Add unlifting pass under private config - #4

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Add unlifting pass under private config#4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.

Reviewed By: zhxchen17

Differential Revision: D46785735

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tugsbayasgalan added a commit to tugsbayasgalan/pytorch that referenced this pull request Jul 17, 2023
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 50e80eab0c214f96bdd2430700eb5bb473d6d193
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Add unlifting pass under private config (#4)
d38a980
Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 9357b25615d97be2426bf74164b9995c57c3b187
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This pull request was exported from Phabricator. Differential Revision: D46785735

larryliu0820 added a commit that referenced this pull request Jul 2, 2025
@larryliu0820
Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced this pull request in psiddh/executorch Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
Test Plan:
Reviewers:
Subscribers:
Tasks:
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metascroy added a commit that referenced this pull request Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit to cmt0/executorch that referenced this pull request Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit to agrima1304/executorch that referenced this pull request Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
kirklandsign pushed a commit that referenced this pull request Jan 14, 2026
@shoumikhin
Create README.md (#4)
078556b
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seyeong-han added a commit to seyeong-han/executorch that referenced this pull request Jun 2, 2026
@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
(review #1).
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 50e80eab0c214f96bdd2430700eb5bb473d6d193
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Add unlifting pass under private config (#4)
d38a980
Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 9357b25615d97be2426bf74164b9995c57c3b187
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larryliu0820 added a commit
that referenced
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Jul 2, 2025
@larryliu0820
Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced
this pull request
in psiddh/executorch
Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
metascroy added a commit
that referenced
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Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
to cmt0/executorch
that referenced
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Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
this pull request
Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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Jan 14, 2026
@shoumikhin
Create README.md (#4)
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Address PRpytorch#19533review: guard test on jinja2cpp; document em…
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…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
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intentionally falsy so the normalized no-tools template path renders
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Add unlifting pass under private config - #4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 50e80eab0c214f96bdd2430700eb5bb473d6d193
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Add unlifting pass under private config (#4)
d38a980
Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 9357b25615d97be2426bf74164b9995c57c3b187
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@larryliu0820
Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced
this pull request
in psiddh/executorch
Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
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metascroy added a commit
that referenced
this pull request
Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
to cmt0/executorch
that referenced
this pull request
Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
this pull request
Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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@shoumikhin
Create README.md (#4)
078556b
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@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
(review #1).

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Add unlifting pass under private config - #4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
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Add unlifting pass under private config (#4)
d38a980
Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
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Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced
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Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
metascroy added a commit
that referenced
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Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
to cmt0/executorch
that referenced
this pull request
Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
this pull request
Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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Jan 14, 2026
@shoumikhin
Create README.md (#4)
078556b
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seyeong-han added a commit
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Jun 2, 2026
@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
(review #1).

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Add unlifting pass under private config - #4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 50e80eab0c214f96bdd2430700eb5bb473d6d193
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Add unlifting pass under private config (#4)
d38a980
Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
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Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced
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in psiddh/executorch
Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
metascroy added a commit
that referenced
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Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
to cmt0/executorch
that referenced
this pull request
Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
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Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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Jan 14, 2026
@shoumikhin
Create README.md (#4)
078556b
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seyeong-han added a commit
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Jun 2, 2026
@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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Add unlifting pass under private config - #4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
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Add unlifting pass under private config (#4)
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Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
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@larryliu0820
Merge pull request#4from pytorch-labs/add_coc
a875876
Add CODE_OF_CONDUCT
psiddh referenced
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in psiddh/executorch
Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
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metascroy added a commit
that referenced
this pull request
Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
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Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
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Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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Create README.md (#4)
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@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
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…pty tools default
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undefined JinjaChatFormatter symbols (review pytorch#4).
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intentionally falsy so the normalized no-tools template path renders
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
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Add unlifting pass under private config (pytorch#104897)
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Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
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Add unlifting pass under private config (#4)
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X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
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Differential Revision: D46785735
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Merge pull request#4from pytorch-labs/add_coc
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Summary: Initial CMSIS-NN custom kernels port (Take#2)
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metascroy added a commit
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@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
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Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
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that referenced
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Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
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@shoumikhin
Create README.md (#4)
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@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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Add unlifting pass under private config - #4
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Summary: We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: zhxchen17
Differential Revision: D46785735
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Add unlifting pass under private config (pytorch#104897)
0520e50
Summary:
Pull Request resolved: pytorch#104897
X-link: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Test Plan: CI
Reviewed By: JacobSzwejbka
Differential Revision: D46785735
fbshipit-source-id: 50e80eab0c214f96bdd2430700eb5bb473d6d193
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Add unlifting pass under private config (#4)
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Summary:
X-link: pytorch/pytorch#104897
Pull Request resolved: pytorch/executorch#4
We wanna do this little by little. For now, I tried only on DissectedPartsModel which needs to use aot_export version.
Reviewed By: JacobSzwejbka
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Merge pull request#4from pytorch-labs/add_coc
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psiddh referenced
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in psiddh/executorch
Jul 28, 2025
sidart
Summary: Initial CMSIS-NN custom kernels port (Take#2)
a523a5b
Test Plan:
Reviewers:
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Tasks:
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metascroy added a commit
that referenced
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Aug 1, 2025
@metascroy
Do not use BNNS copy when dtypes differ in CoreML (#13018)
43d90e5
BNNS copy crashes the process when the dtypes differ
(#11714).
With the example in this PR
(#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame #1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame #2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame #3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame #4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame #5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame #6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame #7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame #8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame #9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame #10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame #11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame #12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame #13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame #14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame #15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame #16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame #17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
cmt0 added a commit
to cmt0/executorch
that referenced
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Aug 15, 2025
@cmt0@facebook-github-bot
Alternative format specifier for %zd
0850437
Summary:
At runtime this format specifier is not correctly handled. The misformatted string get's passed to strlen and eventually causes an assertion.
```
#0 strlen () at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/machine/xtensa/strlen.S:59
pytorch#1 0x610bd83d in _svfprintf_r (data=<optimized out>, fp=<optimized out>, fmt0=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vfprintf.c:1380
pytorch#2 0x610ffcf4 in _vsnprintf_r (ptr=<optimized out>, size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, str=<optimized out>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:66
pytorch#3 vsnprintf (str=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", size=256, fmt=0x20 <error: Cannot access memory at address 0x20>, ap=...)
at /home/xpgcust/tree/RJ-2024.3/tb/p4root/Xtensa/SWConfig/../Target-libs/newlib/newlib/libc/stdio/vsnprintf.c:41
pytorch#4 0x610d4ddd in executorch::runtime::internal::vlogf (level=<optimized out>, timestamp=<optimized out>, filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z",
function=0x60fd5fd7 "resolve_operator", line=735, format=0x60fd6023 "Missing operator: [%zd] %s", args=...) at xplat/executorch/runtime/platform/log.cpp:88
pytorch#5 0x610ce2db in executorch::runtime::internal::logf (level=executorch::runtime::LogLevel::Error, timestamp=3330441403,
filename=0x14012c40 <irt_janus_workq_stack+4960> "Missing operator: [z", function=0x14012d3e <irt_janus_workq_stack+5214> "\026\262\273\200\202", <incomplete sequence \306>,
line=735, format=0x60fd6023 "Missing operator: [%zd] %s")
at /execution-workspace/buck-out/v2/gen/fbsource/e7835b44f7cec64a/xplat/executorch/runtime/platform/__platform__/buck-headers/executorch/runtime/platform/log.h:140
pytorch#6 0x610d8b60 in executorch::runtime::Method::resolve_operator (this=<optimized out>, op_index=1, kernels=<optimized out>, kernel_index=<optimized out>, args=..., n_args=7)
at xplat/executorch/runtime/executor/method.cpp:731
pytorch#7 0x60ff2d70 in executorch::runtime::Method::init (this=0x14012fa0 <irt_janus_workq_stack+5824>, s_plan=<optimized out>, external_data_map=<optimized out>)
at xplat/executorch/runtime/executor/method.cpp:926
pytorch#8 0x610d8c33 in executorch::runtime::Method::load (s_plan=0xb21690b4, program=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000, external_data_map=0x0)
at xplat/executorch/runtime/executor/method.cpp:761
pytorch#9 0x610db216 in executorch::runtime::Program::load_method (this=0xabd4000c, method_name=<optimized out>, memory_manager=0xabd400f0, event_tracer=0xad540000,
named_data_map=<optimized out>) at xplat/executorch/runtime/executor/program.cpp:299
pytorch#10 0x60ff1a80 in MethodContainer::init (this=<optimized out>, modelBuffer=..., weightBuffer=..., methodName=<optimized out>, plannedMemoryBuffers=..., methodAllocator=...,
tempAllocator=..., etDumpBuffer=..., debugBufferDataSink=0x0) at arvr/firmware/silicon/ml/executorch/method_container/src/MethodContainer.cpp:104
pytorch#11 0x610cdef9 in InferenceRunnerExecutorch::initializeExecutorchObjects (this=<optimized out>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:255
pytorch#12 0x610ce06a in InferenceRunnerExecutorch::evaluate (this=0x14013268 <irt_janus_workq_stack+6536>) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunnerExecutorch.cpp:297
pytorch#13 0x610cd186 in execute_model (inferenceRuntimeContext=0x24227680) at arvr/firmware/silicon/turing/tirt/inference/src/InferenceRunner.cpp:60
pytorch#14 0x610ccf0c in tirt_engine_invoke (inference_request=0x24220000) at arvr/firmware/silicon/turing/tirt/engine/src/Engine.cpp:125
pytorch#15 0x610cce25 in tirt::dispatch::tirt_command_process (request=0x24220000) at arvr/firmware/silicon/turing/tirt/command_dispatch/src/tirt_dispatcher.cpp:71
pytorch#16 0x610ccae7 in irt_janus_msg_handler (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, ctx=0x0,
header=0x140137fd <irt_janus_workq_stack+7965>, payload=0x24220000, status=<optimized out>) at arvr/firmware/silicon/turing/tirt/src/IrtIccJanus.cpp:143
--Type <RET> for more, q to quit, c to continue without paging--
pytorch#17 0x610c80ec in _janus_service_handle_message (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>,
service_info=0x140137f8 <irt_janus_workq_stack+7960>) at arvr/firmware/wearables/libs/janus/session/consumer.c:1099
pytorch#18 janus_service (sess=0x60fc5f80 <FDLADSP0::coleman_fdladsp0_cp_tirt_fdladsp0_session_views_cp_iaas_fdlamcu_to_tirt_fdladsp0>, method=JANUS_SERVICE_ONE)
at arvr/firmware/wearables/libs/janus/session/consumer.c:2236
pytorch#19 0x610c9d9a in _work_handler (w=0x14009e08 <s_janus_workq_sessions+8>) at arvr/firmware/wearables/libs/janus/modules/janus_workq/src/workq.c:62
pytorch#20 0x61100409 in triggered_work_handler (work=0x14009e08 <s_janus_workq_sessions+8>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/poll.c:590
pytorch#21 0x610d139f in work_queue_main (workq_ptr=0x14000f98 <s_janus_workq+24>, p2=<optimized out>, p3=<optimized out>) at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/kernel/work.c:688
pytorch#22 0x610c1172 in z_thread_entry (entry=0x610d1344 <work_queue_main>, p1=0x14000f98 <s_janus_workq+24>, p2=0x14001038 <s_janus_workq+184>, p3=0xfffffffd)
at third-party/zephyr/zephyr_rtos/v3.7.0/zephyr/lib/os/thread_entry.c:48
```
Reviewed By: lucylq, JacobSzwejbka
Differential Revision: D79776266
agrima1304 pushed a commit
to agrima1304/executorch
that referenced
this pull request
Aug 26, 2025
@metascroy@agrima1304
Do not use BNNS copy when dtypes differ in CoreML (pytorch#13018)
f82a205
BNNS copy crashes the process when the dtypes differ
(pytorch#11714).
With the example in this PR
(pytorch#11714), we crash the
process on main. Here is the stack trace from LLDB:
```
Process 19234 stopped
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
libsystem_kernel.dylib`__pthread_kill:
-> 0x190ac9388 <+8>: b.lo 0x190ac93a8 ; <+40>
0x190ac938c <+12>: pacibsp 0x190ac9390 <+16>: stp x29, x30, [sp, #-0x10]!
0x190ac9394 <+20>: mov x29, sp
(lldb) bt
* thread pytorch#1, queue = 'com.apple.main-thread', stop reason = signal SIGABRT
* frame #0: 0x0000000190ac9388 libsystem_kernel.dylib`__pthread_kill + 8
frame pytorch#1: 0x0000000190b0288c libsystem_pthread.dylib`pthread_kill + 296
frame pytorch#2: 0x0000000190a0bc60 libsystem_c.dylib`abort + 124
frame pytorch#3: 0x0000000190910174 libsystem_malloc.dylib`malloc_vreport + 892
frame pytorch#4: 0x0000000190913c90 libsystem_malloc.dylib`malloc_report + 64
frame pytorch#5: 0x000000019091821c libsystem_malloc.dylib`___BUG_IN_CLIENT_OF_LIBMALLOC_POINTER_BEING_FREED_WAS_NOT_ALLOCATED + 32
frame pytorch#6: 0x000000019d2f4084 libBNNS.dylib`___lldb_unnamed_symbol1620 + 564
frame pytorch#7: 0x000000019d2f5bac libBNNS.dylib`___lldb_unnamed_symbol1628 + 680
frame pytorch#8: 0x000000019d69ce48 libBNNS.dylib`BNNSCopy + 616
frame pytorch#9: 0x000000030c74d950 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy_using_bnns(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&) + 188
frame pytorch#10: 0x000000030c74cfdc _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(executorchcoreml::MultiArray const&, executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) + 72
frame pytorch#11: 0x000000030c74ceec _portable_lib.cpython-310-darwin.so`executorchcoreml::MultiArray::copy(executorchcoreml::MultiArray&, executorchcoreml::MultiArray::CopyOptions) const + 148
frame pytorch#12: 0x000000030c7488d4 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 376
frame pytorch#13: 0x000000030c748ac8 _portable_lib.cpython-310-darwin.so`invocation function for block in (anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 52
frame pytorch#14: 0x000000019ad33f4c CoreML`CoreML::MultiArrayBuffer::getBytesWithHandler(void (void const*, unsigned long) block_pointer) const + 340
frame pytorch#15: 0x000000019ad34138 CoreML`-[MLMultiArray(ScopedBufferAccess) getBytesWithHandler:] + 152
frame pytorch#16: 0x000000030c7485ec _portable_lib.cpython-310-darwin.so`(anonymous namespace)::copy(MLMultiArray*, executorchcoreml::MultiArray&) + 296
frame pytorch#17: 0x000000030c744f68 _portable_lib.cpython-310-darwin.so`(anonymous namespace)::set_outputs(std::__1::vector<executorchcoreml::MultiArray, std::__1::allocator<executorchcoreml::MultiArray>>&, NSArray<MLMultiArray*>*) + 180
```
With this PR, the process succeeds.
kirklandsign pushed a commit
that referenced
this pull request
Jan 14, 2026
@shoumikhin
Create README.md (#4)
078556b
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seyeong-han added a commit
to seyeong-han/executorch
that referenced
this pull request
Jun 2, 2026
@seyeong-han
Address PRpytorch#19533review: guard test on jinja2cpp; document em…
81b9210
…pty tools default
- test/CMakeLists.txt: exclude test_jinja_chat_formatter.cpp from the test
binary when jinja2cpp isn't built (mirrors the runner CMake guard), so
building tests without the chat_template subdir doesn't fail to link with
undefined JinjaChatFormatter symbols (review pytorch#4).
- jinja_chat_formatter.cpp: document that the empty `tools` list is
intentionally falsy so the normalized no-tools template path renders
(review #1).

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