PiperOrigin-RevId: 164845473

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This change allows more kinds of ops to be fused into the
loop.

Besides increasing spatial locality, fusing the ops can decrease
the code size by not generating an extra loop nest.

This change also adds two kinds of tests:
* Tests to make sure the fusion logic recognizes that fusion
can only occur the op acts elementwise on the operand.
* More tests in fusion_test to test how fused loops are lowered
with the newly added classes of ops.

PiperOrigin-RevId: 164825735

Android demo: revert calls to yuv -> rgb conversion methods so that Java fallback can be used if libtensorflow_demo.so method is not found. Resolves #12110

PiperOrigin-RevId: 164812287

RemoveEMA GT: transforms frozen graphs from the FakeQuantizeTraining GT making it compatible with the QuantizeNodes GT.

PiperOrigin-RevId: 164810566

PiperOrigin-RevId: 164805620

This change introduces an LLVMCompiler class, of which the
CPU and GPU compilers are subclasses. The LLVMCompiler
class provides the ability to inspect LLVM generated compiler
code by registering a callback. The callbacks can
be used to analyze IR before and after optimizations.

This also adds a simple test for the callback mechanism.

PiperOrigin-RevId: 164805348

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Update Android Detect demo to use models exported using the Tensorflow Object Detection API.  Resolves #6738.

PiperOrigin-RevId: 164802542

This changes the `samples_per_second` parameter of the `encode_audio`
and `decode_audio` ops from an `Attr` to an `Input`, so that it can be
given arbitrary tensor values instead of only constants.

This change is important for use cases that want to use a single graph
to encode audio clips at arbitrary sample rates. (In particular, we want
to create a Python function that uses a long-running TensorFlow session
to encode audio; the sample rate cannot be known ahead of time, and we
don't want to have to reconstruct the graph on every call.)

PiperOrigin-RevId: 164799067

PiperOrigin-RevId: 164797105

slightly different semantics.

PiperOrigin-RevId: 164796436

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PiperOrigin-RevId: 164791375

Refactor Keras layers to rely on the core constraint implementation.

PiperOrigin-RevId: 164788653

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This transformation is a simpler (and potentially more efficient)
replacement for `Dataset.map(lambda x: x, num_threads=1,
output_buffer_size=N)`, avoiding the overhead of function invocation
and simplifying the synchronization slightly.
PiperOrigin-RevId: 164781954

[XLA] Fix Broadcast implementation in HloEvaluator to handle the special case of scalar broadcast to be consistent with other backends. Also add a test for scalar broadcast.

PiperOrigin-RevId: 164781786

Updating is possible if operands/uses or computation roots change in
the graph. Updating is not possible if instructions are deleted or if
new instructions are added.

Specific changes:
* Add verification methods for asserting invariants and checking the
  analysis after updating.
* Always add phi values at while instructions. Previously these were
  added only if the phi had different inputs. The advantage of using
  phi's unconditionally is that the set of values is fixed for a
  module. Updates due to changing operands/uses in the graph do not
  create new values.
* Store values in a vector rather than a map. With unconditional phi
  values, the number of HloValues is fixed so the values can be held
  in a vector with stable references to elements.

PiperOrigin-RevId: 164778750

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A common failure mode of the new datasets input pipeline is an
extremely long first sess.run call. It can sometimes appear to
users that things are simply hanging, when instead a large
shuffle buffer is being filled. When filling large shuffle
buffers, we should let users know what's going on.

PiperOrigin-RevId: 164760903

Don't run contrib/timeseries/python/timeseries:state_management_test pip test until crash has been resolved.

PiperOrigin-RevId: 164759761

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Also make version name alpha instead of RC.

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removable from a computation. This is to prevent DCE from removing a
while instruction that includes a send/recv instruction.

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Prevents bad formatting: https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/nn/dynamic_rnn

PiperOrigin-RevId: 164675585