fix hanging due to NanotronParameter.__repr__ (param.data == Nanotron…

…Parameter)
huggingface · Nov 29, 2024 · afdfbf1 · afdfbf1
1 parent b764b97
commit afdfbf1
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Showing 3 changed files with 23 additions and 13 deletions.
diff --git a/src/nanotron/fp8/utils.py b/src/nanotron/fp8/utils.py
@@ -375,8 +375,14 @@ def convert_model_to_fp8(model: NanotronModel, config: FP8Args) -> NanotronModel
             # NOTE: convert it to the residual stream's dtype
             # for p in module.parameters():
             #     p.data = p.data.to(self.config.model.dtype)
-            module.to(dtype=config.resid_dtype)
+            # for p in module.parameters():
+            #     p.data = p.data.to(dtype=config.resid_dtype) if p.data
+            # pass
+            # assert module.weight.data.__class__ == torch.Tensor
+            # module.to(dtype=config.resid_dtype)
             # pass
             # assert module.weight.data.__class__ == torch.Tensor
+            # NOTE: this causes param.data == NanotronParameter
+            assert config.resid_dtype == torch.float32, "not support datatype conversion, because of error 8"
 
     return model
diff --git a/src/nanotron/parallel/parameters.py b/src/nanotron/parallel/parameters.py
@@ -264,8 +264,9 @@ def is_sharded(self) -> bool:
             self, self.NANOTRON_PARAMETER_METADATA_ATTRIBUTE_NAME
         )
 
-    # def __repr__(self):
-    #     return f"NanotronParameter({super().__repr__()})"
+    def __repr__(self):
+        # return f"NanotronParameter({super().__repr__()})"
+        return "NanotronParameter()"
 
     @property
     def data(self):
@@ -293,13 +294,13 @@ def data(self, data):
     def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
         from nanotron.fp8.tensor import FP8Tensor
 
-        print(f"__torch_dispatch__ called with func: {func}, args: {args}, kwargs: {kwargs}")
+        # print(f"__torch_dispatch__ called with func: {func}, args: {args}, kwargs: {kwargs}")
 
-        if func in {torch._tensor_str._str, repr}:
-            return super().__torch_dispatch__(func, types, args, kwargs)
+        # if func in {torch._tensor_str._str, repr}:
+        #     return super().__torch_dispatch__(func, types, args, kwargs)
 
         def unwrap(e):
-            print(f"Unwrapping: {e} (type: {type(e)})")
+            # print(f"Unwrapping: {e} (type: {type(e)})")
             return e._data if e.__class__ == NanotronParameter else e
 
         def wrap(e):

diff --git a/tests/fp8/test_fp8_model.py b/tests/fp8/test_fp8_model.py
@@ -44,12 +44,15 @@ def _test_initialize_fp8_model(parallel_context: ParallelContext, fp8_config: FP
             assert all(
                 p.dtype == fp8_config.resid_dtype for p in module.parameters()
             ), f"name: {name}, __class__: {module.weight.data.__class__}"
-            try:
-                assert all(
-                    p.data.__class__ == nn.Parameter for p in module.parameters()
-                ), f"name: {name}, __class__: {module.weight.data.__class__}"
-            except:
-                assert 1 == 1
+            # try:
+            #     assert all(
+            #         p.data.__class__ == nn.Parameter for p in module.parameters()
+            #     ), f"name: {name}, __class__: {module.weight.data.__class__}"
+            # except:
+            #     assert 1 == 1
+            assert all(
+                p.data.__class__ == nn.Parameter for p in module.parameters()
+            ), f"name: {name}, __class__: {module.weight.data.__class__}"
         else:
             assert all(
                 isinstance(p.data.__class__, FP8Tensor) for p in module.parameters()