Reapply 9cffabc68ca380be937e192be909feff7b144822

This reverts commit 735f117f4d0deb9644d65c8fe8a80add058e7a2b.

Revert "[mlgo][nfc] Better pretty printing of interactive mode reply"

This reverts commit 9cffabc68ca380be937e192be909feff7b144822.

Broke windows builds

[mlgo][nfc] Better pretty printing of interactive mode reply

Also simplified the `-interactive-model-runner-echo-reply` flag to a
bool, because the header will contain the advice spec, so there is a

[mlgo][nfc] Better pretty printing of interactive mode reply

Also simplified the `-interactive-model-runner-echo-reply` flag to a
bool, because the header will contain the advice spec, so there is an
explicit agreement between the compiler and the host as to what that
should be shaped as.

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Fix windows bot breakages due to D143110

[mlgo] Make InteractiveModelRunner actually work with named pipes

Turns out raw_fd_stream doesn't work with named pipes, so we just need
to lower the abstraction. Updated the unittest accordingly. B

[mlgo] Make InteractiveModelRunner actually work with named pipes

Turns out raw_fd_stream doesn't work with named pipes, so we just need
to lower the abstraction. Updated the unittest accordingly. Because
mkfifo's path argument requires a certain naming pattern on Windows
(IIUC), restricted the test to Linux only.

Differential Revision: https://reviews.llvm.org/D143110

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[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] Introduce an "InteractiveModelRunner"

This is a model runner for ML researchers using environments like
CompilerGym. In such environments, researchers host the compiler and
want to be able to

[mlgo] Introduce an "InteractiveModelRunner"

This is a model runner for ML researchers using environments like
CompilerGym. In such environments, researchers host the compiler and
want to be able to observe the problem space (features) at each decision
step of some optimization pass, at which point the compiler is stopped,
waiting for the host makes a decision and provide an advice back to
the compiler, which then continues its normal operation, and so on.

The InteractiveModelRunner supports this scenario for the feature set
exposed by the compiler at a given time. It uses 2 files - ideally FIFO
pipes - one to pass data to the host, the other to get advices back from
the host. This means this scenario is supported with no special
dependencies. The file creation and deletion is the responsibility of
the host. Hooking up this model evaluator to a MLGO-ed pass is the
responsibilty of the pass author, and subsequent patches will do so for
the current set of mlgo passes, and offer an API to easily "just opt in"
by default when mlgo-ing a new pass.

The data protocol is that of the training logger: the host sees a training
log doled out observation by observation by reading from one of the
files, and passes back its advice as a serialized tensor (i.e. tensor value
memory dump) via the other file.

There are some differences wrt the log seen during training: the
interactive model doesn't currently include the outcome (because it should be
identical to the decision, and it's also not present in the "release"
mode); and partial rewards aren't currently communicated back.

The assumption - just like with the training logger - is that the host
is co-located, thus avoiding any endianness concerns. In a distributed
environment, it is up to the hosting infrastructure to intermediate
that.

Differential Revision: https://reviews.llvm.org/D142642

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