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AmosLewis / u-net_brain_mri_vaiq_int8.default.onnx.torch.elide.mlir
Created April 19, 2024 21:58
module {
func.func @torch_jit(%arg0: !torch.vtensor<[32,3,256,256],f32>) -> !torch.vtensor<[32,1,256,256],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
%float1.250000e-01 = torch.constant.float 1.250000e-01
%float4.882810e-04 = torch.constant.float 4.8828125E-4
%float3.125000e-02 = torch.constant.float 3.125000e-02
%float1.953130e-03 = torch.constant.float 0.001953125
%float3.906250e-03 = torch.constant.float 3.906250e-03
%float9.765620e-04 = torch.constant.float 9.765625E-4
%float7.812500e-03 = torch.constant.float 7.812500e-03
%true = torch.constant.bool true
@AmosLewis
AmosLewis / U-2-Net_vaiq_int8.default.onnx.torch.elide.mlir
Created April 19, 2024 21:08
This file has been truncated, but you can view the full file.
module {
func.func @torch_jit(%arg0: !torch.vtensor<[1,3,320,320],f32>) -> (!torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>, !torch.vtensor<[1,1,320,320],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
%0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x3x3x3xf32>) : !torch.vtensor<[64,3,3,3],f32>
%1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x64x3x3xf32>) : !torch.vtensor<[32,64,3,3],f32>
%3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32xf32>) : !torch.vtensor<[32],f32>
%4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x32x3x3xf3
@AmosLewis
AmosLewis / FCN_vaiq_int8.default.onnx.torch.elide.mlir
Created April 19, 2024 01:39
module {
func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>) -> (!torch.vtensor<[1,21,224,224],f32>, !torch.vtensor<[1,21,224,224],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
%0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x3x7x7xf32>) : !torch.vtensor<[64,3,7,7],f32>
%1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x1x1xf32>) : !torch.vtensor<[64,64,1,1],f32>
%3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x3x3xf32>) : !torch.vtensor<[64,64,3,3],f32>
%5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%6 = torch.vtensor.literal(d
@AmosLewis
AmosLewis / DeepLabV3_resnet50_vaiq_int8.default.onnx.torch.elide.mlir
Created April 18, 2024 21:23
module {
func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>) -> (!torch.vtensor<[1,21,224,224],f32>, !torch.vtensor<[1,21,224,224],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
%0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x3x7x7xf32>) : !torch.vtensor<[64,3,7,7],f32>
%1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x1x1xf32>) : !torch.vtensor<[64,64,1,1],f32>
%3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x3x3xf32>) : !torch.vtensor<[64,64,3,3],f32>
%5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%6 = torch.vtensor.literal(d
@AmosLewis
AmosLewis / torch_mlir_build.txt
Last active April 17, 2024 20:38
(mlir_venv) ➜ torch-mlir git:(reduceprod3) ✗ cmake -GNinja -Bbuild \
-DCMAKE_BUILD_TYPE=Debug \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
-DPython3_FIND_VIRTUALENV=ONLY \
-DLLVM_ENABLE_PROJECTS=mlir \
-DLLVM_EXTERNAL_PROJECTS="torch-mlir;torch-mlir-dialects" \
-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \
-DLLVM_EXTERNAL_TORCH_MLIR_DIALECTS_SOURCE_DIR=`pwd`/externals/llvm-external-projects/torch-mlir-dialects \
-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
@AmosLewis
AmosLewis / StdCorrectionModule_basic.mlir
Created March 1, 2024 07:44
#loc = loc(unknown)
module attributes {torch.debug_module_name = "StdCorrectionModule"} {
func.func @forward(%arg0: !torch.vtensor<[?,?,?],f32> loc(unknown)) -> !torch.vtensor<[],f32> {
%int6 = torch.constant.int 6 loc(#loc)
%float2.000000e00 = torch.constant.float 2.000000e+00 loc(#loc)
%float1.000000e00 = torch.constant.float 1.000000e+00 loc(#loc)
%true = torch.constant.bool true loc(#loc)
%int2 = torch.constant.int 2 loc(#loc)
%int1 = torch.constant.int 1 loc(#loc)
%int0 = torch.constant.int 0 loc(#loc)
@AmosLewis
AmosLewis / StdCorrectionModule_basic_debug.cpp
Created March 1, 2024 07:39
(mlir_venv) ➜ torch-mlir git:(reduceprodfix) gdb --arg python -m e2e_testing.main --config=onnx --filter StdCorrectionModule_basic -v
GNU gdb (Ubuntu 12.1-0ubuntu1~22.04) 12.1
Copyright (C) 2022 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type "show copying" and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
@AmosLewis
AmosLewis / BernoulliModule.mlir
Created March 1, 2024 07:32
#loc = loc(unknown)
module attributes {torch.debug_module_name = "BernoulliModule"} {
func.func @forward(%arg0: !torch.vtensor<[?,?,?],f64> loc(unknown)) -> (!torch.vtensor<[],f64>, !torch.vtensor<[],f64>) {
%true = torch.constant.bool true loc(#loc)
%false = torch.constant.bool false loc(#loc)
%int7 = torch.constant.int 7 loc(#loc)
%int2 = torch.constant.int 2 loc(#loc)
%int1 = torch.constant.int 1 loc(#loc)
%none = torch.constant.none loc(#loc)
%float0.000000e00 = torch.constant.float 0.000000e+00 loc(#loc4)
@AmosLewis
AmosLewis / BernoulliModule_basic_debug.cpp
Created March 1, 2024 07:30
(mlir_venv) ➜ torch-mlir git:(reduceprodfix) ✗ gdb --arg python -m e2e_testing.main --config=onnx --filter BernoulliModule_basic -v
GNU gdb (Ubuntu 12.1-0ubuntu1~22.04) 12.1
Copyright (C) 2022 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type "show copying" and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
@AmosLewis
AmosLewis / node_test_reduce_prod_default_axes_keepdims_random_model_torch.mlir
Created February 28, 2024 21:28
module {
func.func @test_reduce_prod_default_axes_keepdims_random(%arg0: !torch.vtensor<[3,2,2],f32>) -> !torch.vtensor<[1,1,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%int0 = torch.constant.int 0
%int0_0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%0 = torch.aten.dim %arg0 : !torch.vtensor<[3,2,2],f32> -> !torch.int
%1 = torch.aten.lt.int %int0_0, %int0 : !torch.int, !torch.int -> !torch.bool
%2 = torch.aten.Int.bool %1 : !torch.bool -> !torch.int
%3 = torch.aten.mul.int %2, %0 : !torch.int, !torch.int -> !torch.int