code: def test_iv_dependent_matmul(type):
      @triton.jit
      def kernel(
          a_ptr,
          b_ptr,
          c_ptr,  #
          M,
          N,
          K,  #
          stride_am,
          stride_ak,  #
          stride_bk,
          stride_bn,  #
          stride_cm,
          stride_cn,  #
          BLOCK_SIZE_M: tl.constexpr,
          BLOCK_SIZE_N: tl.constexpr,
          BLOCK_SIZE_K: tl.constexpr,  #
          type: tl.constexpr,
      ):
          pid = tl.program_id(axis=0)
          num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
          pid_m = pid // num_pid_n
          pid_n = pid % num_pid_n
  
          offs_am = (
              pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
          ) % M
          offs_bn = (
              pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
          ) % N
          offs_k = tl.arange(0, BLOCK_SIZE_K)
          a_ptr = a_ptr + (
              offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
          )
          b_ptr = b_ptr + (
              offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
          )
          a_ptrs = a_ptr
          b_ptrs = b_ptr
          if type == "post_load_two_iters":
              a_ptrs_next = a_ptr + BLOCK_SIZE_K * stride_ak
              b_ptrs_next = b_ptr + BLOCK_SIZE_K * stride_bk
          elif type == "post_load_three_iters":
              a_ptrs_next = a_ptr + BLOCK_SIZE_K * stride_ak
              b_ptrs_next = b_ptr + BLOCK_SIZE_K * stride_bk
              a_ptrs_next_next = a_ptr + 2 * BLOCK_SIZE_K * stride_ak
              b_ptrs_next_next = b_ptr + 2 * BLOCK_SIZE_K * stride_bk
  
          accumulator = tl.zeros(
              (BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32
          )
          for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
              if type == "pre_load":
                  a_ptrs = a_ptr + k * BLOCK_SIZE_K * stride_ak
                  b_ptrs = b_ptr + k * BLOCK_SIZE_K * stride_bk
              elif type == "post_pre_mixed":
                  a_ptrs = a_ptr + k * BLOCK_SIZE_K * stride_ak
              a = tl.load(
                  a_ptrs,
                  mask=offs_k[None, :] < K - k * BLOCK_SIZE_K,
                  other=0.0,
              )
              b = tl.load(
                  b_ptrs,
                  mask=offs_k[:, None] < K - k * BLOCK_SIZE_K,
                  other=0.0,
              )
              accumulator += tl.dot(a, b)
              if type == "post_load":
                  a_ptrs = a_ptr + (k + 1) * BLOCK_SIZE_K * stride_ak
                  b_ptrs = b_ptr + (k + 1) * BLOCK_SIZE_K * stride_bk
              elif type == "post_pre_mixed":
                  b_ptrs = b_ptr + (k + 1) * BLOCK_SIZE_K * stride_bk
              elif type == "post_load_two_iters":
                  a_ptrs = a_ptrs_next
                  b_ptrs = b_ptrs_next
                  a_ptrs_next = (
                      a_ptr + (k + 2) * BLOCK_SIZE_K * stride_ak
                  )
                  b_ptrs_next = (
                      b_ptr + (k + 2) * BLOCK_SIZE_K * stride_bk
                  )
              elif type == "post_load_three_iters":
                  a_ptrs = a_ptrs_next
                  b_ptrs = b_ptrs_next
                  a_ptrs_next = a_ptrs_next_next
                  b_ptrs_next = b_ptrs_next_next
                  a_ptrs_next_next = (
                      a_ptr + (k + 3) * BLOCK_SIZE_K * stride_ak
                  )
                  b_ptrs_next_next = (
                      b_ptr + (k + 3) * BLOCK_SIZE_K * stride_bk
                  )
          c = accumulator.to(tl.float16)
  
          offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
          offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
          c_ptrs = (
              c_ptr
              + stride_cm * offs_cm[:, None]
              + stride_cn * offs_cn[None, :]
          )
          c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
          tl.store(c_ptrs, c, mask=c_mask)
  
      M = 256
      K = 256
      N = 256
      BLOCK_SIZE_K = 32
      BLOCK_SIZE_N = 32
      BLOCK_SIZE_M = 32
  
      a = torch.rand((M, K), device="cuda")
      b = torch.rand((K, N), device="cuda")
  
      torch_output = torch.mm(a, b)
      triton_output = torch.empty_like(
          torch_output, device=torch_output.device
      )
  
      def grid(META):
          return (
              triton.cdiv(M, META["BLOCK_SIZE_M"])
              * triton.cdiv(N, META["BLOCK_SIZE_N"]),
          )
  
      num_stages = 4 if type == "post_load_three_iters" else 3
      kernel[grid](
          a,
          b,
          triton_output,
          M,
          N,
          K,  #
          a.stride(0),
          a.stride(1),
          b.stride(0),
          b.stride(1),  #
          triton_output.stride(0),
          triton_output.stride(1),  #
          BLOCK_SIZE_M=BLOCK_SIZE_M,
          BLOCK_SIZE_N=BLOCK_SIZE_N,
          BLOCK_SIZE_K=BLOCK_SIZE_K,
          type=type,  #
          num_stages=num_stages,
      )
      torch.testing.assert_close(
          torch_output, triton_output, rtol=1e-2, atol=1e-2
      )
  
file_path: triton/python/test/regression/test_functional_regressions.py
imports: import numpy
  import pytest
  import torch
  from numpy.random import RandomState
  import triton
  import triton.language
label_desc: Use image node_id 3 for calling a global function with example usage: test_iv_dependent_matmul(type) and returns: unknown