[AOTI] Add _weight_int4pack_mm to the C shim fallback list (#151059)

Summary: As title

Pull Request resolved: https://github.com/pytorch/pytorch/pull/151059
Approved by: https://github.com/yushangdi

test/inductor/test_aot_inductor.py

@@ -33,6 +33,7 @@ from torch.testing._internal.common_device_type import (
     skipCUDAIf,
 )
 from torch.testing._internal.common_quantization import (
+    _group_quantize_tensor,
     skip_if_no_torchvision,
     skipIfNoFBGEMM,
 )
@@ -42,6 +43,7 @@ from torch.testing._internal.common_utils import (
     IS_FBCODE,
     IS_MACOS,
     IS_WINDOWS,
+    parametrize,
     skipIfRocm,
     skipIfXpu,
     TEST_WITH_ROCM,
@@ -4936,6 +4938,42 @@ class AOTInductorTestsTemplate:
         )
         self.check_model(Model(), example_inputs)
 
+    @skipIfXpu(
+        msg="aten::convert_weight_to_int4pack is not currently implemented for XPU"
+    )
+    @parametrize("m", [32])
+    @parametrize("n", [64])
+    @parametrize("q_group", [32, 64])
+    @parametrize("num_groups", [1, 2])
+    def test__weight_int4pack_mm(self, m, n, q_group, num_groups):
+        if self.device != GPU_TYPE:
+            raise unittest.SkipTest("requires GPU")
+
+        class Model(torch.nn.Module):
+            def __init__(self, weight, scale_and_zeros) -> None:
+                super().__init__()
+                self.weight = weight
+                self.scale_and_zeros = scale_and_zeros
+
+            def forward(self, a):
+                return torch._weight_int4pack_mm(
+                    a, self.weight, q_group, self.scale_and_zeros
+                )
+
+        def convert_weight_to_int4pack(b):
+            b_int32, b_scales_and_zeros = _group_quantize_tensor(
+                b, n_bit=4, q_group_size=q_group
+            )
+            b_int4pack = torch._convert_weight_to_int4pack(b_int32, innerKTiles=2)
+            return b_int4pack, b_scales_and_zeros
+
+        k = q_group * num_groups
+        a = torch.rand((m, k), device=self.device, dtype=torch.bfloat16)
+        b = torch.rand((k, n), device=self.device, dtype=torch.bfloat16)
+        b_int4pack, b_scales_and_zeros_f32 = convert_weight_to_int4pack(b)
+        model = Model(b_int4pack, b_scales_and_zeros_f32)
+        self.check_model(model, (a,))
+
     def test_assert_tensor_meta(self):
         class Module(torch.nn.Module):
             def forward(self, x):

torch/csrc/inductor/aoti_torch/generated/c_shim_cuda.h

@@ -49,6 +49,7 @@ AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda__segment_reduce_backward(AtenTe
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda__thnn_fused_lstm_cell(AtenTensorHandle input_gates, AtenTensorHandle hidden_gates, AtenTensorHandle cx, AtenTensorHandle* input_bias, AtenTensorHandle* hidden_bias, AtenTensorHandle* ret0, AtenTensorHandle* ret1, AtenTensorHandle* ret2);
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda__to_sparse(AtenTensorHandle self, int32_t* layout, const int64_t** blocksize, int64_t blocksize_len_, int64_t* dense_dim, AtenTensorHandle* ret0);
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda__trilinear(AtenTensorHandle i1, AtenTensorHandle i2, AtenTensorHandle i3, const int64_t* expand1, int64_t expand1_len_, const int64_t* expand2, int64_t expand2_len_, const int64_t* expand3, int64_t expand3_len_, const int64_t* sumdim, int64_t sumdim_len_, int64_t unroll_dim, AtenTensorHandle* ret0);
+AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda__weight_int4pack_mm(AtenTensorHandle self, AtenTensorHandle mat2, int64_t qGroupSize, AtenTensorHandle qScaleAndZeros, AtenTensorHandle* ret0);
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda_adaptive_max_pool2d(AtenTensorHandle self, const int64_t* output_size, int64_t output_size_len_, AtenTensorHandle* ret0, AtenTensorHandle* ret1);
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda_adaptive_max_pool2d_backward(AtenTensorHandle grad_output, AtenTensorHandle self, AtenTensorHandle indices, AtenTensorHandle* ret0);
 AOTI_TORCH_EXPORT AOTITorchError aoti_torch_cuda_adaptive_max_pool3d(AtenTensorHandle self, const int64_t* output_size, int64_t output_size_len_, AtenTensorHandle* ret0, AtenTensorHandle* ret1);

torchgen/aoti/fallback_ops.py

@@ -51,6 +51,7 @@ inductor_fallback_ops = {
     "aten._thnn_fused_lstm_cell.default",
     "aten._to_sparse.default",
     "aten._trilinear.default",
+    "aten._weight_int4pack_mm.default",
     "aten._weight_int8pack_mm.default",
     "aten.adaptive_max_pool2d_backward.default",
     "aten.adaptive_max_pool2d.default",