Reenable torchchat download

build/convert_hf_checkpoint.py

@@ -17,7 +17,7 @@
 sys.path.append(str(wd.resolve()))
 sys.path.append(str((wd / "build").resolve()))
 
-from build.model import TransformerArgs
+from build.model import ModelArgs
 
 
 @torch.inference_mode()
@@ -32,7 +32,7 @@ def convert_hf_checkpoint(
     if model_name is None:
         model_name = model_dir.name
 
-    config = TransformerArgs.from_name(model_name)
+    config = ModelArgs.from_name(model_name).text_transformer_args
     print(f"Model config {config.__dict__}")
 
     # Load the json file containing weight mapping

build/model_dist.py

@@ -112,18 +112,19 @@ def forward(self, x: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
 
         # print(f"stage output shape: {x.shape}")
         return x
-
-    @classmethod
-    def from_name(cls, name: str):
-        return cls(TransformerArgs.from_name(name))
-
-    @classmethod
-    def from_table(cls, name: str):
-        return cls(TransformerArgs.from_table(name))
-
-    @classmethod
-    def from_params(cls, params_path: str):
-        return cls(TransformerArgs.from_params(params_path))
+
+    # temporary disable them due to miss essential input
+    # @classmethod
+    # def from_name(cls, name: str):
+    #     return cls(TransformerArgs.from_name(name))
+
+    # @classmethod
+    # def from_table(cls, name: str):
+    #     return cls(TransformerArgs.from_table(name))
+
+    # @classmethod
+    # def from_params(cls, params_path: str):
+    #     return cls(TransformerArgs.from_params(params_path))
 
     @classmethod
     def from_gguf(cls, gguf_path: str, **kwargs):

dist_run.py

@@ -11,12 +11,12 @@
 import torch.distributed as dist
 from torch.distributed.pipelining import PipelineStage, ScheduleGPipe
 
-from build.model import TransformerArgs
+from build.model import ModelArgs
 from build.model_dist import TransformerStage
 
 # Model config
 def main():
-    config = TransformerArgs.from_name("Transformer-2-7b-chat-hf")
+    config = ModelArgs.from_name("Transformer-2-7b-chat-hf").text_transformer_args
     print(config)
 
     # Construct a device mesh with available devices (multi-host or single host)

docs/ADVANCED-USERS.md

@@ -123,14 +123,14 @@ For example, for the stories15M model, this would be expressed as
 
 For models using a configuration not in the list of known
 configurations, you can construct the model by initializing the
-`TransformerArgs` dataclass that controls model construction from a
+`ModelArgs` dataclass that controls model construction from a
 parameter json using the `params-path ${PARAMS_PATH}` containing the
 appropriate model parameters to initialize the `ModelArgs` for the
 model. (We use the model constructor `Model.from_params()`).
 
 The parameter file should be in JSON format specifying these
-parameters. You can find the `TransformerArgs` data class in
-[`model.py`](https://github.com/pytorch/torchchat/blob/main/model.py#L22).
+parameters. You can find the `ModelArgs` data class in
+[`model.py`](https://github.com/pytorch/torchchat/blob/main/build/model.py#L70).
 
 The final way to initialize a torchchat model is from GGUF. You load a
 GGUF model with the option `--load-gguf ${MODELNAME}.gguf`. Presently,