code: def _triton_autotune(
      min_seqlen, max_seqlen, hidden_size, dtype=torch.float16
  ):
      from deepspeed.ops.transformer.inference.triton.matmul_ext import (
          Fp16Matmul,
          matmul,
      )
  
      seqlen = [
          (min_seqlen + i)
          for i in range(
              0,
              max_seqlen - min_seqlen + Fp16Matmul._cache_stride + 1,
              Fp16Matmul._cache_stride,
          )
      ]
      Fp16Matmul._read_autotune_table()
      for N in seqlen:
          A = torch.randn((N, hidden_size), dtype=dtype, device="cuda")
          B = torch.randn(
              (hidden_size, hidden_size), dtype=dtype, device="cuda"
          )
          matmul(A, B)
      Fp16Matmul._update_autotune_table()
  
file_path: deepspeed/deepspeed/ops/transformer/inference/op_binding/vector_matmul.py
imports: import os
  import torch
  from ..config import DeepSpeedInferenceConfig
  from .base import BaseOp
  import deepspeed
label_desc: Use image node_id 4 for calling the VectorMatMulOp obj's underlying member method code with example usage: obj._triton_autotune(min_seqlen, max_seqlen, hidden_size, dtype) without return types