[MLGO] Remove -tfutils-use-simplelogger flag (#72492)

This flag was redundant and the value was not used anywhere, so it
should be removed.

[mlgo] Allow logging the spec for the "advice", if needed

This is for the interactive model runner, so it can confirm the tensor
spec of the advice with its host.

[mlgo] Stream the training data

This leverages the new logging format in that we don't need to buffer
the training data, we can just write it out.

Differential Revision: https://reviews.llvm.org/D1

[mlgo] Stream the training data

This leverages the new logging format in that we don't need to buffer
the training data, we can just write it out.

Differential Revision: https://reviews.llvm.org/D142168

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[nfc][mlgo] Remove abstraction layers for training logger

This follows from D141720

Differential Revision: https://reviews.llvm.org/D141967

Fix OSX build break introduced by D141720

[Analysis] Fix a warning

This patch fixes:

llvm/include/llvm/Analysis/Utils/TrainingLogger.h:94:14: error:
private field 'IncludeReward' is not used
[-Werror,-Wunused-private-field]

[mlgo] Remove the protobuf dependency

The dependency was due to the log format. This change switches to the
previously-introduced (D139370) "dependency-free" logger instead of the
protobuf-based one

[mlgo] Remove the protobuf dependency

The dependency was due to the log format. This change switches to the
previously-introduced (D139370) "dependency-free" logger instead of the
protobuf-based one.

A subsequent change will clean out the unnecessary abstraction left
behind.

This change drops the logger unittest, we have sufficient test coverage
via lit tests, and a unit test would require adding, unnecesarily, a log
reader (the reader is expected to be python, for the ML side, and there
is a reader for that under Analysis/models, used for tests).

Differential Revision: https://reviews.llvm.org/D141720

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[mlgo] Use LLVM_HAVE_TFLITE instead of LLVM_HAVE_TF_API in C++ code (NFC)

We use LLVM_HAVE_TFLITE as the key to enable the mlgo work these days,
and LLVM_HAVE_TF_API is defined whenever LLVM_HAVE_TF

[mlgo] Use LLVM_HAVE_TFLITE instead of LLVM_HAVE_TF_API in C++ code (NFC)

We use LLVM_HAVE_TFLITE as the key to enable the mlgo work these days,
and LLVM_HAVE_TF_API is defined whenever LLVM_HAVE_TF_API is defined.

I'm posting this patch because it's purely mechanical.

I'll post a follow-up patch to remove LLVM_HAVE_TF_API in non-C++
files, and that will not be as mechanical as this one.

Differential Revision: https://reviews.llvm.org/D139863

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Reapply "[mlgo] Dependency-free training mode logger"

This reverts commit 8abe7b11f74bea63d3134c144137b72146da0c7b.

Added the missing cast which was causing a build problem on certain compilers.

Revert "[mlgo] Dependency-free training mode logger"

This reverts commit c5ff6f72342e0a4b0ba2ec9f603bedca86721e80.

This breaks building on macOS:

FAILED: lib/Analysis/CMakeFiles/LLVMAnalysis.dir/T

Revert "[mlgo] Dependency-free training mode logger"

This reverts commit c5ff6f72342e0a4b0ba2ec9f603bedca86721e80.

This breaks building on macOS:

FAILED: lib/Analysis/CMakeFiles/LLVMAnalysis.dir/TensorSpec.cpp.o
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++ -DBUILD_EXAMPLES -DGTEST_HAS_RTTI=0 -D_DEBUG -D__STDC_CONSTANT_MACROS -D__STDC_FORMAT_MACROS -D__STDC_LIMIT_MACROS -I/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/clang-build/lib/Analysis -I/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/lib/Analysis -I/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/clang-build/include -I/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/include -fPIC -fvisibility-inlines-hidden -Werror=date-time -Werror=unguarded-availability-new -Wall -Wextra -Wno-unused-parameter -Wwrite-strings -Wcast-qual -Wmissing-field-initializers -pedantic -Wno-long-long -Wc++98-compat-extra-semi -Wimplicit-fallthrough -Wcovered-switch-default -Wno-noexcept-type -Wnon-virtual-dtor -Wdelete-non-virtual-dtor -Wstring-conversion -Wmisleading-indentation -Wctad-maybe-unsupported -fdiagnostics-color -O3 -DNDEBUG -isysroot /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX11.1.sdk -mmacosx-version-min=10.14 -fno-exceptions -fno-rtti -UNDEBUG -std=c++17 -MD -MT lib/Analysis/CMakeFiles/LLVMAnalysis.dir/TensorSpec.cpp.o -MF lib/Analysis/CMakeFiles/LLVMAnalysis.dir/TensorSpec.cpp.o.d -o lib/Analysis/CMakeFiles/LLVMAnalysis.dir/TensorSpec.cpp.o -c /Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/lib/Analysis/TensorSpec.cpp
In file included from /Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/lib/Analysis/TensorSpec.cpp:16:
In file included from /Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/include/llvm/Analysis/TensorSpec.h:16:
/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/include/llvm/Support/JSON.h:354:29: error: non-constant-expression cannot be narrowed from type 'unsigned long' to 'int64_t' (aka 'long long') in initializer list [-Wc++11-narrowing]
create<int64_t>(int64_t{I});
^
/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/lib/Analysis/TensorSpec.cpp:55:18: note: in instantiation of function template specialization 'llvm::json::Value::Value<unsigned long, void, void, void>' requested here
OS.value(D);
^
/Users/buildslave/jenkins/workspace/clang-stage1-cmake-RA-incremental/llvm-project/llvm/include/llvm/Support/JSON.h:354:29: note: insert an explicit cast to silence this issue
create<int64_t>(int64_t{I});
^
static_cast<int64_t>( )
1 error generated.

https://green.lab.llvm.org/green/job/clang-stage1-cmake-RA-incremental/33120/consoleFull#-145995569149ba4694-19c4-4d7e-bec5-911270d8a58c

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[mlgo] Dependency-free training mode logger

This is the next step in dropping the dependency on protobuf.

The simple logger produces an output consisting of lines of json
strings. Tensor values - w

[mlgo] Dependency-free training mode logger

This is the next step in dropping the dependency on protobuf.

The simple logger produces an output consisting of lines of json
strings. Tensor values - which should constitute the bulk of the data -
are serialized as raw byte buffers. This allows for light-weight reading
of the values.

The next step is to switch the training logic to the new logging format,
following which the protobuf-based logger will be dropped, together with
the training dependency on protobuf.

Subsequent changes will also stop buffering and stream, instead - the
buffering model is just as a convenient point-in-time.

Differential Revision: https://reviews.llvm.org/D139370

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[mlgo][nfc] Virtualize Logger implementation

This is in preparation for dropping the dependency on protobuf. This
first step allows us to subsequently introduce the non-protobuf
implementation behin

[mlgo][nfc] Virtualize Logger implementation

This is in preparation for dropping the dependency on protobuf. This
first step allows us to subsequently introduce the non-protobuf
implementation behind a flag. After that we can update the training side
to ingest the new format, after which we can drop the protobuf
implementation and de-virtualize everything.

Differential Revision: https://reviews.llvm.org/D139062

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[mlgo][nfc] Make `LoggedFeatureSpec` an implementation detail

It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.

Differential Revision: h

[mlgo][nfc] Make `LoggedFeatureSpec` an implementation detail

It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.

Differential Revision: https://reviews.llvm.org/D139031

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[nfc][mlgo] Separate logger and training-mode model evaluator

This just shuffles implementations and declarations around. Now the
logger and the TF C API-based model evaluator are separate.

Differe

[nfc][mlgo] Separate logger and training-mode model evaluator

This just shuffles implementations and declarations around. Now the
logger and the TF C API-based model evaluator are separate.

Differential Revision: https://reviews.llvm.org/D131116

show more ...