diff --git a/ingress/Torch-MLIR/convert-kernel-bench-to-mlir.py b/ingress/Torch-MLIR/convert-kernel-bench-to-mlir.py new file mode 100755 index 0000000..4cf03eb --- /dev/null +++ b/ingress/Torch-MLIR/convert-kernel-bench-to-mlir.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 + +import importlib +import importlib.util + +from pathlib import Path + +from mlir import ir, passmanager +from torch_mlir import fx + +kernels_as_pytorch_folder = Path(__file__).parent / "KernelBench" / "KernelBench" +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") + +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}" + ) + continue + + module_name = kernel_pytorch_file.stem + + kernel_as_mlir_path = mlir_level / (module_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}" + ) + + module_spec = importlib.util.spec_from_file_location( + module_name, kernel_pytorch_file + ) + + if module_spec is None or module_spec.loader is None: + print(f"Error: Could not create module spec for {kernel_pytorch_file}") + continue + + module = importlib.util.module_from_spec(module_spec) + # Execute the module to load its contents + module_spec.loader.exec_module(module) + + if not all( + hasattr(module, a) for a in ("Model", "get_inputs", "get_init_inputs") + ): + print(f"Error: module in file {kernel_pytorch_file} not a proper benchmark") + continue + + # TODO: check hasattr(module, "in_features") etc and adjust to sizes that are more tractable for torch-mlir + + try: + m = fx.export_and_import( + module.Model(*module.get_init_inputs()), + *module.get_inputs(), + output_type=fx.OutputType.LINALG_ON_TENSORS, + ) + except Exception as e: + print(f"Error: got the following error converting {kernel_pytorch_file}") + raise e + + before_clean_up = "//" + str(m)[:-1].replace("\n", "\n//") + "\n" + # Cross boundary from torch-mlir's mlir to environment's mlir + m = ir.Module.parse(str(m), context=ctx) + # Run clean-up, e.g. linalg-"specialization" passes to raise within Linalg. + try: + pm.run(m.operation) # cleanup + except Exception as e: + print(f"Error: got the following error cleaning up {module_name}") + raise e + + with kernel_as_mlir_path.open("w") as f: + print("// Torch-MLIR output:", file=f) + print(before_clean_up, file=f) + print("// MLIR output after clean-up:", file=f) + print(m, file=f) diff --git a/ingress/Torch-MLIR/install-virtualenv.sh b/ingress/Torch-MLIR/install-virtualenv.sh index f48019c..3d70f97 100755 --- a/ingress/Torch-MLIR/install-virtualenv.sh +++ b/ingress/Torch-MLIR/install-virtualenv.sh @@ -9,19 +9,16 @@ else DEVICE_TYPE=$(lspci | grep VGA) fi - -# Install torch-mlir inside a virtual environment echo "First ensure uv is installed" - python -m pip install uv --upgrade echo "Preparing the virtual environment" python -m uv venv torch-mlir-venv --python 3.12 -#echo "Preparing the virtual environment" -#python3 -m venv torch-mlir-venv source torch-mlir-venv/bin/activate -uv pip install --upgrade pip wheel + +echo "Installing mlir-python-bindings and numpy" +uv pip install numpy mlir-python-bindings -f https://makslevental.github.io/wheels # GPU support ("AMD", "NVIDIA", "Intel") EXTRA_INDEX_URL="" @@ -46,9 +43,7 @@ if [ $? != 0 ]; then exit 1 fi - echo "Installing torch-mlir" -# This only seems to work on Ubuntu uv pip install --pre torch-mlir \ --extra-index-url https://download.pytorch.org/whl/nightly/cpu \ -f https://github.com/llvm/torch-mlir-release/releases/expanded_assets/dev-wheels @@ -56,3 +51,6 @@ if [ $? != 0 ]; then exit 1 fi +# TODO(rolf): move to a more appropriate place +echo "Obtaining KernelBench repo" +git clone https://github.com/ScalingIntelligence/KernelBench.git