Update app.py

app.py

@@ -1,94 +1,285 @@
-import csv
-from datetime import datetime
+import argparse
+import json
 import os
-from typing import Optional
-import gradio as gr
+import shutil
+from collections import defaultdict
+from inspect import signature
+from tempfile import TemporaryDirectory
+from typing import Dict, List, Optional, Set
 
-from convert import convert
-from huggingface_hub import HfApi, Repository
+import torch
 
+from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
+from huggingface_hub.file_download import repo_folder_name
+from safetensors.torch import load_file, save_file
+from transformers import AutoConfig
+from transformers.pipelines.base import infer_framework_load_model
 
-DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
-DATA_FILENAME = "data.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
 
-HF_TOKEN = os.environ.get("HF_TOKEN")
+class AlreadyExists(Exception):
+    pass
 
-repo: Optional[Repository] = None
-if HF_TOKEN:
-    repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)
 
+def shared_pointers(tensors):
+    ptrs = defaultdict(list)
+    for k, v in tensors.items():
+        ptrs[v.data_ptr()].append(k)
+    failing = []
+    for ptr, names in ptrs.items():
+        if len(names) > 1:
+            failing.append(names)
+    return failing
 
-def run(token: str, model_id: str) -> str:
-    if token == "" or model_id == "":
-        return """
-        ### Invalid input 🐞
-        
-        Please fill a token and model_id.
-        """
+
+def check_file_size(sf_filename: str, pt_filename: str):
+    sf_size = os.stat(sf_filename).st_size
+    pt_size = os.stat(pt_filename).st_size
+
+    if (sf_size - pt_size) / pt_size > 0.01:
+        raise RuntimeError(
+            f"""The file size different is more than 1%:
+         - {sf_filename}: {sf_size}
+         - {pt_filename}: {pt_size}
+         """
+        )
+
+
+def rename(pt_filename: str) -> str:
+    filename, ext = os.path.splitext(pt_filename)
+    local = f"{filename}.safetensors"
+    local = local.replace("pytorch_model", "model")
+    return local
+
+
+def convert_multi(model_id: str, folder: str) -> List["CommitOperationAdd"]:
+    filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json")
+    with open(filename, "r") as f:
+        data = json.load(f)
+
+    filenames = set(data["weight_map"].values())
+    local_filenames = []
+    for filename in filenames:
+        pt_filename = hf_hub_download(repo_id=model_id, filename=filename)
+
+        sf_filename = rename(pt_filename)
+        sf_filename = os.path.join(folder, sf_filename)
+        convert_file(pt_filename, sf_filename)
+        local_filenames.append(sf_filename)
+
+    index = os.path.join(folder, "model.safetensors.index.json")
+    with open(index, "w") as f:
+        newdata = {k: v for k, v in data.items()}
+        newmap = {k: rename(v) for k, v in data["weight_map"].items()}
+        newdata["weight_map"] = newmap
+        json.dump(newdata, f, indent=4)
+    local_filenames.append(index)
+
+    operations = [
+        CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames
+    ]
+
+    return operations
+
+
+def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
+    pt_filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
+
+    sf_name = "model.safetensors"
+    sf_filename = os.path.join(folder, sf_name)
+    convert_file(pt_filename, sf_filename)
+    operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)]
+    return operations
+
+
+def convert_file(
+    pt_filename: str,
+    sf_filename: str,
+):
+    loaded = torch.load(pt_filename, map_location="cpu")
+    if "state_dict" in loaded:
+        loaded = loaded["state_dict"]
+    shared = shared_pointers(loaded)
+    for shared_weights in shared:
+        for name in shared_weights[1:]:
+            loaded.pop(name)
+
+    # For tensors to be contiguous
+    loaded = {k: v.contiguous() for k, v in loaded.items()}
+
+    dirname = os.path.dirname(sf_filename)
+    os.makedirs(dirname, exist_ok=True)
+    save_file(loaded, sf_filename, metadata={"format": "pt"})
+    check_file_size(sf_filename, pt_filename)
+    reloaded = load_file(sf_filename)
+    for k in loaded:
+        pt_tensor = loaded[k]
+        sf_tensor = reloaded[k]
+        if not torch.equal(pt_tensor, sf_tensor):
+            raise RuntimeError(f"The output tensors do not match for key {k}")
+
+
+def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str:
+    errors = []
+    for key in ["missing_keys", "mismatched_keys", "unexpected_keys"]:
+        pt_set = set(pt_infos[key])
+        sf_set = set(sf_infos[key])
+
+        pt_only = pt_set - sf_set
+        sf_only = sf_set - pt_set
+
+        if pt_only:
+            errors.append(f"{key} : PT warnings contain {pt_only} which are not present in SF warnings")
+        if sf_only:
+            errors.append(f"{key} : SF warnings contain {sf_only} which are not present in PT warnings")
+    return "\n".join(errors)
+
+
+def check_final_model(model_id: str, folder: str):
+    config = hf_hub_download(repo_id=model_id, filename="config.json")
+    shutil.copy(config, os.path.join(folder, "config.json"))
+    config = AutoConfig.from_pretrained(folder)
+
+    _, (pt_model, pt_infos) = infer_framework_load_model(model_id, config, output_loading_info=True)
+    _, (sf_model, sf_infos) = infer_framework_load_model(folder, config, output_loading_info=True)
+
+    if pt_infos != sf_infos:
+        error_string = create_diff(pt_infos, sf_infos)
+        raise ValueError(f"Different infos when reloading the model: {error_string}")
+
+    pt_params = pt_model.state_dict()
+    sf_params = sf_model.state_dict()
+
+    pt_shared = shared_pointers(pt_params)
+    sf_shared = shared_pointers(sf_params)
+    if pt_shared != sf_shared:
+        raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}")
+
+    sig = signature(pt_model.forward)
+    input_ids = torch.arange(10).unsqueeze(0)
+    pixel_values = torch.randn(1, 3, 224, 224)
+    input_values = torch.arange(1000).float().unsqueeze(0)
+    kwargs = {}
+    if "input_ids" in sig.parameters:
+        kwargs["input_ids"] = input_ids
+    if "decoder_input_ids" in sig.parameters:
+        kwargs["decoder_input_ids"] = input_ids
+    if "pixel_values" in sig.parameters:
+        kwargs["pixel_values"] = pixel_values
+    if "input_values" in sig.parameters:
+        kwargs["input_values"] = input_values
+    if "bbox" in sig.parameters:
+        kwargs["bbox"] = torch.zeros((1, 10, 4)).long()
+    if "image" in sig.parameters:
+        kwargs["image"] = pixel_values
+
+    if torch.cuda.is_available():
+        pt_model = pt_model.cuda()
+        sf_model = sf_model.cuda()
+        kwargs = {k: v.cuda() for k, v in kwargs.items()}
+
+    pt_logits = pt_model(**kwargs)[0]
+    sf_logits = sf_model(**kwargs)[0]
+
+    torch.testing.assert_close(sf_logits, pt_logits)
+    print(f"Model {model_id} is ok !")
+
+
+def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
     try:
-        api = HfApi(token=token)
-        is_private = api.model_info(repo_id=model_id).private
-        print("is_private", is_private)
-
-        commit_info = convert(api=api, model_id=model_id)
-        print("[commit_info]", commit_info)
-
-        # save in a (public) dataset:
-        if repo is not None and not is_private:
-            repo.git_pull(rebase=True)
-            print("pulled")
-            with open(DATA_FILE, "a") as csvfile:
-                writer = csv.DictWriter(
-                    csvfile, fieldnames=["model_id", "pr_url", "time"]
-                )
-                writer.writerow(
-                    {
-                        "model_id": model_id,
-                        "pr_url": commit_info.pr_url,
-                        "time": str(datetime.now()),
-                    }
+        discussions = api.get_repo_discussions(repo_id=model_id)
+    except Exception:
+        return None
+    for discussion in discussions:
+        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
+            return discussion
+
+
+def convert_generic(model_id: str, folder: str, filenames: Set[str]) -> List["CommitOperationAdd"]:
+    operations = []
+
+    extensions = set([".bin", ".ckpt"])
+    for filename in filenames:
+        prefix, ext = os.path.splitext(filename)
+        if ext in extensions:
+            pt_filename = hf_hub_download(model_id, filename=filename)
+            _, raw_filename = os.path.split(filename)
+            if raw_filename == "pytorch_model.bin":
+                # XXX: This is a special case to handle `transformers` and the
+                # `transformers` part of the model which is actually loaded by `transformers`.
+                sf_in_repo = "model.safetensors"
+            else:
+                sf_in_repo = f"{prefix}.safetensors"
+            sf_filename = os.path.join(folder, sf_in_repo)
+            convert_file(pt_filename, sf_filename)
+            operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename))
+    return operations
+
+
+def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]:
+    pr_title = "Adding `safetensors` variant of this model"
+    info = api.model_info(model_id)
+    filenames = set(s.rfilename for s in info.siblings)
+
+    with TemporaryDirectory() as d:
+        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
+        os.makedirs(folder)
+        new_pr = None
+        try:
+            operations = None
+            pr = previous_pr(api, model_id, pr_title)
+
+            library_name = getattr(info, "library_name", None)
+            if any(filename.endswith(".safetensors") for filename in filenames) and not force:
+                raise AlreadyExists(f"Model {model_id} is already converted, skipping..")
+            elif pr is not None and not force:
+                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
+                new_pr = pr
+                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
+            elif library_name == "transformers":
+                if "pytorch_model.bin" in filenames:
+                    operations = convert_single(model_id, folder)
+                elif "pytorch_model.bin.index.json" in filenames:
+                    operations = convert_multi(model_id, folder)
+                else:
+                    raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert")
+                check_final_model(model_id, folder)
+            else:
+                operations = convert_generic(model_id, folder, filenames)
+
+            if operations:
+                new_pr = api.create_commit(
+                    repo_id=model_id,
+                    operations=operations,
+                    commit_message=pr_title,
+                    create_pr=True,
                 )
-            commit_url = repo.push_to_hub()
-            print("[dataset]", commit_url)
-
-        return f"""
-        ### Success 🔥
-
-        Yay! This model was successfully converted and a PR was open using your token, here:
-
-        [{commit_info.pr_url}]({commit_info.pr_url})
-        """
-    except Exception as e:
-        return f"""
-        ### Error 😢😢😢
-        
-        {e}
-        """
-
-
-DESCRIPTION = """
-The steps are the following:
-
-- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
-- Input a model id from the Hub
-- Click "Submit"
-- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
-
-⚠️ For now only `pytorch_model.bin` files are supported but we'll extend in the future.
-"""
-
-demo = gr.Interface(
-    title="Convert any model to Safetensors and open a PR",
-    description=DESCRIPTION,
-    allow_flagging="never",
-    article="Check out the [Safetensors repo on GitHub](https://github.com/huggingface/safetensors)",
-    inputs=[
-        gr.Text(max_lines=1, label="your_hf_token"),
-        gr.Text(max_lines=1, label="model_id"),
-    ],
-    outputs=[gr.Markdown(label="output")],
-    fn=run,
-)
-
-demo.launch()
+                print(f"Pr created at {new_pr.pr_url}")
+            else:
+                print("No files to convert")
+        finally:
+            shutil.rmtree(folder)
+        return new_pr
+
+
+if __name__ == "__main__":
+    DESCRIPTION = """
+    Simple utility tool to convert automatically some weights on the hub to `safetensors` format.
+    It is PyTorch exclusive for now.
+    It works by downloading the weights (PT), converting them locally, and uploading them back
+    as a PR on the hub.
+    """
+    parser = argparse.ArgumentParser(description=DESCRIPTION)
+    parser.add_argument(
+        "model_id",
+        type=str,
+        help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
+    )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Create the PR even if it already exists of if the model was already converted.",
+    )
+    args = parser.parse_args()
+    model_id = args.model_id
+    api = HfApi()
+    convert(api, model_id, force=args.force)