| Commit message (Collapse) | Author | Age |
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Update eager notebooks with buttons, licenses, and change filenames
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Verification invalid eager env_ or env_->rendezvous_mgr.
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* BUG: merged duplicated weight columns
* TST: add test case
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Branch 203039240
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Fix gradient of nccl_ops
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Reapplying #20254
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PiperOrigin-RevId: 203039240
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PiperOrigin-RevId: 203039199
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PiperOrigin-RevId: 203037623
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PiperOrigin-RevId: 203029983
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PiperOrigin-RevId: 203029765
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PiperOrigin-RevId: 203027634
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enables applications like auto-batching, jacobians, per-example gradients.
PiperOrigin-RevId: 203026617
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_FORWARD_COMPATIBILITY_HORIZON to something that is provided. Intended use is
for testing new code / behaviour while still the default is the old behaviour.
PiperOrigin-RevId: 203023068
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PiperOrigin-RevId: 203021583
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PiperOrigin-RevId: 203021167
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Now uses the correct path.
This reverts commit 28c511013f960fd94cd0899342084570937ad277.
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PiperOrigin-RevId: 203020841
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PiperOrigin-RevId: 203019816
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Specifically, fix a segmentation fault when converting objects that implement
the Python sequence protocol (i.e., __getitem__, __len__, and __iter__) but
which do not have contiguous keys.
Fixes #20347
However, there are still some discrepancies possible between
tf.convert_to_tensor(o) (or tf.constant(o)) with and without eager execution
enabled. Fixing those is left as a follow up excercise.
Sample differences:
(1) Empty sequences that have numpy conversions defined.
import pandas as pd
import tensorflow as tf
s = pd.Series([]) # Empty series
t = tf.constant(s)
With eager execution enabled, t.dtype ends up with a dtype of float32 (as
py_seq_tensor.cc considers empty lists to be float32)
With graph construction, t.dtype ends up with a dtype of float64 (as
make_tensor_proto() converts 's' to a numpy array and uses its dtype).
(2) Objects that implement __getitem__, __len__, and __iter__, but are not
convertible to numpy arrays (e.g., do not implement __array__):
- With eager execution enabled, these can be converted to a tensor
- For graph construction, the conversion fails.
PiperOrigin-RevId: 203019624
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Pandas input fn accepts DataFrame for Y
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Update beam_search_decoder.py
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PiperOrigin-RevId: 203013884
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PR #20430 contained 151 commits, one of which backleveled PR #20254.
This PR is to re-apply that change. Text of 20254:
PR #20229 included a change to the Eigen version that failed to
compile on ppc64le. rmlarsen created a pull request in Eigen to
fix the compile failure:
https://bitbucket.org/eigen/eigen/pull-requests/410
This patch is to pick up the Eigen version of that patch.
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Branch 202995903
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PiperOrigin-RevId: 203007540
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PiperOrigin-RevId: 203004822
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ProcessState is a singleton that anchors per-process resources.
Up until now that meant only GPU-related memory allocators
since CPU allocation was usually done directly from Allocator::cpu_allocator.
Accordingly process_state.h was in common_runtime/gpu and ProcesState
was only used in GPU builds.
With the upcoming introduction of NUMA node specific CPU allocators
it will be important that most of the TF runtime switch to requesting the
proper NUMA-specific CPU allocator. These allocators will be owned by
and obtained from the ProcessState singleton which will exist in all
builds. The GPU-specific functions are moved to a new
GPUProcessState, also a singleton.
PoolAllocator is also migrated out of common_rumntime/gpu into common_runtime.
PiperOrigin-RevId: 203002666
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to be zeroed.
PiperOrigin-RevId: 203001311
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Branch 202961895
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