Chi_Liu AmosLewis
AmosLewis
/ nonzero.mlir
Created
November 15, 2024 04:54
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| ./build_tools/ci/test_posix.sh | |
| ::group::Run ONNX e2e integration tests | |
| TORCH_VERSION_FOR_COMPARISON = 2.6.0.dev20241107 | |
| Running tests sequentially with progress status | |
| *** RUNNING TEST: AtenNonzero1DModule_one_nonzero *** | |
| Compiling AtenNonzero1DModule_one_nonzero... | |
| ==================== | |
| ONNX RAW IR | |
| module { |
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| module { | |
| func.func @tf2onnx(%arg0: !torch.vtensor<[?,768],f32>, %arg1: !torch.vtensor<[3],si64>, %arg2: !torch.vtensor<[?,256,768],f32>) -> ( !torch.vtensor<[?,256,768],f32>) attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 21 : si64, torch.onnx_meta.producer_name = "tf2onnx", torch.onnx_meta.producer_version = "1.5.2"} { | |
| %reshape = torch.operator "onnx.Reshape"(%arg0, %arg1) : (!torch.vtensor<[?,768],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,256,768],f32> | |
| %866 = torch.operator "onnx.Add"(%reshape, %arg2) : (!torch.vtensor<[?,256,768],f32>, !torch.vtensor<[?,256,768],f32>) -> !torch.vtensor<[?,256,768],f32> | |
| return %866 : !torch.vtensor<[?,256,768],f32> | |
| } | |
| } |
AmosLewis
/ compilation.detail.log
Last active
October 11, 2024 03:02
model--long-t5-tglobal-base-16384-book-summary--pszemraj
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| <unknown>:0: error: failed to legalize unresolved materialization from ('i64') to 'index' that remained live after conversion | |
| <unknown>:0: note: see current operation: %452 = "builtin.unrealized_conversion_cast"(%451) : (i64) -> index | |
| /proj/gdba/shark/chi/src/SHARK-TestSuite/alt_e2eshark/test-run/model--long-t5-tglobal-base-16384-book-summary--pszemraj/model.modified.mlir:1299:12: note: see existing live user here: %2529 = hal.interface.binding.subspan layout(<constants = 20, bindings = [#hal.pipeline.binding<storage_buffer, "ReadOnly|Indirect">, #hal.pipeline.binding<storage_buffer, "ReadOnly|Indirect">, #hal.pipeline.binding<storage_buffer, "ReadOnly|Indirect">, #hal.pipeline.binding<storage_buffer, Indirect>], flags = Indirect>) binding(0) alignment(64) offset(%3) flags("ReadOnly|Indirect") : memref<i64, strided<[], offset: 24>> | |
| %360 = linalg.generic {indexing_maps = [#map12, #map12, #map12, #map12], iterator_types = []} ins(%357, %358, %359 : tensor<i1>, tensor<i64>, tensor<i64>) outs(%94 : tensor<i64 |
AmosLewis
/ onnxscatterreduce.mlir
Created
September 16, 2024 04:48
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| module { | |
| func.func @main_graph(%arg5:!torch.vtensor<[2708],f32>, %arg1: !torch.vtensor<[?],si64>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[2708],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 = "2.1.0"} { | |
| %59 = torch.operator "onnx.ScatterElements"(%arg5, %arg1, %arg2) {torch.onnx.axis = 0 : si64, torch.onnx.reduction = "add"} : (!torch.vtensor<[2708],f32>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[2708],f32> | |
| return %59 : !torch.vtensor<[2708],f32> | |
| } | |
| } |
AmosLewis
/ test-run-vision_int8_cmd_output.txt
Created
August 20, 2024 06:15
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| python ./run.py --tolerance 0.001 0.001 --cachedir /proj/gdba/shark/cache --ireebuild ../../iree-build -f onnx -g models --mode onnx --report -j 12 -r test-ru | |
| n-vision_int8 --testsfile list1_vision_int8_run | |
| /proj/gdba/shark/chi/src/SHARK-TestSuite/e2eshark/e2e_venv/lib/python3.10/site-packages/torchvision/io/image.py:14: UserWarning: Failed to load image Python extension: '/proj/gdba/shark/chi/src/SHARK-TestSuite/e2eshark/e2e_venv/lib/python3.10/site-packages/torchvision/image.so: undefined symbol: _ZNK3c1011StorageImpl27throw_data_ptr_access_errorEv'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source? | |
| warn( | |
| Starting e2eshark tests. Using 12 processes | |
| Cache Directory: /proj/gdba/shark/cache | |
| Tolerance for comparing floating point (atol, rtol) = (0.001, 0.001) | |
| Note: No Torch MLIR build provided using --torchmlir |
AmosLewis
/ module_torch_jit_dispatch_33.mlir
Created
August 10, 2024 00:17
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| hal.executable public @torch_jit_dispatch_33 { | |
| hal.executable.variant public @embedded_elf_x86_64 target(<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", native_vector_size = 16 : i64, target_triple = "x86_64-unknown-unknown-eabi-elf"}>) { | |
| hal.executable.export public @torch_jit_dispatch_33_quantized_batch_matmul_56x56x512x128_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} { | |
| ^bb0(%arg0: !hal.device): | |
| %x, %y, %z = flow.dispatch.workgroup_count_from_slice | |
| hal.return %x, %y, %z : index, index, index | |
| } | |
| builtin.module { | |
| func.func @torch_jit_dispatch_33_quantized_batch_matmul_5 |
AmosLewis
/ module_torch_jit_dispatch_25.mlir
Created
August 10, 2024 00:16
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| hal.executable public @torch_jit_dispatch_25 { | |
| hal.executable.variant public @embedded_elf_x86_64 target(<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", native_vector_size = 16 : i64, target_triple = "x86_64-unknown-unknown-eabi-elf"}>) { | |
| hal.executable.export public @torch_jit_dispatch_25_quantized_batch_matmul_56x56x128x512_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} { | |
| ^bb0(%arg0: !hal.device): | |
| %x, %y, %z = flow.dispatch.workgroup_count_from_slice | |
| hal.return %x, %y, %z : index, index, index | |
| } | |
| builtin.module { | |
| func.func @torch_jit_dispatch_25_quantized_batch_matmul_5 |
AmosLewis
/ module_torch_jit_dispatch_23.mlir
Created
August 10, 2024 00:16
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| hal.executable public @torch_jit_dispatch_23 { | |
| hal.executable.variant public @embedded_elf_x86_64 target(<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", native_vector_size = 16 : i64, target_triple = "x86_64-unknown-unknown-eabi-elf"}>) { | |
| hal.executable.export public @torch_jit_dispatch_23_quantized_batch_matmul_56x56x512x128_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} { | |
| ^bb0(%arg0: !hal.device): | |
| %x, %y, %z = flow.dispatch.workgroup_count_from_slice | |
| hal.return %x, %y, %z : index, index, index | |
| } | |
| builtin.module { | |
| func.func @torch_jit_dispatch_23_quantized_batch_matmul_5 |
AmosLewis
/ module_torch_jit_dispatch_15.mlir
Created
August 10, 2024 00:15
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| hal.executable public @torch_jit_dispatch_15 { | |
| hal.executable.variant public @embedded_elf_x86_64 target(<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", native_vector_size = 16 : i64, target_triple = "x86_64-unknown-unknown-eabi-elf"}>) { | |
| hal.executable.export public @torch_jit_dispatch_15_quantized_batch_matmul_56x56x128x512_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} { | |
| ^bb0(%arg0: !hal.device): | |
| %x, %y, %z = flow.dispatch.workgroup_count_from_slice | |
| hal.return %x, %y, %z : index, index, index | |
| } | |
| builtin.module { | |
| func.func @torch_jit_dispatch_15_quantized_batch_matmul_5 |
AmosLewis
/ dpn68_vaiq_iree_failed.log
Created
August 8, 2024 22:49
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| failed to translate executables | |
| failed to translate executables | |
| dpn68_vaiq.default.onnx.linalg.mlir:1243:12: error: One or more operations with large vector sizes (8192 bytes) were found: | |
| %180 = linalg.generic {indexing_maps = [#map1, #map2], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%179 : tensor<1x64x56x56xf32>) outs(%107 : tensor<1x64x56x56xi8>) { | |
| ^ | |
| dpn68_vaiq.default.onnx.linalg.mlir:9:3: note: called from | |
| func.func @main_graph(%arg0: tensor<1x3x224x224xf32>) -> tensor<1x1000xf32> { | |
| ^ | |
| <unknown>:0: note: %cst_3 = arith.constant dense<1.562500e-02> : vector<200704xf32> |
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