Repository: huggingface/transformers
Pull request: Expand quantized data type support for tensor parallelism 

# What does this PR do?
Update the tensor parallel for support generic quantized tensor with data type like int8 and scalar tensor.

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Fixes # (issue)
- [#37720]
[rank0]: File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/integrations/tensor_parallel.py", line 658, in shard_and_distribute_module
[rank0]: param = tp_layer.partition_tensor(
[rank0]: File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/integrations/tensor_parallel.py", line 385, in partition_tensor
[rank0]: return nn.Parameter(parameter) #, requires_grad=False)
[rank0]: File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/parameter.py", line 49, in new
[rank0]: t = data.detach().requires_grad_(requires_grad)
[rank0]: RuntimeError: only Tensors of floating point dtype can require gradients

- 0-dim tensor not support [:]
Get the scaler value of a 0-dim PySafeSlice [#380]


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Changed files:
filename: src/transformers/integrations/tensor_parallel.py
patch: @@ -307,7 +307,8 @@ def partition_tensor(self, param, empty_param, param_type, param_casting_dtype,
             parameter = parameter.contiguous()
         if self.use_dtensor:
             parameter = DTensor.from_local(parameter, device_mesh, shard, run_check=False)
-        return nn.Parameter(parameter)
+        requires_grad = True if parameter.is_floating_point() else False
+        return nn.Parameter(parameter, requires_grad=requires_grad)
 
     @staticmethod
     def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_mesh):
@@ -329,7 +330,8 @@ def partition_tensor(self, param, empty_param, param_type, param_casting_dtype,
             parameter = parameter.contiguous()
         if self.use_dtensor:
             parameter = DTensor.from_local(parameter, device_mesh, [Shard(-2)], run_check=False)
-        return nn.Parameter(parameter)
+        requires_grad = True if parameter.is_floating_point() else False
+        return nn.Parameter(parameter, requires_grad=requires_grad)
 
 
 class RowwiseParallel(TensorParallelLayer):
@@ -381,7 +383,8 @@ def partition_tensor(self, param, empty_param, param_type, param_casting_dtype,
             parameter = parameter.contiguous()
         if self.use_dtensor:
             parameter = DTensor.from_local(parameter, device_mesh, shard, run_check=False)
-        return nn.Parameter(parameter)
+        requires_grad = True if parameter.is_floating_point() else False
+        return nn.Parameter(parameter, requires_grad=requires_grad)
 
     @staticmethod
     def _prepare_input_fn(input_layouts, desired_input_layouts, mod, inputs, device_mesh):
@@ -443,7 +446,8 @@ def partition_tensor(self, param, empty_param, param_type, param_casting_dtype,
             parameter = parameter.contiguous()
         if self.use_dtensor:
             parameter = DTensor.from_local(parameter, device_mesh, [Shard(-1)], run_check=False)
-        return nn.Parameter(parameter)
+        requires_grad = True if parameter.is_floating_point() else False
+        return nn.Parameter(parameter, requires_grad=requires_grad)
 
 
 class SequenceParallel(TensorParallelLayer):
@@ -521,13 +525,14 @@ def partition_tensor(self, param, empty_param, param_type, param_casting_dtype,
         # colwise shard weight/bias to Shard(0), weight be Shard(-2) (0 if you have 1 dim only)
         # means Colwise as Linear is input * weight^T + bias, where
         # weight would become Shard(1)
-        parameter = param[:]
+        parameter = param[...]
         parameter = parameter.to(param_casting_dtype)
         if to_contiguous:
             parameter = parameter.contiguous()
         if self.use_dtensor:
             parameter = DTensor.from_local(parameter, device_mesh, [Replicate()], run_check=False)
-        return nn.Parameter(parameter)
+        requires_grad = True if parameter.is_floating_point() else False
+        return nn.Parameter(parameter, requires_grad=requires_grad)
 
 
 SUPPORTED_TP_STYLES = {