def test_group_layernorm_no_cuda_illegal_memory_access(self):
        """
        This subgraph has led to CUDA illegal memory issues before.
        Adding it as a unit test to ensure there are no regressions.
        """
        batch_size = IntVar(values=[1, 2048], name="batch_size")

        unsqueeze_46_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_46_0",
        )
        unsqueeze_58_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_58_0",
        )
        unsqueeze_70_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_70_0",
        )
        unsqueeze_131_0 = Tensor(
            shape=[batch_size, 3, 1],
            is_input=True,
            name="unsqueeze_131_0",
        )
        main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_weight = Tensor(
            shape=[IntImm(256)],
            is_input=True,
            name="main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_weight",
        )
        main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_bias = Tensor(
            shape=[IntImm(256)],
            is_input=True,
            name="main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_bias",
        )

        unsqueeze_83_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_83_0",
        )
        unsqueeze_95_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_95_0",
        )
        unsqueeze_107_0 = Tensor(
            shape=[batch_size, 7680, 1],
            is_input=True,
            name="unsqueeze_107_0",
        )
        unsqueeze_358_0 = Tensor(
            shape=[batch_size, 3, 1],
            is_input=True,
            name="unsqueeze_358_0",
        )
        main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_weight = Tensor(
            shape=[IntImm(256)],
            is_input=True,
            name="main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_weight",
        )
        main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_bias = Tensor(
            shape=[IntImm(256)],
            is_input=True,
            name="main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_bias",
        )

        concatenate_71_0 = ops.concatenate()(
            inputs=[unsqueeze_46_0, unsqueeze_58_0, unsqueeze_70_0],
            dim=2,
        )
        bmm_rrr_132_0 = ops.bmm_rrr()(concatenate_71_0, unsqueeze_131_0)
        reshape_133_0 = ops.reshape()(bmm_rrr_132_0, shape=[-1, 30, 256])
        layernorm_134_0 = ops.layernorm(normalized_shape=[IntImm(256)])(
            reshape_133_0,
            main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_weight,
            main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_bias,
        )
        permute021_136_0 = ops.permute021()(layernorm_134_0)

        concatenate_108_0 = ops.concatenate()(
            inputs=[unsqueeze_83_0, unsqueeze_95_0, unsqueeze_107_0],
            dim=2,
        )
        bmm_rrr_359_0 = ops.bmm_rrr()(concatenate_108_0, unsqueeze_358_0)
        reshape_360_0 = ops.reshape()(bmm_rrr_359_0, shape=[-1, 30, 256])
        layernorm_361_0 = ops.layernorm(normalized_shape=[IntImm(256)])(
            reshape_360_0,
            main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_weight,
            main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_bias,
        )
        permute021_363_0 = ops.permute021()(layernorm_361_0)

        outputs = [permute021_136_0, permute021_363_0]

        for i, output in enumerate(outputs):
            output._attrs["is_output"] = True
            output._attrs["name"] = f"output_{i}"

        model = compile_model(
            outputs,
            detect_target(),
            "./tmp",
            "test_group_layernorm_repro",
        )

        pt_inputs = {
            "unsqueeze_46_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_58_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_70_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_131_0": get_random_torch_tensor(shape=[1024, 3, 1]),
            "main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_weight": get_random_torch_tensor(
                shape=[256]
            ),
            "main_module_base_forward_module_over_arch_bottom_arch_list_1_dime_shared_arch_layer_norm__norm_bias": get_random_torch_tensor(
                shape=[256]
            ),
            "unsqueeze_83_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_95_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_107_0": get_random_torch_tensor(shape=[1024, 7680, 1]),
            "unsqueeze_358_0": get_random_torch_tensor(shape=[1024, 3, 1]),
            "main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_weight": get_random_torch_tensor(
                shape=[256]
            ),
            "main_module_base_forward_module_over_arch_bottom_arch_list_0_dime_shared_arch_layer_norm__norm_bias": get_random_torch_tensor(
                shape=[256]
            ),
        }
        pt_outputs = {
            "output_0": get_torch_empty_tensor(shape=[1024, 256, 30]),
            "output_1": get_torch_empty_tensor(shape=[1024, 256, 30]),
        }

        model.run_with_tensors(pt_inputs, pt_outputs)

        This subgraph has led to CUDA illegal memory issues before.
        Adding it as a unit test to ensure there are no regressions.
        
tests/unittest/compiler/test_strided_group_layernorm.py