[AOTI][reland] Fix assert_function call in cpu autotune template (#135920)

Summary: Reland https://github.com/pytorch/pytorch/pull/135086. In the ABI-compatible mode, assert_function should be AOTI_TORCH_CHECK.

Test Plan: CI

Differential Revision: D62500592

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135920
Approved by: https://github.com/chenyang78

test/inductor/test_aot_inductor.py

@@ -3270,7 +3270,9 @@ class AOTInductorTestsTemplate:
             model, example_inputs_list, dynamic_shapes=dynamic_shapes
         )
 
-    @common_utils.parametrize("max_autotune", [False, True])
+    # max_autotune is disabled due to https://github.com/pytorch/pytorch/issues/135106
+    # @common_utils.parametrize("max_autotune", [False, True])
+    @common_utils.parametrize("max_autotune", [False])
     def test_misc_1(self, max_autotune):
         if self.device == "cpu" and IS_MACOS and max_autotune:
             raise unittest.SkipTest("max_autotune not supported on macos")

test/inductor/test_cpu_cpp_wrapper.py

@@ -87,13 +87,7 @@ if TEST_WITH_ROCM:
         }
     )
 if config.abi_compatible:
-    xfail_list = [
-        *[
-            func
-            for func in dir(test_cpu_select_algorithm.TestSelectAlgorithmCPU())
-            if func.startswith("test_linear_with_pointwise")
-        ],
-    ]
+    xfail_list = []
     for test_name in xfail_list:
         test_failures_cpp_wrapper[test_name] = test_torchinductor.TestFailure(
             ("cpp_wrapper",), is_skip=False
@@ -103,6 +97,11 @@ if config.abi_compatible:
         ] = test_torchinductor.TestFailure(("cpp_wrapper",), is_skip=False)
     skip_list = [
         "test_multihead_attention_cpu",
+        *[
+            func
+            for func in dir(test_cpu_select_algorithm.TestSelectAlgorithmCPU())
+            if func.startswith("test_linear_with_pointwise")
+        ],
     ]
     for test_name in skip_list:
         test_failures_cpp_wrapper[test_name] = test_torchinductor.TestFailure(

torch/_inductor/codegen/cpp.py

@@ -2146,9 +2146,7 @@ class CppKernel(Kernel):
 
     @property
     def assert_function(self) -> str:
-        if V.graph.aot_mode:
-            # TODO: Using AOTI_TORCH_CHECK is causing performance drop for some models
-            # compared with JIT Inductor which uses TORCH_CHECK
+        if config.abi_compatible:
             return "AOTI_TORCH_CHECK"
         else:
             return "TORCH_CHECK"

torch/_inductor/codegen/cpp_micro_gemm.py

@@ -332,8 +332,8 @@ class CppMicroGemmFP32Vec(CppMicroGemm):
 
     TEMPLATE_ENTRY = r"""
 {{declare_kernel}} {
-    TORCH_CHECK(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}");
-    TORCH_CHECK(K % {{block_k}} == 0, "K dimension must be multiple of {{block_k}}");
+    {{kernel.assert_function}}(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}");
+    {{kernel.assert_function}}(K % {{block_k}} == 0, "K dimension must be multiple of {{block_k}}");
     // TODO(jgong5): loop unroll for M and N
     for (int64_t m = 0; m < M; m += {{block_m}}) {
         int64_t block_m = std::min<int64_t>(M - m, {{block_m}});
@@ -364,7 +364,7 @@ class CppMicroGemmFP32Vec(CppMicroGemm):
                     break;
 {%- endfor %}
                 default:
-                    {{kernel.assert_function}}(false, "Unsupported block_m: ", block_m);
+                    {{kernel.assert_function}}(false, "Unsupported block_m: {{block_m}}");
                 }
             }
         }
@@ -509,8 +509,8 @@ class CppMicroGemmAMX(CppMicroGemm):
 
     TEMPLATE_ENTRY = r"""
 {{declare_kernel}} {
-    TORCH_CHECK(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}");
-    TORCH_CHECK(K % 2 == 0, "K dimension must be multiple of 2");
+    {{kernel.assert_function}}(N % {{block_n}} == 0, "N dimension must be multiple of {{block_n}}");
+    {{kernel.assert_function}}(K % 2 == 0, "K dimension must be multiple of 2");
     // TODO(jgong5): loop unroll for M and N
     for (int64_t m = 0; m < M; m += {{block_m}}) {
         int64_t block_m = std::min<int64_t>(M - m, {{block_m}});

torch/_inductor/codegen/cpp_prefix.h

@@ -26,6 +26,7 @@
 #include <c10/util/generic_math.h>
 #include <c10/util/Half.h>
 #include <c10/util/TypeCast.h>
+#include <torch/csrc/inductor/aoti_torch/c/shim.h>
 
 #if defined(CPU_CAPABILITY_AVX512) || defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_ZVECTOR) || defined(CPU_CAPABILITY_NEON) || defined(CPU_CAPABILITY_VSX)
 #define INDUCTOR_USE_VECTOR_TYPES() 1

torch/_inductor/codegen/cpp_template.py

@@ -111,11 +111,10 @@ class CppTemplate(KernelTemplate):
     def header(self) -> IndentedBuffer:
         res = IndentedBuffer()
         res.writeline(codecache.cpp_prefix())
-        res.splice(
-            """
-                #include "c10/util/Unroll.h"
-            """
-        )
+        # TODO: add c10::ForcedUnroll test to test_aoti_abi_check
+        res.splice("""#include <c10/util/Unroll.h>""")
+        if config.abi_compatible:
+            res.splice("""#include <torch/csrc/inductor/aoti_torch/c/shim.h>""")
         enable_kernel_profile = config.cpp.enable_kernel_profile and sys.platform in [
             "linux",
             "win32",

torch/_inductor/graph.py

@@ -854,7 +854,6 @@ class GraphLowering(torch.fx.Interpreter):
     def allocate_non_dup_const_name(
         self, name: Optional[str], data: Union[Tensor]
     ) -> str:
-        orig_name = name
         if not config.aot_inductor.use_runtime_constant_folding:
             for constant_name, value in self.constants.items():
                 if (
@@ -871,7 +870,7 @@ class GraphLowering(torch.fx.Interpreter):
 
         if name is None:
             name = f"constant{len(self.constants)}"
-        assert name is not None
+        orig_name = name
         if name[0].isdigit():
             name = f"constant_{name}"
         name = self.qualify_name(name)