| Commit message (Collapse) | Author | Age |
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The current logic tries to bubble the forward pass tensor to the outermost
graph. That might not always be do-able e.g. when the cond is inside a while loop
it will need to know accumulator logic for while_loop. So instead, the cond_grad
now captures tensors from the forward If op's graph. When the grad If op is
built these tensors will be appropriately captured by the surrounding FuncGraph.
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Heuristic NCHW/NHWC layout assignment works great; we've never had to flip this
flag. Might as well remove it and simplify things a bit.
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CudnnConvolutionAlgorithmPicker::PickBestAlgorithm.
Using a struct lets us return additional data -- namely, the elapsed time to
run the best algo -- without adding a fourth entry to the tuple, which would be
confusing.
No functional change.
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instead of assertMultiLineEqual if input is too large
(https://bugs.python.org/issue11763). This change is switching to use unified_diff
in that case.
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e821380d69a549dc64900693942789d21aa4df5e
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the main logic for INTEL_MKL_ML_ONLY is getting removed in PR#22783.
#22783
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Fixes #21405.
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stateless_random_uniform now take minval+maxval and handles ints,
and stateless_normal/stateless_truncated_normal take mean+stddev.
Additionally, all of the stateless functions now have proper doc
strings.
This is step one of moving stateless random numbers out of contrib.
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The extra spaces were confusing bash's string-line-continuation from
the backslash `\` on the previous line.
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`MapAndBatchDataset` whose user-provided functions have the property that each output argument take its value directly from an input argument (e.g. `lambda x, y: y, x`). This specialization can produce the result without having to schedule the function using the executor.
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Enable GPU tests for cond_v2.
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1) define bazel config
build:<bazel_config_name> --define <option_name>s=true
2) set the config
build --config=<bazel_config_name>
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the model.
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preserve operand layouts.
Add an std::function member to the HloVerifier for a backend to specify the
function object used to determine whether an instruction can change layouts.
Use the function object to find out the non-layout-changing instructions and
check that such instructions should produce results with the same layouts as
its operands.
Add test cases.
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logits that are -inf will be given 0 probability and logits that are
inf will result in a nan output. Likewise if all logits are -inf the
output will also be nan.
This is done by using where operators, mostly because 0 * inf = nan
and x/0 = sign(x) inf following the IEEE 754 standard. However these
results are not mathematically correct in the context of the sparsemax
algorithm.
Fixes: https://github.com/tensorflow/tensorflow/issues/15564
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attr values that are not overridden e.g. transpose_a in the matmul op).
This is required for backward compatibility (a binary built via an older version
of TF should still run on a newer version of TF, where some ops may have added
attrs).
For non-eager graph building, the default attr values of graph ops are added by
tensorflow::AddDefaultsToNodeDef().
We ran into this issue when running the same S4TF test cases via eager APIs --
some tests failed due to "missing attrs", but are fixed by this patch.
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the optimizer isn't set, e.g. loading weights and then predict.
- Add load_weights for `KerasTpuModel`.
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Automated rollback of PR #21945
END_PUBLIC
Automated rollback of commit 863f61412fcc654840c6b67473b742ea4e5e964e. Revert #21945.
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Note to users manually patching ptxas from a later toolkit version:
Building NCCL requires the same version of ptxas and nvlink.
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library.
Change XlaBinaryOp::Broadcast to use the BroadcastTo lowering, since it produces fewer extraneous reshapes and transposes. Even if the reshapes and transposes would later optimize away, this yields more readable output and makes life easier for HLO rewrites that run early.
Change in preparation for removing reshapes from SoftmaxCrossEntropyWithLogits.
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