1 files changed, 88 insertions, 0 deletions
diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc
new file mode 100644
index 0000000000..5af6a79f3e
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc
@@ -0,0 +1,88 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// XLA-specific MatMul Op.
+
+#include "tensorflow/compiler/tf2xla/xla_compilation_device.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+namespace {
+
+class MatMulOp : public XlaOpKernel {
+ public:
+  explicit MatMulOp(OpKernelConstruction* ctx, bool is_sparse = false)
+      : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_));
+    if (is_sparse) {
+      // SparseMatMul is actually dense matmul with a hint that one or
+      // both of the inputs may contain a lot of zeroes. On CPU these
+      // inputs are dynamically converted to sparse representation
+      // before multiplication. For now in XLA we ignore the hints
+      // and always do dense multiplication.
+      bool dummy_is_sparse;
+      OP_REQUIRES_OK(ctx, ctx->GetAttr("a_is_sparse", &dummy_is_sparse));
+      OP_REQUIRES_OK(ctx, ctx->GetAttr("b_is_sparse", &dummy_is_sparse));
+    }
+  }
+
+  ~MatMulOp() override = default;
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    const TensorShape a_shape = ctx->InputShape(0);
+    const TensorShape b_shape = ctx->InputShape(1);
+
+    // Check that the dimensions of the two matrices are valid.
+    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_shape),
+                errors::InvalidArgument("In[0] is not a matrix"));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b_shape),
+                errors::InvalidArgument("In[1] is not a matrix"));
+    int first_index = transpose_a_ ? 0 : 1;
+    int second_index = transpose_b_ ? 1 : 0;
+
+    OP_REQUIRES(ctx,
+                a_shape.dim_size(first_index) == b_shape.dim_size(second_index),
+                errors::InvalidArgument("Matrix size-compatible: In[0]: ",
+                                        a_shape.DebugString(), ", In[1]: ",
+                                        b_shape.DebugString()));
+
+    xla::ComputationDataHandle a = ctx->Input(0);
+    xla::ComputationDataHandle b = ctx->Input(1);
+    auto lhs = (transpose_a_) ? ctx->builder()->Transpose(a, {1, 0}) : a;
+    auto rhs = (transpose_b_) ? ctx->builder()->Transpose(b, {1, 0}) : b;
+    ctx->SetOutput(0, ctx->builder()->Dot(lhs, rhs));
+  }
+
+ private:
+  bool transpose_a_;
+  bool transpose_b_;
+};
+
+REGISTER_XLA_OP("MatMul", MatMulOp);
+
+class SparseMatMulOp : public MatMulOp {
+ public:
+  explicit SparseMatMulOp(OpKernelConstruction* ctx) : MatMulOp(ctx, true) {}
+
+  ~SparseMatMulOp() override = default;
+};
+
+REGISTER_XLA_OP("SparseMatMul", SparseMatMulOp);
+
+}  // namespace
+}  // namespace tensorflow