class_docstr: fp16 matrix multiplication kernel
  dtypes: fp16 x fp16 = fp16
code: def _check_parity(
      A,
      B,
      output_dtype,
      SA=None,
      SB=None,
      qblock_size=None,
      ref_dtype=torch.float32,
      tol=0.01,
      use_triton=True,
      bias=None,
      activation="",
  ):
      torch_output = __class__._ref_forward(
          A, B, ref_dtype=ref_dtype, bias=bias, activation=activation
      )
      triton_output = __class__.forward(
          A, B, use_triton=use_triton, bias=bias, activation=activation
      )
      assert torch.allclose(
          triton_output.cpu().type(torch_output.dtype),
          torch_output.cpu(),
          rtol=tol,
      )
      print(f"{__class__.__name__}: PASSed the parity check")
      return triton_output, torch_output
  
imports: import torch
  import triton
  import os
  from filelock import FileLock
  import deepspeed.ops.transformer.inference.triton.triton_matmul_kernel
  import pickle
  from io import open
  import deepspeed
  from pathlib import Path
  import atexit
label_desc: Use image node_id 8 for calling the Fp16Matmul obj's underlying member method code with example usage: obj._check_parity(A, B, output_dtype, SA, SB, qblock_size, ref_dtype, tol, use_triton, bias, activation) and returns: triton_output,  torch_output