Implement download subcommand, optional positional model name argument (

#234)

* Implement download option

* Add support for model aliases

* Support model name as a positional parameter

* Merge GenerateArgs changes

* Run lint

* Revert chat subcommand/arg changes

* Add mistral-7b-instruct alias, fix lints

* Add model config for known models

* Move known model config to config/models.json

* Make model names case-insensitive

* Move known model configuration from build/model.py to config/model_config.py

* Fix lints

* Fixing issues after rebasing

* Update README

.github/workflows/pull.yml

@@ -361,6 +361,11 @@ jobs:
           cat ./output_eager2
           echo "Tests complete."
 
+      - name: Test download
+        run: |
+
+          python torchchat.py generate stories15M
+
   test-tinystories-eager:
     strategy:
       matrix:

.gitignore

@@ -5,3 +5,5 @@ __pycache__/
 
 # C extensions
 *.so
+
+.model-artifacts/

README.md

@@ -32,14 +32,10 @@ python torchchat.py --help
 
 ```
 
-### Dowenload a Model and Tokenizer
+### Generating Text
 
 ```
-#download a model
-python torchchat.py download llama2
-
-#generate text using the model
-
+python torchchat.py generate stories15M
 ```
 That’s all there is to it!
 Read on to learn how to use the full power of torchchat.
@@ -48,7 +44,15 @@ Read on to learn how to use the full power of torchchat.
 For the full details on all commands and parameters run `python torchchat.py --help`
 
 ### Download
-TODO: Fill this out
+For supported models, torchchat can download model weights. Most models use HuggingFace as the distribution channel, so you will need to create a HuggingFace
+account and install `huggingface-cli`.
+
+To install `huggingface-cli`, run `pip install huggingface-cli`. After installing, create a user access token [as documented here](https://huggingface.co/docs/hub/en/security-tokens). Run `huggingface-cli login`, which will prompt for the newly created token. Once this is done, torchchat will be able to download model artifacts from
+HuggingFace.
+
+```
+python torchchat.py download llama2
+```
 
 ### Chat
 Designed for interactive and conversational use.
@@ -69,7 +73,7 @@ For more information run `python torchchat.py generate --help`
 
 **Examples**
 ```
-#Generate for Mac with some parameters
+python torchchat.py generate llama2 --device=cpu --dtype=fp16
 ```
 
 ### Export
@@ -80,7 +84,7 @@ For more information run `python torchchat.py export --help`
 **Examples**
 
 ```
-#Export Example
+python torchchat.py export stories15M --output-pte-path=stories15m.pte
 ```
 
 ### Browser

build/builder.py

@@ -14,7 +14,7 @@
 import torch
 import torch._dynamo.config
 import torch._inductor.config
-
+from config.model_config import resolve_model_config
 from quantize import name_to_dtype, quantize_model
 
 from sentencepiece import SentencePieceProcessor
@@ -42,7 +42,7 @@ class BuilderArgs:
     def __post_init__(self):
         if not (
             (self.checkpoint_path and self.checkpoint_path.is_file())
-            or (self.checkpoint_dir and self.checkpoint_path.is_dir())
+            or (self.checkpoint_dir and self.checkpoint_dir.is_dir())
             or (self.gguf_path and self.gguf_path.is_file())
             or (self.dso_path and Path(self.dso_path).is_file())
             or (self.pte_path and Path(self.pte_path).is_file())
@@ -73,7 +73,17 @@ def from_args(cls, args):  # -> BuilderArgs:
         # Handle disabled checkpoint_dir option
         checkpoint_dir = None
         if hasattr(args, "checkpoint_dir"):
-            checkpoint_dir = args.checkpoint_dir    
+            checkpoint_dir = args.checkpoint_dir
+
+        checkpoint_path = args.checkpoint_path
+        if args.model:  # Using a named, well-known model
+            model_config = resolve_model_config(args.model)
+
+            checkpoint_path = (
+                Path(args.model_directory)
+                / model_config.name
+                / model_config.checkpoint_file
+            )
 
         is_chat_model = False
         if args.is_chat_model:
@@ -94,8 +104,8 @@ def from_args(cls, args):  # -> BuilderArgs:
                     is_chat_model = True
 
         return cls(
-            checkpoint_path=args.checkpoint_path,
             checkpoint_dir=checkpoint_dir,
+            checkpoint_path=checkpoint_path,
             params_path=args.params_path,
             params_table=args.params_table,
             gguf_path=args.gguf_path,
@@ -134,9 +144,12 @@ def from_args(cls, args):  # -> TokenizerArgs:
 
         if args.tokenizer_path:
             tokenizer_path = args.tokenizer_path
+        elif args.model:  # Using a named, well-known model
+            model_config = resolve_model_config(args.model)
+            tokenizer_path = Path(args.model_directory) / model_config.name / "tokenizer.model"
         elif args.checkpoint_path:
             tokenizer_path = args.checkpoint_path.parent / "tokenizer.model"
-        elif args.checkpoint_dir:
+        elif hasattr(args, "checkpoint_dir") and args.checkpoint_dir:
             tokenizer_path = args.checkpoint_dir / "tokenizer.model"
         else:
             raise RuntimeError("cannot find tokenizer model")
@@ -356,4 +369,10 @@ def validate_args(model: Transformer, tokenizer_args: TokenizerArgs):
     is_tiktoken = tokenizer_args.is_tiktoken
     if use_tiktoken != is_tiktoken:
         raise RuntimeError(f"model-specified tokenizer ({tokenizer_setting_to_name(use_tiktoken)} does not match provided tokenizer ({tokenizer_setting_to_name(is_tiktoken)}")
-
+
+def resolve_model_name(model: str) -> str:
+    # If the provided model name is an alias, retrieve the full path.
+    if model in model_aliases:
+        return model_aliases[model]
+    else:
+        return model

scripts/convert_hf_checkpoint.py → build/convert_hf_checkpoint.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 import json
+import os
 import re
 import sys
 from pathlib import Path
@@ -22,19 +23,20 @@
 @torch.inference_mode()
 def convert_hf_checkpoint(
     *,
-    checkpoint_dir: Optional[Path] = None,
+    model_dir: Optional[Path] = None,
     model_name: Optional[str] = None,
+    remove_bin_files: bool = False,
 ) -> None:
-    if checkpoint_dir is None:
-        checkpoint_dir = Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf")
+    if model_dir is None:
+        model_dir = Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf")
     if model_name is None:
-        model_name = checkpoint_dir.name
+        model_name = model_dir.name
 
     config = ModelArgs.from_name(model_name)
     print(f"Model config {config.__dict__}")
 
     # Load the json file containing weight mapping
-    model_map_json = checkpoint_dir / "pytorch_model.bin.index.json"
+    model_map_json = model_dir / "pytorch_model.bin.index.json"
 
     assert model_map_json.is_file()
 
@@ -56,7 +58,7 @@ def convert_hf_checkpoint(
         "model.norm.weight": "norm.weight",
         "lm_head.weight": "output.weight",
     }
-    bin_files = {checkpoint_dir / bin for bin in bin_index["weight_map"].values()}
+    bin_files = {model_dir / bin for bin in bin_index["weight_map"].values()}
 
     def permute(w, n_heads):
         dim = config.dim
@@ -97,8 +99,13 @@ def permute(w, n_heads):
             del final_result[key]
             del final_result[key.replace("wq", "wk")]
             del final_result[key.replace("wq", "wv")]
-    print(f"Saving checkpoint to {checkpoint_dir / 'model.pth'}")
-    torch.save(final_result, checkpoint_dir / "model.pth")
+    print(f"Saving checkpoint to {model_dir / 'model.pth'}. This may take a while.")
+    torch.save(final_result, model_dir / "model.pth")
+    print("Done.")
+
+    if remove_bin_files:
+        for file in bin_files:
+            os.remove(file)
 
 
 if __name__ == "__main__":
@@ -114,6 +121,6 @@ def permute(w, n_heads):
 
     args = parser.parse_args()
     convert_hf_checkpoint(
-        checkpoint_dir=args.checkpoint_dir,
+        model_dir=args.checkpoint_dir,
         model_name=args.model_name,
     )

cli.py

@@ -14,6 +14,11 @@
 def check_args(args, name: str) -> None:
     pass
 
+def add_arguments_for_download(parser):
+    # Only download specific options should be here
+    _add_arguments_common(parser)
+
+
 def add_arguments_for_generate(parser):
     # Only generate specific options should be here
     _add_arguments_common(parser)
@@ -39,6 +44,19 @@ def add_arguments_for_browser(parser):
     )
 
 def _add_arguments_common(parser):
+    # Model specification. TODO Simplify this.
+    # A model can be specified using a positional model name or HuggingFace
+    # path. Alternatively, the model can be specified via --gguf-path or via
+    # an explicit --checkpoint-dir, --checkpoint-path, or --tokenizer-path.
+
+    parser.add_argument(
+        "model",
+        type=str,
+        nargs="?",
+        default=None,
+        help="Model name for well-known models.",
+    )
+
     # TODO: Refactor this so that only common options are here
     # and subcommand-specific options are inside individual
     # add_arguments_for_generate, add_arguments_for_export etc.
@@ -168,6 +186,18 @@ def _add_arguments_common(parser):
         default=None,
         help="maximum length sequence to evaluate",
     )
+    parser.add_argument(
+        "--hf-token",
+        type=str,
+        default=None,
+        help="A HuggingFace API token to use when downloading model artifacts",
+    )
+    parser.add_argument(
+        "--model-directory",
+        type=Path,
+        default=".model-artifacts",
+        help="The directory to store downloaded model artifacts",
+    )
 
 
 def arg_init(args):

config/data/models.json

@@ -0,0 +1,28 @@
+{
+    "meta-llama/Llama-2-7b-chat-hf": {
+        "aliases": ["llama2", "llama2-7b"],
+        "distribution_channel": "HuggingFaceSnapshot",
+        "distribution_path": "meta-llama/Llama-2-7b-chat-hf"
+    },
+    "mistralai/Mistral-7B-Instruct-v0.2": {
+        "aliases": ["mistral-7b-instruct"],
+        "distribution_channel": "HuggingFaceSnapshot",
+        "distribution_path": "mistralai/Mistral-7B-Instruct-v0.2"
+    },
+    "stories15M": {
+        "distribution_channel": "DirectDownload",
+        "distribution_path": [
+            "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt",
+            "https://github.com/karpathy/llama2.c/raw/master/tokenizer.model"
+        ],
+        "checkpoint_file": "stories15M.pt"
+    },
+    "stories110M": {
+        "distribution_channel": "DirectDownload",
+        "distribution_path": [
+            "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt",
+            "https://github.com/karpathy/llama2.c/raw/master/tokenizer.model"
+        ],
+        "checkpoint_file": "stories110M.pt"
+    }
+}

config/model_config.py

@@ -0,0 +1,86 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from typing import Dict, Sequence, Union
+
+"""
+Known Model Configs:
+
+For models that are known to work with torchchat, we provide a config under
+config/data/models.json to support automatically downloading the model and
+converting to the expected format for use with torchchat.
+
+There are two supported distribution channels:
+
+1) HuggingFaceSnapshot: Download a model from HuggingFace.
+2) DirectDownload: Download a list of model artifacts from URLs. No conversion
+   is done.
+"""
+
+
+# Specifies the distribution channel to download model artifacts from. Enum
+# variants are specified as strings to simplify JSON (de)serialization.
+class ModelDistributionChannel(str, Enum):
+    # Download a full model snapshot from HuggingFace, such as
+    # meta-llama/Llama-2-7b-chat-hf and convert to torchchat format.
+    HuggingFaceSnapshot = "HuggingFaceSnapshot"
+
+    # Download one or more files over HTTP(S).
+    DirectDownload = "DirectDownload"
+
+
+@dataclass
+class ModelConfig:
+    name: str = field(default="")
+    aliases: Sequence[str] = field(default_factory=list)
+    distribution_path: Union[str, Sequence[str]] = field(default="")
+    distribution_channel: ModelDistributionChannel = field(
+        default=ModelDistributionChannel.HuggingFaceSnapshot
+    )
+    checkpoint_file: str = field(default="model.pth")
+
+
+# Keys are stored in lowercase.
+model_aliases: Dict[str, str] = None
+model_configs: Dict[str, ModelConfig] = None
+
+
+def resolve_model_config(model: str) -> ModelConfig:
+    global model_aliases
+    global model_configs
+
+    model = model.lower()
+
+    # Lazy load model config from JSON.
+    if not model_configs:
+        model_aliases = {}
+        model_configs = {}
+
+        with open(
+            Path(__file__).parent.parent / "config" / "data" / "models.json", "r"
+        ) as f:
+            model_config_dict = json.load(f)
+
+        for key, value in model_config_dict.items():
+            config = ModelConfig(**value)
+            config.name = key
+
+            key = key.lower()
+            model_configs[key] = config
+
+            for alias in config.aliases:
+                model_aliases[alias.lower()] = key
+
+    if model in model_aliases:
+        model = model_aliases[model]
+
+    if model not in model_configs:
+        raise ValueError(f"Unknown model '{model}'.")
+
+    return model_configs[model]