docstr: Test caching the same graph, but under conditions that introduce guards
  for tensor sizes < int32.
docstr_tok: ["Test","caching","the","same","graph",",","but","under","conditions","that","introduce","guards","for","tensor","sizes","<","int32","."]
code: def test_cache_load_with_guards_int32_bounds(self, device, dtype):
      """
      Test caching the same graph, but under conditions that introduce guards
      for tensor sizes < int32.
      """
      if device == "cuda" and not HAS_CUDA:
          raise unittest.SkipTest("requires CUDA")
      if (
          device == "cuda"
          and dtype == torch.bfloat16
          and not SM80OrLater
      ):
          raise unittest.SkipTest("requires SM80 or later")
  
      def fn(x, y):
          return (x + x, y + y)
  
      compiled_fn = torch.compile(fn, dynamic=True)
  
      # Iterate over different shapes, varying whether the total
      # size is below or above int32. For each combination, we expect
      # different guards around whether the symbolic sizes do or do
      # not exceed int32.
      shapes = (
          ((5, 6), (7, 8)),
          ((5, 6), (47000, 47001)),
          ((47000, 47001), (5, 6)),
      )
      for a_shape, b_shape in shapes:
          a = torch.rand(a_shape, device=device, dtype=dtype)
          b = torch.rand(b_shape, device=device, dtype=dtype)
  
          # AVOID a dynamo reset here. We expect guards to have been
          # added that will be violated with the new shape. We should
          # see a recompilation (along with a cache miss).
          counters.clear()
          res1 = compiled_fn(a, b)
          self.assertGreater(
              counters["inductor"]["fxgraph_cache_miss"], 0
          )
          self.assertEqual(counters["inductor"]["fxgraph_cache_hit"], 0)
  
          # A second call should hit. (Reset here to force compilation).
          counters.clear()
          torch._dynamo.reset()
          res2 = compiled_fn(a, b)
          self.assertEqual(
              counters["inductor"]["fxgraph_cache_miss"], 0
          )
          self.assertGreater(
              counters["inductor"]["fxgraph_cache_hit"], 0
          )
  
          self.assertEqual(res1, res2)
  
imports: import functools
  import pickle
  import tempfile
  import unittest
  from unittest.mock import patch
  import torch
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch._inductor import config
  from torch._inductor.codecache import AsyncCompile, FxGraphCachePickler, FxGraphHashDetails, TensorMetadata, TensorMetadataAndValues
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_device_type import largeTensorTest
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize
  from torch.testing._internal.inductor_utils import HAS_CUDA
  from torch.utils._triton import has_triton
label_desc: Use image node_id 6 for calling the TestFxGraphCache obj's underlying member method code with example usage: obj.test_cache_load_with_guards_int32_bounds(device, dtype) and returns: unknown,  unknown