Merge changes from github.

END_PUBLIC

---
Commit 1e1b3d902 authored by Pete Warden<pete@petewarden.com>
Committed by gunan<gunan@google.com>:
Changed output directory for Pi CI build to fix permissions problem with nightlies (#13257)

* Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change for Pi that's no longer needed

* Fixed Pi Zero OpenBLAS build problems and tidied up directories used

* More robust checks in Pi build script

* Changed output directory for Pi CI build to fix permissions problem

---
Commit fe3a2e65c authored by Yan Facai (???)<facai.yan@gmail.com>
Committed by drpngx<drpngx@users.noreply.github.com>:
check invalid string type for dest_nodes in extract_sub_graph (#13057)

* BUG: check str type

* TST: add unit test

* CLN: remove list check

* CLN: use warning

* CLN: 2 indent

* CLN: raise TypeError if not list

* CLN: check string only

---
Commit 225ab7629 authored by Jean Wanka<jm.wanka@gmail.com>
Committed by Jean Wanka<jm.wanka@gmail.com>:
Fix polynomial decay with cycle for global step=0

For polynomial decay with cycle=True the learning rate at
step 0 becomes NaN, because in the process of calculating it we
devide by 0. This change should fix it, by setting the multiplier
for the decay steps to one for global_step=0.

---
Commit 286f57061 authored by Bjarke Hammersholt Roune<broune@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make Service::TransferToClient not attempt to manipulate the literal when the transfer failed, preventing a crash and allowing the caller to see the reason for the failed transfer.

PiperOrigin-RevId: 169770126

---
Commit e0501bc4d authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Shanqing Cai<cais@google.com>:
Fix GRUBlockCell parameter naming inconsistency (#13153)

* Fix GRUBlockCell parameter naming inconsistency

This fix tries to fix the issue in 13137 where
parameter `cell_size` is used instead of `num_units`.
This is inconsistent with other RNN cells.

This fix adds support of `num_units` while at the same
time maintains backward compatiblility for `cell_size`.

This fix fixes 13137.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Add `@deprecated_args` for 'cell_size' in `GRUBlockCell`

This commit adds `@deprecated_args` for 'cell_size' in `GRUBlockCell`

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

* Address review comment

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

---
Commit 02a2eba05 authored by Pete Warden<pete@petewarden.com>
Committed by gunan<gunan@google.com>:
Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change that's no longer needed (#13251)

* Fix for RTLD_GLOBAL breakage of Pi builds, and removed Eigen version change for Pi that's no longer needed

* Fixed Pi Zero OpenBLAS build problems and tidied up directories used

* More robust checks in Pi build script

---
Commit 8ef722253 authored by Sanjoy Das<sanjoy@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove a redundant setName.

The EmitComputation should have emitted a function with the right name, so use a
CHECK instead.

PiperOrigin-RevId: 169764856

---
Commit 1b94147dc authored by Neal Wu<wun@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix broken GitHub links in tensorflow and tensorflow_models resulting from The Great Models Move (a.k.a. the research subfolder)

PiperOrigin-RevId: 169763373

---
Commit b1ada5f0c authored by Justine Tunney<jart@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix TensorBoard python -m invoke in docs

PiperOrigin-RevId: 169758752

---
Commit 2957cd894 authored by Mustafa Ispir<ispir@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Local run option of estimator training.

PiperOrigin-RevId: 169756384

---
Commit 1dc2fe7ac authored by Gunhan Gulsoy<gunan@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 166264198

PiperOrigin-RevId: 169998124

1 files changed, 60 insertions, 50 deletions

diff --git a/tensorflow/core/kernels/mkl_aggregate_ops.cc b/tensorflow/core/kernels/mkl_aggregate_ops.cc
index 51ba127def..935eb81dd0 100644
--- a/tensorflow/core/kernels/mkl_aggregate_ops.cc
+++ b/tensorflow/core/kernels/mkl_aggregate_ops.cc
@@ -54,17 +54,62 @@ class MklAddNOp : public OpKernel {
     GetMklShape(ctx, 1, &(mkl_context.input2_shape));
     bool input2_in_mkl_format = mkl_context.input2_shape.IsMklTensor();
 
+    // handle the case of a scalar
+    if (!input1_in_mkl_format && input0.dims() == 0) {
+      const TensorShape& o_shape = input0.shape();
+      Tensor* out_tensor = nullptr;
+      mkl_context.output_shape.SetMklTensor(false);
+      AllocateOutputSetMklShape(ctx, 0, &out_tensor, o_shape,
+                                mkl_context.output_shape);
+      float user_i1 = (input0.scalar<T>()());
+      ;
+      float user_i2 = (input1.scalar<T>()());
+      ;
+      out_tensor->scalar<T>()() = std::plus<float>{}(user_i1, user_i2);
+      return;
+    }
+
     mkl_context.in_dims = input1_in_mkl_format
         ? mkl_context.input1_shape.GetDimension()
         : input0.dims();
     mkl_context.in_dims = input2_in_mkl_format
         ? mkl_context.input2_shape.GetDimension()
         : input1.dims();
+
+    // If there is nothing to compute, return.
+    if (!input1_in_mkl_format && !input2_in_mkl_format) {
+      const TensorShape& o_shape = input0.shape();
+      if (o_shape.num_elements() == 0) {
+        Tensor* out_tensor = nullptr;
+        mkl_context.output_shape.SetMklTensor(false);
+        AllocateOutputSetMklShape(ctx, 0, &out_tensor, o_shape,
+                                  mkl_context.output_shape);
+        return;
+      }
+    }
+
+    mkl_context.in_sizes = new size_t[mkl_context.in_dims];
+    mkl_context.in_strides = new size_t[mkl_context.in_dims];
     // Generate size, stride for input if input is in MKL format.
-    ExtractMklOpParams(&mkl_context.in1_sizes,
-     &mkl_context.in1_strides, input0, &mkl_context.input1_shape);
-    ExtractMklOpParams(&mkl_context.in2_sizes,
-     &mkl_context.in2_strides, input1, &mkl_context.input2_shape);
+    if (input1_in_mkl_format || input2_in_mkl_format) {
+      const MklShape* tmp_mkl_shape = (input1_in_mkl_format)
+                                          ? &mkl_context.input1_shape
+                                          : &mkl_context.input2_shape;
+      for (int i = 0; i < mkl_context.in_dims; i++) {
+        mkl_context.in_sizes[i] = tmp_mkl_shape->GetSizes()[i];
+        mkl_context.in_strides[i] = tmp_mkl_shape->GetStrides()[i];
+      }
+    } else {
+      for (int i = 0; i < mkl_context.in_dims; i++) {
+        mkl_context.in_sizes[i] =
+            input0.dim_size((mkl_context.in_dims - 1) - i);
+      }
+      mkl_context.in_strides[0] = 1;
+      for (int i = 1; i < mkl_context.in_dims; i++) {
+        mkl_context.in_strides[i] =
+            mkl_context.in_strides[i - 1] * mkl_context.in_sizes[i - 1];
+      }
+    }
 
     std::vector<float> coeff(2, 1.0);
     mkl_context.MklCreateInputLayouts(ctx);
@@ -82,7 +127,7 @@ class MklAddNOp : public OpKernel {
      mkl_context.output_shape.SetMklLayout(mkl_context.Eltwise, dnnResourceDst);
 
      mkl_context.output_shape.SetTfLayout(
-        mkl_context.in_dims, mkl_context.in1_sizes, mkl_context.in1_strides);
+         mkl_context.in_dims, mkl_context.in_sizes, mkl_context.in_strides);
      if (input1_in_mkl_format == true) {
       mkl_context.output_shape.SetTfDimOrder(mkl_context.in_dims,
       mkl_context.input1_shape.GetTfToMklDimMap());
@@ -113,44 +158,11 @@ class MklAddNOp : public OpKernel {
     mkl_context.MklCleanup();
   }
 
-  void ExtractMklOpParams(size_t** out_sizes, size_t** out_strides,
-    const Tensor& input, const MklShape* input_shape) {
-    bool input_in_mkl_format = input_shape->IsMklTensor();
-    int in_dims = input_in_mkl_format
-                              ? input_shape->GetDimension()
-                              : input.dims();
-    size_t* in_sizes = new size_t[in_dims];
-    size_t* in_strides = new size_t[in_dims];
-
-    if (input_in_mkl_format) {
-      for (int i = 0; i < in_dims; i++) {
-        in_sizes[i] = input_shape->GetSizes()[i];
-        in_strides[i] = input_shape->GetStrides()[i];
-      }
-    } else {
-      for (int i = 0; i < in_dims; i++) {
-        in_sizes[i] =
-            input.dim_size((in_dims - 1) - i);
-      }
-      in_strides[0] = 1;
-      for (int i = 1; i < in_dims; i++) {
-        in_strides[i] =
-            in_strides[i - 1] * in_sizes[i - 1];
-      }
-    }
-    *out_sizes = in_sizes;
-    *out_strides = in_strides;
-  }
-
-
  private:
   typedef struct {
     int in_dims;
-    size_t* in1_sizes;
-    size_t* in1_strides;
-
-    size_t* in2_sizes;
-    size_t* in2_strides;
+    size_t* in_sizes = nullptr;
+    size_t* in_strides = nullptr;
     dnnPrimitive_t Eltwise = nullptr;
     dnnPrimitiveAttributes_t attributes = nullptr;
     void* Eltwise_res[dnnResourceNumber];
@@ -160,18 +172,16 @@ class MklAddNOp : public OpKernel {
     void MklCreateInputLayouts(OpKernelContext* context) {
       bool input1_in_mkl_format = input1_shape.IsMklTensor();
       if (!input1_in_mkl_format) {
-        CHECK_EQ(
-            dnnLayoutCreate_F32(&lt_input1, in_dims, in1_sizes, in1_strides),
-            E_SUCCESS);
+        CHECK_EQ(dnnLayoutCreate_F32(&lt_input1, in_dims, in_sizes, in_strides),
+                 E_SUCCESS);
       } else {
         lt_input1 = static_cast<dnnLayout_t>(input1_shape.GetCurLayout());
       }
 
       bool input2_in_mkl_format = input2_shape.IsMklTensor();
       if (!input2_in_mkl_format) {
-        CHECK_EQ(
-            dnnLayoutCreate_F32(&lt_input2, in_dims, in2_sizes, in2_strides),
-            E_SUCCESS);
+        CHECK_EQ(dnnLayoutCreate_F32(&lt_input2, in_dims, in_sizes, in_strides),
+                 E_SUCCESS);
       } else {
         lt_input2 = static_cast<dnnLayout_t>(input2_shape.GetCurLayout());
       }
@@ -246,15 +256,15 @@ class MklAddNOp : public OpKernel {
       bool input1_in_mkl_format = input1_shape.IsMklTensor();
       bool input2_in_mkl_format = input2_shape.IsMklTensor();
       dnnDelete_F32(Eltwise);
+      if (!input1_in_mkl_format || !input2_in_mkl_format) {
+        delete[] in_sizes;
+        delete[] in_strides;
+      }
       if (!input1_in_mkl_format) {
          dnnLayoutDelete_F32(lt_input1);
-         delete [] in1_sizes;
-         delete [] in1_strides;
       }
       if (!input2_in_mkl_format) {
          dnnLayoutDelete_F32(lt_input2);
-         delete [] in2_sizes;
-         delete [] in2_strides;
       }
     }
   } MklAddNOpContext;