[ingress][pytorch] Basic KernelBench to MLIR conversion

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "ingress/KernelBench/KernelBench"]
+	path = ingress/KernelBench/KernelBench
+	url = https://github.com/ScalingIntelligence/KernelBench.git

ingress/KernelBench/convert-kernel-bench-to-mlir.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+
+import sys
+from pathlib import Path
+
+from mlir import ir, passmanager
+from lighthouse.ingress import torch as torch_ingress
+
+
+kernels_as_pytorch_folder = Path(__file__).parent / "KernelBench" / "KernelBench"
+
+if not (kernels_as_pytorch_folder.exists() and kernels_as_pytorch_folder.is_dir()):
+    print(
+        "ERROR: KernelBench repo not found.\n"
+        "NOTE: Pull in dependency with: git submodule update "
+        + str(kernels_as_pytorch_folder.parent.relative_to(Path.cwd()))
+        + "",
+        file=sys.stderr,
+    )
+    sys.exit(1)
+
+
+kernels_as_pytorch_level1 = kernels_as_pytorch_folder / "level1"
+kernels_as_pytorch_level2 = kernels_as_pytorch_folder / "level2"
+
+kernels_as_mlir_folder = Path(__file__).parent / "cache"
+kernels_as_mlir_level1 = kernels_as_mlir_folder / "level1"
+kernels_as_mlir_level1.mkdir(parents=True, exist_ok=True)
+kernels_as_mlir_level2 = kernels_as_mlir_folder / "level2"
+kernels_as_mlir_level2.mkdir(parents=True, exist_ok=True)
+
+level1, level2 = Path("level1"), Path("level2")
+ignore_list = [
+    level1 / "12_Matmul_with_diagonal_matrices_.py",  # torch.operator "torch.aten.diag"
+    level1
+    / "34_InstanceNorm.py",  # LLVM ERROR: SmallVector unable to grow. Requested capacity (93898875033000)
+    level1
+    / "72_conv_transposed_3D_asymmetric_input_asymmetric_kernel___strided_padded_grouped_.py",  # Bare exception during torch-backend-to-linalg-on-tensors-backend-pipeline
+    level1
+    / "89_cumsum.py",  # Dialect `tm_tensor' not found for custom op 'tm_tensor.scan'
+    level1
+    / "90_cumprod.py",  # Dialect `tm_tensor' not found for custom op 'tm_tensor.scan'
+    level1
+    / "91_cumsum_reverse.py",  # Dialect `tm_tensor' not found for custom op 'tm_tensor.scan'
+    level1
+    / "92_cumsum_exclusive.py",  # Dialect `tm_tensor' not found for custom op 'tm_tensor.scan'
+    level1
+    / "93_masked_cumsum.py",  # Dialect `tm_tensor' not found for custom op 'tm_tensor.scan'
+    level1
+    / "95_CrossEntropyLoss.py",  # Bare exception during torch-backend-to-linalg-on-tensors-backend-pipeline
+    level1
+    / "96_HuberLoss.py",  # Bare exception during torch-backend-to-linalg-on-tensors-backend-pipeline
+    level1
+    / "97_ScaledDotProductAttention.py",  # AssertionError: Torch not compiled with CUDA enabled
+    level1
+    / "99_TripletMarginLoss.py",  # Bare exception during torch-backend-to-linalg-on-tensors-backend-pipeline
+    level2
+    / "17_Conv2d_InstanceNorm_Divide.py",  # LLVM ERROR: SmallVector unable to grow. Requested capacity (94899412484104)
+    level2
+    / "18_Matmul_Sum_Max_AvgPool_LogSumExp_LogSumExp.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "28_BMM_InstanceNorm_Sum_ResidualAdd_Multiply.py",  # LLVM ERROR: SmallVector unable to grow. Requested capacity (94899412484104)
+    level2
+    / "42_ConvTranspose2d_GlobalAvgPool_BiasAdd_LogSumExp_Sum_Multiply.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "43_Conv3d_Max_LogSumExp_ReLU.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "45_Gemm_Sigmoid_LogSumExp.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "51_Gemm_Subtract_GlobalAvgPool_LogSumExp_GELU_ResidualAdd.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "52_Conv2d_Activation_BatchNorm.py",  # failed to legalize operation 'torch.operator'
+    level2 / "55_Matmul_MaxPool_Sum_Scale.py",  # MLIR file too big: 16G
+    level2 / "59_Matmul_Swish_Scaling.py",  # MLIR file too big: 16G
+    level2 / "56_Matmul_Sigmoid_Sum.py",  # MLIR file too big: 16G
+    level2 / "66_Matmul_Dropout_Softmax.py",  # MLIR file too big: 4G
+    level2 / "68_Matmul_Min_Subtract.py",  # MLIR file too big: 4G
+    level2 / "94_Gemm_BiasAdd_Hardtanh_Mish_GroupNorm.py",  # MLIR file too big: 1G
+    level2 / "33_Gemm_Scale_BatchNorm.py",  # MLIR file too big: 1G
+    level2 / "88_Gemm_GroupNorm_Swish_Multiply_Swish.py",  # MLIR file too big: 1G
+    level2 / "75_Gemm_GroupNorm_Min_BiasAdd.py",  # MLIR file too big: 1G
+    level2 / "84_Gemm_BatchNorm_Scaling_Softmax.py",  # MLIR file too big: 1G
+    level2 / "97_Matmul_BatchNorm_BiasAdd_Divide_Swish.py",  # MLIR file too big: 1G
+    level2 / "62_Matmul_GroupNorm_LeakyReLU_Sum.py",  # MLIR file too big: 1G
+    level2 / "30_Gemm_GroupNorm_Hardtanh.py",  # MLIR file too big: 1G
+    level2 / "95_Matmul_Add_Swish_Tanh_GELU_Hardtanh.py",  # MLIR file too big: 1G
+    level2 / "29_Matmul_Mish_Mish.py",  # MLIR file too big: 1G
+    level2 / "99_Matmul_GELU_Softmax.py",  # MLIR file too big: 1G
+    level2 / "98_Matmul_AvgPool_GELU_Scale_Max.py",  # MLIR file too big: 1G
+    level2 / "80_Gemm_Max_Subtract_GELU.py",  # MLIR file too big: 1G
+    level2 / "81_Gemm_Swish_Divide_Clamp_Tanh_Clamp.py",  # MLIR file too big: 1G
+    level2 / "12_Gemm_Multiply_LeakyReLU.py",  # MLIR file too big: 1G
+    level2 / "53_Gemm_Scaling_Hardtanh_GELU.py",  # MLIR file too big: 1G
+    level2 / "9_Matmul_Subtract_Multiply_ReLU.py",  # MLIR file too big: 1G
+    level2 / "70_Gemm_Sigmoid_Scaling_ResidualAdd.py",  # MLIR file too big: 1G
+    level2 / "86_Matmul_Divide_GELU.py",  # MLIR file too big: 1G
+    level2 / "63_Gemm_ReLU_Divide.py",  # MLIR file too big: 1G
+    level2 / "76_Gemm_Add_ReLU.py",  # MLIR file too big: 1G
+    level2 / "14_Gemm_Divide_Sum_Scaling.py",  # MLIR file too big: 1G
+    level2 / "39_Gemm_Scale_BatchNorm.py",  # MLIR file too big: 256M
+    level2 / "41_Gemm_BatchNorm_GELU_ReLU.py",  # MLIR file too big: 256M
+    level2 / "40_Matmul_Scaling_ResidualAdd.py",  # MLIR file too big: 256M
+    level2 / "37_Matmul_Swish_Sum_GroupNorm.py",  # MLIR file too big: 64.3M
+    level2
+    / "58_ConvTranspose3d_LogSumExp_HardSwish_Subtract_Clamp.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "64_Gemm_LogSumExp_LeakyReLU_LeakyReLU_GELU_GELU.py",  # error: failed to legalize operation 'torch.constant.int'
+    level2
+    / "79_Conv3d_Multiply_InstanceNorm_Clamp_Multiply_Max.py",  # LLVM ERROR: SmallVector unable to grow. Requested capacity (94312016449768)
+    level2
+    / "92_Conv2d_GroupNorm_Tanh_HardSwish_ResidualAdd_LogSumExp.py",  # error: failed to legalize operation 'torch.constant.int'
+]
+
+
+ctx = ir.Context()
+pm = passmanager.PassManager(context=ctx)
+pm.add("linalg-specialize-generic-ops")
+
+print("Output directory:", kernels_as_mlir_folder)
+exitcode = 0
+for pytorch_level, mlir_level in (
+    (kernels_as_pytorch_level1, kernels_as_mlir_level1),
+    (kernels_as_pytorch_level2, kernels_as_mlir_level2),
+):
+    for kernel_pytorch_file in pytorch_level.iterdir():
+        level_and_kernel = (
+            Path(kernel_pytorch_file.parent.name) / kernel_pytorch_file.name
+        )
+        if level_and_kernel in ignore_list or not kernel_pytorch_file.is_file():
+            print(
+                f"Skipping: {kernel_pytorch_file.parent.name}/{kernel_pytorch_file.name}",
+                file=sys.stderr,
+            )
+            continue
+
+        kernel_name = kernel_pytorch_file.stem
+
+        kernel_as_mlir_path = mlir_level / (kernel_name + ".mlir")
+        if kernel_as_mlir_path.exists():
+            print(
+                f"Already in cache: {kernel_pytorch_file.parent.name}/{kernel_pytorch_file.name}"
+            )
+            continue
+        print(
+            f"Processing: {kernel_pytorch_file.parent.name}/{kernel_pytorch_file.name}"
+        )
+        mlir_kernel = torch_ingress.import_from_file(
+            kernel_pytorch_file, ir_context=ctx
+        )
+        assert isinstance(mlir_kernel, ir.Module)
+
+        try:
+            pm.run(mlir_kernel.operation)  # cleanup
+        except Exception as e:
+            print(
+                f"ERROR: got the following error cleaning up '{kernel_name}'",
+                file=sys.stderr,
+            )
+            raise e
+
+        with kernel_as_mlir_path.open("w") as f:
+            print("// MLIR output after conversion and clean-up:", file=f)
+            print(mlir_kernel, file=f)