class_docstr: fp16 matrix multiplication kernel
  dtypes: fp16 x fp16 = fp16
code: def _context_4d_matmul(prob, input, head_size):
      assert prob.is_contiguous(), "matrix prob must be contiguous"
      assert input.is_contiguous(), "matrix input must be contiguous"
  
      batches = input.shape[0]
      d_model = input.shape[-1] // 3
      num_of_heads = d_model // head_size
  
      v = input[:, :, d_model * 2 :]
  
      v = v.view(batches, -1, num_of_heads, head_size)
  
      # checks constraints
      assert (
          prob.shape[0] == v.shape[0]
          and prob.shape[1] == v.shape[2]
          and prob.shape[2] == v.shape[1]
          and prob.shape[3] == v.shape[1]
      ), "incompatible dimensions"
      B, H, M, K = prob.shape
      B, K, H, N = v.shape
  
      assert K > 1, "inner-product dimension K should be larger than 1"
  
      # allocates output
      output = torch.empty((B, M, H, N), device=v.device, dtype=v.dtype)
      grid = lambda META: (
          triton.cdiv(M, META["BLOCK_SIZE_M"])
          * triton.cdiv(N, META["BLOCK_SIZE_N"]),
          H,
          B,
      )
  
      __class__._4d_kernel[grid](
          prob,
          v,
          output,
          M,
          N,
          K,
          M // __class__._cache_stride,
          N // __class__._cache_stride,
          K // __class__._cache_stride,
          prob.stride(0),
          prob.stride(1),
          prob.stride(2),
          prob.stride(3),
          v.stride(0),
          v.stride(2),
          v.stride(1),
          v.stride(3),
          # Here we also transpose the output when writing to memory.
          output.stride(0),
          output.stride(2),
          output.stride(1),
          output.stride(3),
          scale=-1,
          MASK=False,
      )
      return output.view(batches, -1, d_model)
  
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 6 for calling the Fp16Matmul obj's underlying member method code with example usage: obj._context_4d_matmul(prob, input, head_size) and returns: output