Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/50191
DISC is a e2e flow, including both compiler side and runtime side. For
runtime side, we have different targeting environments (e.g. tensorflow,
pytorch, or sometimes even a standalone binary). In order to simplify
the design of the compiler side, we design a Runtime Abstraction Layer
(RAL) to sperate the compiler side and runtime side. Thus the compiler
side only need to target RAL itself and it is the responsibility of RAL
to handle the differences between different targeting environments.
One of the most important functions of RAL is to manage stateful
resources. To this end, it provides a context object, and hides all
stateful operations behind this context, thus the compiler side itself
doesn't need to care about the resource initialization. For example, a
kernel must be loaded before it can be launched on GPU. However, the
loading operation should only be taken once during the whole lifetime of
the context in order to achieve the best performance. Based on the
initialization-free interfaces provided by RAL, compiler side can focus
on its core optimization logic and lets the RAL to manage the resource
status.
The context mentioned above is passed as a parameter to the entry
function and all RAL APIs should always use the context as their first
argument. This CR also provides a pass to help to ensure this property.
The pass rewrites the entry function to make sure their first argument
is the context. For entry function, the pass also rewrites its inputs
and outputs. To be concrete, all the original inputs and outputs of the
entry function are received from and sent to RAL through a sequence of
RAL API calls correspondingly. The motivation behind this is to hide the
implementation details of I/Os. This design may also potentially enable
partial execution of the compiled module when some of the inputs are
ready.
Copybara import of the project:
--
c4f20a89aed71181e75bcc5265723b88bde23240 by Wenyi Zhao <reyizero@gmail.com>:
[MLIR][DISC] Add RAL (Runtime abstraction layer) Dialect
DISC is a e2e flow, including both compiler side and runtime side. For
runtime side, we have different targeting environments (e.g. tensorflow,
pytorch, or sometimes even a standalone binary). In order to simplify
the design of the compiler side, we design a Runtime Abstraction Layer
(RAL) to sperate the compiler side and runtime side. Thus the compiler
side only need to target RAL itself and it is the responsibility of RAL
to handle the differences between different targeting environments.
One of the most important functions of RAL is to manage stateful
resources. To this end, it provides a context object, and hides all
stateful operations behind this context, thus the compiler side itself
doesn't need to care about the resource initialization. For example, a
kernel must be loaded before it can be launched on GPU. However, the
loading operation should only be taken once during the whole lifetime of
the context in order to achieve the best performance. Based on the
initialization-free interfaces provided by RAL, compiler side can focus
on its core optimization logic and lets the RAL to manage the resource
status.
The context mentioned above is passed as a parameter to the entry
function and all RAL APIs should always use the context as their first
argument. This CR also provides a pass to help to ensure this property.
The pass rewrites the entry function to make sure their first argument
is the context. For entry function, the pass also rewrites its inputs
and outputs. To be concrete, all the original inputs and outputs of the
entry function are received from and sent to RAL through a sequence of
RAL API calls correspondingly. The motivation behind this is to hide the
implementation details of I/Os. This design may also potentially enable
partial execution of the compiled module when some of the inputs are
ready.
--
1991d4f80ab6087943956e1c0fec4940a22ab08d by Wenyi Zhao <reyizero@gmail.com>:
fix
PiperOrigin-RevId: 379317586
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/49598
This PR implements logic for lowering memref.tensor_load ops that are
inserted during `mhlo-legalize-to-lmhlo`
Copybara import of the project:
--
80eb377af4e02182e1aecc943a41ca5d7d1c2100 by Wenyi Zhao <reyizero@gmail.com>:
[MLIR][DISC] legalize tensor_load inserted during hlo-to-lhlo conversion
This PR implements logic for lowering memref.tensor_load ops that are
inserted during `mhlo-legalize-to-lmhlo`.
--
ac452fe3dcd591211cd5c59be9189fe2f7153b41 by Wenyi Zhao <reyizero@gmail.com>:
minor fix
--
6b36017f8632a06adbc3e05a62975fa641d0260f by Wenyi Zhao <reyizero@gmail.com>:
minor refine
--
846005cc76d0033112e47825c2e9a97790b6925f by Wenyi Zhao <reyizero@gmail.com>:
minor fix
--
f6a4becaa287d5ca323b2d152a4d0ae053730fd9 by Wenyi Zhao <reyizero@gmail.com>:
fix
--
5555749f60f7fce8f57962860ef65efccf0362ba by Wenyi Zhao <reyizero@gmail.com>:
fix
--
8873b9b6d9315c1199ca9f7c133ecf377ecd2fa6 by Wenyi Zhao <reyizero@gmail.com>:
fix
PiperOrigin-RevId: 376942547
Add a pass to cluster unranked C/HLO operations in one
`chlo.rank_specialization_cluster` op. The C/HLO operations are moved to the
body of the operation. Later passes can use this to rank-specialize all these
operations together.
PiperOrigin-RevId: 373336725
For now, the pass only reifies the required shape computations. Moving
broadcasts will follow to allow for fusion across them.
PiperOrigin-RevId: 362033715
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/44499
The file `sink_constants_to_control_flow.cc` includes the header
`PassDetail.h`, which itself includes `mhlo_passes.h.inc`. The latter is
not guaranteed to be already generated since there was no dependency set
to MLIRMhloPassIncGen.
Copybara import of the project:
--
0ff51ccc88c1ba049eb2e9555afb54079bea39c9 by Marius Brehler <marius.brehler@iml.fraunhofer.de>:
Add missing dep on MLIRMhloPassIncGen target
The file `sink_constants_to_control_flow.cc` includes the header
`PassDetail.h`, which itself includes `mhlo_passes.h.inc`. The latter is
not guaranteed to be already generated since there was no dependency set
to MLIRMhloPassIncGen.
PiperOrigin-RevId: 340485068
Add `tan` op and lowering to CHLO dialect, move CHLO lowerings to
`chlo_legalize_to_hlo_patterns` and extend missing patterns.
PiperOrigin-RevId: 331506094
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/43137
This PR removes lhlo-copy-removal pass entirely and replace its usages with ```mlir::createCopyRemovalPass()```.
--
7ce1a06f507c8db46c6d7b43c7870cf56002e18e by Ehsan Toosi <ehsan.nadjaran_toosi@dfki.de>:
[mlir][lhlo] Replace lhlo-copy-removal pass with mlir-copy-removal pass
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/43137 from dfki-ehna:using_mlir_copy_removal 7ce1a06f507c8db46c6d7b43c7870cf56002e18e
PiperOrigin-RevId: 331498501
Add `tan` op and lowering to CHLO dialect, move CHLO lowerings to
`chlo_legalize_to_hlo_patterns` and extend missing patterns.
PiperOrigin-RevId: 331128170
Add `tan` op and lowering to CHLO dialect, move CHLO lowerings to
`chlo_legalize_to_hlo_patterns` and extend missing patterns.
PiperOrigin-RevId: 331125286
Start of pass to legalize MHLO control flow to SCF for further optimization in common form. The current version just matches a very simple instance (which also happens to occur a few times). Exposes some further canonicalization opportunities that aren't yet addressed.
PiperOrigin-RevId: 329017723
Geoffrey Martin-Noble
Wenyi Zhao
A. Unique TensorFlower
Hanhan Wang
Christian Sigg
Alexander Belyaev
Marius Brehler
Ehsan Toosi
Jacques Pienaar
Mehdi Amini