class_docstr: Block-Sparse MatMul class; this class handles three types of matrix-multiplication:
     - sparse = dense X dense
     - dense = sparse X dense
     - dense = dense X sparse
  
  For more details about sparsity config, please see `Generative Modeling with Sparse Transformers`: https://arxiv.org/abs/1904.10509
class_docstr_tok: ["Block-Sparse","MatMul","class",";","this","class","handles","three","types","of","matrix-multiplication",":","-","sparse","=","dense","X","dense","-","dense","=","sparse","X","dense","-","dense","=","dense","X","sparse","For","more","details","about","sparsity","config",",","please","see","`","Generative","Modeling","with","Sparse","Transformers","`",":","https",":","\/\/arxiv.org\/abs\/1904.10509"]
code: def _validate_inputs(self, a, b):
      if a.device != b.device:
          raise ValueError(
              f"Inputs must be on the same device; got {a.device} for tensor A "
              f"and {b.device} for tensor B"
          )
      if not get_accelerator().on_accelerator(a):
          raise ValueError("Only GPU devices are supported for now")
  
      # When autocast is enabled, torch.matmul autocasts to float16, so we do the same here
      if torch.is_autocast_enabled():
          a, b = a.half(), b.half()
      elif a.dtype != b.dtype:
          raise ValueError(
              f"Inputs must be the same dtype; got {a.dtype} for A and {b.dtype} for B"
          )
  
      mode, trans_a, trans_b = self.mode, self.trans_a, self.trans_b
      if mode != "sdd":
          # One input is sparse
          dense, dense_name, sparse, sparse_name = (
              (a, "A", b, "B") if mode == "dds" else (b, "B", a, "A")
          )
          dense_inner = dense.shape[self.dense_inner_dim]
          if dense_inner != self.dense_inner_size:
              raise ValueError(
                  f"Expected tensor {dense_name} to have size {self.dense_inner_size} at dim "
                  f"{self.dense_inner_dim % dense.ndim}, got {dense_inner}."
              )
  
          if (
              sparse.shape[-len(self.sparse_shape) :]
              != self.sparse_shape
          ):
              raise ValueError(
                  f"Expected tensor with trailing dimensions of shape {self.sparse_shape} for argument "
                  f"{sparse_name}, got {sparse.shape}"
              )
  
      def add_extra_dims(x):
          # Add extra leading singleton dimensions if needed
          dims_needed = 4 - x.ndim
          if dims_needed > 0:
              singletons = [1] * dims_needed
              x = x.view(*singletons, *x.shape)
          elif dims_needed < 0:
              raise ValueError(
                  "Tensors with more than 4 dimensions are not currently supported"
              )
  
          return x
  
      # Pad shapes with leading singleton dimensions
      a = add_extra_dims(a)
      b = add_extra_dims(b)
  
      return a, b
  
imports: import importlib
  import torch
  import triton
  import triton.language
  import triton._C.libtriton
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 5 for calling the MatMul obj's underlying member method code with example usage: obj._validate_inputs(a, b) and returns: a,  b, x