From 842246a4fd2172c56fa293b7e68b531ffee26572 Mon Sep 17 00:00:00 2001
From: Bruce Fontaine <bfontain@google.com>
Date: Mon, 9 Mar 2020 16:46:04 -0700
Subject: [PATCH] Update documentation of
tf.tpu.experimental.(shared_)embedding_column to reflect that a user should
pass in the same function if they use the same dynamic learning rate for
multiple tables.
PiperOrigin-RevId: 299967731
Change-Id: I97c9ecf89c932774ec8daafdc65c3376570a886a
---
tensorflow/python/tpu/feature_column.py | 10 ++++++++--
tensorflow/python/tpu/feature_column_v2.py | 10 ++++++++--
2 files changed, 16 insertions(+), 4 deletions(-)
diff --git a/tensorflow/python/tpu/feature_column.py b/tensorflow/python/tpu/feature_column.py
index 5f9535dc3c6..6039a57ce90 100644
--- a/tensorflow/python/tpu/feature_column.py
+++ b/tensorflow/python/tpu/feature_column.py
@@ -86,7 +86,10 @@ def embedding_column(categorical_column,
and any sequence longer will be truncated. This must be positive for
sequence features and 0 for non-sequence features.
learning_rate_fn: A function that takes global step and returns learning
- rate for the embedding table.
+ rate for the embedding table. If you intend to use the same learning rate
+ for multiple embedding tables, please ensure that you pass the exact same
+ python function to all calls of embedding_column, otherwise performence
+ may suffer.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
@@ -196,7 +199,10 @@ def shared_embedding_columns(categorical_columns,
sequence shorter then this will be padded with 0 embeddings and any
sequence longer will be truncated.
learning_rate_fn: A function that takes global step and returns learning
- rate for the embedding table.
+ rate for the embedding table. If you intend to use the same learning rate
+ for multiple embedding tables, please ensure that you pass the exact same
+ python function to all calls of shared_embedding_columns, otherwise
+ performence may suffer.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
diff --git a/tensorflow/python/tpu/feature_column_v2.py b/tensorflow/python/tpu/feature_column_v2.py
index a51a3153c76..d9820425467 100644
--- a/tensorflow/python/tpu/feature_column_v2.py
+++ b/tensorflow/python/tpu/feature_column_v2.py
@@ -107,7 +107,10 @@ def embedding_column_v2(categorical_column,
and any sequence longer will be truncated. This must be positive for
sequence features and 0 for non-sequence features.
learning_rate_fn: A function that takes global step and returns learning
- rate for the embedding table.
+ rate for the embedding table. If you intend to use the same learning rate
+ for multiple embedding tables, please ensure that you pass the exact same
+ python function to all calls of embedding_column, otherwise performence
+ may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core".
If specifying "tpu_tensor_core", a tensor_core_shape must be supplied.
@@ -266,7 +269,10 @@ def shared_embedding_columns_v2(categorical_columns,
sequence shorter then this will be padded with 0 embeddings and any
sequence longer will be truncated.
learning_rate_fn: A function that takes global step and returns learning
- rate for the embedding table.
+ rate for the embedding table. If you intend to use the same learning rate
+ for multiple embedding tables, please ensure that you pass the exact same
+ python function to all calls of shared_embedding_columns, otherwise
+ performence may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core". If
specifying "tpu_tensor_core", a tensor_core_shape must be supplied.