code: def static_persistent_tma_warp_specialized_matmul_kernel(  #
      a_ptr,
      b_ptr,
      c_ptr,  #
      M,
      N,
      K,  #
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_cm,
      stride_cn,  #
      BLOCK_M: tl.constexpr,
      BLOCK_N: tl.constexpr,
      BLOCK_K: tl.constexpr,  #
      NUM_SMS: tl.constexpr,  #
  ):
      start_tile = tl.program_id(axis=0)
      m_tiles = tl.cdiv(M, BLOCK_M)
      n_tiles = tl.cdiv(N, BLOCK_N)
      k_tiles = tl.cdiv(K, BLOCK_K)
      num_tiles = m_tiles * n_tiles
  
      pre_pid_m = start_tile // n_tiles
      pre_pid_n = start_tile % n_tiles
  
      block_offset_m = pre_pid_m * BLOCK_M
      block_offset_n = pre_pid_n * BLOCK_N
      a_tile_ptr = tl.make_block_ptr(
          base=a_ptr,
          shape=(M, K),
          strides=(stride_am, stride_ak),
          offsets=(block_offset_m, 0),
          block_shape=(BLOCK_M, BLOCK_K),
          order=(1, 0),
      )
      b_tile_ptr = tl.make_block_ptr(
          base=b_ptr,
          shape=(K, N),
          strides=(stride_bk, stride_bn),
          offsets=(0, block_offset_n),
          block_shape=(BLOCK_K, BLOCK_N),
          order=(0, 1),
      )
      for tile_id in range(start_tile, num_tiles, NUM_SMS):
          pid_m = tile_id // n_tiles
          pid_n = tile_id % n_tiles
          accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
          if tile_id >= NUM_SMS:
              a_tile_ptr = tl.advance(
                  a_tile_ptr,
                  [(pid_m - pre_pid_m) * BLOCK_M, -k_tiles * BLOCK_K],
              )
              b_tile_ptr = tl.advance(
                  b_tile_ptr,
                  [-k_tiles * BLOCK_K, (pid_n - pre_pid_n) * BLOCK_N],
              )
  
          for k in range(0, K, BLOCK_K):
              a = tl.load(a_tile_ptr)
              b = tl.load(b_tile_ptr)
              accumulator += tl.dot(a, b)
              a_tile_ptr = tl.advance(a_tile_ptr, [0, BLOCK_K])
              b_tile_ptr = tl.advance(b_tile_ptr, [BLOCK_K, 0])
  
          offs_m = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
          offs_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
  
          c_ptrs = (
              c_ptr
              + offs_m[:, None] * stride_cm
              + offs_n[None, :] * stride_cn
          )
          tl.store(c_ptrs, accumulator)
          pre_pid_m = pid_m
          pre_pid_n = pid_n
  
imports: import itertools
  import pytest
  import torch
  from torch.testing import assert_close
  import triton
  import triton.language
  from triton.runtime import driver
label_desc: Use image node_id 9 for calling a global function with example usage: static_persistent_tma_warp_specialized_matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_M, BLOCK_N, BLOCK_K, NUM_SMS) without return types