undo random changes

Signed-off-by: Bill Nell <[email protected]>

Author: bnellnm

csrc/custom_all_reduce.cuh

@@ -602,4 +602,4 @@ class CustomAllreduce {
  * template void vllm::CustomAllreduce::allreduce<half>(cudaStream_t, half *,
  half *, int, int, int);
 */
-}  // namespace vllm
+}  // namespace vllm

vllm/distributed/parallel_state.py

@@ -34,10 +34,10 @@
 
 import torch
 import torch.distributed
-from torch.distributed import Backend, ProcessGroup
 from pplx_kernels.nvshmem import (nvshmem_alloc_empty_unique_id,
-                                  nvshmem_get_unique_id, nvshmem_init,
-                                  nvshmem_finalize)
+                                  nvshmem_finalize, nvshmem_get_unique_id,
+                                  nvshmem_init)
+from torch.distributed import Backend, ProcessGroup
 
 import vllm.envs as envs
 from vllm.distributed.device_communicators.base_device_communicator import (
@@ -917,6 +917,7 @@ def init_distributed_environment(
 
 PPLX_DID_INIT: bool = False
 
+
 @run_once
 def pplx_init(rank, world_size):
     if world_size > 1:
@@ -1131,7 +1132,6 @@ def destroy_model_parallel():
     _DP = None
 
 
-
 def destroy_distributed_environment():
     global _WORLD
     if _WORLD:

vllm/model_executor/models/mllama.py

@@ -1245,31 +1245,6 @@ def unpack_data(self,
                 output_tensor[i, :t.size(0)] = t
             return output_tensor
 
-    def unpack_data(self,
-                    image_data: Union[List[torch.Tensor], torch.Tensor],
-                    padding_value=0) -> torch.Tensor:
-        if isinstance(image_data, torch.Tensor):
-            # torch.Tensor
-            return image_data
-        else:
-            assert isinstance(
-                image_data[0],
-                torch.Tensor), "Image data is not properly batched."
-            # List[torch.Tensor]
-            bsz = len(image_data)
-            max_length = max(t.size(0) for t in image_data)
-            trailing_dims = image_data[0].shape[1:]
-            for data in image_data:
-                cur_trailing_dims = data.shape[1:]
-                assert cur_trailing_dims == trailing_dims
-            output_tensor = torch.full((bsz, max_length, *trailing_dims),
-                                       padding_value,
-                                       dtype=image_data[0].dtype,
-                                       device=image_data[0].device)
-            for i, t in enumerate(image_data):
-                output_tensor[i, :t.size(0)] = t
-            return output_tensor
-
     def _parse_and_validate_image_input(self, **kwargs: object):
         # tensor with the same shape will be batched together by
         # MultiModalKwargs.batch, so pixel_values here can be: