Remove sharded ckpt from export_llama

examples/models/llama/export_llama_lib.py

@@ -229,12 +229,6 @@ def build_args_parser() -> argparse.ArgumentParser:
         help="Path to the checkpoint .pth file. When not provided, the model will be initialized with random weights.",
     )
 
-    parser.add_argument(
-        "--checkpoint_dir",
-        default=None,
-        help="checkpoint directory. Use with a sharded checkpoint, not for the standard llama2 model. Note, checkpoint_dir takes precedence over checkpoint if both are set.",
-    )
-
     parser.add_argument(
         "--adapter_checkpoint",
         required=False,
@@ -678,18 +672,12 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
         if llm_config.base.checkpoint
         else None
     )
-    checkpoint_dir = (
-        canonical_path(llm_config.base.checkpoint_dir)
-        if llm_config.base.checkpoint_dir
-        else None
-    )
     params_path = (
         canonical_path(llm_config.base.params) if llm_config.base.params else None
     )
     output_dir_path = canonical_path(llm_config.export.output_dir, dir=True)
 
     llm_config.base.checkpoint = checkpoint_path
-    llm_config.base.checkpoint_dir = checkpoint_dir
     llm_config.base.params = params_path
     llm_config.export.output_dir = output_dir_path
 

examples/models/llama/model.py

@@ -7,8 +7,7 @@
 # pyre-unsafe
 
 import json
-import os
-from typing import Dict, Optional, Tuple
+from typing import Optional
 
 import torch
 from executorch.examples.models.checkpoint import (
@@ -18,7 +17,6 @@
 
 from executorch.examples.models.llama.llama_transformer import construct_transformer
 from executorch.examples.models.llama.model_args import ModelArgs
-from executorch.examples.models.llama.rope import Rope
 
 from executorch.extension.llm.export.config.llm_config import LlmConfig
 from torchao.utils import TorchAOBaseTensor
@@ -39,12 +37,9 @@ def convert_to_llama_checkpoint(**kwargs):
 
 class Llama2Model(EagerModelBase):
     def __init__(self, llm_config: Optional[LlmConfig] = None):
-        resource_dir = get_default_model_resource_dir(__file__)
-
         self.llm_config = llm_config if llm_config else LlmConfig()
 
         checkpoint_path = self.llm_config.base.checkpoint
-        checkpoint_dir = self.llm_config.base.checkpoint_dir
         params_path = self.llm_config.base.params
 
         # Adapter checkpoint and config.
@@ -72,37 +67,8 @@ def __init__(self, llm_config: Optional[LlmConfig] = None):
         # Follow the instruction in https://github.com/facebookresearch/llama to download the model.
         device = "cpu"
         # flake8: noqa: TOR102
-        cps = []
-        # Load sharded checkpoint.
         checkpoint = {}
-        if checkpoint_dir is not None:
-            # Load multiple checkpoint; ignore the single path.
-            checkpoint_path = None
-            for i in range(4):
-                cp_name = f"consolidated.{i}.pth"
-                print(f"Loading {cp_name}")
-                cps.append(
-                    torch.load(
-                        os.path.join(checkpoint_dir, cp_name),
-                        map_location=device,
-                        mmap=True,
-                    )
-                )
-            checkpoint = {}
-            for key in cps[0].keys():
-                if not torch.allclose(cps[0][key], cps[1][key]):
-                    values = (cps[0][key], cps[1][key], cps[2][key], cps[3][key])
-                    if "wo" in key or "w2" in key:
-                        # Concat on dim=1 for "wo" and "w2".
-                        checkpoint[key] = torch.cat(values, dim=1)
-                    else:
-                        # Concat on dim=0 for everything else.
-                        checkpoint[key] = torch.cat(values, dim=0)
-                else:
-                    # Do not duplicate layers shared between each checkpoint.
-                    checkpoint[key] = cps[0][key]
-        # Load single checkpoint.
-        elif checkpoint_path:
+        if checkpoint_path:
             checkpoint = torch.load(checkpoint_path, map_location=device, mmap=True)
 
         # If given checkpoint is fairseq, convert to llama checkpoint.

extension/llm/export/config/llm_config.py

@@ -76,7 +76,6 @@ class BaseConfig:
             If left empty, the model will either be initialized with random weights
             if it is a Llama model or the weights will be downloaded from HuggingFace
             if it is a non-Llama model.
-        checkpoint_dir: Path to directory containing sharded checkpoint files.
         adapter_checkpoint: Path to the adapter.pt file from torchtune. Used if
             the model has trained LoRA adapters. Must provide
             adapter_config.json.
@@ -99,7 +98,6 @@ class BaseConfig:
     model_class: ModelType = ModelType.llama3
     params: Optional[str] = None
     checkpoint: Optional[str] = None
-    checkpoint_dir: Optional[str] = None
     adapter_checkpoint: Optional[str] = None
     adapter_config: Optional[str] = None
     tokenizer_path: Optional[str] = None
@@ -527,8 +525,6 @@ def from_args(cls, args: argparse.Namespace) -> "LlmConfig":  # noqa: C901
             llm_config.base.params = args.params
         if hasattr(args, "checkpoint"):
             llm_config.base.checkpoint = args.checkpoint
-        if hasattr(args, "checkpoint_dir"):
-            llm_config.base.checkpoint_dir = args.checkpoint_dir
         if hasattr(args, "adapter_checkpoint"):
             llm_config.base.adapter_checkpoint = args.adapter_checkpoint
         if hasattr(args, "adapter_config"):