skip three pyhpc models with dynamic shape test (#120599)

As reported in https://github.com/pytorch/pytorch/issues/119434, `pyhpc_isoneutral_mixing`, `pyhpc_equation_of_state` and `pyhpc_turbulent_kinetic_energy` failed with dynamic shape testing, we propose to skip the dynamic batch size testing of these 3 models in this PR.

* Error msg is
```
  File "/localdisk/leslie/torch_inductor_community/pytorch/benchmarks/dynamo/common.py", line 3879, in run
    assert marked, f"nothing in example_inputs had a dim with {batch_size}"
AssertionError: nothing in example_inputs had a dim with 1048576
```

* Root Cause is
  *  Benchmark code will only annotate the inputs' dim as dynamic when its size equals to batch size c617e7b407/benchmarks/dynamo/common.py (L3867-L3871). If it fails to find any dim equals to batch size, above error throws.
  * However, for these 3 models, none of the inputs' dim will equal to input batch size since the [relationship of dim sizes](26b85eadde/torchbenchmark/models/pyhpc_equation_of_state/__init__.py (L12-L16))
  ```
    shape = (
        math.ceil(2 * size ** (1/3)),
        math.ceil(2 * size ** (1/3)),
        math.ceil(0.25 * size ** (1/3)),
    )
  ```
  * Another thing is `pyhpc_isoneutral_mixing`, `pyhpc_equation_of_state` can pass the dynamic batch size accuracy testing, because the batch size has been set to 4 in accuracy testing (c617e7b407/benchmarks/dynamo/common.py (L3456)) and `math.ceil(2 * size ** (1/3))` happens equaling to 4.

* Since the dim sizes of input has above relationship, running the these models in dynamic shape, we may need to annotate `dim[0](s0) = dim[2](s1) * 8`, per the discussion in https://github.com/pytorch/pytorch/issues/117477#issuecomment-1897108756 @avikchaudhuri, looks like we are not expressible for this case. So, I think we may need to skip the dynamic batch size testing for these 3 models.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/120599
Approved by: https://github.com/jgong5, https://github.com/desertfire

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_torchbench_inference.csv

@@ -262,6 +262,10 @@ pyhpc_isoneutral_mixing,pass,0
 
 
 
+pyhpc_turbulent_kinetic_energy,pass,0
+
+
+
 pytorch_CycleGAN_and_pix2pix,pass,0
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv

@@ -190,6 +190,10 @@ pyhpc_isoneutral_mixing,pass,0
 
 
 
+pyhpc_turbulent_kinetic_energy,pass,0
+
+
+
 pytorch_CycleGAN_and_pix2pix,pass,0
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_torchbench_inference.csv

@@ -262,6 +262,10 @@ pyhpc_isoneutral_mixing,pass,0
 
 
 
+pyhpc_turbulent_kinetic_energy,pass,0
+
+
+
 pytorch_CycleGAN_and_pix2pix,pass,0
 
 

benchmarks/dynamo/common.py

@@ -135,6 +135,9 @@ CI_SKIP_DYNAMIC_BATCH_ONLY = {
     # We should be able to graphbreak there.
     "doctr_det_predictor",
     "dlrm",
+    "pyhpc_isoneutral_mixing",
+    "pyhpc_equation_of_state",
+    "pyhpc_turbulent_kinetic_energy",
 }
 
 # These models currently fail accuracy with eager Adam optimizer