| Commit message (Collapse) | Author | Age |
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The argument is actually still used for adding summaries to collections.
PiperOrigin-RevId: 161459624
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concatenated file. This is a bit more usable since we can feed these files
directly to opt, llc etc.
PiperOrigin-RevId: 161459565
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loads 'py_test' from tensorflow.bzl.
PiperOrigin-RevId: 161457087
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PiperOrigin-RevId: 161453063
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PiperOrigin-RevId: 161452440
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the fix.
PiperOrigin-RevId: 161452438
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PiperOrigin-RevId: 161445783
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instance group APIs, with support (in conjunction with UnionClusterResolver) for mapping multiple instance groups into one TensorFlow job (see the `testUnionMultipleInstanceRetrieval` test for details).
This should simplify creating and using standardized grpc TensorFlow server based instances using Compute Engine instance groups for distributed training.
PiperOrigin-RevId: 161443891
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Fixes issue #11091
PiperOrigin-RevId: 161427084
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PiperOrigin-RevId: 161426590
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In particular, BytesFreedIfScheduled is the O(n^2) kernel of our
scheduling algorithm, and it used to do one hashtable lookup for each
input and output buffer of its instruction. This patch gets rid of all
of the hashtable lookups, replacing them with simple pointer
dereferences.
PiperOrigin-RevId: 161413495
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Using '1' automatically gets converted to int32, but when the user explicitly
uses an int64 for the 'begin' argument, we want to make sure that 'strides'
is of the same dtype, so we make sure we use a 1 of the proper dtype.
Adds a test that failed before and passes with this change. Also
tested the indexing case in addition to the _baseslice case, and added
tests for both 'begin' as a Tensor as well as a non-Tensor.
Fixes #11380.
RELNOTES: Fixes 'strides' and 'begin' dtype mismatch when slicing using int64 Tensor index in python. See #11380.
PiperOrigin-RevId: 161411479
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PiperOrigin-RevId: 161404589
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PiperOrigin-RevId: 161401043
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PiperOrigin-RevId: 161396592
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argument/resource update shapes to XlaCompiler.
Change in preparation for adding implementations of the Stack... operators in the Tensorflow/XLA bridge. The representation of a Stack will be a tuple of (stack value, stack size). xla::Shape can represent tuples, but TensorShape cannot.
PiperOrigin-RevId: 161391774
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PiperOrigin-RevId: 161391365
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PiperOrigin-RevId: 161388633
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For the `pt` or `print_tensor` command, the numeric summary can be activated with the `-s` option flag, e.g.,
tfdbg> pt -s cross_entropy/Log:0
Numeric summaries are applicable only to non-empty tensors of Boolean and numeric (int*, float*, complex*) types.
PiperOrigin-RevId: 161350396
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PiperOrigin-RevId: 161337922
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PiperOrigin-RevId: 161305803
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PiperOrigin-RevId: 161300540
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PiperOrigin-RevId: 161278460
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This transformation acts like `tf.contrib.data.Dataset.flat_map()` but
produces output elements from `k` input elements in an interleaved fashion,
optionally in blocks of consecutive elements of length `b`.
PiperOrigin-RevId: 161277614
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VarIsInitializedOp when working on ResourceVariables
PiperOrigin-RevId: 161272800
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HloInstruction::slide_strides(int64).
This is so that the function matches the naming
convention of the other slice accessors.
PiperOrigin-RevId: 161272516
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lead to inlining failures.
PiperOrigin-RevId: 161271376
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since they appear to make the test more flaky
PiperOrigin-RevId: 161265638
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The fix is to change StringPiece to string in our temporary maps, since the
backing memory for the StringPiece was actually dead by the time we used the
temporary maps.
Note that FunctionDef::attr() is a proto2::Map, which has a value_type that
behaves similarly to std::pair. Here's a re-written snippet of the original
GetSetAttrs code, which makes the bug more apparent:
std::map<StringPiece, AttrValue> GetSetAttrs(const FunctionDef& fdef) {
std::map<StringPiece, AttrValue> attrs;
for (std::pair<string, AttrValue> pair : fdef.attr()) {
attrs[pair.first] = pair.second;
}
return attrs;
}
The problem is that the `pair` that captures each entry of fdef.attr() creates a
copy of the underlying string, and we're converting that into a StringPiece.
The underlying memory for the string is dead on each iteration of the loop.
I've also added a test that fails with the old implementation, and passes with
the new implementation.
PiperOrigin-RevId: 161265130
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Turns out using the StreamInterface objects leads to "invalid resource handle"
errors, so we have to use the cudaStream_t directly. This change is based on
similar code in cuda_solvers.cc.
PiperOrigin-RevId: 161261085
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PiperOrigin-RevId: 161258773
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PiperOrigin-RevId: 161257957
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(i.e., CUB reduces) a more GPU memory to launch. Prior to this, calling
WhereOp with the BFC allocator in non-opt mode led to "out of memory" error
when launching the kernel.
PiperOrigin-RevId: 161255358
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This change also adds helper class BinaryDatasetOpKernel, that simplifies parsing two input
datasets.
RELNOTES: Add `ConcatenateDataset` to concatenate two datasets.
PiperOrigin-RevId: 161254998
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PiperOrigin-RevId: 161254616
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SAME.
This CL introduces
- a new expression for the total padding which is much easier to parse than the previous one
- a new section which derives this expression, explaining why it is used.
PiperOrigin-RevId: 161254603
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Correct Keras version number.
PiperOrigin-RevId: 161252947
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PiperOrigin-RevId: 161247019
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PiperOrigin-RevId: 161245173
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PiperOrigin-RevId: 161242660
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Switched it to py_test for timeout=, but forgot to add PY2AND3.
PiperOrigin-RevId: 161235480
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PiperOrigin-RevId: 161234072
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strategy.
PiperOrigin-RevId: 161233946
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value of another variable v, make sure that the initializer of v is run first.
PiperOrigin-RevId: 161226052
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PiperOrigin-RevId: 161221736
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PiperOrigin-RevId: 161218103
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PiperOrigin-RevId: 161214648
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PiperOrigin-RevId: 161209972
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PiperOrigin-RevId: 161203568
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