cli -- llama inference configure

toolchain/cli/download.py

@@ -9,7 +9,7 @@ from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
 from toolchain.cli.subcommand import Subcommand
 
 
-DEFAULT_OUTPUT_DIR = "/tmp/llama_toolchain_cache/"
+DEFAULT_CHECKPOINT_DIR = f"{os.path.expanduser('~')}/.llama/checkpoints/"
 
 
 class Download(Subcommand):
@@ -43,13 +43,6 @@ class Download(Subcommand):
             type=str,
             help="Name of the repository on Hugging Face Hub eg. llhf/Meta-Llama-3.1-70B-Instruct",
         )
-        self.parser.add_argument(
-            "--output-dir",
-            type=Path,
-            required=False,
-            default=None,
-            help=f"Directory in which to save the model. Defaults to `{DEFAULT_OUTPUT_DIR}<model_name>`.",
-        )
         self.parser.add_argument(
             "--hf-token",
             type=str,
@@ -57,18 +50,21 @@ class Download(Subcommand):
             default=os.getenv("HF_TOKEN", None),
             help="Hugging Face API token. Needed for gated models like Llama2. Will also try to read environment variable `HF_TOKEN` as default.",
         )
+        self.parser.add_argument(
+            "--ignore-patterns",
+            type=str,
+            required=False,
+            default="*.safetensors",
+            help="If provided, files matching any of the patterns are not downloaded. Defaults to ignoring "
+            "safetensors files to avoid downloading duplicate weights.",
+        )
 
     def _run_download_cmd(self, args: argparse.Namespace):
         model_name = args.repo_id.split("/")[-1]
 
-        os.makedirs(DEFAULT_OUTPUT_DIR, exist_ok=True)
+        os.makedirs(output_dir, exist_ok=True)
-        output_dir = args.output_dir
-        model_name = args.repo_id.split("/")[-1]
-        if output_dir is None:
-            output_dir = Path(DEFAULT_OUTPUT_DIR) / model_name
-        else:
-            output_dir = Path(output_dir) / model_name
 
+        output_dir = Path(output_dir) / model_name
         try:
             true_output_dir = snapshot_download(
                 args.repo_id,
@@ -76,6 +72,7 @@ class Download(Subcommand):
                 # "auto" will download to cache_dir and symlink files to local_dir
                 # avoiding unnecessary duplicate copies
                 local_dir_use_symlinks="auto",
+                ignore_patterns=args.ignore_patterns,
                 token=args.hf_token,
             )
         except GatedRepoError:

toolchain/cli/inference/configure.py

@@ -0,0 +1,80 @@
+import argparse
+import os
+import textwrap
+
+from pathlib import Path
+
+from toolchain.cli.subcommand import Subcommand
+
+
+CONFIGS_BASE_DIR = f"{os.path.expanduser('~')}/.llama/configs/"
+
+class InferenceConfigure(Subcommand):
+    """Llama cli for configuring llama toolchain configs"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "configure",
+            prog="llama inference configure",
+            description="Configure llama toolchain inference configs",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama inference configure
+                """
+            ),
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_inference_configure_cmd)
+
+    def _add_arguments(self):
+        pass
+
+    def read_user_inputs(self):
+        checkpoint_dir = input("Enter the checkpoint directory for the model (e.g., ~/.llama/checkpoints/Meta-Llama-3-8B/): ")
+        model_parallel_size = input("Enter model parallel size (e.g., 1 for 8B / 8 for 70B and 405B): ")
+
+        return checkpoint_dir, model_parallel_size
+
+    def write_output_yaml(
+        self, 
+        checkpoint_dir, 
+        model_parallel_size, 
+        yaml_output_path
+    ):
+        yaml_content = textwrap.dedent(f"""
+            model_inference_config:
+                impl_type: "inline"
+                inline_config:
+                    checkpoint_type: "pytorch"
+                    checkpoint_dir: {checkpoint_dir}/
+                    tokenizer_path: {checkpoint_dir}/tokenizer.model
+                    model_parallel_size: {model_parallel_size}
+                    max_seq_len: 2048
+                    max_batch_size: 1
+            """)
+        with open(yaml_output_path, 'w') as yaml_file:
+            yaml_file.write(yaml_content.strip())
+
+        print(f"YAML configuration has been written to {yaml_output_path}")
+
+    def _run_inference_configure_cmd(self, args: argparse.Namespace) -> None:
+        checkpoint_dir, model_parallel_size = self.read_user_inputs()
+        checkpoint_dir = os.path.expanduser(checkpoint_dir)
+        
+        if not (
+            checkpoint_dir.endswith("original") or
+            checkpoint_dir.endswith("original/")
+        ):
+            checkpoint_dir = os.path.join(checkpoint_dir, "original")
+
+        os.makedirs(CONFIGS_BASE_DIR, exist_ok=True)
+        yaml_output_path = Path(CONFIGS_BASE_DIR) / "inference.yaml"
+
+        self.write_output_yaml(
+            checkpoint_dir,
+            model_parallel_size,
+            yaml_output_path,
+        )

toolchain/cli/inference/inference.py

@@ -1,6 +1,7 @@
 import argparse
 import textwrap
 
+from toolchain.cli.inference.configure import InferenceConfigure
 from toolchain.cli.inference.start import InferenceStart
 from toolchain.cli.subcommand import Subcommand
 
@@ -26,3 +27,4 @@ class InferenceParser(Subcommand):
 
         # Add sub-commandsa
         InferenceStart.create(subparsers)
+        InferenceConfigure.create(subparsers)