This is originally from @claverru's PR:
https://github.com/tensorflow/tensorflow/pull/31191

PiperOrigin-RevId: 286007122
Change-Id: Ic5fb32de79d1210d11554291b41e69cc3ebbbd12

https://gitlab.com/libeigen/eigen/commit/7252163335f56f23fcc7381c1efdea47161005fa

PiperOrigin-RevId: 286006460
Change-Id: Ic78a1d7e41bbe7f877a32e67b996e70b9a824122

This matches the behavior for the (long-deprecated) DT_STRING_REF handle version, and avoids string copies and allocations on each invocation of the kernel.

PiperOrigin-RevId: 286005273
Change-Id: If1767f86bdf5ebc0480cb9d61703dcd895942eec

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Change-Id: I683b17ae42cf90835056558598e972a3441b1a01

This class provides a simplified mechanism for defining a switch over a set of types using llvm casting functionality. More specifically, this allows for defining a switch over a value of type T where each case corresponds to a type(CaseT) that can be used with dyn_cast<CaseT>(...). An example is shown below:

// Traditional piece of code:
Operation *op = ...;
if (auto constant = dyn_cast<ConstantOp>(op))
  ...;
else if (auto return = dyn_cast<ReturnOp>(op))
  ...;
else
  ...;

// New piece of code:
Operation *op = ...;
TypeSwitch<Operation *>(op)
  .Case<ConstantOp>([](ConstantOp constant) { ... })
  .Case<ReturnOp>([](ReturnOp return) { ... })
  .Default([](Operation *op) { ... });

Aside from the above, TypeSwitch supports return values, void return, multiple types per case, etc. The usability is intended to be very similar to StringSwitch.

(Using c++14 template lambdas makes everything even nicer)
More complex example of how this makes certain things easier:
LogicalResult process(Constant op);
LogicalResult process(ReturnOp op);
LogicalResult process(FuncOp op);

TypeSwitch<Operation *, LogicalResult>(op)
  .Case<ConstantOp, ReturnOp, FuncOp>([](auto op) { return process(op); })
  .Default([](Operation *op) { return op->emitError() << "could not be processed"; });

PiperOrigin-RevId: 286003613
Change-Id: Ia497a9ec65fbbbcd72e23651d828fe17fee5096d

This will allow the bridge logger to opt in to all of the functionality that is already available, e.g. module scope printing, change detection, etc.

PiperOrigin-RevId: 285999034
Change-Id: Iabd13e548aee0a987615079ba52a159a4f56d6f4

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Change-Id: I9a35925eb4694c07be66b9df4dd98e809cefc74c

Fixes the TF upgrade script to not delete files when there is a parsing error and the output mode is `in-place`.

PiperOrigin-RevId: 285990161
Change-Id: Ideb70c5bda9cbec65100893b48687ded167e46dc

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Change-Id: I7f37b1497c56e09115d38256ff11eb221463ffb3

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Change-Id: I1790012208d0a091d7b9c134ba86755b6d3524c8

The change omits necessary checks to is_initialized(), which could lead to data races.

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Change-Id: I12d40188473b855e398437514237b72eddb0443f

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Change-Id: Ic4dbe5e054d3383995c3a8b301f0c3d7544d4b8e

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Change-Id: Ida0f58ce622cb627653ac7898a2c5d427ac0cbfe

It cannot correctly reason about whether the buffer is initialized or not.

PiperOrigin-RevId: 285981325
Change-Id: I51751f822253a7d6c49830a4b6a3382cbc5a10b5

Add pattern rewrite which splits a vector TransferReadOp into slices according to the unrolling/slicing scheme of its ExtractSlicesOp user.

PiperOrigin-RevId: 285975613
Change-Id: I422c94f143c1c8937021ceda3d2947bf102dea90

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Change-Id: Ib83e53d6e34a7def18f5ac55c4d2838ca91b799e

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Change-Id: I5a36ba4909b8c312fabe591e8107f1b1c7fd45f9

This is a general code cleanup and should be a NFC.

PiperOrigin-RevId: 285972718
Change-Id: Idbcc9664cff11287834b14e7522ddcd78342cfa1

Update vector op unrolling transformation to generate ExtractSlicesOp and InsertSlicesOp (instead of less structured chain of StridedSliceOps and InsertStridedSliceOps).

PiperOrigin-RevId: 285968051
Change-Id: I18a1404f90bdaf5eea8466238cfefad34047fdf4

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Change-Id: I3eae0b134cd2e954bfa0ac31e6a7411b3a5bb7df

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Change-Id: I7961e539f271f97036b32f58260d1973cb1728c6

For some reason, unlike other code in the repository, these files used the Status constructor with an enum error code and StrCat(), rather than the convenient wrapper.

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Change-Id: Iecd0bfc275349891f157ceaaaa94e7110d1f2910

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Change-Id: I314facccd02de2d9f14d879e84dd458ec9db2309

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Change-Id: Iced3f91ab8332abcf4e858685c3469547b52efc8

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Change-Id: I6e832665d94a0b2cdf1f4024dff407cf2f7cf1a5

The current method is apparently too complex to be inlined, and profiling shows a significant fraction of the overhead is in function dispatch. This change improves the throughput of the sparse_to_dense_op microbenchmark, which stresses IndicesValid(), by between 7% (BM_SparseToDense/5M) and 39% (BM_SparseToDense/986k).

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Change-Id: Ibc547842a527e6634fa037338738962768690db8

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Change-Id: Ie2861ae7106ef34545561397024ef441c020b2ad

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Change-Id: I0b5fd98d0f76e93e4ccc399b6ebe84f229a7364a

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Change-Id: I68e174c654ee975067065dbfcb8b59d08d1fdb00

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Change-Id: Idfe131765d1b691937b26692623dbfb9c125f066

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Change-Id: I0404ae4a9759ca01042db4684bc2282062137418

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Change-Id: I2116497da6a683c5fb62e574794e4b1e04dee571

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Change-Id: I67da00e81f6ca60e939b5f77502be1249d59a930

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Change-Id: I20b55062c56c5b04c8dc0fc03ad6df6f5f02c8f5

- Add new method for GenerateTestSpec to generate testspec from TFLite.

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Change-Id: I803bd619013f7410bd56283a715e46c8719d4810

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Change-Id: I252217310d589d4c331b0287f5fc328d7bb05b75

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Change-Id: If4d365661a0376ee815fd02fe2a16dd823fca13d

third_party/tensorflow/compiler/mlir/tfrt/tfrt_delegate.

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Change-Id: I716a73a66d418a58a6a930175e7673b9b59ae671

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Change-Id: Ifa5b65fe41bea505d1454a8c43b56959eaa04431

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Change-Id: I10b5eacb8861ffc07384ea76e4e4597283f69fcb