| Commit message (Collapse) | Author | Age |
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Adjust TPUDistributionStrategy API to remove duplicate master address.
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the checkpoint. Therefore StopAtCheckpointStepHook should not be used in chief mode.
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Allows a change any time within process.
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This CL renames the various inputs to the Gather HLO to be more mnemonic by
making it more obviously a batch dynamic-slice. The replacements I made are:
s/elided_window_dims/collapsed_slice_dims/g
s/window_bounds/slice_sizes/g
s/gather_dims_to_operand_dims/start_index_map/g
s/gather_indices/start_indices/g
s/output_window_dims/offset_dims/g
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operator.
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Provides a bit more isolation.
Now with fewer reference cycles. Factors out the ResourceVariable temporary graph destruction logic into a utility so the temporary graph-building-graph doesn't bother reference cycle tests.
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Throw in TFRecordWriter.write() if write fails
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Add session_creator and a couple properties to worker context which then are used to configure monitored sessions.
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Distribution Strategy
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This change contains no code changes. Only doc-strings.
We can't use relative links in code files, so we don't have much choice but to link to tensorflow.org/
The deleted links were to docs that no longer exist.
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TFE_OpSetAttrString was holding on to the 'value' pointer
after it returned. This bug was introduced in commit
2b0805301e4531dd7c2ed677d932f6408675460e
which caused TFE_OpSetAttrString to invoke
AttrBuilder& AttrBuilder::Set(StringPiece attr_name, StringPiece&& value);
instead of:
AttrBuilder& AttrBuilder::Set(StringPiece attr_name, T&& value)
(where the latter copies 'value' when T is a StringPiece or const char*
and the former aliases the memory pointed to by StringPiece).
In this process, I realized that
AttrBuilder::Set(StringPiece attr_name, StringPiece&& value)
was never being invoked (other than in this buggy situation),
so I removed it altogether.
Without the changes to attr_builder.{h,cc}, the newly added test
fails - complaining that "NHWC" is not a valid value for the "padding"
attribute.
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gradients are quantized, quantization fails for any node that is a producer of gradients since the _FollowedByFakeQuant method prevents any node followed by a fake quant from being properly quantized.
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I patched the doc generator to generate markdown links.
Ran the doc converter, and copied the output into docs_src.
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than float.
Previously, reductions of non-float floating point types would check fail at runtime in the arg_literal.Get<float>() call.
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This change removes the dependency between Executor and the concrete representation of NodeExecStats as a protobuf object, is a step towards supporting multiple representations.
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The convolutions are rewritten to convolutions with feature_group_count = 1.
Adapt hlo_verifier and shape_inference to handle feature_group_count > 1.
Add the new pass to the backends, and write a depthwise convolution test.
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This is a first step towards supporting grouped convolutions, which are a
generalization of depthwise convolution.
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This is a simple op that takes an array of strings and returns an array of the
same shape, containing each string length.
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This prevents most of TensorFlow from needing to depend
on libprotoc.
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For expediency in rolling out support for tokens used for ordering side-effecting ops, infeed and outfeed *optionally* took a token operand. This CL removes that option so all infeed and outfeed instructions take a token operand.
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Based on CL from Chris Ying and contributions from Y. You and Wang Tao.
Introduced by "Large Batch Training of Convolutional Networks" by Y. You,
I. Gitman, and B. Ginsburg. (https://arxiv.org/abs/1708.03888)
Implements the LARS learning rate scheme presented in the paper above. This
optimizer is useful when scaling the batch size to up to 32K without
significant performance degradation. It is recommended to use the optimizer
in conjunction with:
- Gradual learning rate warm-up
- Linear learning rate scaling
- Poly rule learning rate decay
With this optimizer, ResNet-50 now converges to 76.3% top-1 accuracy at batch 32K on a JF Pod.
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