Release commit

Signed-off-by: Stephen Augustus <saugustus2@bloomberg.net>

examples/run_page_embedding.py

@@ -0,0 +1,180 @@
+# Copyright 2024 Bloomberg Finance L.P.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from pathlib import Path
+
+import accelerate
+import safetensors
+import torch
+import transformers
+from accelerate import Accelerator
+from loguru import logger
+from tqdm import tqdm
+
+from m3docrag.datasets.m3_docvqa import M3DocVQADataset
+from m3docrag.retrieval import ColPaliRetrievalModel
+from m3docrag.utils.args import parse_args
+from m3docrag.utils.distributed import (
+    barrier,
+    global_rank,
+    is_distributed,
+    local_rank,
+    log_runtime_info,
+    print_gpu_stats,
+)
+from m3docrag.utils.paths import (
+    LOCAL_DATA_DIR,
+    LOCAL_MODEL_DIR,
+)
+
+logger.info(torch.__version__)
+logger.info(transformers.__version__)
+logger.info(accelerate.__version__)
+
+
+def main():
+    args = parse_args()
+
+    log_runtime_info()
+    print_gpu_stats()
+
+    accelerator = Accelerator()
+
+    if not is_distributed() or global_rank() == 0:
+        logger.info(f"Process {global_rank()}:{local_rank()} - args {args}")
+
+    if is_distributed():
+        barrier()
+
+    local_data_dir = Path(LOCAL_DATA_DIR) / args.data_name
+    local_retrieval_model_dir = (
+        Path(LOCAL_MODEL_DIR) / args.retrieval_model_name_or_path
+    )
+    local_retrieval_adapter_model_dir = (
+        Path(LOCAL_MODEL_DIR) / args.retrieval_adapter_model_name_or_path
+    )
+
+    # Download datasets / model checkpoints
+    if not is_distributed() or global_rank() == 0:
+        if not local_data_dir.exists():
+            raise ValueError(f"Data directory {local_data_dir} does not exist")
+
+        assert args.use_retrieval, args.use_retrieval
+
+        if not local_retrieval_model_dir.exists():
+            raise ValueError(
+                f"Retrieval model directory {local_retrieval_model_dir} does not exist"
+            )
+
+        if args.retrieval_model_type == "colpali":
+            if not local_retrieval_adapter_model_dir.exists():
+                raise ValueError(
+                    f"Retrieval adapter model directory {local_retrieval_adapter_model_dir} does not exist"
+                )
+
+    if is_distributed():
+        barrier()
+
+    if args.retrieval_model_type == "colpali":
+        colpali_model = ColPaliRetrievalModel(
+            backbone_name_or_path=local_retrieval_model_dir,
+            adapter_name_or_path=local_retrieval_adapter_model_dir,
+        )
+        retrieval_model = colpali_model
+
+    if args.data_name == "m3-docvqa":
+        dataset = M3DocVQADataset(args=args)
+
+        def collate_fn(examples):
+            out = {}
+            if args.retrieval_model_type == "colpali":
+                for k in ["doc_id", "images"]:
+                    out[k] = [ex[k] for ex in examples]
+            return out
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset=dataset,
+        collate_fn=collate_fn,
+        batch_size=1,
+        shuffle=False,
+        batch_sampler=None,
+        sampler=None,
+        drop_last=False,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    retrieval_model.model, data_loader = accelerator.prepare(
+        retrieval_model.model, data_loader
+    )
+
+    all_results = []
+
+    save_dir = Path(args.output_dir)
+    save_dir.mkdir(exist_ok=True, parents=True)
+    logger.info(f"Results will be saved at: {save_dir}")
+
+    for i, datum in enumerate(tqdm(data_loader)):
+        print(f"{i} / {len(data_loader)}")
+
+        if args.data_name == "mp-docvqa":
+            page_name = datum["page_name"][0]
+            logger.info(page_name)
+        else:
+            doc_id = datum["doc_id"][0]
+            logger.info(doc_id)
+
+        if args.retrieval_model_type == "colpali":
+            images = datum["images"][0]
+
+            doc_embs = colpali_model.encode_images(
+                images=images,
+                batch_size=args.per_device_eval_batch_size,
+                to_cpu=True,
+                use_tqdm=False,
+            )
+
+            # [n_pages, n_tokens, emb_dim]
+            doc_embs = torch.stack(doc_embs, dim=0)
+
+        # Store embedding as BF16 by default
+        doc_embs = doc_embs.to(torch.bfloat16)
+
+        logger.info(doc_embs.shape)
+        if args.retrieval_model_type == "colpali":
+            logger.info(doc_embs[0, 0, :5])
+
+        # Save the embedding
+        if args.data_name == "mp-docvqa":
+            local_save_fname = f"{page_name}.safetensors"
+        else:
+            local_save_fname = f"{doc_id}.safetensors"
+        local_save_path = save_dir / local_save_fname
+
+        if args.retrieval_model_type == "colpali":
+            safetensors.torch.save_file({"embeddings": doc_embs}, local_save_path)
+
+        all_results.append({"save_path": local_save_path})
+
+    logger.info(
+        f"Process {global_rank()}:{local_rank()} Results correctly saved at {save_dir}"
+    )
+
+    if is_distributed():
+        barrier()
+
+
+if __name__ == "__main__":
+    main()