path.expanduser('~'), '.cache', 'pdelfin', cache_key) @@ -84,7 +102,7 @@ class DatabaseManager: result = self.cursor.fetchone() return result is not None and result[0] == etag - def add_index_entries(self, index_entries: List['BatchInferenceLine']): + def add_index_entries(self, index_entries: List[BatchInferenceRecord]): if index_entries: self.cursor.executemany(""" INSERT INTO page_results (s3_path, page_num, start_index, length, finish_reason, error) @@ -92,6 +110,28 @@ class DatabaseManager: """, [(entry.s3_path, entry.page_num, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) self.conn.commit() + def get_index_entries(self, s3_path: str) -> List[BatchInferenceRecord]: + self.cursor.execute(""" + SELECT s3_path, page_num, start_index, length, finish_reason, error + FROM page_results + WHERE s3_path = ? + ORDER BY page_num ASC + """, (s3_path,)) + + rows = self.cursor.fetchall() + + return [ + self.BatchInferenceRecord( + s3_path=row[0], + page_num=row[1], + start_index=row[2], + length=row[3], + finish_reason=row[4], + error=row[5] + ) + for row in rows + ] + def update_processed_file(self, s3_path, etag): self.cursor.execute(""" INSERT INTO processed_files (s3_path, etag) @@ -114,23 +154,46 @@ class DatabaseManager: except sqlite3.IntegrityError: print(f"PDF with s3_path '{s3_path}' already exists.") - def get_pdf_status(self, s3_path: str) -> Optional[str]: - self.cursor.execute("SELECT status FROM pdfs WHERE s3_path = ?", (s3_path,)) - result = self.cursor.fetchone() - return result[0] if result else None + def get_pdf(self, s3_path: str) -> Optional[PDFRecord]: + self.cursor.execute(""" + SELECT s3_path, num_pages, status + FROM pdfs + WHERE s3_path = ? + """, (s3_path,)) + + row = self.cursor.fetchone() + + if row: + return self.PDFRecord( + s3_path=row[0], + num_pages=row[1], + status=row[2] + ) + return None + + def get_pdfs_by_status(self, status: str) -> List[PDFRecord]: + self.cursor.execute(""" + SELECT s3_path, num_pages, status + FROM pdfs + WHERE status == ? + """, (status, )) + + rows = self.cursor.fetchall() + + return [ + self.PDFRecord( + s3_path=row[0], + num_pages=row[1], + status=row[2] + ) + for row in rows + ] def close(self): self.conn.close() -@dataclass(frozen=True) -class BatchInferenceLine: - s3_path: str - page_num: int # 1 indexed! - start_index: int - length: int - finish_reason: str - error: Optional[str] + def parse_s3_path(s3_path): @@ -202,7 +265,7 @@ def parse_custom_id(custom_id: str) -> Tuple[str, int]: page_num = int(custom_id[custom_id.rindex("-") + 1:]) return s3_path, page_num -def process_jsonl_content(s3_path) -> List[BatchInferenceLine]: +def process_jsonl_content(s3_path) -> List[DatabaseManager.BatchInferenceRecord]: content = get_s3_bytes(s3_path).decode("utf-8") start_index = 0 @@ -217,7 +280,7 @@ def process_jsonl_content(s3_path) -> List[BatchInferenceLine]: assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" - index_entries.append(BatchInferenceLine( + index_entries.append(DatabaseManager.BatchInferenceRecord( s3_path=s3_path, page_num=page_num, start_index=start_index, @@ -247,19 +310,28 @@ def get_pdf_num_pages(s3_path: str) -> Optional[int]: return None -def get_pdf_batch_inference_lines(s3_path: str, pages: list[int]) -> list[dict]: - results = [] +def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list[dict]: + db = DatabaseManager(s3_workspace) + + existing_pages = db.get_index_entries(pdf.s3_path) + new_queries = [] + try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: - tf.write(get_s3_bytes(s3_path)) + tf.write(get_s3_bytes(pdf.s3_path)) tf.flush() - for page in pages: - results.append(build_page_query(tf.name, s3_path, page)) - except Exception as ex: - print(f"Warning, could not get batch inferences lines for {s3_path} due to {ex}") + for page in range(1, pdf.num_pages + 1): + # Is there an existing page that has no error + if any(page.is_usable() for page in existing_pages): + continue - return results + # TODO: Later, you may want to retry with different sampling parameters or do something else + new_queries.append(build_page_query(tf.name, pdf.s3_path, page)) + except Exception as ex: + print(f"Warning, could not get batch inferences lines for {pdf.s3_path} due to {ex}") + + return new_queries if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') @@ -275,7 +347,7 @@ if __name__ == '__main__': # One shared executor to rule them all executor = ProcessPoolExecutor() - # If you have new PDFs, add them to the list + # If you have new PDFs, step one is to add them to the list if args.add_pdfs: assert args.add_pdfs.startswith("s3://"), "PDFs must live on s3" @@ -317,4 +389,19 @@ if __name__ == '__main__': db.update_processed_file(s3_path, etag=etag) # Now query each pdf, if you have all of the pages needed (all pages present, error is null and finish_reason is stop), then you assemble it into a dolma document and output it - # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use \ No newline at end of file + # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use + future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} + + for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): + pdf = future_to_path[future] + inference_lines = future.result() + + + + + + # TODO + # 1. build a class that will manage taking in dicts and outputting them as jsonls of up to the max size to the bucket + # you'll need one for new batch inference lines, and one for finished dolma docs + # 2. Have a way to apply basic spam + language filter if you can during add pdfs step + # 3. For retrying, make it so you retry several times with different sampling parameters \ No newline at end of file From 39333f2c963d3e789fbab498ab41c38bb050e8e8 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 17:09:11 +0000 Subject: [PATCH 02/30] New pipeline stuff --- pdelfin/assemblepipeline.py | 79 +++++++++++++++++++++++++++++++++++-- 1 file changed, 76 insertions(+), 3 deletions(-) diff --git a/pdelfin/assemblepipeline.py b/pdelfin/assemblepipeline.py index fd62216..a04de09 100644 --- a/pdelfin/assemblepipeline.py +++ b/pdelfin/assemblepipeline.py @@ -7,6 +7,7 @@ import argparse import glob import tempfile import posixpath +import smart_open from dataclasses import dataclass from pypdf import PdfReader @@ -193,7 +194,66 @@ class DatabaseManager: self.conn.close() +# Writes batches of lines out to a set of files, keeping each file below some maximum size +class BatchWriter: + def __init__(self, output_prefix: str, max_size_mb: int = 250): + self.output_prefix = output_prefix + self.max_size = max_size_mb * 1024 * 1024 # Convert MB to bytes + self.batch = [] + self.batch_size = 0 + parsed = urlparse(output_prefix) + self.is_s3 = parsed.scheme in ('s3', 's3a', 's3n') + + if not self.is_s3: + os.makedirs(output_prefix, exist_ok=True) + + def _compute_hash(self, content: str) -> str: + """Compute a 20-character SHA1 hash of the given content.""" + sha1 = hashlib.sha1() + sha1.update(content.encode('utf-8')) + return sha1.hexdigest()[:20] + + def _get_output_path(self, hash_str: str) -> str: + """Generate the full output path with hash in the filename.""" + parsed = urlparse(self.output_prefix) + if self.is_s3: + bucket = parsed.netloc + key = parsed.path.lstrip('/') + if key and not key.endswith('/'): + key += '/' + full_key = posixpath.join(key, f"output_{hash_str}.jsonl") + return f"s3://{bucket}/{full_key}" + else: + filename = f"output_{hash_str}.jsonl" + return os.path.join(self.output_prefix, filename) + + def write_line(self, line: str): + line_size = len(line.encode('utf-8')) + 1 # +1 for newline + + if self.batch_size + line_size > self.max_size: + self._write_batch() + + self.batch.append(line) + self.batch_size += line_size + + def _write_batch(self): + if not self.batch: + return + + batch_content = "\n".join(self.batch) + "\n" + hash_str = self._compute_hash(batch_content) + output_path = self._get_output_path(hash_str) + + with smart_open.open(output_path, 'w') as f_out: + f_out.write(batch_content) + print(f"Wrote batch to {output_path}") + + self.batch = [] + self.batch_size = 0 + + def close(self): + self._write_batch() def parse_s3_path(s3_path): @@ -315,6 +375,10 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list existing_pages = db.get_index_entries(pdf.s3_path) new_queries = [] + + # Shortcut out of downloading the actual PDF + if set(page.page_num for page in existing_pages if page.is_usable()) == set(range(1, pdf.num_pages + 1)): + return [] try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: @@ -337,7 +401,7 @@ if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/)') parser.add_argument('--add_pdfs', help='Glob path to add PDFs (s3) to the workspace', default=None) - parser.add_argument('--file_size_limit', type=int, default=250, help='Max file size in MB') + parser.add_argument('--max_size_mb', type=int, default=250, help='Max file size in MB') args = parser.parse_args() db = DatabaseManager(args.workspace) @@ -383,21 +447,30 @@ if __name__ == '__main__': for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): s3_path, etag = future_to_path[future] - inference_lines = future.result() + inference_records = future.result() - db.add_index_entries(inference_lines) + db.add_index_entries(inference_records) db.update_processed_file(s3_path, etag=etag) # Now query each pdf, if you have all of the pages needed (all pages present, error is null and finish_reason is stop), then you assemble it into a dolma document and output it # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} + potentially_done_pdfs = [] + new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{round}", args.max_size_mb) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): pdf = future_to_path[future] inference_lines = future.result() + if len(inference_lines) == 0: + potentially_done_pdfs.append(pdf) + for line in inference_lines: + new_inference_writer.write_line(json.dumps(line)) + new_inference_writer.close() + + # Now, finally, assemble any potentially done docs into dolma documents # TODO From f2f578cca9ddde35d2ca1ce87fe205802263c238 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 17:23:09 +0000 Subject: [PATCH 03/30] More pipeline code --- pdelfin/assemblepipeline.py | 65 ++++++++++++++++++++++++++++++++++--- 1 file changed, 60 insertions(+), 5 deletions(-) diff --git a/pdelfin/assemblepipeline.py b/pdelfin/assemblepipeline.py index a04de09..37494bc 100644 --- a/pdelfin/assemblepipeline.py +++ b/pdelfin/assemblepipeline.py @@ -6,6 +6,7 @@ import json import argparse import glob import tempfile +import datetime import posixpath import smart_open @@ -93,6 +94,14 @@ class DatabaseManager: self.cursor.execute("SELECT value FROM metadata WHERE key=?", (key,)) result = self.cursor.fetchone() return result[0] if result else None + + def set_metadata(self, key: str, value: str) -> None: + self.cursor.execute(""" + INSERT INTO metadata (key, value) + VALUES (?, ?) + ON CONFLICT(key) DO UPDATE SET value=excluded.value + """, (key, value)) + self.conn.commit() def get_current_round(self): round_value = self.get_metadata("round") @@ -385,18 +394,52 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list tf.write(get_s3_bytes(pdf.s3_path)) tf.flush() - for page in range(1, pdf.num_pages + 1): + for target_page_num in range(1, pdf.num_pages + 1): # Is there an existing page that has no error - if any(page.is_usable() for page in existing_pages): + if any(page.is_usable() and page.page_num == target_page_num for page in existing_pages): continue # TODO: Later, you may want to retry with different sampling parameters or do something else - new_queries.append(build_page_query(tf.name, pdf.s3_path, page)) + new_queries.append(build_page_query(tf.name, pdf.s3_path, target_page_num)) except Exception as ex: print(f"Warning, could not get batch inferences lines for {pdf.s3_path} due to {ex}") return new_queries +def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> dict: + db = DatabaseManager(s3_workspace) + existing_pages = db.get_index_entries(pdf.s3_path) + document_text = "" + + for target_page_num in range(1, pdf.num_pages + 1): + target_page = next(page for page in existing_pages if page.is_usable() and page.page_num == target_page_num) + + target_row = get_s3_bytes(target_page.s3_path, + start_index=target_page.start_index, + end_index=target_page.start_index+target_page.length) + + target_data = json.loads(target_row.decode("utf-8")) + + document_text += target_data + "\n" + + metadata = { + "Source-File": pdf.s3_path, + "pdf-total-pages": pdf.num_pages, + } + id_ = hashlib.sha1(document_text.encode()).hexdigest() + + dolma_doc = { + "id": id_, + "text": document_text, + "source": "s2pdf", + "added": datetime.datetime.now().strftime("%Y-%m-%d"), + "created": datetime.datetime.now().strftime("%Y-%m-%d"), + "metadata": metadata, + } + + return dolma_doc + + if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/)') @@ -456,6 +499,7 @@ if __name__ == '__main__': # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} potentially_done_pdfs = [] + lines_written = 0 new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{round}", args.max_size_mb) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): @@ -466,15 +510,26 @@ if __name__ == '__main__': potentially_done_pdfs.append(pdf) for line in inference_lines: + lines_written += 1 new_inference_writer.write_line(json.dumps(line)) new_inference_writer.close() + if lines_written > 0: + db.set_metadata("round", str(db.get_current_round() + 1)) + # Now, finally, assemble any potentially done docs into dolma documents + future_to_path = {executor.submit(build_dolma_doc, args.workspace, pdf): pdf for pdf in potentially_done_pdfs} + new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb) + for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): + pdf = future_to_path[future] + dolma_doc = future.result() + new_output_writer.write_line(json.dumps(dolma_doc)) + + new_output_writer.close() + # TODO - # 1. build a class that will manage taking in dicts and outputting them as jsonls of up to the max size to the bucket - # you'll need one for new batch inference lines, and one for finished dolma docs # 2. Have a way to apply basic spam + language filter if you can during add pdfs step # 3. For retrying, make it so you retry several times with different sampling parameters \ No newline at end of file From cd8e28e459e776b67a4e9038ee47612a4f3a7f73 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 18:19:17 +0000 Subject: [PATCH 04/30] Pipeline working hopefully soon --- pdelfin/assemblepipeline.py | 57 +++++++++++++++++++++++-------------- 1 file changed, 35 insertions(+), 22 deletions(-) diff --git a/pdelfin/assemblepipeline.py b/pdelfin/assemblepipeline.py index 37494bc..930ae9a 100644 --- a/pdelfin/assemblepipeline.py +++ b/pdelfin/assemblepipeline.py @@ -27,7 +27,8 @@ s3 = boto3.client('s3') class DatabaseManager: @dataclass(frozen=True) class BatchInferenceRecord: - s3_path: str + inference_s3_path: str + pdf_s3_path: str page_num: int # 1 indexed! start_index: int length: int @@ -56,7 +57,8 @@ class DatabaseManager: def _initialize_tables(self): self.cursor.execute(""" CREATE TABLE IF NOT EXISTS page_results ( - s3_path TEXT, + inference_s3_path TEXT, + pdf_s3_path TEXT, page_num INTEGER, start_index BIGINT, length BIGINT, @@ -115,29 +117,30 @@ class DatabaseManager: def add_index_entries(self, index_entries: List[BatchInferenceRecord]): if index_entries: self.cursor.executemany(""" - INSERT INTO page_results (s3_path, page_num, start_index, length, finish_reason, error) + INSERT INTO page_results (inference_s3_path, pdf_s3_path, page_num, start_index, length, finish_reason, error) VALUES (?, ?, ?, ?, ?, ?) - """, [(entry.s3_path, entry.page_num, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) + """, [(entry.inference_s3_path, entry.pdf_s3_path, entry.page_num, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) self.conn.commit() def get_index_entries(self, s3_path: str) -> List[BatchInferenceRecord]: self.cursor.execute(""" - SELECT s3_path, page_num, start_index, length, finish_reason, error + SELECT inference_s3_path, pdf_s3_path, page_num, start_index, length, finish_reason, error FROM page_results WHERE s3_path = ? - ORDER BY page_num ASC + ORDER BY inference_s3_path DESC start_index ASC page_num ASC """, (s3_path,)) rows = self.cursor.fetchall() return [ self.BatchInferenceRecord( - s3_path=row[0], - page_num=row[1], - start_index=row[2], - length=row[3], - finish_reason=row[4], - error=row[5] + inference_s3_path=row[0], + pdf_s3_path=row[1], + page_num=row[2], + start_index=row[3], + length=row[4], + finish_reason=row[5], + error=row[6] ) for row in rows ] @@ -186,6 +189,7 @@ class DatabaseManager: SELECT s3_path, num_pages, status FROM pdfs WHERE status == ? + ORDER BY s3_path DESC """, (status, )) rows = self.cursor.fetchall() @@ -334,8 +338,8 @@ def parse_custom_id(custom_id: str) -> Tuple[str, int]: page_num = int(custom_id[custom_id.rindex("-") + 1:]) return s3_path, page_num -def process_jsonl_content(s3_path) -> List[DatabaseManager.BatchInferenceRecord]: - content = get_s3_bytes(s3_path).decode("utf-8") +def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchInferenceRecord]: + content = get_s3_bytes(inference_s3_path).decode("utf-8") start_index = 0 index_entries = [] @@ -345,12 +349,13 @@ def process_jsonl_content(s3_path) -> List[DatabaseManager.BatchInferenceRecord] try: data = json.loads(line) - s3_path, page_num = parse_custom_id(data["custom_id"]) + pdf_s3_path, page_num = parse_custom_id(data["custom_id"]) assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" index_entries.append(DatabaseManager.BatchInferenceRecord( - s3_path=s3_path, + inference_s3_path=inference_s3_path, + pdf_s3_path=pdf_s3_path, page_num=page_num, start_index=start_index, length=line_length, @@ -410,17 +415,22 @@ def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> dict: db = DatabaseManager(s3_workspace) existing_pages = db.get_index_entries(pdf.s3_path) document_text = "" + last_page_start_index = 0 + pdf_page_spans = [] for target_page_num in range(1, pdf.num_pages + 1): target_page = next(page for page in existing_pages if page.is_usable() and page.page_num == target_page_num) - target_row = get_s3_bytes(target_page.s3_path, - start_index=target_page.start_index, - end_index=target_page.start_index+target_page.length) + target_row = get_s3_bytes(target_page.pdf_s3_path, + start_index=target_page.start_index, + end_index=target_page.start_index+target_page.length) target_data = json.loads(target_row.decode("utf-8")) - document_text += target_data + "\n" + document_text += target_data["natural_text"] + "\n" + + pdf_page_spans.append([last_page_start_index, len(document_text), target_page_num]) + last_page_start_index = len(document_text) metadata = { "Source-File": pdf.s3_path, @@ -431,10 +441,13 @@ def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> dict: dolma_doc = { "id": id_, "text": document_text, - "source": "s2pdf", + "source": "pdelfin", "added": datetime.datetime.now().strftime("%Y-%m-%d"), "created": datetime.datetime.now().strftime("%Y-%m-%d"), "metadata": metadata, + "attributes": { + "pdf_page_numbers": pdf_page_spans + } } return dolma_doc @@ -500,7 +513,7 @@ if __name__ == '__main__': future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} potentially_done_pdfs = [] lines_written = 0 - new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{round}", args.max_size_mb) + new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{db.get_current_round()}", args.max_size_mb) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): pdf = future_to_path[future] From af992bd6036a42878518492db9020a64fe60d9bf Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 18:23:22 +0000 Subject: [PATCH 05/30] More refactoring --- .../{assemblepipeline.py => birrpipeline.py} | 10 +- pdelfin/runpipeline.py | 275 ------------------ 2 files changed, 5 insertions(+), 280 deletions(-) rename pdelfin/{assemblepipeline.py => birrpipeline.py} (98%) delete mode 100644 pdelfin/runpipeline.py diff --git a/pdelfin/assemblepipeline.py b/pdelfin/birrpipeline.py similarity index 98% rename from pdelfin/assemblepipeline.py rename to pdelfin/birrpipeline.py index 930ae9a..ae5fb8c 100644 --- a/pdelfin/assemblepipeline.py +++ b/pdelfin/birrpipeline.py @@ -67,7 +67,7 @@ class DatabaseManager: ) """) self.cursor.execute(""" - CREATE INDEX IF NOT EXISTS idx_path ON page_results(s3_path) + CREATE INDEX IF NOT EXISTS idx_path ON page_results(pdf_s3_path) """) self.cursor.execute(""" CREATE TABLE IF NOT EXISTS pdfs ( @@ -122,13 +122,13 @@ class DatabaseManager: """, [(entry.inference_s3_path, entry.pdf_s3_path, entry.page_num, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) self.conn.commit() - def get_index_entries(self, s3_path: str) -> List[BatchInferenceRecord]: + def get_index_entries(self, pdf_s3_path: str) -> List[BatchInferenceRecord]: self.cursor.execute(""" SELECT inference_s3_path, pdf_s3_path, page_num, start_index, length, finish_reason, error FROM page_results - WHERE s3_path = ? - ORDER BY inference_s3_path DESC start_index ASC page_num ASC - """, (s3_path,)) + WHERE pdf_s3_path = ? + ORDER BY inference_s3_path DESC, start_index ASC, page_num ASC + """, (pdf_s3_path,)) rows = self.cursor.fetchall() diff --git a/pdelfin/runpipeline.py b/pdelfin/runpipeline.py deleted file mode 100644 index 82f9968..0000000 --- a/pdelfin/runpipeline.py +++ /dev/null @@ -1,275 +0,0 @@ -# The way this script works is it gets a list of pdfs to process -# and an output/scratch folder location either locally or in s3 to work with -# On the first run, with an empty output folder, it will queue up each page in each pdf to go into a VLM -# Then, the user queues up that task in birr, and it outputs to a new subfolder in the same location -# Then, you run your script again, and it will see that you have some valid output files -# If so, then it will check those output files, and if it has a complete document, it will build a dolma doc for it, and that's considered done -# For any remaining pages that got errored out, or failed due to stop_reason not being "stop" (ex. over length) -# Then, it will queue up another set of tasks, hopefully much smaller, to send into batch inference again -# This process will keep going, until you run it with the --fallback option, at which point it will -# just use a basic text extraction on any remaining pages, and assemble the rest of the dolma docs -# -# -# -import os -import glob -import random -import argparse -import boto3 -import json -import hashlib -from pypdf import PdfReader -from tqdm import tqdm -from typing import Generator, List -from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed -from urllib.parse import urlparse - -from pdelfin.data.renderpdf import render_pdf_to_base64png -from pdelfin.prompts import build_finetuning_prompt -from pdelfin.prompts.anchor import get_anchor_text -from pdelfin.filter import PdfFilter - -import logging -import smart_open -import posixpath # Import posixpath for S3 path handling - -logging.getLogger("pypdf").setLevel(logging.ERROR) - -pdf_filter = PdfFilter() - -def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> dict: - image_base64 = render_pdf_to_base64png(local_pdf_path, page, 1024) - anchor_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport") - - return { - "custom_id": f"{pretty_pdf_path}-{page}", - "chat_messages": [ - { - "role": "user", - "content": [ - {"type": "text", "text": build_finetuning_prompt(anchor_text)}, - {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}} - ], - } - ], - "temperature": 0.1, - "max_tokens": 6000, - } - -def fetch_s3_file(s3_url: str, local_path: str) -> str: - parsed = urlparse(s3_url) - bucket_name = parsed.netloc - key = parsed.path.lstrip('/') - - s3 = boto3.client('s3') - s3.download_file(bucket_name, key, local_path) - return local_path - -def process_pdf(pdf_path: str, no_filter: bool) -> List[dict]: - if pdf_path.startswith("s3://"): - local_pdf_path = os.path.join("/tmp", os.path.basename(pdf_path)) - fetch_s3_file(pdf_path, local_pdf_path) - else: - local_pdf_path = pdf_path - - if (not no_filter) and pdf_filter.filter_out_pdf(local_pdf_path): - print(f"Skipping {local_pdf_path} due to common filter") - return [] - - pretty_pdf_path = pdf_path - - pdf = PdfReader(local_pdf_path) - num_pages = len(pdf.pages) - - sample_pages = list(range(1, num_pages + 1)) - result = [] - for page in sample_pages: - try: - query = build_page_query(local_pdf_path, pretty_pdf_path, page) - result.append(query) - except Exception as e: - print(f"Error processing page {page} of {pdf_path}: {e}") - - return result - -def is_glob_pattern(path: str) -> bool: - return any(char in path for char in ['*', '?', '[', ']']) - -def expand_s3_glob(s3_glob: str) -> list: - parsed = urlparse(s3_glob) - bucket_name = parsed.netloc - prefix = os.path.dirname(parsed.path.lstrip('/')).rstrip('/') + "/" - pattern = os.path.basename(parsed.path) - - s3 = boto3.client('s3') - paginator = s3.get_paginator('list_objects_v2') - page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix) - - matched_files = [] - for page in page_iterator: - for obj in page.get('Contents', []): - key = obj['Key'] - if key.endswith('.pdf') and glob.fnmatch.fnmatch(key, posixpath.join(prefix, pattern)): - matched_files.append(f"s3://{bucket_name}/{key}") - - return matched_files - -def compute_hash(content: str) -> str: - """Compute a 20-character SHA1 hash of the given content.""" - sha1 = hashlib.sha1() - sha1.update(content.encode('utf-8')) - return sha1.hexdigest()[:20] - -def get_smart_open_write_path(output_path: str, hash_str: str) -> str: - """Generate the full output path with hash in the filename.""" - parsed = urlparse(output_path) - if parsed.scheme in ('s3', 's3a', 's3n'): - bucket = parsed.netloc - key = parsed.path.lstrip('/') - # Ensure the key is treated as a directory by appending a slash if not present - if key and not key.endswith('/'): - key += '/' - # Use posixpath to correctly join S3 paths - full_key = posixpath.join(key, f"output_{hash_str}.jsonl") - return f"s3://{bucket}/{full_key}" - else: - dir_path = output_path - filename = f"output_{hash_str}.jsonl" - return os.path.join(dir_path, filename) - -def main(): - parser = argparse.ArgumentParser( - description="Given a bunch of PDFs, prepares a mise/birr workflow to run them through a conversion mechanism" - ) - parser.add_argument( - "pdf_paths", - nargs='*', - help=( - "List of PDF paths to process. If a single argument contains glob patterns (e.g., *.pdf or s3://bucket/pdfs/*.pdf), " - "it will be expanded accordingly." - ) - ) - parser.add_argument( - "--path_list", - type=str, - help="Path to a file containing paths to PDFs, one per line." - ) - parser.add_argument( - "--max_size_mb", - type=int, - default=250, - help="Max number of MBs of entries to put in each birr workitem" - ) - parser.add_argument( - "--no_filter", - action="store_true", - help="Disables the basic spam/language filtering so that ALL pdfs listed are used" - ) - parser.add_argument( - "--output", - type=str, - default="mise_batch_data", - help="Output destination (can be a local path or an S3 URI)" - ) - args = parser.parse_args() - - pdf_paths = [] - - # Load PDF paths from positional arguments or path_list - if args.pdf_paths: - for path in args.pdf_paths: - if is_glob_pattern(path): - glob_path = path - if glob_path.startswith("s3://"): - # Handle S3 globbing - expanded_paths = expand_s3_glob(glob_path) - pdf_paths.extend(expanded_paths) - else: - # Handle local filesystem globbing - expanded_paths = glob.glob(glob_path, recursive=True) - pdf_paths.extend(expanded_paths) - else: - pdf_paths.append(path) - - if args.path_list: - with open(args.path_list, 'r') as f: - for line in f: - path = line.strip() - if path: - pdf_paths.append(path) - - # Remove duplicates and shuffle - pdf_paths = list(set(pdf_paths)) - random.shuffle(pdf_paths) - - print(f"Loaded and shuffled {len(pdf_paths)} paths to use.") - - # Prepare for output - output_dir = args.output - max_file_size = args.max_size_mb * 1024 * 1024 # Convert MB to bytes - - # Determine if output is S3 - parsed_output = urlparse(output_dir) - is_s3 = parsed_output.scheme in ('s3', 's3a', 's3n') - - # Initialize variables for batching - batch = [] - batch_size = 0 - pdfs_with_output = 0 - - # Function to write a batch - def write_batch(batch: List[dict]): - nonlocal output_dir - if not batch: - return - batch_content = "\n".join(json.dumps(entry) for entry in batch) + "\n" - hash_str = compute_hash(batch_content) - output_path_with_hash = get_smart_open_write_path(output_dir, hash_str) - with smart_open.open(output_path_with_hash, 'w') as f_out: - f_out.write(batch_content) - print(f"Wrote batch to {output_path_with_hash}") - - # Using ProcessPoolExecutor to process files concurrently - with ProcessPoolExecutor() as executor: - futures = [] - - with tqdm(desc="Processing PDFs", leave=False, total=len(pdf_paths)) as pb: - for pdf_path in pdf_paths: - futures.append(executor.submit(process_pdf, pdf_path, args.no_filter)) - - for future in as_completed(futures): - try: - request_results = future.result() # Get the result from the process - - if request_results: - pdfs_with_output += 1 # Increment if there's at least one result - - for request_obj in request_results: - request_json = json.dumps(request_obj) - request_size = len(request_json.encode('utf-8')) + 1 # +1 for newline - - # Check if adding this entry would exceed the max size - if batch_size + request_size > max_file_size: - # Write the current batch - write_batch(batch) - # Reset the batch - batch = [] - batch_size = 0 - - # Add the entry to the batch - batch.append(request_obj) - batch_size += request_size - - pb.update(1) - except Exception as e: - print(f"Error processing a PDF: {str(e)}") - - # Write any remaining batch - write_batch(batch) - - # Print the number of PDFs that resulted in at least one output - print(f"Number of sampled PDFs that produced at least one output: {pdfs_with_output}") - print(f"Now you should run these prompts through mise/birr") - -if __name__ == "__main__": - main() From 77a850d7ef21f3d50314f06abbcc92c66da167ae Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 18:42:50 +0000 Subject: [PATCH 06/30] Tracking rounds of inference better --- pdelfin/birrpipeline.py | 83 +++++++++++++++++++++++++++-------------- 1 file changed, 54 insertions(+), 29 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index ae5fb8c..1714956 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -9,6 +9,7 @@ import tempfile import datetime import posixpath import smart_open +import logging from dataclasses import dataclass from pypdf import PdfReader @@ -24,12 +25,17 @@ from pdelfin.prompts.anchor import get_anchor_text # Global s3 client for the whole script, feel free to adjust params if you need it s3 = boto3.client('s3') +# Quiet logs from pypdf and smart open +logging.getLogger("pypdf").setLevel(logging.ERROR) +logging.getLogger("smart_open").setLevel(logging.ERROR) + class DatabaseManager: @dataclass(frozen=True) class BatchInferenceRecord: inference_s3_path: str pdf_s3_path: str page_num: int # 1 indexed! + round: int start_index: int length: int finish_reason: str @@ -60,6 +66,7 @@ class DatabaseManager: inference_s3_path TEXT, pdf_s3_path TEXT, page_num INTEGER, + round INTEGER, start_index BIGINT, length BIGINT, finish_reason TEXT, @@ -117,14 +124,14 @@ class DatabaseManager: def add_index_entries(self, index_entries: List[BatchInferenceRecord]): if index_entries: self.cursor.executemany(""" - INSERT INTO page_results (inference_s3_path, pdf_s3_path, page_num, start_index, length, finish_reason, error) + INSERT INTO page_results (inference_s3_path, pdf_s3_path, page_num, round, start_index, length, finish_reason, error) VALUES (?, ?, ?, ?, ?, ?) - """, [(entry.inference_s3_path, entry.pdf_s3_path, entry.page_num, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) + """, [(entry.inference_s3_path, entry.pdf_s3_path, entry.page_num, entry.round, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) self.conn.commit() def get_index_entries(self, pdf_s3_path: str) -> List[BatchInferenceRecord]: self.cursor.execute(""" - SELECT inference_s3_path, pdf_s3_path, page_num, start_index, length, finish_reason, error + SELECT inference_s3_path, pdf_s3_path, page_num, round, start_index, length, finish_reason, error FROM page_results WHERE pdf_s3_path = ? ORDER BY inference_s3_path DESC, start_index ASC, page_num ASC @@ -137,14 +144,24 @@ class DatabaseManager: inference_s3_path=row[0], pdf_s3_path=row[1], page_num=row[2], - start_index=row[3], - length=row[4], - finish_reason=row[5], - error=row[6] + round=row[3], + start_index=row[4], + length=row[5], + finish_reason=row[6], + error=row[7] ) for row in rows ] + def get_last_indexed_round(self) -> int: + self.cursor.execute(""" + SELECT MAX(round) + FROM page_results + """) + + result = self.cursor.fetchone() + return -1 if result[0] is None else result[0] + def update_processed_file(self, s3_path, etag): self.cursor.execute(""" INSERT INTO processed_files (s3_path, etag) @@ -309,8 +326,6 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di ], } ], - "temperature": 0.8, - "max_tokens": 6000, } @@ -338,7 +353,7 @@ def parse_custom_id(custom_id: str) -> Tuple[str, int]: page_num = int(custom_id[custom_id.rindex("-") + 1:]) return s3_path, page_num -def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchInferenceRecord]: +def process_jsonl_content(inference_s3_path: str, cur_round: int) -> List[DatabaseManager.BatchInferenceRecord]: content = get_s3_bytes(inference_s3_path).decode("utf-8") start_index = 0 @@ -357,6 +372,7 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI inference_s3_path=inference_s3_path, pdf_s3_path=pdf_s3_path, page_num=page_num, + round=data["round"], start_index=start_index, length=line_length, finish_reason=data["outputs"][0]["finish_reason"], @@ -386,6 +402,7 @@ def get_pdf_num_pages(s3_path: str) -> Optional[int]: def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list[dict]: db = DatabaseManager(s3_workspace) + cur_round = db.get_current_round() existing_pages = db.get_index_entries(pdf.s3_path) new_queries = [] @@ -393,7 +410,7 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list # Shortcut out of downloading the actual PDF if set(page.page_num for page in existing_pages if page.is_usable()) == set(range(1, pdf.num_pages + 1)): return [] - + try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: tf.write(get_s3_bytes(pdf.s3_path)) @@ -405,7 +422,7 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list continue # TODO: Later, you may want to retry with different sampling parameters or do something else - new_queries.append(build_page_query(tf.name, pdf.s3_path, target_page_num)) + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) except Exception as ex: print(f"Warning, could not get batch inferences lines for {pdf.s3_path} due to {ex}") @@ -498,7 +515,6 @@ if __name__ == '__main__': ] print(f"Found {len(inference_output_paths)} new batch inference results to index") - future_to_path = {executor.submit(process_jsonl_content, s3_path): (s3_path, etag) for s3_path, etag in inference_output_paths} for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): @@ -510,28 +526,34 @@ if __name__ == '__main__': # Now query each pdf, if you have all of the pages needed (all pages present, error is null and finish_reason is stop), then you assemble it into a dolma document and output it # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use - future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} - potentially_done_pdfs = [] - lines_written = 0 - new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{db.get_current_round()}", args.max_size_mb) + if db.get_last_indexed_round() < db.get_current_round() - 1: + print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference/round_{db.get_current_round() - 1}") + potentially_done_pdfs = [] + else: + print(f"\nCreating batch inference files for new PDFs") + future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} + potentially_done_pdfs = [] + lines_written = 0 + new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{db.get_current_round()}", args.max_size_mb) - for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): - pdf = future_to_path[future] - inference_lines = future.result() + for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): + pdf = future_to_path[future] + inference_lines = future.result() - if len(inference_lines) == 0: - potentially_done_pdfs.append(pdf) + if len(inference_lines) == 0: + potentially_done_pdfs.append(pdf) - for line in inference_lines: - lines_written += 1 - new_inference_writer.write_line(json.dumps(line)) + for line in inference_lines: + lines_written += 1 + new_inference_writer.write_line(json.dumps(line)) - new_inference_writer.close() + new_inference_writer.close() - if lines_written > 0: - db.set_metadata("round", str(db.get_current_round() + 1)) + if lines_written > 0: + db.set_metadata("round", str(db.get_current_round() + 1)) # Now, finally, assemble any potentially done docs into dolma documents + print(f"\nAssembling {len(potentially_done_pdfs)} potentially finished PDFs into Dolma documents at {args.workspace}/output") future_to_path = {executor.submit(build_dolma_doc, args.workspace, pdf): pdf for pdf in potentially_done_pdfs} new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb) @@ -542,7 +564,10 @@ if __name__ == '__main__': new_output_writer.write_line(json.dumps(dolma_doc)) new_output_writer.close() + + print("\nWork finished, waiting for all workers to finish cleaning up") + executor.shutdown(wait=True) # TODO # 2. Have a way to apply basic spam + language filter if you can during add pdfs step - # 3. For retrying, make it so you retry several times with different sampling parameters \ No newline at end of file + # 3. For retrying, make it so you retry several times with different sampling parameters From 879b974af28e934e71c3992f9e8e61801673cd3a Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 20:06:07 +0000 Subject: [PATCH 07/30] More and more fixes --- pdelfin/birrpipeline.py | 84 +++++++++++++++++++++++++++-------------- 1 file changed, 55 insertions(+), 29 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 1714956..afaf31b 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -125,7 +125,7 @@ class DatabaseManager: if index_entries: self.cursor.executemany(""" INSERT INTO page_results (inference_s3_path, pdf_s3_path, page_num, round, start_index, length, finish_reason, error) - VALUES (?, ?, ?, ?, ?, ?) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) """, [(entry.inference_s3_path, entry.pdf_s3_path, entry.page_num, entry.round, entry.start_index, entry.length, entry.finish_reason, entry.error) for entry in index_entries]) self.conn.commit() @@ -258,7 +258,10 @@ class BatchWriter: filename = f"output_{hash_str}.jsonl" return os.path.join(self.output_prefix, filename) - def write_line(self, line: str): + def write_line(self, line: Optional[str]): + if line is None or len(line.strip()) == 0: + return + line_size = len(line.encode('utf-8')) + 1 # +1 for newline if self.batch_size + line_size > self.max_size: @@ -334,10 +337,17 @@ def get_s3_bytes(s3_path: str, start_index: Optional[int] = None, end_index: Opt # Build the range header if start_index and/or end_index are specified range_header = None - if start_index is not None or end_index is not None: - range_value = f"bytes={start_index or 0}-" - if end_index is not None: - range_value += str(end_index) + if start_index is not None and end_index is not None: + # Range: bytes=start_index-end_index + range_value = f"bytes={start_index}-{end_index}" + range_header = {'Range': range_value} + elif start_index is not None and end_index is None: + # Range: bytes=start_index- + range_value = f"bytes={start_index}-" + range_header = {'Range': range_value} + elif start_index is None and end_index is not None: + # Range: bytes=-end_index (last end_index bytes) + range_value = f"bytes=-{end_index}" range_header = {'Range': range_value} if range_header: @@ -353,40 +363,46 @@ def parse_custom_id(custom_id: str) -> Tuple[str, int]: page_num = int(custom_id[custom_id.rindex("-") + 1:]) return s3_path, page_num -def process_jsonl_content(inference_s3_path: str, cur_round: int) -> List[DatabaseManager.BatchInferenceRecord]: - content = get_s3_bytes(inference_s3_path).decode("utf-8") +def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchInferenceRecord]: + content_bytes = get_s3_bytes(inference_s3_path) start_index = 0 index_entries = [] - lines = content.splitlines(keepends=True) + lines = content_bytes.splitlines(keepends=True) # Split content into lines as bytes for line in lines: - line_length = len(line) + line_length = len(line) # Length in bytes try: - data = json.loads(line) + # Decode the line for JSON processing + line_str = line.decode('utf-8') + data = json.loads(line_str) pdf_s3_path, page_num = parse_custom_id(data["custom_id"]) assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" + # Try to parse the actual model response JSON + model_response_json = json.loads(data["outputs"][0]["text"]) + index_entries.append(DatabaseManager.BatchInferenceRecord( inference_s3_path=inference_s3_path, pdf_s3_path=pdf_s3_path, page_num=page_num, round=data["round"], - start_index=start_index, - length=line_length, + start_index=start_index, # Byte offset in the original file + length=line_length, # Length in bytes finish_reason=data["outputs"][0]["finish_reason"], error=data.get("completion_error", None) )) except json.JSONDecodeError: - pass # Handle JSON decode errors if necessary + print(f"Error with JSON Decoding of infrence in {inference_s3_path}") except Exception as e: print(f"Error processing line: {e}") - start_index += line_length + start_index += line_length # Increment by the number of bytes return index_entries + def get_pdf_num_pages(s3_path: str) -> Optional[int]: try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: @@ -428,7 +444,7 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list return new_queries -def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> dict: +def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> Optional[dict]: db = DatabaseManager(s3_workspace) existing_pages = db.get_index_entries(pdf.s3_path) document_text = "" @@ -436,15 +452,23 @@ def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> dict: pdf_page_spans = [] for target_page_num in range(1, pdf.num_pages + 1): - target_page = next(page for page in existing_pages if page.is_usable() and page.page_num == target_page_num) + target_pages = [page for page in existing_pages if page.is_usable() and page.page_num == target_page_num] - target_row = get_s3_bytes(target_page.pdf_s3_path, - start_index=target_page.start_index, - end_index=target_page.start_index+target_page.length) + if len(target_pages) == 0: + return None + + target_page = target_pages[0] + + target_row = get_s3_bytes(target_page.inference_s3_path, + start_index=target_page.start_index, + end_index=target_page.start_index+target_page.length - 1) target_data = json.loads(target_row.decode("utf-8")) - document_text += target_data["natural_text"] + "\n" + target_output = json.loads(target_data["outputs"][0]["text"]) + + if target_output["natural_text"] is not None: + document_text += target_output["natural_text"] + "\n" pdf_page_spans.append([last_page_start_index, len(document_text), target_page_num]) last_page_start_index = len(document_text) @@ -482,7 +506,7 @@ if __name__ == '__main__': print(f"Current round is {db.get_current_round()}\n") # One shared executor to rule them all - executor = ProcessPoolExecutor() + executor = ProcessPoolExecutor(max_workers=1) # If you have new PDFs, step one is to add them to the list if args.add_pdfs: @@ -491,10 +515,10 @@ if __name__ == '__main__': print(f"Querying all PDFs at {args.add_pdfs}") all_pdfs = expand_s3_glob(args.add_pdfs) - print(f"Found {len(all_pdfs)} total pdf paths") + print(f"Found {len(all_pdfs):,} total pdf paths") all_pdfs = [pdf for pdf in all_pdfs if not db.pdf_exists(pdf)] - print(f"Need to import {len(all_pdfs)} total new pdf paths") + print(f"Need to import {len(all_pdfs):,} total new pdf paths") future_to_path = {executor.submit(get_pdf_num_pages, s3_path): s3_path for s3_path in all_pdfs} for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): @@ -514,7 +538,7 @@ if __name__ == '__main__': if not db.is_file_processed(s3_path, etag) ] - print(f"Found {len(inference_output_paths)} new batch inference results to index") + print(f"Found {len(inference_output_paths):,} new batch inference results to index") future_to_path = {executor.submit(process_jsonl_content, s3_path): (s3_path, etag) for s3_path, etag in inference_output_paths} for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): @@ -527,14 +551,14 @@ if __name__ == '__main__': # Now query each pdf, if you have all of the pages needed (all pages present, error is null and finish_reason is stop), then you assemble it into a dolma document and output it # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use if db.get_last_indexed_round() < db.get_current_round() - 1: - print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference/round_{db.get_current_round() - 1}") - potentially_done_pdfs = [] + print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference_inputs/round_{db.get_current_round() - 1}") + potentially_done_pdfs = db.get_pdfs_by_status("pending") else: print(f"\nCreating batch inference files for new PDFs") future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} potentially_done_pdfs = [] lines_written = 0 - new_inference_writer = BatchWriter(f"{args.workspace}/inference/round_{db.get_current_round()}", args.max_size_mb) + new_inference_writer = BatchWriter(f"{args.workspace}/inference_inputs/round_{db.get_current_round()}", args.max_size_mb) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): pdf = future_to_path[future] @@ -553,7 +577,7 @@ if __name__ == '__main__': db.set_metadata("round", str(db.get_current_round() + 1)) # Now, finally, assemble any potentially done docs into dolma documents - print(f"\nAssembling {len(potentially_done_pdfs)} potentially finished PDFs into Dolma documents at {args.workspace}/output") + print(f"\nAssembling {len(potentially_done_pdfs):,} potentially finished PDFs into Dolma documents at {args.workspace}/output") future_to_path = {executor.submit(build_dolma_doc, args.workspace, pdf): pdf for pdf in potentially_done_pdfs} new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb) @@ -565,6 +589,8 @@ if __name__ == '__main__': new_output_writer.close() + + print("\nWork finished, waiting for all workers to finish cleaning up") executor.shutdown(wait=True) From 1ed9e4c9470e6dcf281d300e514a01f46cb5d9a8 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 20:28:54 +0000 Subject: [PATCH 08/30] Runs to the end now --- pdelfin/birrpipeline.py | 101 +++++++++++++++++++++++++++++++--------- 1 file changed, 79 insertions(+), 22 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index afaf31b..e5cd81b 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -8,13 +8,14 @@ import glob import tempfile import datetime import posixpath -import smart_open +import threading import logging from dataclasses import dataclass from pypdf import PdfReader from tqdm import tqdm -from typing import Optional, List, Tuple, Dict +from functools import partial +from typing import Optional, List, Tuple, Dict, Callable, Any from urllib.parse import urlparse from concurrent.futures import ProcessPoolExecutor, as_completed @@ -120,6 +121,14 @@ class DatabaseManager: self.cursor.execute("SELECT etag FROM processed_files WHERE s3_path = ?", (s3_path,)) result = self.cursor.fetchone() return result is not None and result[0] == etag + + def update_processed_file(self, s3_path, etag): + self.cursor.execute(""" + INSERT INTO processed_files (s3_path, etag) + VALUES (?, ?) + ON CONFLICT(s3_path) DO UPDATE SET etag=excluded.etag + """, (s3_path, etag)) + self.conn.commit() def add_index_entries(self, index_entries: List[BatchInferenceRecord]): if index_entries: @@ -162,14 +171,6 @@ class DatabaseManager: result = self.cursor.fetchone() return -1 if result[0] is None else result[0] - def update_processed_file(self, s3_path, etag): - self.cursor.execute(""" - INSERT INTO processed_files (s3_path, etag) - VALUES (?, ?) - ON CONFLICT(s3_path) DO UPDATE SET etag=excluded.etag - """, (s3_path, etag)) - self.conn.commit() - def pdf_exists(self, s3_path: str) -> bool: self.cursor.execute("SELECT 1 FROM pdfs WHERE s3_path = ?", (s3_path,)) return self.cursor.fetchone() is not None @@ -184,6 +185,14 @@ class DatabaseManager: except sqlite3.IntegrityError: print(f"PDF with s3_path '{s3_path}' already exists.") + def update_pdf_status(self, s3_path: str, new_status: str) -> None: + self.cursor.execute(""" + UPDATE pdfs + SET status = ? + WHERE s3_path = ? + """, (new_status, s3_path)) + self.conn.commit() + def get_pdf(self, s3_path: str) -> Optional[PDFRecord]: self.cursor.execute(""" SELECT s3_path, num_pages, status @@ -226,11 +235,13 @@ class DatabaseManager: # Writes batches of lines out to a set of files, keeping each file below some maximum size class BatchWriter: - def __init__(self, output_prefix: str, max_size_mb: int = 250): + def __init__(self, output_prefix: str, max_size_mb: int = 250, after_flush: Optional[Callable[[List[str]], Any]] = None): self.output_prefix = output_prefix self.max_size = max_size_mb * 1024 * 1024 # Convert MB to bytes self.batch = [] self.batch_size = 0 + self.after_flush = after_flush + self.threads = [] parsed = urlparse(output_prefix) self.is_s3 = parsed.scheme in ('s3', 's3a', 's3n') @@ -259,14 +270,14 @@ class BatchWriter: return os.path.join(self.output_prefix, filename) def write_line(self, line: Optional[str]): - if line is None or len(line.strip()) == 0: + if line is None or not line.strip(): return - + line_size = len(line.encode('utf-8')) + 1 # +1 for newline if self.batch_size + line_size > self.max_size: self._write_batch() - + self.batch.append(line) self.batch_size += line_size @@ -274,19 +285,53 @@ class BatchWriter: if not self.batch: return - batch_content = "\n".join(self.batch) + "\n" + batch_lines = self.batch.copy() + batch_content = "\n".join(batch_lines) + "\n" hash_str = self._compute_hash(batch_content) output_path = self._get_output_path(hash_str) - with smart_open.open(output_path, 'w') as f_out: - f_out.write(batch_content) - print(f"Wrote batch to {output_path}") + # Start a new thread to write the batch + thread = threading.Thread( + target=self._write_batch_to_file, + args=(batch_content, output_path, batch_lines) + ) + thread.start() + self.threads.append(thread) + # Clear the batch and batch_size self.batch = [] self.batch_size = 0 + def _write_batch_to_file(self, batch_content: str, output_path: str, batch_lines: List[str]): + if self.is_s3: + self._write_to_s3(batch_content, output_path) + else: + with open(output_path, 'w', encoding='utf-8') as f_out: + f_out.write(batch_content) + + # After writing, call the after_flush callback if it is set + if self.after_flush: + self.after_flush(batch_lines) + + def _write_to_s3(self, content: str, s3_path: str): + # Parse the s3_path to get bucket and key + parsed = urlparse(s3_path) + bucket = parsed.netloc + key = parsed.path.lstrip('/') + + s3 = boto3.client('s3') + s3.put_object( + Bucket=bucket, + Key=key, + Body=content.encode('utf-8'), + ContentType='text/plain; charset=utf-8' + ) + def close(self): self._write_batch() + # Wait for all threads to finish + for thread in self.threads: + thread.join() def parse_s3_path(s3_path): @@ -493,6 +538,12 @@ def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> Option return dolma_doc +def mark_pdfs_done(s3_workspace: str, dolma_doc_lines: list[str]): + db = DatabaseManager(s3_workspace) + + for line in dolma_doc_lines: + db.update_pdf_status(json.loads(line)["metadata"]["Source-File"], "completed") + if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') @@ -569,7 +620,9 @@ if __name__ == '__main__': for line in inference_lines: lines_written += 1 - new_inference_writer.write_line(json.dumps(line)) + + if line is not None: + new_inference_writer.write_line(json.dumps(line)) new_inference_writer.close() @@ -579,20 +632,24 @@ if __name__ == '__main__': # Now, finally, assemble any potentially done docs into dolma documents print(f"\nAssembling {len(potentially_done_pdfs):,} potentially finished PDFs into Dolma documents at {args.workspace}/output") future_to_path = {executor.submit(build_dolma_doc, args.workspace, pdf): pdf for pdf in potentially_done_pdfs} - new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb) + new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb, after_flush=partial(mark_pdfs_done, args.workspace)) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): pdf = future_to_path[future] dolma_doc = future.result() - - new_output_writer.write_line(json.dumps(dolma_doc)) + + if dolma_doc is not None: + new_output_writer.write_line(json.dumps(dolma_doc)) new_output_writer.close() + print("\nFinal statistics:") + # Output the number of documents in each status "pending" and "completed" print("\nWork finished, waiting for all workers to finish cleaning up") executor.shutdown(wait=True) + db.close() # TODO # 2. Have a way to apply basic spam + language filter if you can during add pdfs step From 194af5ff520b0e6631eef70b23be9abdc8bb1072 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 20:31:37 +0000 Subject: [PATCH 09/30] Robustness --- pdelfin/birrpipeline.py | 39 +++++++++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index e5cd81b..c9c98a4 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -426,20 +426,34 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" # Try to parse the actual model response JSON - model_response_json = json.loads(data["outputs"][0]["text"]) + try: + model_response_json = json.loads(data["outputs"][0]["text"]) + + index_entries.append(DatabaseManager.BatchInferenceRecord( + inference_s3_path=inference_s3_path, + pdf_s3_path=pdf_s3_path, + page_num=page_num, + round=data["round"], + start_index=start_index, # Byte offset in the original file + length=line_length, # Length in bytes + finish_reason=data["outputs"][0]["finish_reason"], + error=data.get("completion_error", None) + )) + except json.JSONDecodeError: + index_entries.append(DatabaseManager.BatchInferenceRecord( + inference_s3_path=inference_s3_path, + pdf_s3_path=pdf_s3_path, + page_num=page_num, + round=data["round"], + start_index=start_index, # Byte offset in the original file + length=line_length, # Length in bytes + finish_reason="error", + error="Could not parse model JSON output", + )) - index_entries.append(DatabaseManager.BatchInferenceRecord( - inference_s3_path=inference_s3_path, - pdf_s3_path=pdf_s3_path, - page_num=page_num, - round=data["round"], - start_index=start_index, # Byte offset in the original file - length=line_length, # Length in bytes - finish_reason=data["outputs"][0]["finish_reason"], - error=data.get("completion_error", None) - )) except json.JSONDecodeError: print(f"Error with JSON Decoding of infrence in {inference_s3_path}") + # TODO Maybe this needs to add an index error that this json is bad except Exception as e: print(f"Error processing line: {e}") @@ -645,7 +659,8 @@ if __name__ == '__main__': print("\nFinal statistics:") # Output the number of documents in each status "pending" and "completed" - + # For each round, outputs a report of how many pages were processed, how many had errors + print("\nWork finished, waiting for all workers to finish cleaning up") executor.shutdown(wait=True) From 350061906e952da2b82076e0acfa15b9a8b89a49 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 20:48:33 +0000 Subject: [PATCH 10/30] Adding nicer output stats --- pdelfin/birrpipeline.py | 37 ++++++++++++++++++++++++++++++++----- 1 file changed, 32 insertions(+), 5 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index c9c98a4..97c837d 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -447,13 +447,13 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI round=data["round"], start_index=start_index, # Byte offset in the original file length=line_length, # Length in bytes - finish_reason="error", + finish_reason=data["outputs"][0]["finish_reason"], error="Could not parse model JSON output", )) except json.JSONDecodeError: print(f"Error with JSON Decoding of infrence in {inference_s3_path}") - # TODO Maybe this needs to add an index error that this json is bad + # TODO Maybe this needs to add an index error that this json is bad, but that would mean a birr error except Exception as e: print(f"Error processing line: {e}") @@ -571,7 +571,7 @@ if __name__ == '__main__': print(f"Current round is {db.get_current_round()}\n") # One shared executor to rule them all - executor = ProcessPoolExecutor(max_workers=1) + executor = ProcessPoolExecutor() # If you have new PDFs, step one is to add them to the list if args.add_pdfs: @@ -641,6 +641,7 @@ if __name__ == '__main__': new_inference_writer.close() if lines_written > 0: + print(f"Added {lines_written:,} new batch inference requests") db.set_metadata("round", str(db.get_current_round() + 1)) # Now, finally, assemble any potentially done docs into dolma documents @@ -658,8 +659,34 @@ if __name__ == '__main__': new_output_writer.close() print("\nFinal statistics:") - # Output the number of documents in each status "pending" and "completed" - # For each round, outputs a report of how many pages were processed, how many had errors + + # Output the number of PDFs in each status "pending" and "completed" + pending_pdfs = db.get_pdfs_by_status("pending") + completed_pdfs = db.get_pdfs_by_status("completed") + + print(f"Pending PDFs: {len(pending_pdfs):,}") + print(f"Completed PDFs: {len(completed_pdfs):,}") + + # For each round, outputs a report of how many pages were processed, how many had errors, and a breakdown by (error, finish_reason) + total_rounds = db.get_last_indexed_round() + 1 + for round_num in range(total_rounds): + print(f"\nStatistics for round {round_num}:") + + db.cursor.execute(""" + SELECT COUNT(*), error, finish_reason + FROM page_results + WHERE round = ? + GROUP BY error, finish_reason + """, (round_num,)) + + results = db.cursor.fetchall() + + total_pages = sum(count for count, _, _ in results) + print(f"Total pages processed: {total_pages:,}") + + for count, error, finish_reason in results: + error_str = error if error is not None else "None" + print(f" (error: {error_str}, finish_reason: {finish_reason}) -> {count:,} pages") print("\nWork finished, waiting for all workers to finish cleaning up") From 6d536830011916006287a0a03301eab5516ce269 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Mon, 14 Oct 2024 21:37:14 +0000 Subject: [PATCH 11/30] More stats hopefully running faster --- pdelfin/birrpipeline.py | 18 +++++++++++------ pdelfin/data/renderpdf.py | 42 ++++++++++++++++++++++++++++++++------- pdelfin/prompts/anchor.py | 11 +++++++--- tests/test_anchor.py | 20 +++++++++++++++---- 4 files changed, 71 insertions(+), 20 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 97c837d..39fd169 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -496,8 +496,17 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list if any(page.is_usable() and page.page_num == target_page_num for page in existing_pages): continue - # TODO: Later, you may want to retry with different sampling parameters or do something else - new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) + has_errored_previously = sum(page.page_num == target_page_num for page in existing_pages) + + if has_errored_previously: + # TODO For now this just retries the page 3 times, which is nothing special + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) + + # But you can try to do some fancier things, such as rotating the page, removing the pdf hints all together, etc + else: + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) except Exception as ex: print(f"Warning, could not get batch inferences lines for {pdf.s3_path} due to {ex}") @@ -670,8 +679,6 @@ if __name__ == '__main__': # For each round, outputs a report of how many pages were processed, how many had errors, and a breakdown by (error, finish_reason) total_rounds = db.get_last_indexed_round() + 1 for round_num in range(total_rounds): - print(f"\nStatistics for round {round_num}:") - db.cursor.execute(""" SELECT COUNT(*), error, finish_reason FROM page_results @@ -682,13 +689,12 @@ if __name__ == '__main__': results = db.cursor.fetchall() total_pages = sum(count for count, _, _ in results) - print(f"Total pages processed: {total_pages:,}") + print(f"\nInference Round {round_num} - {total_pages:,} pages processed:") for count, error, finish_reason in results: error_str = error if error is not None else "None" print(f" (error: {error_str}, finish_reason: {finish_reason}) -> {count:,} pages") - print("\nWork finished, waiting for all workers to finish cleaning up") executor.shutdown(wait=True) db.close() diff --git a/pdelfin/data/renderpdf.py b/pdelfin/data/renderpdf.py index 9f4294e..60dfafe 100644 --- a/pdelfin/data/renderpdf.py +++ b/pdelfin/data/renderpdf.py @@ -2,14 +2,42 @@ import subprocess import base64 import io from pypdf import PdfReader - from PIL import Image -def render_pdf_to_base64png(local_pdf_path: str, page: int, target_longest_image_dim: int=2048): - pdf = PdfReader(local_pdf_path) - pdf_page = pdf.pages[page - 1] - longest_dim = max(pdf_page.mediabox.width, pdf_page.mediabox.height) +def get_pdf_media_box_width_height(local_pdf_path: str, page_num: int) -> tuple[float, float]: + """ + Get the MediaBox dimensions for a specific page in a PDF file using the pdfinfo command. + + :param pdf_file: Path to the PDF file + :param page_num: The page number for which to extract MediaBox dimensions + :return: A dictionary containing MediaBox dimensions or None if not found + """ + # Construct the pdfinfo command to extract info for the specific page + command = ['pdfinfo', '-f', str(page_num), '-l', str(page_num), '-box', local_pdf_path] + + # Run the command using subprocess + result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + + # Check if there is any error in executing the command + if result.returncode != 0: + raise ValueError(f"Error running pdfinfo: {result.stderr}") + + # Parse the output to find MediaBox + output = result.stdout + media_box = None + + for line in output.splitlines(): + if 'MediaBox' in line: + media_box = line.split(':')[1].strip().split() + media_box = [float(x) for x in media_box] + return abs(media_box[0] - media_box[2]), abs(media_box[3] - media_box[1]) + + raise ValueError("MediaBox not found in the PDF info.") + + +def render_pdf_to_base64png(local_pdf_path: str, page_num: int, target_longest_image_dim: int=2048): + longest_dim = max(get_pdf_media_box_width_height(local_pdf_path, page_num)) # Convert PDF page to PNG using pdftoppm pdftoppm_result = subprocess.run( @@ -17,9 +45,9 @@ def render_pdf_to_base64png(local_pdf_path: str, page: int, target_longest_image "pdftoppm", "-png", "-f", - str(page), + str(page_num), "-l", - str(page), + str(page_num), "-r", str(target_longest_image_dim * 72 / longest_dim), # 72 pixels per point is the conversion factor local_pdf_path, diff --git a/pdelfin/prompts/anchor.py b/pdelfin/prompts/anchor.py index 36b91a6..592809e 100644 --- a/pdelfin/prompts/anchor.py +++ b/pdelfin/prompts/anchor.py @@ -13,6 +13,7 @@ import re import ftfy from dataclasses import dataclass from typing import Literal, List +from functools import lru_cache import pypdfium2 as pdfium import pymupdf @@ -119,10 +120,14 @@ class PageReport: text_elements: List[TextElement] image_elements: List[ImageElement] +@lru_cache(maxsize=5) +def _get_cached_pdf_reader(local_pdf_path: str) -> PdfReader: + # Cached, because you are going to often iterate through a whole pdf, so this will make it a lot faster on subsequent iterations + return PdfReader(local_pdf_path) -def _pdf_report(local_pdf_path: str, page: int) -> PageReport: - reader = PdfReader(local_pdf_path) - page = reader.pages[page - 1] +def _pdf_report(local_pdf_path: str, page_num: int) -> PageReport: + reader = _get_cached_pdf_reader(local_pdf_path) + page = reader.pages[page_num - 1] resources = page.get("/Resources", {}) xobjects = resources.get("/XObject", {}) text_elements, image_elements = [], [] diff --git a/tests/test_anchor.py b/tests/test_anchor.py index 225ea27..ea76bf1 100644 --- a/tests/test_anchor.py +++ b/tests/test_anchor.py @@ -2,11 +2,12 @@ import unittest import os import json import io +import glob from pypdf import PdfReader from pdelfin.prompts.anchor import _pdf_report, _linearize_pdf_report, get_anchor_text - +from pdelfin.data.renderpdf import get_pdf_media_box_width_height class AnchorTest(unittest.TestCase): def testExtractText(self): @@ -103,8 +104,6 @@ class AnchorTest(unittest.TestCase): self.assertLess(len(anchor_text), 4000) - - class BuildSilverTest(unittest.TestCase): def testSmallPage(self): local_pdf_path = os.path.join(os.path.dirname(__file__), "gnarly_pdfs", "small_page_size.pdf") @@ -124,4 +123,17 @@ class BuildSilverTest(unittest.TestCase): print(width, height) - assert max(width, height) == 2048 \ No newline at end of file + assert max(width, height) == 2048 + +class TestRenderPdf(unittest.TestCase): + def testFastMediaBoxMatchesPyPdf(self): + for file in glob.glob(os.path.join(os.path.dirname(__file__), "gnarly_pdfs", "*.pdf")): + reader = PdfReader(file) + print("checking", file) + + for page_num in range(1, len(reader.pages) + 1): + w1, h1 = get_pdf_media_box_width_height(file, page_num) + pypdfpage = reader.pages[page_num - 1] + + self.assertEqual(w1, pypdfpage.mediabox.width) + self.assertEqual(h1, pypdfpage.mediabox.height) \ No newline at end of file From fc8fcfaeba086c4724ff823a2c67ae6e82c9c43f Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 15:13:25 +0000 Subject: [PATCH 12/30] Fixing dataloader hopefully --- pdelfin/train/dataloader.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/pdelfin/train/dataloader.py b/pdelfin/train/dataloader.py index adba62b..ee17643 100644 --- a/pdelfin/train/dataloader.py +++ b/pdelfin/train/dataloader.py @@ -5,6 +5,7 @@ import re import os import base64 import glob +import pypdf, pypdf.errors from functools import partial from typing import Any, Dict, Optional @@ -190,9 +191,12 @@ def extract_openai_batch_query(query: Dict[str, Any]) -> Dict[str, Any]: 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") - + + try: + raw_page_text = get_anchor_text(tf.name, page_num, pdf_engine="pdfreport") + except pypdf.errors.PdfReadError: + raw_page_text = None + return { "custom_id": custom_id, "input_prompt_text": input_prompt_text, @@ -239,6 +243,7 @@ def build_batch_query_response_vision_dataset(query_glob_path: str, response_glo logger.info("Mapping query data") query_data = query_data["train"] query_data = query_data.map(extract_openai_batch_query, remove_columns=query_data.column_names, num_proc=num_proc) + query_data = query_data.filter(lambda x: x["raw_page_text"] is not None, num_proc=num_proc) logger.info("Mapping response data") response_data = response_data["train"] @@ -258,6 +263,7 @@ def build_batch_query_response_vision_dataset(query_glob_path: str, response_glo ) # Don't include data where the model cut off due to a length issue, or moderation issue + logger.info("Filtering on finish_reason == stop") final_dataset = final_dataset.filter(lambda x: x["finish_reason"] == "stop", num_proc=num_proc) # Pick things that have a reasonable image size only @@ -265,6 +271,7 @@ def build_batch_query_response_vision_dataset(query_glob_path: str, response_glo width, height = get_png_dimensions_from_base64(x["input_prompt_image_base64"]) return 1800 <= max(width, height) <= 2200 + logger.info("Filtering on image size") final_dataset = final_dataset.filter(pick_image_sizes, num_proc=num_proc) return final_dataset From 6d61ae4aa8535452e06d780670ec7513a3b5d368 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 16:02:08 +0000 Subject: [PATCH 13/30] Some pipeline cleanup stuff --- pdelfin/birrpipeline.py | 75 ++++++++++++++++++++++++++--------------- 1 file changed, 48 insertions(+), 27 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 39fd169..5ea818b 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -10,6 +10,7 @@ import datetime import posixpath import threading import logging +import urllib3.exceptions from dataclasses import dataclass from pypdf import PdfReader @@ -113,10 +114,6 @@ class DatabaseManager: """, (key, value)) self.conn.commit() - def get_current_round(self): - round_value = self.get_metadata("round") - return int(round_value) if round_value else 0 - def is_file_processed(self, s3_path, etag): self.cursor.execute("SELECT etag FROM processed_files WHERE s3_path = ?", (s3_path,)) result = self.cursor.fetchone() @@ -215,7 +212,7 @@ class DatabaseManager: SELECT s3_path, num_pages, status FROM pdfs WHERE status == ? - ORDER BY s3_path DESC + ORDER BY s3_path DESC, num_pages DESC """, (status, )) rows = self.cursor.fetchall() @@ -452,8 +449,8 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI )) except json.JSONDecodeError: - print(f"Error with JSON Decoding of infrence in {inference_s3_path}") - # TODO Maybe this needs to add an index error that this json is bad, but that would mean a birr error + print(f"Error with JSON Decoding of inference in {inference_s3_path}") + # TODO Maybe this needs to add an index error that this json is bad except Exception as e: print(f"Error processing line: {e}") @@ -475,9 +472,8 @@ def get_pdf_num_pages(s3_path: str) -> Optional[int]: return None -def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list[dict]: +def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord, cur_round: int) -> list[dict]: db = DatabaseManager(s3_workspace) - cur_round = db.get_current_round() existing_pages = db.get_index_entries(pdf.s3_path) new_queries = [] @@ -499,12 +495,11 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> list has_errored_previously = sum(page.page_num == target_page_num for page in existing_pages) if has_errored_previously: - # TODO For now this just retries the page 3 times, which is nothing special - new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) - new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) - new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) + # Retry the page up to 3 times + for _ in range(3): + new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) - # But you can try to do some fancier things, such as rotating the page, removing the pdf hints all together, etc + # Optionally, you can implement more complex retry logic here else: new_queries.append({**build_page_query(tf.name, pdf.s3_path, target_page_num), "round": cur_round}) except Exception as ex: @@ -567,6 +562,30 @@ def mark_pdfs_done(s3_workspace: str, dolma_doc_lines: list[str]): for line in dolma_doc_lines: db.update_pdf_status(json.loads(line)["metadata"]["Source-File"], "completed") +def get_current_round(s3_workspace: str) -> int: + bucket, prefix = parse_s3_path(s3_workspace) + inference_inputs_prefix = posixpath.join(prefix, 'inference_inputs/') + paginator = s3.get_paginator('list_objects_v2') + page_iterator = paginator.paginate(Bucket=bucket, Prefix=inference_inputs_prefix, Delimiter='/') + + round_numbers = [] + for page in page_iterator: + for common_prefix in page.get('CommonPrefixes', []): + round_prefix = common_prefix.get('Prefix') + # Extract 'round_X' from the prefix + round_dir = posixpath.basename(posixpath.dirname(round_prefix)) + if round_dir.startswith('round_'): + try: + round_num = int(round_dir[len('round_'):]) + round_numbers.append(round_num) + except ValueError: + pass + if round_numbers: + current_round = max(round_numbers) + 1 + else: + current_round = 0 + return current_round + if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') @@ -577,7 +596,9 @@ if __name__ == '__main__': db = DatabaseManager(args.workspace) print(f"Loaded db at {db.db_path}") - print(f"Current round is {db.get_current_round()}\n") + + current_round = get_current_round(args.workspace) + print(f"Current round is {current_round}\n") # One shared executor to rule them all executor = ProcessPoolExecutor() @@ -617,22 +638,27 @@ if __name__ == '__main__': for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): s3_path, etag = future_to_path[future] - inference_records = future.result() - db.add_index_entries(inference_records) - db.update_processed_file(s3_path, etag=etag) + try: + inference_records = future.result() + + db.add_index_entries(inference_records) + db.update_processed_file(s3_path, etag=etag) + except urllib3.exceptions.SSLError: + print(f"Cannot load inference file {s3_path} due to SSL error, will retry another time") + # Now query each pdf, if you have all of the pages needed (all pages present, error is null and finish_reason is stop), then you assemble it into a dolma document and output it # If you don't have every page, or if you have pages with errors, then you output a new batch of inference items to use - if db.get_last_indexed_round() < db.get_current_round() - 1: - print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference_inputs/round_{db.get_current_round() - 1}") + if db.get_last_indexed_round() < current_round - 1: + print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference_inputs/round_{current_round - 1}") potentially_done_pdfs = db.get_pdfs_by_status("pending") else: print(f"\nCreating batch inference files for new PDFs") - future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf): pdf for pdf in db.get_pdfs_by_status("pending")} + future_to_path = {executor.submit(build_pdf_queries, args.workspace, pdf, current_round): pdf for pdf in db.get_pdfs_by_status("pending")} potentially_done_pdfs = [] lines_written = 0 - new_inference_writer = BatchWriter(f"{args.workspace}/inference_inputs/round_{db.get_current_round()}", args.max_size_mb) + new_inference_writer = BatchWriter(f"{args.workspace}/inference_inputs/round_{current_round}", args.max_size_mb) for future in tqdm(as_completed(future_to_path), total=len(future_to_path)): pdf = future_to_path[future] @@ -651,7 +677,6 @@ if __name__ == '__main__': if lines_written > 0: print(f"Added {lines_written:,} new batch inference requests") - db.set_metadata("round", str(db.get_current_round() + 1)) # Now, finally, assemble any potentially done docs into dolma documents print(f"\nAssembling {len(potentially_done_pdfs):,} potentially finished PDFs into Dolma documents at {args.workspace}/output") @@ -698,7 +723,3 @@ if __name__ == '__main__': print("\nWork finished, waiting for all workers to finish cleaning up") executor.shutdown(wait=True) db.close() - - # TODO - # 2. Have a way to apply basic spam + language filter if you can during add pdfs step - # 3. For retrying, make it so you retry several times with different sampling parameters From 4669eb71344c6c7b078359d58c202570fe995d15 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 16:22:55 +0000 Subject: [PATCH 14/30] Adjusting workflow so I can do s2 pdfs --- pdelfin/birrpipeline.py | 58 ++++++++++++++++++++++++++--------------- 1 file changed, 37 insertions(+), 21 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 5ea818b..5b578f7 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -10,6 +10,7 @@ import datetime import posixpath import threading import logging +import boto3.session import urllib3.exceptions from dataclasses import dataclass @@ -25,7 +26,8 @@ from pdelfin.prompts import build_finetuning_prompt from pdelfin.prompts.anchor import get_anchor_text # Global s3 client for the whole script, feel free to adjust params if you need it -s3 = boto3.client('s3') +workspace_s3 = boto3.client('s3') +pdf_s3 = boto3.client('s3') # Quiet logs from pypdf and smart open logging.getLogger("pypdf").setLevel(logging.ERROR) @@ -338,13 +340,13 @@ def parse_s3_path(s3_path): bucket, _, prefix = path.partition('/') return bucket, prefix -def expand_s3_glob(s3_glob: str) -> Dict[str, str]: +def expand_s3_glob(s3_client, s3_glob: str) -> Dict[str, str]: parsed = urlparse(s3_glob) bucket_name = parsed.netloc prefix = os.path.dirname(parsed.path.lstrip('/')).rstrip('/') + "/" pattern = os.path.basename(parsed.path) - paginator = s3.get_paginator('list_objects_v2') + paginator = s3_client.get_paginator('list_objects_v2') page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix) matched_files = {} @@ -374,7 +376,7 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di } -def get_s3_bytes(s3_path: str, start_index: Optional[int] = None, end_index: Optional[int] = None) -> bytes: +def get_s3_bytes(s3_client, s3_path: str, start_index: Optional[int] = None, end_index: Optional[int] = None) -> bytes: bucket, key = parse_s3_path(s3_path) # Build the range header if start_index and/or end_index are specified @@ -393,9 +395,9 @@ def get_s3_bytes(s3_path: str, start_index: Optional[int] = None, end_index: Opt range_header = {'Range': range_value} if range_header: - obj = s3.get_object(Bucket=bucket, Key=key, Range=range_header['Range']) + obj = s3_client.get_object(Bucket=bucket, Key=key, Range=range_header['Range']) else: - obj = s3.get_object(Bucket=bucket, Key=key) + obj = s3_client.get_object(Bucket=bucket, Key=key) return obj['Body'].read() @@ -406,7 +408,7 @@ def parse_custom_id(custom_id: str) -> Tuple[str, int]: return s3_path, page_num def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchInferenceRecord]: - content_bytes = get_s3_bytes(inference_s3_path) + content_bytes = get_s3_bytes(workspace_s3, inference_s3_path) start_index = 0 index_entries = [] @@ -462,7 +464,7 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI def get_pdf_num_pages(s3_path: str) -> Optional[int]: try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: - tf.write(get_s3_bytes(s3_path)) + tf.write(get_s3_bytes(pdf_s3, s3_path)) tf.flush() reader = PdfReader(tf.name) @@ -484,7 +486,7 @@ def build_pdf_queries(s3_workspace: str, pdf: DatabaseManager.PDFRecord, cur_rou try: with tempfile.NamedTemporaryFile("wb+", suffix=".pdf") as tf: - tf.write(get_s3_bytes(pdf.s3_path)) + tf.write(get_s3_bytes(pdf_s3, pdf.s3_path)) tf.flush() for target_page_num in range(1, pdf.num_pages + 1): @@ -522,9 +524,9 @@ def build_dolma_doc(s3_workspace: str, pdf: DatabaseManager.PDFRecord) -> Option target_page = target_pages[0] - target_row = get_s3_bytes(target_page.inference_s3_path, - start_index=target_page.start_index, - end_index=target_page.start_index+target_page.length - 1) + target_row = get_s3_bytes(workspace_s3, target_page.inference_s3_path, + start_index=target_page.start_index, + end_index=target_page.start_index+target_page.length - 1) target_data = json.loads(target_row.decode("utf-8")) @@ -565,7 +567,7 @@ def mark_pdfs_done(s3_workspace: str, dolma_doc_lines: list[str]): def get_current_round(s3_workspace: str) -> int: bucket, prefix = parse_s3_path(s3_workspace) inference_inputs_prefix = posixpath.join(prefix, 'inference_inputs/') - paginator = s3.get_paginator('list_objects_v2') + paginator = workspace_s3.get_paginator('list_objects_v2') page_iterator = paginator.paginate(Bucket=bucket, Prefix=inference_inputs_prefix, Delimiter='/') round_numbers = [] @@ -590,10 +592,20 @@ def get_current_round(s3_workspace: str) -> int: if __name__ == '__main__': parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline') parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/)') - parser.add_argument('--add_pdfs', help='Glob path to add PDFs (s3) to the workspace', default=None) + parser.add_argument('--add_pdfs', help='Path to add pdfs stored in s3 to the workspace, can be a glob path s3://bucket/prefix/*.pdf or path to file containing list of pdf paths', default=None) + parser.add_argument('--workspace_profile', help='S3 configuration profile for accessing the workspace', default=None) + parser.add_argument('--pdf_profile', help='S3 configuration profile for accessing the raw pdf documents', default=None) parser.add_argument('--max_size_mb', type=int, default=250, help='Max file size in MB') args = parser.parse_args() + if args.workspace_profile: + workspace_session = boto3.Session(profile_name=args.workspace_profile) + workspace_s3 = workspace_session.resource("s3") + + if args.pdf_profile: + pdf_session = boto3.Session(profile_name=args.pdf_profile) + pdf_s3 = pdf_session.resource("s3") + db = DatabaseManager(args.workspace) print(f"Loaded db at {db.db_path}") @@ -605,12 +617,16 @@ if __name__ == '__main__': # If you have new PDFs, step one is to add them to the list if args.add_pdfs: - assert args.add_pdfs.startswith("s3://"), "PDFs must live on s3" - - print(f"Querying all PDFs at {args.add_pdfs}") - - all_pdfs = expand_s3_glob(args.add_pdfs) - print(f"Found {len(all_pdfs):,} total pdf paths") + if args.add_pdfs.startswith("s3://"): + print(f"Querying all PDFs at {args.add_pdfs}") + + all_pdfs = expand_s3_glob(pdf_s3, args.add_pdfs) + print(f"Found {len(all_pdfs):,} total pdf paths") + elif os.path.exists(args.add_pdfs): + with open(args.add_pdfs, "r") as f: + all_pdfs = [line for line in f.readlines() if len(line.strip()) > 0] + else: + raise ValueError("add_pdfs argument needs to be either an s3 glob search path, or a local file contains pdf paths (one per line)") all_pdfs = [pdf for pdf in all_pdfs if not db.pdf_exists(pdf)] print(f"Need to import {len(all_pdfs):,} total new pdf paths") @@ -626,7 +642,7 @@ if __name__ == '__main__': # Now build an index of all the pages that were processed within the workspace so far print("Indexing all batch inference sent to this workspace") - inference_output_paths = expand_s3_glob(f"{args.workspace}/inference_outputs/*.jsonl") + inference_output_paths = expand_s3_glob(workspace_s3, f"{args.workspace}/inference_outputs/*.jsonl") inference_output_paths = [ (s3_path, etag) for s3_path, etag in inference_output_paths.items() From a7fae0e6599912966a19536f7d2a34e406182a88 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 16:36:54 +0000 Subject: [PATCH 15/30] fix --- pdelfin/birrpipeline.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 5b578f7..5739962 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -600,11 +600,11 @@ if __name__ == '__main__': if args.workspace_profile: workspace_session = boto3.Session(profile_name=args.workspace_profile) - workspace_s3 = workspace_session.resource("s3") + workspace_s3 = workspace_session.client("s3") if args.pdf_profile: pdf_session = boto3.Session(profile_name=args.pdf_profile) - pdf_s3 = pdf_session.resource("s3") + pdf_s3 = pdf_session.client("s3") db = DatabaseManager(args.workspace) print(f"Loaded db at {db.db_path}") @@ -624,7 +624,7 @@ if __name__ == '__main__': print(f"Found {len(all_pdfs):,} total pdf paths") elif os.path.exists(args.add_pdfs): with open(args.add_pdfs, "r") as f: - all_pdfs = [line for line in f.readlines() if len(line.strip()) > 0] + all_pdfs = [line.strip() for line in f.readlines() if len(line.strip()) > 0] else: raise ValueError("add_pdfs argument needs to be either an s3 glob search path, or a local file contains pdf paths (one per line)") From b8cd41402272ed00f17e4f6890242442322ced40 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 16:54:19 +0000 Subject: [PATCH 16/30] tiny fix --- pdelfin/birrpipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 5739962..da924f7 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -695,7 +695,7 @@ if __name__ == '__main__': print(f"Added {lines_written:,} new batch inference requests") # Now, finally, assemble any potentially done docs into dolma documents - print(f"\nAssembling {len(potentially_done_pdfs):,} potentially finished PDFs into Dolma documents at {args.workspace}/output") + print(f"\nAssembling potentially finished PDFs into Dolma documents at {args.workspace}/output") future_to_path = {executor.submit(build_dolma_doc, args.workspace, pdf): pdf for pdf in potentially_done_pdfs} new_output_writer = BatchWriter(f"{args.workspace}/output", args.max_size_mb, after_flush=partial(mark_pdfs_done, args.workspace)) From 42cf6a639fbfb6d4ff322760e7ebdb4670be87df Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 18:37:31 +0000 Subject: [PATCH 17/30] Dolma viewer --- .gitignore | 2 +- pdelfin/viewer/dolmaviewer.py | 89 +++++++++++++++++++++++++++++++++++ pdelfin/viewer/template.html | 49 +++++++++++++++++++ 3 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 pdelfin/viewer/dolmaviewer.py create mode 100644 pdelfin/viewer/template.html diff --git a/.gitignore b/.gitignore index dbef8a7..a3f69ec 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,7 @@ wandb/ *histogram.png *.json - +dolma_previews/* /*.html diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py new file mode 100644 index 0000000..c8b8f8d --- /dev/null +++ b/pdelfin/viewer/dolmaviewer.py @@ -0,0 +1,89 @@ +import os +import json +import html +import argparse +import boto3 +from botocore.exceptions import NoCredentialsError, PartialCredentialsError +from jinja2 import Template +import smart_open + +def read_jsonl(path): + with smart_open.smart_open(path, 'r', encoding='utf-8') as f: + for line in f: + yield line.strip() + +def parse_s3_path(path): + # s3://bucket_name/key_name + path = path[5:] # Remove 's3://' + bucket_name, key_name = path.split('/', 1) + return bucket_name, key_name + +def generate_presigned_url(s3_client, bucket_name, key_name): + try: + response = s3_client.generate_presigned_url('get_object', + Params={'Bucket': bucket_name, 'Key': key_name}, + ExpiresIn=3600) # Link expires in 1 hour + return response + except (NoCredentialsError, PartialCredentialsError): + print("Error: AWS credentials not found or incomplete.") + return None + +def main(jsonl_path, output_dir, template_path): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Load the Jinja template + with open(os.path.join(os.path.dirname(__file__), template_path), 'r', encoding='utf-8') as template_file: + template_content = template_file.read() + template = Template(template_content) + + # Initialize S3 client for generating presigned URLs + workspace_session = boto3.Session(profile_name="s2") + s3_client = workspace_session.client("s3") + + for line in read_jsonl(jsonl_path): + if not line: + continue + data = json.loads(line) + id_ = data.get('id') + text = data.get('text', '') + attributes = data.get('attributes', {}) + pdf_page_numbers = attributes.get('pdf_page_numbers', []) + metadata = data.get('metadata', {}) + source_file = metadata.get('Source-File') + + # Escape HTML special characters + text = html.escape(text) + + # Process the text and split it according to page spans + pages = [] + for span in pdf_page_numbers: + start_index, end_index, page_num = span + page_text = text[start_index:end_index] + # Replace line breaks with
tags + page_text = page_text.replace('\n', '
\n') + pages.append({'page_num': page_num, 'text': page_text}) + + # Generate pre-signed URL if source_file is an S3 path + s3_link = None + if source_file and source_file.startswith('s3://'): + bucket_name, key_name = parse_s3_path(source_file) + s3_link = generate_presigned_url(s3_client, bucket_name, key_name) + + # Render the HTML using the Jinja template + html_content = template.render(id=id_, pages=pages, s3_link=s3_link) + + # Write the HTML content to a file + filename = f'{id_}.html' + filepath = os.path.join(output_dir, filename) + with open(filepath, 'w', encoding='utf-8') as f: + f.write(html_content) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Generate HTML pages from a JSONL file with pre-signed S3 links.') + parser.add_argument('jsonl_path', help='Path to the JSONL file (local or s3://)') + parser.add_argument('--output_dir', default='dolma_previews', help='Directory to save HTML files') + parser.add_argument('--template_path', default='template.html', help='Path to the Jinja2 template file') + args = parser.parse_args() + + main(args.jsonl_path, args.output_dir, args.template_path) \ No newline at end of file diff --git a/pdelfin/viewer/template.html b/pdelfin/viewer/template.html new file mode 100644 index 0000000..caa515f --- /dev/null +++ b/pdelfin/viewer/template.html @@ -0,0 +1,49 @@ + + + + + {{ id }} + + + +
+ {% if s3_link %} + + {% endif %} + {% for page in pages %} + {{ page.text|safe }} + {% endfor %} +
+ + From 4a280e55dfe4f7f12497993b584d48ce58efe59f Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 21:03:28 +0000 Subject: [PATCH 18/30] Nicer dolma viewer --- pdelfin/data/renderpdf.py | 2 +- pdelfin/viewer/dolmaviewer.py | 21 ++++++++++++++++----- pdelfin/viewer/template.html | 27 +++++++++++++++++++++++---- 3 files changed, 40 insertions(+), 10 deletions(-) diff --git a/pdelfin/data/renderpdf.py b/pdelfin/data/renderpdf.py index 60dfafe..7e058b8 100644 --- a/pdelfin/data/renderpdf.py +++ b/pdelfin/data/renderpdf.py @@ -63,7 +63,7 @@ def render_pdf_to_base64png(local_pdf_path: str, page_num: int, target_longest_i def render_pdf_to_base64webp(local_pdf_path: str, page: int, target_longest_image_dim: int=1024): base64_png = render_pdf_to_base64png(local_pdf_path, page, target_longest_image_dim) - png_image = Image.open(io.BytesIO(base64_png.encode("utf-8"))) + png_image = Image.open(io.BytesIO(base64.b64decode(base64_png))) webp_output = io.BytesIO() png_image.save(webp_output, format="WEBP") diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py index c8b8f8d..5e66741 100644 --- a/pdelfin/viewer/dolmaviewer.py +++ b/pdelfin/viewer/dolmaviewer.py @@ -6,6 +6,11 @@ import boto3 from botocore.exceptions import NoCredentialsError, PartialCredentialsError from jinja2 import Template import smart_open +import cached_path + +from tqdm import tqdm + +from pdelfin.data.renderpdf import render_pdf_to_base64webp def read_jsonl(path): with smart_open.smart_open(path, 'r', encoding='utf-8') as f: @@ -41,7 +46,7 @@ def main(jsonl_path, output_dir, template_path): workspace_session = boto3.Session(profile_name="s2") s3_client = workspace_session.client("s3") - for line in read_jsonl(jsonl_path): + for line in tqdm(read_jsonl(jsonl_path)): if not line: continue data = json.loads(line) @@ -52,8 +57,11 @@ def main(jsonl_path, output_dir, template_path): metadata = data.get('metadata', {}) source_file = metadata.get('Source-File') - # Escape HTML special characters - text = html.escape(text) + # Generate base64 PNG image of the corresponding PDF page + if source_file and source_file.startswith('s3://'): + local_pdf_path = cached_path.cached_path(source_file) + else: + local_pdf_path = source_file # Process the text and split it according to page spans pages = [] @@ -61,8 +69,11 @@ def main(jsonl_path, output_dir, template_path): start_index, end_index, page_num = span page_text = text[start_index:end_index] # Replace line breaks with
tags - page_text = page_text.replace('\n', '
\n') - pages.append({'page_num': page_num, 'text': page_text}) + page_text = html.escape(page_text).replace('\n', '
\n') + + base64_image = render_pdf_to_base64webp(local_pdf_path, page_num) + + pages.append({'page_num': page_num, 'text': page_text, 'image': base64_image}) # Generate pre-signed URL if source_file is an S3 path s3_link = None diff --git a/pdelfin/viewer/template.html b/pdelfin/viewer/template.html index caa515f..fe9cb2c 100644 --- a/pdelfin/viewer/template.html +++ b/pdelfin/viewer/template.html @@ -22,15 +22,27 @@ line-height: 1.8; } .page-section { - display: block; + display: flex; + flex-direction: row; + margin-bottom: 20px; transition: background-color 0.3s ease; } .page-section:hover { background-color: #f5f5f5; } - .page-section p { - text-align: justify; + .page-section .text { + flex: 2; padding: 10px; + text-align: justify; + } + .page-section .image { + flex: 1; + padding: 10px; + } + .page-section img { + max-width: 100%; + height: auto; + border: 1px solid #ccc; } @@ -42,7 +54,14 @@ {% endif %} {% for page in pages %} - {{ page.text|safe }} +
+
{{ page.text|safe }}
+ {% if page.image %} +
+ Page {{ page.page_num }} Image +
+ {% endif %} +
{% endfor %} From 4ef14ec813e0d61d7758bd84f95bcb381a53a4d8 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 22:26:31 +0000 Subject: [PATCH 19/30] More stats --- pdelfin/birrpipeline.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index da924f7..9ab777e 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -714,8 +714,8 @@ if __name__ == '__main__': pending_pdfs = db.get_pdfs_by_status("pending") completed_pdfs = db.get_pdfs_by_status("completed") - print(f"Pending PDFs: {len(pending_pdfs):,}") - print(f"Completed PDFs: {len(completed_pdfs):,}") + print(f"Pending PDFs: {len(pending_pdfs):,} ({sum(doc.num_pages for doc in pending_pdfs):,} pages)") + print(f"Completed PDFs: {len(completed_pdfs):,} ({sum(doc.num_pages for doc in completed_pdfs):,} pages)") # For each round, outputs a report of how many pages were processed, how many had errors, and a breakdown by (error, finish_reason) total_rounds = db.get_last_indexed_round() + 1 From 9eb252f8f6f31bd8984e94e2a16c03a2025c62ba Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Tue, 15 Oct 2024 22:43:31 +0000 Subject: [PATCH 20/30] Better tracking of completion_errors --- pdelfin/birrpipeline.py | 48 +++++++++++++++++++++++++---------------- 1 file changed, 30 insertions(+), 18 deletions(-) diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 9ab777e..127ceed 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -422,12 +422,7 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI data = json.loads(line_str) pdf_s3_path, page_num = parse_custom_id(data["custom_id"]) - assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" - - # Try to parse the actual model response JSON - try: - model_response_json = json.loads(data["outputs"][0]["text"]) - + if data.get("completion_error", None) is not None: index_entries.append(DatabaseManager.BatchInferenceRecord( inference_s3_path=inference_s3_path, pdf_s3_path=pdf_s3_path, @@ -435,20 +430,37 @@ def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchI round=data["round"], start_index=start_index, # Byte offset in the original file length=line_length, # Length in bytes - finish_reason=data["outputs"][0]["finish_reason"], + finish_reason="completion_error", error=data.get("completion_error", None) )) - except json.JSONDecodeError: - index_entries.append(DatabaseManager.BatchInferenceRecord( - inference_s3_path=inference_s3_path, - pdf_s3_path=pdf_s3_path, - page_num=page_num, - round=data["round"], - start_index=start_index, # Byte offset in the original file - length=line_length, # Length in bytes - finish_reason=data["outputs"][0]["finish_reason"], - error="Could not parse model JSON output", - )) + else: + # Try to parse the actual model response JSON + assert "outputs" in data and len(data["outputs"]) > 0, "No outputs from model detected" + + try: + model_response_json = json.loads(data["outputs"][0]["text"]) + + index_entries.append(DatabaseManager.BatchInferenceRecord( + inference_s3_path=inference_s3_path, + pdf_s3_path=pdf_s3_path, + page_num=page_num, + round=data["round"], + start_index=start_index, # Byte offset in the original file + length=line_length, # Length in bytes + finish_reason=data["outputs"][0]["finish_reason"], + error=data.get("completion_error", None) + )) + except json.JSONDecodeError: + index_entries.append(DatabaseManager.BatchInferenceRecord( + inference_s3_path=inference_s3_path, + pdf_s3_path=pdf_s3_path, + page_num=page_num, + round=data["round"], + start_index=start_index, # Byte offset in the original file + length=line_length, # Length in bytes + finish_reason=data["outputs"][0]["finish_reason"], + error="Could not parse model JSON output", + )) except json.JSONDecodeError: print(f"Error with JSON Decoding of inference in {inference_s3_path}") From 35558dbddcf1f0dbd3df689b33cb77c78705bc2d Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:05:07 +0000 Subject: [PATCH 21/30] Make the prompt hint randomly select lines --- pdelfin/prompts/anchor.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/pdelfin/prompts/anchor.py b/pdelfin/prompts/anchor.py index 592809e..d994017 100644 --- a/pdelfin/prompts/anchor.py +++ b/pdelfin/prompts/anchor.py @@ -10,6 +10,7 @@ # coherency score best of these three import subprocess import re +import random import ftfy from dataclasses import dataclass from typing import Literal, List @@ -335,7 +336,10 @@ def _linearize_pdf_report(report: PageReport, max_length: int = 4000) -> str: ] # Sort remaining elements by their positions (e.g., x-coordinate and then y-coordinate) - remaining_elements.sort(key=lambda x: (x[3][0], x[3][1])) + # remaining_elements.sort(key=lambda x: (x[3][0], x[3][1])) + + # Shuffle remaining elements randomly + random.shuffle(remaining_elements) # Add elements until reaching max_length for elem_type, elem, s, position in remaining_elements: From 2cd863ddce5857ac6b577f3e03cc9a8859553613 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:05:44 +0000 Subject: [PATCH 22/30] Dolma viewer improvements --- pdelfin/viewer/dolmaviewer.py | 100 +- pdelfin/viewer/template.html | 2 +- .../gnarly_pdfs/tobacco_missed_tokens_pg1.pdf | 2002 +++++++++++++++++ tests/test_anchor.py | 9 + 4 files changed, 2068 insertions(+), 45 deletions(-) create mode 100644 tests/gnarly_pdfs/tobacco_missed_tokens_pg1.pdf diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py index 5e66741..1bfb13d 100644 --- a/pdelfin/viewer/dolmaviewer.py +++ b/pdelfin/viewer/dolmaviewer.py @@ -7,8 +7,8 @@ from botocore.exceptions import NoCredentialsError, PartialCredentialsError from jinja2 import Template import smart_open import cached_path - from tqdm import tqdm +from concurrent.futures import ThreadPoolExecutor, as_completed from pdelfin.data.renderpdf import render_pdf_to_base64webp @@ -33,6 +33,46 @@ def generate_presigned_url(s3_client, bucket_name, key_name): print("Error: AWS credentials not found or incomplete.") return None +def process_document(data, s3_client, template, output_dir): + id_ = data.get('id') + text = data.get('text', '') + attributes = data.get('attributes', {}) + pdf_page_numbers = attributes.get('pdf_page_numbers', []) + metadata = data.get('metadata', {}) + source_file = metadata.get('Source-File') + + # Generate base64 image of the corresponding PDF page + if source_file and source_file.startswith('s3://'): + local_pdf_path = cached_path.cached_path(source_file) + else: + local_pdf_path = source_file + + pages = [] + for span in pdf_page_numbers: + start_index, end_index, page_num = span + page_text = text[start_index:end_index] + # Replace line breaks with
tags + page_text = html.escape(page_text).replace('\n', '
\n') + + base64_image = render_pdf_to_base64webp(local_pdf_path, page_num) + + pages.append({'page_num': page_num, 'text': page_text, 'image': base64_image}) + + # Generate pre-signed URL if source_file is an S3 path + s3_link = None + if source_file and source_file.startswith('s3://'): + bucket_name, key_name = parse_s3_path(source_file) + s3_link = generate_presigned_url(s3_client, bucket_name, key_name) + + # Render the HTML using the Jinja template + html_content = template.render(id=id_, pages=pages, s3_link=s3_link) + + # Write the HTML content to a file + filename = f'{id_}.html' + filepath = os.path.join(output_dir, filename) + with open(filepath, 'w', encoding='utf-8') as f: + f.write(html_content) + def main(jsonl_path, output_dir, template_path): if not os.path.exists(output_dir): os.makedirs(output_dir) @@ -46,49 +86,21 @@ def main(jsonl_path, output_dir, template_path): workspace_session = boto3.Session(profile_name="s2") s3_client = workspace_session.client("s3") - for line in tqdm(read_jsonl(jsonl_path)): - if not line: - continue - data = json.loads(line) - id_ = data.get('id') - text = data.get('text', '') - attributes = data.get('attributes', {}) - pdf_page_numbers = attributes.get('pdf_page_numbers', []) - metadata = data.get('metadata', {}) - source_file = metadata.get('Source-File') + # Create ThreadPoolExecutor + with ThreadPoolExecutor() as executor: + futures = [] + for line in read_jsonl(jsonl_path): + if not line: + continue + data = json.loads(line) + future = executor.submit(process_document, data, s3_client, template, output_dir) + futures.append(future) - # Generate base64 PNG image of the corresponding PDF page - if source_file and source_file.startswith('s3://'): - local_pdf_path = cached_path.cached_path(source_file) - else: - local_pdf_path = source_file - - # Process the text and split it according to page spans - pages = [] - for span in pdf_page_numbers: - start_index, end_index, page_num = span - page_text = text[start_index:end_index] - # Replace line breaks with
tags - page_text = html.escape(page_text).replace('\n', '
\n') - - base64_image = render_pdf_to_base64webp(local_pdf_path, page_num) - - pages.append({'page_num': page_num, 'text': page_text, 'image': base64_image}) - - # Generate pre-signed URL if source_file is an S3 path - s3_link = None - if source_file and source_file.startswith('s3://'): - bucket_name, key_name = parse_s3_path(source_file) - s3_link = generate_presigned_url(s3_client, bucket_name, key_name) - - # Render the HTML using the Jinja template - html_content = template.render(id=id_, pages=pages, s3_link=s3_link) - - # Write the HTML content to a file - filename = f'{id_}.html' - filepath = os.path.join(output_dir, filename) - with open(filepath, 'w', encoding='utf-8') as f: - f.write(html_content) + for future in tqdm(as_completed(futures), total=len(futures)): + try: + future.result() + except Exception as e: + print(f"An error occurred: {e}") if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate HTML pages from a JSONL file with pre-signed S3 links.') @@ -97,4 +109,4 @@ if __name__ == '__main__': parser.add_argument('--template_path', default='template.html', help='Path to the Jinja2 template file') args = parser.parse_args() - main(args.jsonl_path, args.output_dir, args.template_path) \ No newline at end of file + main(args.jsonl_path, args.output_dir, args.template_path) diff --git a/pdelfin/viewer/template.html b/pdelfin/viewer/template.html index fe9cb2c..cdb5d03 100644 --- a/pdelfin/viewer/template.html +++ b/pdelfin/viewer/template.html @@ -58,7 +58,7 @@
{{ page.text|safe }}
{% if page.image %}
- Page {{ page.page_num }} Image + Page {{ page.page_num }} Image
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"tobacco_missed_tokens_pg1.pdf") + + anchor_text = get_anchor_text(local_pdf_path, 1, pdf_engine="pdfreport") + + print(anchor_text) + print(len(anchor_text)) + self.assertLess(len(anchor_text), 4000) + class BuildSilverTest(unittest.TestCase): def testSmallPage(self): From 96682b2ecbba47441592dab84e640dbb2d02aa41 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:18:27 +0000 Subject: [PATCH 23/30] Refactoring --- .gitignore | 1 + pdelfin/birrpipeline.py | 73 ++----------------- pdelfin/s3_utils.py | 70 ++++++++++++++++++ pdelfin/viewer/dolmaviewer.py | 3 +- ...emplate.html => dolmaviewer_template.html} | 7 +- tests/test_dataloader.py | 1 + 6 files changed, 81 insertions(+), 74 deletions(-) create mode 100644 pdelfin/s3_utils.py rename pdelfin/viewer/{template.html => dolmaviewer_template.html} (90%) diff --git a/.gitignore b/.gitignore index a3f69ec..411792f 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ wandb/ *histogram.png *.json dolma_previews/* +s2_previews/* /*.html diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 127ceed..0c8406c 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -24,6 +24,8 @@ from concurrent.futures import ProcessPoolExecutor, as_completed from pdelfin.data.renderpdf import render_pdf_to_base64png from pdelfin.prompts import build_finetuning_prompt from pdelfin.prompts.anchor import get_anchor_text +from pdelfin.s3_utils import parse_s3_path, expand_s3_glob, get_s3_bytes, put_s3_bytes + # Global s3 client for the whole script, feel free to adjust params if you need it workspace_s3 = boto3.client('s3') @@ -303,7 +305,7 @@ class BatchWriter: def _write_batch_to_file(self, batch_content: str, output_path: str, batch_lines: List[str]): if self.is_s3: - self._write_to_s3(batch_content, output_path) + put_s3_bytes(workspace_s3, output_path, batch_content.encode("utf-8")) else: with open(output_path, 'w', encoding='utf-8') as f_out: f_out.write(batch_content) @@ -312,20 +314,6 @@ class BatchWriter: if self.after_flush: self.after_flush(batch_lines) - def _write_to_s3(self, content: str, s3_path: str): - # Parse the s3_path to get bucket and key - parsed = urlparse(s3_path) - bucket = parsed.netloc - key = parsed.path.lstrip('/') - - s3 = boto3.client('s3') - s3.put_object( - Bucket=bucket, - Key=key, - Body=content.encode('utf-8'), - ContentType='text/plain; charset=utf-8' - ) - def close(self): self._write_batch() # Wait for all threads to finish @@ -333,31 +321,6 @@ class BatchWriter: thread.join() -def parse_s3_path(s3_path): - if not s3_path.startswith('s3://'): - raise ValueError('s3_path must start with s3://') - path = s3_path[5:] - bucket, _, prefix = path.partition('/') - return bucket, prefix - -def expand_s3_glob(s3_client, s3_glob: str) -> Dict[str, str]: - parsed = urlparse(s3_glob) - bucket_name = parsed.netloc - prefix = os.path.dirname(parsed.path.lstrip('/')).rstrip('/') + "/" - pattern = os.path.basename(parsed.path) - - paginator = s3_client.get_paginator('list_objects_v2') - page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix) - - matched_files = {} - for page in page_iterator: - for obj in page.get('Contents', []): - key = obj['Key'] - if glob.fnmatch.fnmatch(key, posixpath.join(prefix, pattern)): - matched_files[f"s3://{bucket_name}/{key}"] = obj['ETag'].strip('"') - - return matched_files - def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> dict: image_base64 = render_pdf_to_base64png(local_pdf_path, page, 1024) anchor_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport") @@ -376,32 +339,6 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di } -def get_s3_bytes(s3_client, s3_path: str, start_index: Optional[int] = None, end_index: Optional[int] = None) -> bytes: - bucket, key = parse_s3_path(s3_path) - - # Build the range header if start_index and/or end_index are specified - range_header = None - if start_index is not None and end_index is not None: - # Range: bytes=start_index-end_index - range_value = f"bytes={start_index}-{end_index}" - range_header = {'Range': range_value} - elif start_index is not None and end_index is None: - # Range: bytes=start_index- - range_value = f"bytes={start_index}-" - range_header = {'Range': range_value} - elif start_index is None and end_index is not None: - # Range: bytes=-end_index (last end_index bytes) - range_value = f"bytes=-{end_index}" - range_header = {'Range': range_value} - - if range_header: - obj = s3_client.get_object(Bucket=bucket, Key=key, Range=range_header['Range']) - else: - obj = s3_client.get_object(Bucket=bucket, Key=key) - - return obj['Body'].read() - - def parse_custom_id(custom_id: str) -> Tuple[str, int]: s3_path = custom_id[:custom_id.rindex("-")] page_num = int(custom_id[custom_id.rindex("-") + 1:]) @@ -577,7 +514,9 @@ def mark_pdfs_done(s3_workspace: str, dolma_doc_lines: list[str]): db.update_pdf_status(json.loads(line)["metadata"]["Source-File"], "completed") def get_current_round(s3_workspace: str) -> int: - bucket, prefix = parse_s3_path(s3_workspace) + path = s3_workspace[5:] + bucket, _, prefix = path.partition('/') + inference_inputs_prefix = posixpath.join(prefix, 'inference_inputs/') paginator = workspace_s3.get_paginator('list_objects_v2') page_iterator = paginator.paginate(Bucket=bucket, Prefix=inference_inputs_prefix, Delimiter='/') diff --git a/pdelfin/s3_utils.py b/pdelfin/s3_utils.py new file mode 100644 index 0000000..18aa4c4 --- /dev/null +++ b/pdelfin/s3_utils.py @@ -0,0 +1,70 @@ +import os +import glob +import posixpath + +from typing import Optional +from urllib.parse import urlparse + + +def parse_s3_path(s3_path: str) -> tuple[str, str]: + if not s3_path.startswith('s3://'): + raise ValueError('s3_path must start with s3://') + parsed = urlparse(s3_path) + bucket = parsed.netloc + key = parsed.path.lstrip('/') + + return bucket, key + + +def expand_s3_glob(s3_client, s3_glob: str) -> dict[str, str]: + parsed = urlparse(s3_glob) + bucket_name = parsed.netloc + prefix = os.path.dirname(parsed.path.lstrip('/')).rstrip('/') + "/" + pattern = os.path.basename(parsed.path) + + paginator = s3_client.get_paginator('list_objects_v2') + page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix) + + matched_files = {} + for page in page_iterator: + for obj in page.get('Contents', []): + key = obj['Key'] + if glob.fnmatch.fnmatch(key, posixpath.join(prefix, pattern)): + matched_files[f"s3://{bucket_name}/{key}"] = obj['ETag'].strip('"') + + return matched_files + +def get_s3_bytes(s3_client, s3_path: str, start_index: Optional[int] = None, end_index: Optional[int] = None) -> bytes: + bucket, key = parse_s3_path(s3_path) + + # Build the range header if start_index and/or end_index are specified + range_header = None + if start_index is not None and end_index is not None: + # Range: bytes=start_index-end_index + range_value = f"bytes={start_index}-{end_index}" + range_header = {'Range': range_value} + elif start_index is not None and end_index is None: + # Range: bytes=start_index- + range_value = f"bytes={start_index}-" + range_header = {'Range': range_value} + elif start_index is None and end_index is not None: + # Range: bytes=-end_index (last end_index bytes) + range_value = f"bytes=-{end_index}" + range_header = {'Range': range_value} + + if range_header: + obj = s3_client.get_object(Bucket=bucket, Key=key, Range=range_header['Range']) + else: + obj = s3_client.get_object(Bucket=bucket, Key=key) + + return obj['Body'].read() + +def put_s3_bytes(s3_client, s3_path: str, data: bytes): + bucket, key = parse_s3_path(s3_path) + + s3_client.put_object( + Bucket=bucket, + Key=key, + Body=data, + ContentType='text/plain; charset=utf-8' + ) \ No newline at end of file diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py index 1bfb13d..ab63c3e 100644 --- a/pdelfin/viewer/dolmaviewer.py +++ b/pdelfin/viewer/dolmaviewer.py @@ -6,7 +6,6 @@ import boto3 from botocore.exceptions import NoCredentialsError, PartialCredentialsError from jinja2 import Template import smart_open -import cached_path from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor, as_completed @@ -106,7 +105,7 @@ if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate HTML pages from a JSONL file with pre-signed S3 links.') parser.add_argument('jsonl_path', help='Path to the JSONL file (local or s3://)') parser.add_argument('--output_dir', default='dolma_previews', help='Directory to save HTML files') - parser.add_argument('--template_path', default='template.html', help='Path to the Jinja2 template file') + parser.add_argument('--template_path', default='dolmaviewer_template.html', help='Path to the Jinja2 template file') args = parser.parse_args() main(args.jsonl_path, args.output_dir, args.template_path) diff --git a/pdelfin/viewer/template.html b/pdelfin/viewer/dolmaviewer_template.html similarity index 90% rename from pdelfin/viewer/template.html rename to pdelfin/viewer/dolmaviewer_template.html index cdb5d03..1b932e5 100644 --- a/pdelfin/viewer/template.html +++ b/pdelfin/viewer/dolmaviewer_template.html @@ -48,17 +48,14 @@
- {% if s3_link %} - - {% endif %} {% for page in pages %}
{{ page.text|safe }}
{% if page.image %}
+ Page {{ page.page_num }} Image +
{% endif %}
diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 8f30652..559f80b 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -66,6 +66,7 @@ class TestBatchQueryResponseDataset(unittest.TestCase): initial_len = len(ds) from pdelfin.train.dataprep import filter_by_max_seq_len + print("Filtering on max sequence length") ds = ds.filter(partial(filter_by_max_seq_len, processor=processor, max_prompt_len=2200, max_response_len=2200)) formatted_dataset = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor)) From 19e56ec7ce106f26b275d909615175276bb41963 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:21:25 +0000 Subject: [PATCH 24/30] dolma viewer runs much faster now --- pdelfin/viewer/dolmaviewer.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py index ab63c3e..0487601 100644 --- a/pdelfin/viewer/dolmaviewer.py +++ b/pdelfin/viewer/dolmaviewer.py @@ -3,12 +3,14 @@ import json import html import argparse import boto3 +import tempfile from botocore.exceptions import NoCredentialsError, PartialCredentialsError from jinja2 import Template import smart_open from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor, as_completed +from pdelfin.s3_utils import get_s3_bytes from pdelfin.data.renderpdf import render_pdf_to_base64webp def read_jsonl(path): @@ -42,9 +44,11 @@ def process_document(data, s3_client, template, output_dir): # Generate base64 image of the corresponding PDF page if source_file and source_file.startswith('s3://'): - local_pdf_path = cached_path.cached_path(source_file) + local_pdf = tempfile.NamedTemporaryFile("wb+", suffix=".pdf") + local_pdf.write(get_s3_bytes(s3_client, source_file)) + local_pdf.flush() else: - local_pdf_path = source_file + raise ValueError("Expecting s3 files only") pages = [] for span in pdf_page_numbers: @@ -53,10 +57,12 @@ def process_document(data, s3_client, template, output_dir): # Replace line breaks with
tags page_text = html.escape(page_text).replace('\n', '
\n') - base64_image = render_pdf_to_base64webp(local_pdf_path, page_num) + base64_image = render_pdf_to_base64webp(local_pdf.name, page_num) pages.append({'page_num': page_num, 'text': page_text, 'image': base64_image}) + local_pdf.close() + # Generate pre-signed URL if source_file is an S3 path s3_link = None if source_file and source_file.startswith('s3://'): From baa82a4a9a2a45f6bf01d2026e7290d4e1f45ef7 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:37:08 +0000 Subject: [PATCH 25/30] Fixing links, rendering tables --- pdelfin/viewer/dolmaviewer.py | 7 +++- pdelfin/viewer/dolmaviewer_template.html | 47 +++++++++++++++++++++++- 2 files changed, 51 insertions(+), 3 deletions(-) diff --git a/pdelfin/viewer/dolmaviewer.py b/pdelfin/viewer/dolmaviewer.py index 0487601..8f56361 100644 --- a/pdelfin/viewer/dolmaviewer.py +++ b/pdelfin/viewer/dolmaviewer.py @@ -9,6 +9,7 @@ from jinja2 import Template import smart_open from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor, as_completed +import markdown2 from pdelfin.s3_utils import get_s3_bytes from pdelfin.data.renderpdf import render_pdf_to_base64webp @@ -54,8 +55,10 @@ def process_document(data, s3_client, template, output_dir): for span in pdf_page_numbers: start_index, end_index, page_num = span page_text = text[start_index:end_index] - # Replace line breaks with
tags - page_text = html.escape(page_text).replace('\n', '
\n') + + # Detect and convert Markdown to HTML + page_text = html.escape(page_text, quote=True).replace('<br>', '
') + page_text = markdown2.markdown(page_text, extras=["tables"]) base64_image = render_pdf_to_base64webp(local_pdf.name, page_num) diff --git a/pdelfin/viewer/dolmaviewer_template.html b/pdelfin/viewer/dolmaviewer_template.html index 1b932e5..7f89422 100644 --- a/pdelfin/viewer/dolmaviewer_template.html +++ b/pdelfin/viewer/dolmaviewer_template.html @@ -44,6 +44,51 @@ height: auto; border: 1px solid #ccc; } + + table { + width: 100%; + border-collapse: collapse; /* Ensures that borders are collapsed to give a clean look */ + margin-bottom: 1.5em; /* Adds some space below the table */ + } + + th, td { + border: 1px solid #ddd; /* 1px solid border for table cells */ + padding: 12px 15px; /* Adds some padding for better spacing inside the cells */ + text-align: left; /* Aligns text to the left */ + vertical-align: top; /* Aligns content to the top of the cell */ + font-size: 14px; /* Adjusts font size for readability */ + } + + th { + background-color: #f4f4f4; /* Light background for table headers */ + font-weight: bold; /* Bolds header text */ + text-transform: uppercase; /* Makes header text uppercase */ + letter-spacing: 0.05em; /* Adds slight spacing between letters for readability */ + border-bottom: 2px solid #ccc; /* Slightly thicker bottom border for headers */ + } + + tr:nth-child(even) { + background-color: #f9f9f9; /* Alternates row background color */ + } + + tr:hover { + background-color: #f1f1f1; /* Highlights row on hover for better interaction */ + } + + td img { + max-width: 100%; /* Ensures any images in table cells scale properly */ + height: auto; + display: block; + } + + table caption { + caption-side: bottom; /* Position caption at the bottom of the table */ + text-align: right; + font-size: 12px; + color: #777; + padding: 5px 0; + } + @@ -53,7 +98,7 @@
{{ page.text|safe }}
{% if page.image %} From a7cd7467c3664e15a711fd58394014ee6bc879be Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 16:45:07 +0000 Subject: [PATCH 26/30] mathjax --- pdelfin/viewer/dolmaviewer_template.html | 18 ++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/pdelfin/viewer/dolmaviewer_template.html b/pdelfin/viewer/dolmaviewer_template.html index 7f89422..5189a78 100644 --- a/pdelfin/viewer/dolmaviewer_template.html +++ b/pdelfin/viewer/dolmaviewer_template.html @@ -90,6 +90,22 @@ } + + +
@@ -106,5 +122,7 @@
{% endfor %}
+ + diff --git a/pyproject.toml b/pyproject.toml index c379c72..45b0570 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ dependencies = [ "Pillow", "ftfy", "bleach", - "duckdb", + "markdown2", ] license = {file = "LICENSE"} From a2546e0b0428b5490484b4a4a446b0914856bd26 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 17:06:03 +0000 Subject: [PATCH 27/30] more stuff --- .../discoverworld_crazy_tables.pdf | Bin 0 -> 3054585 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/gnarly_pdfs/discoverworld_crazy_tables.pdf diff --git a/tests/gnarly_pdfs/discoverworld_crazy_tables.pdf b/tests/gnarly_pdfs/discoverworld_crazy_tables.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6ecb26d2f015f5e751fd72ff4d69c568421eb009 GIT binary patch literal 3054585 zcmd>m2|Sf;x3^g&QiM|5R3!5}@1mkIMp1?xGL?CjVN*orp$M@RGKEZ;Vv`JoWQa_e z=Xti-dtcwZrJmso8wm*=$R#moG{0^-!C 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18:06:27 +0000 Subject: [PATCH 28/30] Organizing around a new style of dataloader --- .gitignore | 1 + pdelfin/birrpipeline.py | 7 +- pdelfin/data/renderpdf.py | 61 +++++++++- pdelfin/s3_utils.py | 7 +- pdelfin/train/dataloader.py | 235 +++++++++--------------------------- tests/test_dataloader.py | 24 +--- 6 files changed, 128 insertions(+), 207 deletions(-) diff --git a/.gitignore b/.gitignore index 411792f..61856c5 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ wandb/ *.json dolma_previews/* s2_previews/* +gnarly_previews/* /*.html diff --git a/pdelfin/birrpipeline.py b/pdelfin/birrpipeline.py index 0c8406c..7ed1062 100644 --- a/pdelfin/birrpipeline.py +++ b/pdelfin/birrpipeline.py @@ -24,7 +24,7 @@ from concurrent.futures import ProcessPoolExecutor, as_completed from pdelfin.data.renderpdf import render_pdf_to_base64png from pdelfin.prompts import build_finetuning_prompt from pdelfin.prompts.anchor import get_anchor_text -from pdelfin.s3_utils import parse_s3_path, expand_s3_glob, get_s3_bytes, put_s3_bytes +from pdelfin.s3_utils import parse_custom_id, expand_s3_glob, get_s3_bytes, put_s3_bytes # Global s3 client for the whole script, feel free to adjust params if you need it @@ -339,11 +339,6 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di } -def parse_custom_id(custom_id: str) -> Tuple[str, int]: - s3_path = custom_id[:custom_id.rindex("-")] - page_num = int(custom_id[custom_id.rindex("-") + 1:]) - return s3_path, page_num - def process_jsonl_content(inference_s3_path: str) -> List[DatabaseManager.BatchInferenceRecord]: content_bytes = get_s3_bytes(workspace_s3, inference_s3_path) diff --git a/pdelfin/data/renderpdf.py b/pdelfin/data/renderpdf.py index 7e058b8..2987411 100644 --- a/pdelfin/data/renderpdf.py +++ b/pdelfin/data/renderpdf.py @@ -67,4 +67,63 @@ def render_pdf_to_base64webp(local_pdf_path: str, page: int, target_longest_imag webp_output = io.BytesIO() png_image.save(webp_output, format="WEBP") - return base64.b64encode(webp_output.getvalue()).decode("utf-8") \ No newline at end of file + return base64.b64encode(webp_output.getvalue()).decode("utf-8") + + +def get_png_dimensions_from_base64(base64_data) -> tuple[int, int]: + """ + Returns the (width, height) of a PNG image given its base64-encoded data, + without base64-decoding the entire data or loading the PNG itself + + Should be really fast to support filtering + + Parameters: + - base64_data (str): Base64-encoded PNG image data. + + Returns: + - tuple: (width, height) of the image. + + Raises: + - ValueError: If the data is not a valid PNG image or the required bytes are not found. + """ + # PNG signature is 8 bytes + png_signature_base64 = base64.b64encode(b'\x89PNG\r\n\x1a\n').decode('ascii') + if not base64_data.startswith(png_signature_base64[:8]): + raise ValueError('Not a valid PNG file') + + # Positions in the binary data where width and height are stored + width_start = 16 # Byte position where width starts (0-based indexing) + width_end = 20 # Byte position where width ends (exclusive) + height_start = 20 + height_end = 24 + + # Compute the byte range needed (from width_start to height_end) + start_byte = width_start + end_byte = height_end + + # Calculate base64 character positions + # Each group of 3 bytes corresponds to 4 base64 characters + base64_start = (start_byte // 3) * 4 + base64_end = ((end_byte + 2) // 3) * 4 # Add 2 to ensure we cover partial groups + + # Extract the necessary base64 substring + base64_substring = base64_data[base64_start:base64_end] + + # Decode only the necessary bytes + decoded_bytes = base64.b64decode(base64_substring) + + # Compute the offset within the decoded bytes + offset = start_byte % 3 + + # Extract width and height bytes + width_bytes = decoded_bytes[offset:offset+4] + height_bytes = decoded_bytes[offset+4:offset+8] + + if len(width_bytes) < 4 or len(height_bytes) < 4: + raise ValueError('Insufficient data to extract dimensions') + + # Convert bytes to integers + width = int.from_bytes(width_bytes, 'big') + height = int.from_bytes(height_bytes, 'big') + + return width, height \ No newline at end of file diff --git a/pdelfin/s3_utils.py b/pdelfin/s3_utils.py index 18aa4c4..5b208a5 100644 --- a/pdelfin/s3_utils.py +++ b/pdelfin/s3_utils.py @@ -67,4 +67,9 @@ def put_s3_bytes(s3_client, s3_path: str, data: bytes): Key=key, Body=data, ContentType='text/plain; charset=utf-8' - ) \ No newline at end of file + ) + +def parse_custom_id(custom_id: str) -> tuple[str, int]: + s3_path = custom_id[:custom_id.rindex("-")] + page_num = int(custom_id[custom_id.rindex("-") + 1:]) + return s3_path, page_num \ No newline at end of file diff --git a/pdelfin/train/dataloader.py b/pdelfin/train/dataloader.py index ee17643..a320d63 100644 --- a/pdelfin/train/dataloader.py +++ b/pdelfin/train/dataloader.py @@ -10,12 +10,14 @@ import pypdf, pypdf.errors from functools import partial from typing import Any, Dict, Optional from logging import Logger +from filelock import FileLock 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 +from pdelfin.s3_utils import parse_custom_id, get_s3_bytes, parse_s3_path # Configure logging logging.basicConfig(level=logging.INFO) @@ -68,145 +70,12 @@ def load_jsonl_into_ds(s3_glob_path: str, first_n_files: int = None) -> Dataset: return dataset -def get_png_dimensions_from_base64(base64_data) -> tuple[int, int]: - """ - Returns the (width, height) of a PNG image given its base64-encoded data, - without base64-decoding the entire data or loading the PNG itself - - Should be really fast to support filtering - - Parameters: - - base64_data (str): Base64-encoded PNG image data. - - Returns: - - tuple: (width, height) of the image. - - Raises: - - ValueError: If the data is not a valid PNG image or the required bytes are not found. - """ - # PNG signature is 8 bytes - png_signature_base64 = base64.b64encode(b'\x89PNG\r\n\x1a\n').decode('ascii') - if not base64_data.startswith(png_signature_base64[:8]): - raise ValueError('Not a valid PNG file') - - # Positions in the binary data where width and height are stored - width_start = 16 # Byte position where width starts (0-based indexing) - width_end = 20 # Byte position where width ends (exclusive) - height_start = 20 - height_end = 24 - - # Compute the byte range needed (from width_start to height_end) - start_byte = width_start - end_byte = height_end - - # Calculate base64 character positions - # Each group of 3 bytes corresponds to 4 base64 characters - base64_start = (start_byte // 3) * 4 - base64_end = ((end_byte + 2) // 3) * 4 # Add 2 to ensure we cover partial groups - - # Extract the necessary base64 substring - base64_substring = base64_data[base64_start:base64_end] - - # Decode only the necessary bytes - decoded_bytes = base64.b64decode(base64_substring) - - # Compute the offset within the decoded bytes - offset = start_byte % 3 - - # Extract width and height bytes - width_bytes = decoded_bytes[offset:offset+4] - height_bytes = decoded_bytes[offset+4:offset+8] - - if len(width_bytes) < 4 or len(height_bytes) < 4: - raise ValueError('Insufficient data to extract dimensions') - - # Convert bytes to integers - width = int.from_bytes(width_bytes, 'big') - height = int.from_bytes(height_bytes, 'big') - - 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 - """ - custom_id = query.get("custom_id", "") - body = query.get("body", {}) - messages = body.get("messages", []) - - input_prompt_text = "" - input_prompt_image_base64 = "" - - for message in messages: - if message.get("role") != "user": - continue # We are only interested in user messages - - contents = message.get("content", []) - for content_item in contents: - if content_item.get("type") == "text": - input_prompt_text = content_item.get("text", "") - elif content_item.get("type") == "image_url": - image_url = content_item.get("image_url", {}).get("url", "") - if image_url.startswith("data:image"): - # Extract base64 part from data URL - try: - base64_data = image_url.split(",", 1)[1] - input_prompt_image_base64 = base64_data - except IndexError: - input_prompt_image_base64 = "" - - # This code builds the finetuning prompt from the original openai prompt by extracting the "pdf_report hint anchor text" from that - # and reusing it - # # At this point, the input_prompt_text is the raw text that was passed to the OpenAI model - # # 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 - # match = re.search(pattern, input_prompt_text, re.DOTALL) - - # if match: - # raw_page_text = match.group(1).strip() - # else: - # 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) - - try: - raw_page_text = get_anchor_text(tf.name, page_num, pdf_engine="pdfreport") - except pypdf.errors.PdfReadError: - raw_page_text = None - - return { - "custom_id": custom_id, - "input_prompt_text": input_prompt_text, - "input_prompt_image_base64": input_prompt_image_base64, - "raw_page_text": raw_page_text, - } - - def extract_openai_batch_response(example): custom_id = example.get("custom_id", None) + + # Parse the custom id into an s3 document path and page number (1indexed) + s3_path, page_num = parse_custom_id(custom_id) + response_body = example.get("response", {}).get("body", {}) choices = response_body.get("choices", []) response = "" @@ -217,61 +86,71 @@ def extract_openai_batch_response(example): response = message.get("content", "") finish_reason = first_choice.get("finish_reason", "") - return {"custom_id": custom_id, "response": response, "finish_reason": finish_reason} + # TODO Maybe in the future we can parse the response (which is a structured JSON document itself) + # into its own columns + return {"s3_path": s3_path, "page_num": page_num, "response": response, "finish_reason": finish_reason} -def merge_query_response(query_example, response_data: Dataset, response_map: dict[str, int]): - custom_id = query_example["custom_id"] +def _cache_s3_file(s3_path: str, local_cache_dir: str): + """ + Downloads an S3 object to a local cache directory, ensuring no two writers corrupt the same file. + """ + bucket, key = parse_s3_path(s3_path) - if custom_id not in response_map: - return { - "response": None, - "finish_reason": None, - } + # Define the local file path + local_file_path = os.path.join(local_cache_dir, key.replace("/", "_")) + os.makedirs(os.path.dirname(local_file_path), exist_ok=True) + lock_file = f"{local_file_path}.lock" - response_row = response_data[response_map[custom_id]] + # Use a file lock to prevent concurrent writes + with FileLock(lock_file): + if not os.path.exists(local_file_path): + logger.info(f"Downloading {s3_path} to {local_file_path}") + 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') + ) + s3_client.download_file(bucket, key, local_file_path) + else: + logger.info(f"File {local_file_path} already exists, skipping download.") - return {"response": response_row["response"], "finish_reason": response_row["finish_reason"]} + return local_file_path +def cache_s3_files(dataset: Dataset, pdf_cache_location: str, num_proc: int = 32) -> Dataset: + """ + Caches all S3 paths in the dataset to the local cache directory. + """ + # Define the download function to use in parallel processing + def cache_file(example): + s3_path = example["s3_path"] + if s3_path: + # Download the file and cache it locally + local_path = _cache_s3_file(s3_path, pdf_cache_location) + return {"local_pdf_path": local_path} + return {"local_pdf_path": None} -def build_batch_query_response_vision_dataset(query_glob_path: str, response_glob_path: str, num_proc: int=32) -> Dataset: - logger.info("Loading query and response datasets") - query_data = load_jsonl_into_ds(query_glob_path) + # Map the caching function to the dataset (with parallelism if needed) + dataset = dataset.map(cache_file, num_proc=num_proc, load_from_cache_file=False) + + return dataset + +def build_finetuning_dataset(response_glob_path: str, pdf_cache_location: Optional[str]=None, num_proc: int=32) -> Dataset: + if pdf_cache_location is None: + pdf_cache_location = os.path.join(os.path.expanduser('~'), '.cache', 'pdelfin_pdfs') + + logger.info("Loading fine tuning dataset from OpenAI style batch responses") response_data = load_jsonl_into_ds(response_glob_path) - - # Map the datasets down to the core fields that we're going to need to make them easier to process - logger.info("Mapping query data") - query_data = query_data["train"] - query_data = query_data.map(extract_openai_batch_query, remove_columns=query_data.column_names, num_proc=num_proc) - query_data = query_data.filter(lambda x: x["raw_page_text"] is not None, num_proc=num_proc) - - logger.info("Mapping response data") response_data = response_data["train"] + response_data = response_data.map(extract_openai_batch_response, remove_columns=response_data.column_names, num_proc=num_proc) - # What we're going to do, is build an in-memory map for the response data from custom_id to row - # This will let us do quick lookups when we do a merge step, but it will not scale past a certain point - logger.info("Building custom_id to row map") - custom_id_to_response_row = {} - for row_id, entry in enumerate(response_data): - custom_id_to_response_row[entry["custom_id"]] = row_id - - logger.info("Running merge map") - final_dataset = query_data.map( - partial(merge_query_response, response_data=response_data, response_map=custom_id_to_response_row), - num_proc=num_proc - ) - # Don't include data where the model cut off due to a length issue, or moderation issue logger.info("Filtering on finish_reason == stop") - final_dataset = final_dataset.filter(lambda x: x["finish_reason"] == "stop", num_proc=num_proc) + final_dataset = response_data.filter(lambda x: x["finish_reason"] == "stop", num_proc=num_proc) - # Pick things that have a reasonable image size only - def pick_image_sizes(x): - width, height = get_png_dimensions_from_base64(x["input_prompt_image_base64"]) - return 1800 <= max(width, height) <= 2200 - logger.info("Filtering on image size") - final_dataset = final_dataset.filter(pick_image_sizes, num_proc=num_proc) + # Cache all the s3_paths that were accessed to a local storage location, + final_dataset = cache_s3_files(final_dataset, pdf_cache_location, num_proc) return final_dataset diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 559f80b..b633ab2 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -6,8 +6,7 @@ from functools import partial from transformers import AutoProcessor from pdelfin.train.dataloader import ( - build_batch_query_response_vision_dataset, - extract_openai_batch_query, + build_finetuning_dataset, extract_openai_batch_response, load_jsonl_into_ds, list_dataset_files @@ -24,9 +23,8 @@ class TestBatchQueryResponseDataset(unittest.TestCase): print(ds) print(ds["train"]) - def testCombinedQueryResponse(self): - ds = build_batch_query_response_vision_dataset( - query_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_eval/*.jsonl", + def testFinetuningDS(self): + ds = build_finetuning_dataset( response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_eval/*.json", ) @@ -38,22 +36,6 @@ class TestBatchQueryResponseDataset(unittest.TestCase): print(ds[0]) - def testLocalDS(self): - ds = build_batch_query_response_vision_dataset( - query_glob_path="/root/openai_batch_data_v5_1_train/*.jsonl", - response_glob_path="/root/openai_batch_data_v5_1_train_done/*.json", - ) - - print(ds) - - ds.to_parquet("/root/trainds_parquet/bigds.parquet") - - processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") - from pdelfin.train.dataprep import filter_by_max_seq_len - ds = ds.filter(partial(filter_by_max_seq_len, processor=processor, max_prompt_len=1000)) - - print(ds[0]) - def testPlotSequenceLengthHistogram(self): import plotly.express as px From 3c1b7de293ff71c43c37ed729478497e2f0cc384 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 18:26:25 +0000 Subject: [PATCH 29/30] Refactoring of train dataloaders --- pdelfin/prompts/anchor.py | 4 +- pdelfin/train/dataloader.py | 3 + pdelfin/train/dataprep.py | 160 ++++++------------------------------ pyproject.toml | 1 + tests/test_dataloader.py | 61 +++----------- 5 files changed, 41 insertions(+), 188 deletions(-) diff --git a/pdelfin/prompts/anchor.py b/pdelfin/prompts/anchor.py index d994017..579ea1b 100644 --- a/pdelfin/prompts/anchor.py +++ b/pdelfin/prompts/anchor.py @@ -26,7 +26,7 @@ from pypdf.generic import RectangleObject from pdelfin.prompts._adv_anchor import mult -def get_anchor_text(local_pdf_path: str, page: int, pdf_engine: Literal["pdftotext", "pdfium", "pymupdf", "pypdf", "topcoherency", "pdfreport"]) -> str: +def get_anchor_text(local_pdf_path: str, page: int, pdf_engine: Literal["pdftotext", "pdfium", "pymupdf", "pypdf", "topcoherency", "pdfreport"], target_length: int=4000) -> str: assert page > 0, "Pages are 1-indexed in pdf-land" if pdf_engine == "pdftotext": @@ -54,7 +54,7 @@ def get_anchor_text(local_pdf_path: str, page: int, pdf_engine: Literal["pdftote return best_option elif pdf_engine == "pdfreport": - return _linearize_pdf_report(_pdf_report(local_pdf_path, page)) + return _linearize_pdf_report(_pdf_report(local_pdf_path, page), max_length=target_length) else: raise NotImplementedError("Unknown engine") diff --git a/pdelfin/train/dataloader.py b/pdelfin/train/dataloader.py index a320d63..0e39d25 100644 --- a/pdelfin/train/dataloader.py +++ b/pdelfin/train/dataloader.py @@ -23,6 +23,9 @@ from pdelfin.s3_utils import parse_custom_id, get_s3_bytes, parse_s3_path logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) +# Quiet logs from pypdf and smart open +logging.getLogger("pypdf").setLevel(logging.ERROR) +logging.getLogger("smart_open").setLevel(logging.ERROR) def list_dataset_files(s3_glob_path: str): """ diff --git a/pdelfin/train/dataprep.py b/pdelfin/train/dataprep.py index de04da5..f3ed5e1 100644 --- a/pdelfin/train/dataprep.py +++ b/pdelfin/train/dataprep.py @@ -4,19 +4,15 @@ from PIL import Image import base64 import torch # Make sure to import torch as it's used in the DataCollator +from pdelfin.prompts.anchor import get_anchor_text from pdelfin.prompts import build_finetuning_prompt - -def filter_by_max_seq_len(example, processor, max_prompt_len: int=2200, max_response_len: int=2200): - if len(processor.tokenizer.tokenize(example["input_prompt_text"])) > max_prompt_len: - return False - - if len(processor.tokenizer.tokenize(example["response"])) > max_response_len: - return False - - return True +from pdelfin.data.renderpdf import render_pdf_to_base64png -def prepare_data_for_qwen2_training(example, processor, add_batch_dim=False): +def prepare_data_for_qwen2_training(example, processor, target_longest_image_dim: int, target_anchor_text_len: int): + anchor_text = get_anchor_text(example["local_pdf_path"], example["page_num"], pdf_engine="pdfreport", target_length=target_anchor_text_len) + base64_page_image = render_pdf_to_base64png(example["local_pdf_path"], example["page_num"], target_longest_image_dim=target_longest_image_dim) + # Prepare messages messages = [ { @@ -24,9 +20,9 @@ def prepare_data_for_qwen2_training(example, processor, add_batch_dim=False): "content": [ { "type": "image", - "image": example["input_prompt_image_base64"] # Placeholder + "image": base64_page_image }, - {"type": "text", "text": build_finetuning_prompt(example["raw_page_text"])}, + {"type": "text", "text": build_finetuning_prompt(anchor_text)}, ], } ] @@ -36,14 +32,7 @@ def prepare_data_for_qwen2_training(example, processor, add_batch_dim=False): ) # Decode image from base64 - main_image = Image.open(BytesIO(base64.b64decode(example["input_prompt_image_base64"]))) - - # Right now, we are going to downsample to 1024 on the longest dimension, because - # 2048 as we passed to OpenAI is too large for training - width, height = main_image.size - assert 1800 <= max(width, height) <= 2200, f"Image size {width}x{height} invalid" - main_image = main_image.resize((width // 2, height // 2), Image.LANCZOS) - + main_image = Image.open(BytesIO(base64.b64decode(base64_page_image))) # Process inputs using processor inputs = processor( @@ -84,36 +73,30 @@ def prepare_data_for_qwen2_training(example, processor, add_batch_dim=False): labels_full = np.full_like(input_ids, fill_value=-100) labels_full[len(inputs.input_ids[0]):] = labels.input_ids[0] + # TODO Maybe cap the max length + # Return as dict, including pixel_values - if add_batch_dim: - return { - "input_ids": input_ids[np.newaxis, ...], - "attention_mask": attention_mask[np.newaxis, ...], - "labels": labels_full[np.newaxis, ...], - "pixel_values": inputs.pixel_values[np.newaxis, ...], - "image_grid_thw": inputs["image_grid_thw"] - } - else: - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "labels": labels_full, - "pixel_values": inputs.pixel_values, - "image_grid_thw": inputs["image_grid_thw"][0] - } + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "labels": labels_full, + "pixel_values": inputs.pixel_values, + "image_grid_thw": inputs["image_grid_thw"][0] + } -def batch_prepare_data_for_qwen2_training(batch, processor): +def batch_prepare_data_for_qwen2_training(batch, processor, target_longest_image_dim: int, target_anchor_text_len: int): # Process each example in the batch using the helper function processed_examples = [] - for i in range(len(batch["input_prompt_image_base64"])): + for i in range(len(batch["response"])): example = { - "input_prompt_image_base64": batch["input_prompt_image_base64"][i], - "input_prompt_text": batch["input_prompt_text"][i], - "raw_page_text": batch["raw_page_text"][i], + "local_pdf_path": batch["local_pdf_path"][i], + "page_num": batch["page_num"][i], "response": batch["response"][i] } - processed_example = prepare_data_for_qwen2_training(example, processor) + processed_example = prepare_data_for_qwen2_training(example, processor, + target_longest_image_dim=target_longest_image_dim, + target_anchor_text_len=target_anchor_text_len) processed_examples.append(processed_example) return { @@ -124,96 +107,3 @@ def batch_prepare_data_for_qwen2_training(batch, processor): "image_grid_thw": [x["image_grid_thw"] for x in processed_examples], } - -def prepare_data_for_qwen2_inference(example, processor): - # Prepare messages - messages = [ - { - "role": "user", - "content": [ - { - "type": "image", - "image": example["input_prompt_image_base64"] # Placeholder - }, - {"type": "text", "text": example["input_prompt_text"]}, - ], - } - ] - # Apply chat template to get the text - text = processor.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - - # Decode image from base64 - main_image = Image.open(BytesIO(base64.b64decode(example["input_prompt_image_base64"]))) - - # Right now, we are going to downsample to 1024 on the longest dimension, because - # 2048 as we passed to OpenAI is too large for training - width, height = main_image.size - if 1800 <= max(width, height) <= 2200: - main_image = main_image.resize((width // 2, height // 2), Image.LANCZOS) - - - # Process inputs using processor - inputs = processor( - text=[text], - images=[main_image], - padding=True, - return_tensors="np", - ) - - input_ids = inputs["input_ids"][0] - - # All columns will participate in attention fully - attention_mask = np.ones_like(input_ids) - - # Return as dict, including pixel_values - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "pixel_values": inputs.pixel_values, - "image_grid_thw": inputs["image_grid_thw"][0] - } - - -def batch_prepare_data_for_qwen2_inference(batch, processor): - # Process each example in the batch using the helper function - processed_examples = [] - for i in range(len(batch["input_prompt_image_base64"])): - example = { - "input_prompt_image_base64": batch["input_prompt_image_base64"][i], - "input_prompt_text": batch["input_prompt_text"][i], - "raw_page_text": batch["raw_page_text"][i], - } - processed_example = prepare_data_for_qwen2_inference(example, processor) - processed_examples.append(processed_example) - - return { - "input_ids": [x["input_ids"] for x in processed_examples], - "attention_mask": [x["attention_mask"] for x in processed_examples], - "pixel_values": [x["pixel_values"] for x in processed_examples], - "image_grid_thw": [x["image_grid_thw"] for x in processed_examples], - } - -# Define a custom data collator -class DataCollatorForVisionLanguageModeling: - def __init__(self, processor): - self.processor = processor - - def __call__(self, features): - input_ids = [f['input_ids'] for f in features] - attention_mask = [f['attention_mask'] for f in features] - labels = [f['labels'] for f in features] - pixel_values = [f['pixel_values'] for f in features] - - # Pad input_ids, attention_mask, labels - batch = self.processor.pad( - {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}, - return_tensors="pt", - padding=True, - ) - - # Stack pixel_values - batch['pixel_values'] = torch.stack([torch.tensor(pv) for pv in pixel_values]) - - return batch diff --git a/pyproject.toml b/pyproject.toml index 45b0570..dd3ea9c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,7 @@ dependencies = [ "ftfy", "bleach", "markdown2", + "filelock", ] license = {file = "LICENSE"} diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index b633ab2..a74aee6 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -12,7 +12,7 @@ from pdelfin.train.dataloader import ( list_dataset_files ) -from pdelfin.train.dataprep import batch_prepare_data_for_qwen2_training, prepare_data_for_qwen2_training +from pdelfin.train.dataprep import batch_prepare_data_for_qwen2_training class TestBatchQueryResponseDataset(unittest.TestCase): @@ -31,28 +31,27 @@ class TestBatchQueryResponseDataset(unittest.TestCase): print(ds) processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") - from pdelfin.train.dataprep import filter_by_max_seq_len - ds = ds.filter(partial(filter_by_max_seq_len, processor=processor, max_prompt_len=1000)) + + ds = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor, target_longest_image_dim=1024, target_anchor_text_len=6000)) print(ds[0]) def testPlotSequenceLengthHistogram(self): import plotly.express as px - ds = build_batch_query_response_vision_dataset( - query_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_eval/*.jsonl", + ds = build_finetuning_dataset( response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_eval/*.json", ) + + processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") + + ds = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor, target_longest_image_dim=1024, target_anchor_text_len=6000)) + processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") initial_len = len(ds) - from pdelfin.train.dataprep import filter_by_max_seq_len - print("Filtering on max sequence length") - ds = ds.filter(partial(filter_by_max_seq_len, processor=processor, max_prompt_len=2200, max_response_len=2200)) - - formatted_dataset = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor)) - train_dataloader = DataLoader(formatted_dataset, batch_size=1, num_workers=30, shuffle=False) + train_dataloader = DataLoader(ds, batch_size=1, num_workers=30, shuffle=False) max_seen_len = 0 steps = 0 @@ -81,43 +80,3 @@ class TestBatchQueryResponseDataset(unittest.TestCase): ) fig.write_image("sequence_lengths_histogram.png") - - def testExtractBatch(self): - query_data = load_jsonl_into_ds("s3://ai2-oe-data/jakep/openai_batch_data_v2_mini/*.jsonl", first_n_files=3) - query_data = query_data["train"] - query_data = query_data.map(extract_openai_batch_query, remove_columns=query_data.column_names) - - print(query_data) - print(query_data[0]["custom_id"], query_data[0]["input_prompt_text"]) - - def testExtractResponse(self): - response_data = load_jsonl_into_ds("s3://ai2-oe-data/jakep/openai_batch_done_v2/*.json", first_n_files=3) - response_data = response_data["train"] - - response_data = response_data.map(extract_openai_batch_response, remove_columns=response_data.column_names) - - print(response_data) - print(response_data[0]) - - def testPyArrowDirectJson(self): - 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_eval/*.json" - - all_files = list_dataset_files(query_glob_path) - - import pyarrow as pa - 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) - From d4f64ed82a1780df3a462e7e5b2853f377cf17e9 Mon Sep 17 00:00:00 2001 From: Jake Poznanski Date: Wed, 16 Oct 2024 18:37:52 +0000 Subject: [PATCH 30/30] Config work --- pdelfin/train/config/qwen2vl-7b-lora.yaml | 34 ++++++++--------------- pdelfin/train/core/config.py | 30 ++------------------ 2 files changed, 15 insertions(+), 49 deletions(-) diff --git a/pdelfin/train/config/qwen2vl-7b-lora.yaml b/pdelfin/train/config/qwen2vl-7b-lora.yaml index 87a24b7..38d3a09 100644 --- a/pdelfin/train/config/qwen2vl-7b-lora.yaml +++ b/pdelfin/train/config/qwen2vl-7b-lora.yaml @@ -7,43 +7,33 @@ wandb: project: pdelfin entity: ai2-llm -# TODO This is not used -format: - instruction_template: "Original:" - response_template: "Rewritten:" - # Template from here: https://github.com/QwenLM/Qwen2/blob/main/examples/sft/finetune.py#L30 - chat_template: | - {% for message in messages %} - {{'<|im_start|>' + message['role'] + '\n' + message['content']}} - {% if loop.last %} - {{ '<|im_end|>'}} - {% else %} - {{ '<|im_end|>\n' }} - {% endif %} - {% endfor %} - generate: - max_length: 4096 + max_length: 8192 train_data: seed: 1337 sources: - # These tend to be really big, so it's only practical to host them as parquets on weka, otherwise you may OOM or just never finish dataloading - name: openai_batch_data_v5_1_train - parquet_path: /data/jakep/pdfdata/openai_batch_data_v5_1_parquet/*.parquet - - name: openai_batch_data_v5_1_train - parquet_path: /data/jakep/pdfdata/openai_batch_data_v5_1_iabooks_parquet/*.parquet + response_glob_path: /data/jakep/pdfdata/openai_batch_data_v5_1_train_done/*.json + target_longest_image_dim: 1024 + target_anchor_text_len: 6000 + - name: openai_batch_data_v5_1_iabooks_train + response_glob_path: /data/jakep/pdfdata/openai_batch_data_v5_1_iabooks_train_done/*.json + target_longest_image_dim: 1024 + target_anchor_text_len: 6000 valid_data: metric_for_best_model: openai_batch_data_v5_1_eval_loss sources: # These tend to be small, so you can load from s3 it's no big deal - name: openai_batch_data_v5_1_eval - 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_eval/*.json + target_longest_image_dim: 1024 + target_anchor_text_len: 6000 - name: openai_batch_data_v5_1_iabooks_eval - query_glob_path: s3://ai2-oe-data/jakep/pdfdata/openai_batch_data_v5_1_iabooks_eval/*.jsonl response_glob_path: s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_iabooks_eval/*.json + target_longest_image_dim: 1024 + target_anchor_text_len: 6000 diff --git a/pdelfin/train/core/config.py b/pdelfin/train/core/config.py index 6e677fc..c3389ae 100644 --- a/pdelfin/train/core/config.py +++ b/pdelfin/train/core/config.py @@ -22,28 +22,6 @@ class ModelConfig: model_revision: Optional[str] = field(help="The model revision to use for the model.", default=None) -@dataclass -class FormatConfig: - """Configuration for formatting the text that is input to the model.""" - - new_line_symbol: str = field( - help="The symbol to use for new lines in the text; default is '\\n'.", - default="\n", - ) - system_message: Optional[str] = field( - help="The system message to use for formatting the text; default is no system message.", - default=None, - ) - instruction_template: str = field( - help="The template to use for formatting the input text", default="Original:" - ) - response_template: str = field(help="The template to use for formatting the output text", default="Rewrite:") - chat_template: Optional[str] = field( - help="The template to use for formatting the chat text. If None, the default chat template will be used.", - default=None, - ) - - @dataclass class GenerateConfig: max_length: int = field(help="The maximum length of the generated text", default=4096) @@ -75,9 +53,9 @@ class AwsConfig: @dataclass class SourceConfig: name: str = field(help="The name of the source") - parquet_path: Optional[str] = field(help="The s3/glob path to a bunch of parquet files for a preprocessed dataset.", default=None) - query_glob_path: Optional[str] = field(help="The s3 bucket pointing to the inputs sent to OpenAI to generate the silver data", default=None) - response_glob_path: Optional[str] = field(help="The s3 bucket pointing to the batch api response json's sent back from open ai", default=None) + response_glob_path: str = field(help="The s3 bucket pointing to the batch api response json's sent back from open ai") + target_longest_image_dim: int = field(help="Dimensions to render the pdf page image to") + target_anchor_text_len: int = field(help="Maximum amount of anchor text (aka prompt hint)") @dataclass @@ -141,7 +119,6 @@ class TrainConfig: lora: Optional[LoraConfig] = field(default=None, help="The LoRA configuration") aws: AwsConfig = field(default=AwsConfig(), help="Configuration for AWS S3") wandb: WandbConfig = field(default=WandbConfig(), help="Configuration for Weights and Biases") - format: FormatConfig = field(default=FormatConfig(), help="Configuration for formatting the input/output text") train_data: DataConfig = field(default=DataConfig(), help="Configuration for the training data") valid_data: DataConfig = field(default=DataConfig(), help="Configuration for the validation data") generate: GenerateConfig = field(default=GenerateConfig(), help="Configuration for text generation") @@ -158,5 +135,4 @@ class DemoConfig: share: bool = field(default=False, help="Share the demo publicly.") model: ModelConfig = field(default=ModelConfig()) - format: FormatConfig = field(default=FormatConfig()) generate: GenerateConfig = field(default=GenerateConfig())

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