| Commit message (Collapse) | Author | Age |
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This patch uses take by value and move idiom to optimize copying of constructor arguments.
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value}}` and `^^key:value^^`. This change consolidate these two format.
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switching from assertAllEqual to assertAllClose.
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microcontrollers
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- Remove unnecessary test_session() boilerplate when executing eagerly
- Use self.cached_session() instead of self.test_session() when using graphs
self.test_session() has been deprecated in 9962eb5e84b15e309410071b06c2ed2d6148ed44 as its name confuses readers of the test. Moving to cached_session() instead which is more explicit about:
* the fact that the session may be reused.
* the session is not closed even when doing a "with self.test_session()" statement.
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shapes.
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HLO instruction to its operands.
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self -> _parameters -> self cycles were creating work for Python's garbage collector in training loops, where Distribution objects may be created repeatedly when executing eagerly. This CL just fixes that narrow memory issue; I'm not convinced dict(locals()) is super efficient, so we may want to follow up on that for performance.
Adds a few unit tests tests with run_test_in_graph_and_eager_modes(assert_no_eager_garbage=True). It'd be nice to expand this coverage over time.
Includes a small test_util simplification to support this (TFP tests don't like reset_default_graph for some reason). Testing for cycles in the TFP repo will need to wait on the Normal changes from the TF repo syncing.
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`MapDataset`.
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* simplify contraction by collapsing inner dims into single dimension
* get rid of expensive reverse op
~5X improvement when compiled with AVX.
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Fixes #21266
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monotonic unary functions.
Add the ability to flip Max <-> Min if the function is non-increasing, e.g. Max(Neg(x)) => Neg(Min(x)).
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HLO transformations would forget to propagate the feature depth attribute.
Making these attributes mandatory, while slightly less convenient for tests,
makes HLO transformations more robust.
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wdirons:21833_disable_gpu_test_scatter_add_ndim_op_test
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metrics combined with while loops.
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get an owned device mgr from the input session.
One use case is in S4TF, we run a graph session to enqueue a tensor into a fifo
queue, and then call TFE_Execute() on a dequeue op over the same queue, as a way
to transfer a tensor from TF to host (tensor tranfer in the other direction also
works).
To make this work, we need TFE_Context and the the TF_Session to use the same
ResourceMgr object (attached to a Device, which is in turn owned by DeviceMgr),
so that both can access the fifo queue resource op.
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transposed matrix.
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