From 3e1a0792fb593953860162d57320c8602fd199eb Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Tue, 9 Oct 2018 09:32:50 -0700
Subject: Create SDCAOptimizerV2 op to fix the "adaptative" typo.

PiperOrigin-RevId: 216370193
---
 .../api_def/base_api/api_def_SdcaOptimizerV2.pbtxt | 171 +++++++++++++++++++++
 tensorflow/core/kernels/sdca_ops.cc                |   8 +-
 tensorflow/core/ops/sdca_ops.cc                    |  28 ++++
 3 files changed, 206 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_SdcaOptimizerV2.pbtxt

(limited to 'tensorflow/core')

diff --git a/tensorflow/core/api_def/base_api/api_def_SdcaOptimizerV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_SdcaOptimizerV2.pbtxt
new file mode 100644
index 0000000000..c615dee8c7
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_SdcaOptimizerV2.pbtxt
@@ -0,0 +1,171 @@
+op {
+  graph_op_name: "SdcaOptimizerV2"
+  visibility: HIDDEN
+  in_arg {
+    name: "sparse_example_indices"
+    description: <<END
+a list of vectors which contain example indices.
+END
+  }
+  in_arg {
+    name: "sparse_feature_indices"
+    description: <<END
+a list of vectors which contain feature indices.
+END
+  }
+  in_arg {
+    name: "sparse_feature_values"
+    description: <<END
+a list of vectors which contains feature value
+associated with each feature group.
+END
+  }
+  in_arg {
+    name: "dense_features"
+    description: <<END
+a list of matrices which contains the dense feature values.
+END
+  }
+  in_arg {
+    name: "example_weights"
+    description: <<END
+a vector which contains the weight associated with each
+example.
+END
+  }
+  in_arg {
+    name: "example_labels"
+    description: <<END
+a vector which contains the label/target associated with each
+example.
+END
+  }
+  in_arg {
+    name: "sparse_indices"
+    description: <<END
+a list of vectors where each value is the indices which has
+corresponding weights in sparse_weights. This field maybe omitted for the
+dense approach.
+END
+  }
+  in_arg {
+    name: "sparse_weights"
+    description: <<END
+a list of vectors where each value is the weight associated with
+a sparse feature group.
+END
+  }
+  in_arg {
+    name: "dense_weights"
+    description: <<END
+a list of vectors where the values are the weights associated
+with a dense feature group.
+END
+  }
+  in_arg {
+    name: "example_state_data"
+    description: <<END
+a list of vectors containing the example state data.
+END
+  }
+  out_arg {
+    name: "out_example_state_data"
+    description: <<END
+a list of vectors containing the updated example state
+data.
+END
+  }
+  out_arg {
+    name: "out_delta_sparse_weights"
+    description: <<END
+a list of vectors where each value is the delta
+weights associated with a sparse feature group.
+END
+  }
+  out_arg {
+    name: "out_delta_dense_weights"
+    description: <<END
+a list of vectors where the values are the delta
+weights associated with a dense feature group.
+END
+  }
+  attr {
+    name: "loss_type"
+    description: <<END
+Type of the primal loss. Currently SdcaSolver supports logistic,
+squared and hinge losses.
+END
+  }
+  attr {
+    name: "adaptive"
+    default_value {
+      b: True
+    }
+    description: <<END
+Whether to use Adaptive SDCA for the inner loop.
+END
+  }
+  attr {
+    name: "num_sparse_features"
+    description: <<END
+Number of sparse feature groups to train on.
+END
+  }
+  attr {
+    name: "num_sparse_features_with_values"
+    description: <<END
+Number of sparse feature groups with values
+associated with it, otherwise implicitly treats values as 1.0.
+END
+  }
+  attr {
+    name: "num_dense_features"
+    description: <<END
+Number of dense feature groups to train on.
+END
+  }
+  attr {
+    name: "l1"
+    description: <<END
+Symmetric l1 regularization strength.
+END
+  }
+  attr {
+    name: "l2"
+    description: <<END
+Symmetric l2 regularization strength.
+END
+  }
+  attr {
+    name: "num_loss_partitions"
+    description: <<END
+Number of partitions of the global loss function.
+END
+  }
+  attr {
+    name: "num_inner_iterations"
+    description: <<END
+Number of iterations per mini-batch.
+END
+  }
+  summary: "Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for"
+  description: <<END
+linear models with L1 + L2 regularization. As global optimization objective is
+strongly-convex, the optimizer optimizes the dual objective at each step. The
+optimizer applies each update one example at a time. Examples are sampled
+uniformly, and the optimizer is learning rate free and enjoys linear convergence
+rate.
+
+[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).<br>
+Shai Shalev-Shwartz, Tong Zhang. 2012
+
+$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
+
+[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).<br>
+Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
+Peter Richtarik, Martin Takac. 2015
+
+[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).<br>
+Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
+END
+}
diff --git a/tensorflow/core/kernels/sdca_ops.cc b/tensorflow/core/kernels/sdca_ops.cc
index 3bd4168dc7..d0e0b15da7 100644
--- a/tensorflow/core/kernels/sdca_ops.cc
+++ b/tensorflow/core/kernels/sdca_ops.cc
@@ -83,7 +83,11 @@ struct ComputeOptions {
           context, false,
           errors::InvalidArgument("Unsupported loss type: ", loss_type));
     }
-    OP_REQUIRES_OK(context, context->GetAttr("adaptative", &adaptive));
+    auto s = context->GetAttr("adaptative", &adaptive);
+    if (!s.ok()) {
+      s = context->GetAttr("adaptive", &adaptive);
+    }
+    OP_REQUIRES_OK(context, s);
     OP_REQUIRES_OK(
         context, context->GetAttr("num_sparse_features", &num_sparse_features));
     OP_REQUIRES_OK(context, context->GetAttr("num_sparse_features_with_values",
@@ -245,6 +249,8 @@ class SdcaOptimizer : public OpKernel {
 };
 REGISTER_KERNEL_BUILDER(Name("SdcaOptimizer").Device(DEVICE_CPU),
                         SdcaOptimizer);
+REGISTER_KERNEL_BUILDER(Name("SdcaOptimizerV2").Device(DEVICE_CPU),
+                        SdcaOptimizer);
 
 class SdcaShrinkL1 : public OpKernel {
  public:
diff --git a/tensorflow/core/ops/sdca_ops.cc b/tensorflow/core/ops/sdca_ops.cc
index fdf53a55dd..51d248f2d6 100644
--- a/tensorflow/core/ops/sdca_ops.cc
+++ b/tensorflow/core/ops/sdca_ops.cc
@@ -65,6 +65,34 @@ REGISTER_OP("SdcaOptimizer")
     .Output("out_delta_dense_weights: num_dense_features * float")
     .SetShapeFn(ApplySdcaOptimizerShapeFn);
 
+// The SdcaOptimizerV2 op fixes the "adaptative" typo in v1.
+REGISTER_OP("SdcaOptimizerV2")
+    .Attr(
+        "loss_type: {'logistic_loss', 'squared_loss', 'hinge_loss',"
+        "'smooth_hinge_loss', 'poisson_loss'}")
+    .Attr("adaptive : bool=false")
+    .Attr("num_sparse_features: int >= 0")
+    .Attr("num_sparse_features_with_values: int >= 0")
+    .Attr("num_dense_features: int >= 0")
+    .Attr("l1: float")
+    .Attr("l2: float")
+    .Attr("num_loss_partitions: int >= 1")
+    .Attr("num_inner_iterations: int >= 1")
+    .Input("sparse_example_indices: num_sparse_features * int64")
+    .Input("sparse_feature_indices: num_sparse_features * int64")
+    .Input("sparse_feature_values: num_sparse_features_with_values * float")
+    .Input("dense_features: num_dense_features * float")
+    .Input("example_weights: float")
+    .Input("example_labels: float")
+    .Input("sparse_indices: num_sparse_features * int64")
+    .Input("sparse_weights: num_sparse_features * float")
+    .Input("dense_weights: num_dense_features * float")
+    .Input("example_state_data: float")
+    .Output("out_example_state_data: float")
+    .Output("out_delta_sparse_weights: num_sparse_features * float")
+    .Output("out_delta_dense_weights: num_dense_features * float")
+    .SetShapeFn(ApplySdcaOptimizerShapeFn);
+
 REGISTER_OP("SdcaShrinkL1")
     .Attr("num_features: int >= 0")
     .Attr("l1: float")
-- 
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