NIM supporting changes for nemo.export for NeMo 2.0 (part II) (#11669)

* Remove trt_compile from __init__ as it triggers imports from nemo.utils Signed-off-by: Jan Lasek <[email protected]> * Get tokenizer for NeMo 2 from model.yaml using local SP or HF classes Signed-off-by: Jan Lasek <[email protected]> * Apply isort and black reformatting Signed-off-by: janekl <[email protected]> --------- Signed-off-by: Jan Lasek <[email protected]> Signed-off-by: janekl <[email protected]> Co-authored-by: janekl <[email protected]>
NVIDIA · Dec 23, 2024 · fc54cee · fc54cee
1 parent 054bd46
commit fc54cee
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Showing 2 changed files with 52 additions and 7 deletions.
diff --git a/nemo/export/__init__.py b/nemo/export/__init__.py
@@ -11,5 +11,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-from nemo.export.tensorrt_lazy_compiler import trt_compile
diff --git a/nemo/export/trt_llm/nemo_ckpt_loader/nemo_file.py b/nemo/export/trt_llm/nemo_ckpt_loader/nemo_file.py
@@ -38,6 +38,13 @@
 from nemo.export.tarutils import TarPath, ZarrPathStore
 from nemo.export.tiktoken_tokenizer import TiktokenTokenizer
 
+try:
+    from nemo.lightning import io
+
+    HAVE_NEMO2 = True
+except (ImportError, ModuleNotFoundError):
+    HAVE_NEMO2 = False
+
 LOGGER = logging.getLogger("NeMo")
 
 
@@ -289,14 +296,54 @@ def copy_tokenizer_files(config, out_dir):
     return config
 
 
+def get_tokenizer_from_nemo2_context(model_context_dir: Path):
+    """
+    Retrieve tokenizer configuration from NeMo 2.0 context and instantiate the tokenizer.
+
+    Args:
+        model_context_dir (Path): Path to the model context directory.
+
+    Returns:
+        The instantiated tokenizer (various classes possible).
+    """
+
+    if HAVE_NEMO2:
+        # Use NeMo tokenizer loaded from the NeMo 2.0 model context
+        tokenizer_spec = io.load_context(model_context_dir, subpath="model.tokenizer")
+        return build_tokenizer(tokenizer_spec)
+    else:
+        # Use local nemo.export SentencePieceTokenizer implementation
+        # or directly a HuggingFace tokenizer based on the model config
+        with (model_context_dir / "model.yaml").open("r") as stream:
+            model_config = yaml.safe_load(stream)
+
+        tokenizer_config = model_config["tokenizer"]
+        target_class = tokenizer_config["_target_"]
+        tokenizer_module = "nemo.collections.common.tokenizers."
+        assert target_class.startswith(tokenizer_module)
+        target_class = target_class.removeprefix(tokenizer_module)
+
+        if target_class == "sentencepiece_tokenizer.SentencePieceTokenizer":
+            tokenizer = SentencePieceTokenizer(
+                model_path=str(model_context_dir / tokenizer_config["model_path"]),
+                special_tokens=tokenizer_config.get("special_tokens", None),
+                legacy=tokenizer_config.get("legacy", False),
+            )
+        elif target_class == "huggingface.auto_tokenizer.AutoTokenizer":
+            tokenizer = AutoTokenizer.from_pretrained(
+                str(model_context_dir / tokenizer_config["pretrained_model_name"])
+            )
+        else:
+            raise ValueError(f"Unsupported tokenizer type: {tokenizer_module}{target_class}.")
+
+    return tokenizer
+
+
 def get_tokenizer(tokenizer_dir_or_path: Union[str, Path]) -> PreTrainedTokenizer:
     """Loads the tokenizer from the decoded NeMo weights dir."""
     tokenizer_dir_or_path = Path(tokenizer_dir_or_path)
     if (tokenizer_dir_or_path / "nemo_context").exists():
-        from nemo.lightning import io
-
-        tokenizer_spec = io.load_context((tokenizer_dir_or_path / "nemo_context"), subpath="model.tokenizer")
-        return build_tokenizer(tokenizer_spec)
+        return get_tokenizer_from_nemo2_context(tokenizer_dir_or_path / "nemo_context")
     elif os.path.exists(os.path.join(tokenizer_dir_or_path, "vocab.json")):
         vocab_path = tokenizer_dir_or_path / "vocab.json" if tokenizer_dir_or_path.is_dir() else tokenizer_dir_or_path
         tokenizer_config = {"library": "tiktoken", "vocab_file": str(vocab_path)}
@@ -476,7 +523,7 @@ def load_nemo_model(nemo_ckpt: Union[str, Path], nemo_export_dir: Union[str, Pat
                     elif k == "activation_func":
                         nemo_model_config["activation"] = v["_target_"].rsplit('.', 1)[-1]
             else:
-                from nemo.lightning import io
+                assert HAVE_NEMO2, "nemo_toolkit>=2.0.0 is required to load the model context."
 
                 config = io.load_context(io_folder, subpath="model.config")