Torchchat CLI pipeline for Multimodal Models

.github/workflows/pull.yml

@@ -458,7 +458,6 @@ jobs:
           pip3 list
           python3 -c 'import torch;print(f"torch: {torch.__version__, torch.version.git_version}")'
           python3 -c 'import torchvision;print(f"torchvision: {torchvision.__version__, torchvision.version.git_version}")'
-          python3 -c 'import torchaudio;print(f"torchaudio: {torchaudio.__version__, torchaudio.version.git_version}")'
 
           cd ../..
           echo "Inside: ${PWD}"

torchchat/cli/builder.py

@@ -15,15 +15,26 @@
 import torch._dynamo.config
 import torch._inductor.config
 import torch.nn as nn
+
+try:
+    from _torchchat_test_script import flamingo_meta_to_tune
+except ImportError:
+    pass
+
 from distributed import (
     init_distributed,
     launch_distributed,
     ParallelDims,
     parallelize_llama,
 )
+
 from torch.distributed.device_mesh import DeviceMesh
 
-from torchchat.model import Model
+from torchtune.models.convert_weights import meta_to_tune
+
+from torchtune.training import set_default_dtype
+
+from torchchat.model import Model, ModelType
 
 from torchchat.model_config.model_config import resolve_model_config
 from torchchat.utils.build_utils import (
@@ -35,10 +46,6 @@
 from torchchat.utils.measure_time import measure_time
 from torchchat.utils.quantize import quantize_model
 
-from torchtune.models.convert_weights import meta_to_tune
-
-
-
 
 @dataclass
 class BuilderArgs:
@@ -143,7 +150,6 @@ def from_args(cls, args):  # -> BuilderArgs:
                     if "chat" in path_basename or "instruct" in path_basename:
                         is_chat_model = True
 
-
         output_pte_path = getattr(args, "output_pte_path", None)
         output_dso_path = getattr(args, "output_dso_path", None)
         if output_pte_path and args.dtype.startswith("fast"):
@@ -234,7 +240,12 @@ def validate_model(
 
         is_tiktoken = self.is_tiktoken
         is_sentencepiece = self.is_sentencepiece
-        use_tiktoken = model.config.transformer_args["text"].use_tiktoken
+        text_args = model.config.transformer_args.get("text")
+        if text_args is None:
+            # TODO: Will be refactored: Currently, the only model that doesn't have text in transfomer_args is Flamingo
+            use_tiktoken = model.config.model_type == ModelType.Flamingo
+        else:
+            use_tiktoken = text_args.use_tiktoken
 
         if not (is_tiktoken == use_tiktoken) or not (is_sentencepiece != use_tiktoken):
             raise RuntimeError(
@@ -266,7 +277,9 @@ def from_args(cls, args):  # -> TokenizerArgs:
             raise RuntimeError("cannot find tokenizer model")
 
         if not tokenizer_path.is_file():
-            raise RuntimeError(f"did not find tokenizer at {tokenizer_path}")
+            raise RuntimeError(
+                f"did not find tokenizer at {os.path.abspath(tokenizer_path)}"
+            )
 
         return cls(
             tokenizer_path=tokenizer_path,
@@ -335,7 +348,9 @@ def _load_model_default(builder_args, only_config=False):
 
     if builder_args.params_table and builder_args.params_table.endswith("Tune"):
         print("Loading Tune checkpoint")
-        meta_checkpoint = torch.load(str(builder_args.checkpoint_path), mmap=True, weights_only=True)
+        meta_checkpoint = torch.load(
+            str(builder_args.checkpoint_path), mmap=True, weights_only=True
+        )
         checkpoint = meta_to_tune(meta_checkpoint)
     elif builder_args.checkpoint_dir is not None:
         # Load multiple checkpoint; ignore the single path.
@@ -372,8 +387,17 @@ def _load_model_default(builder_args, only_config=False):
     if "model" in checkpoint and "stories" in str(builder_args.checkpoint_path):
         checkpoint = checkpoint["model"]
 
-    checkpoint = {"model." + k: v for k, v in checkpoint.items()}
-    model.load_state_dict(checkpoint, assign=True, strict=True)
+    if model.config.model_type == ModelType.Flamingo:
+        # TODO: Refactor this. For now, overwrite the model with model loaded from params_path
+        with set_default_dtype(builder_args.precision), torch.device(
+            builder_args.device
+        ):
+            model = Model.from_params(builder_args.params_path)
+        state_dict = flamingo_meta_to_tune(checkpoint)
+        model.model.load_state_dict(state_dict)
+    else:
+        checkpoint = {"model." + k: v for k, v in checkpoint.items()}
+        model.load_state_dict(checkpoint, assign=True, strict=True)
 
     return model
 

torchchat/cli/cli.py

@@ -46,7 +46,7 @@ def check_args(args, verb: str) -> None:
     # different semantics.
     if (
         verb not in INVENTORY_VERBS
-        and args.model
+        and getattr(args, "model", None)
         and not is_model_downloaded(args.model, args.model_directory)
     ):
         download_and_convert(args.model, args.model_directory, args.hf_token)
@@ -320,6 +320,13 @@ def _add_generation_args(parser, verb: str) -> None:
             help="Number of samples",
         )
 
+    generator_parser.add_argument(
+        "--image-prompts",
+        nargs="+",
+        type=str,
+        default=None,
+        help="Paths to image files used as image prompts for multimodal models. Currently, 1 image input is supported.",
+    )
     generator_parser.add_argument(
         "--chat",
         action="store_true",