Merge branch 'master' into r0.13

Author: avijit-nervana

python/ngraph_bridge/__init__.in.py

@@ -34,6 +34,7 @@
 from tensorflow.python.framework import ops
 
 from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.framework import load_library
 
 import ctypes
 
@@ -92,8 +93,9 @@
    (TF_INSTALLED_VER[1] == TF_NEEDED_VER[1]) and \
    ((TF_INSTALLED_VER[2].split('-'))[0] == (TF_NEEDED_VER[2].split('-'))[0]):
     libpath = os.path.dirname(__file__)
-    ngraph_bridge_lib = ctypes.cdll.LoadLibrary(
-        os.path.join(libpath, 'libngraph_bridge.' + ext))
+    full_lib_path = os.path.join(libpath, 'libngraph_bridge.' + ext)
+    _ = load_library.load_op_library(full_lib_path)
+    ngraph_bridge_lib = ctypes.cdll.LoadLibrary(full_lib_path)
 else:
     raise ValueError(
         "Error: Installed TensorFlow version {0}\nnGraph bridge built with: {1}"
@@ -227,4 +229,3 @@ def get_disabled_ops():
   "nGraph bridge built with Grappler: " + str(ngraph_bridge_lib.ngraph_tf_is_grappler_enabled()) + "\n" \
   "nGraph bridge built with Variables and Optimizers Enablement: " \
       + str(ngraph_bridge_lib.ngraph_tf_are_variables_enabled())
-

src/ngraph_builder.cc

@@ -750,6 +750,9 @@ static Status TranslateBatchMatMulOp(
   shared_ptr<ng::Node> ng_lhs, ng_rhs;
   TF_RETURN_IF_ERROR(GetInputNodes(ng_op_map, op, &ng_lhs, &ng_rhs));
 
+  std::string backend_name;
+  TF_RETURN_IF_ERROR(ngraph_bridge::GetNodeBackend(op, &backend_name));
+
   auto ng_lhs_shape = ng_lhs->get_shape();
   auto ng_rhs_shape = ng_rhs->get_shape();
 
@@ -781,77 +784,138 @@ static Status TranslateBatchMatMulOp(
 
   auto ng_lhs_axes = out_axes;
   auto ng_rhs_axes = out_axes;
-  if (tf_adj_x) {
-    ng_lhs_axes.push_back(n_dims - 1);
-    ng_lhs_axes.push_back(n_dims - 2);
-    ng_lhs = ng::builder::numpy_transpose(ng_lhs, ng_lhs_axes);
-  }
-  if (tf_adj_y) {
-    ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 2);
-    ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 1);
-    ng_rhs = ng::builder::numpy_transpose(ng_rhs, ng_rhs_axes);
-  } else {
-    ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 1);
-    ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 2);
-    ng_rhs = ng::builder::numpy_transpose(ng_rhs, ng_rhs_axes);
-  }
 
-  ng_lhs_shape = ng_lhs->get_shape();
-  ng_rhs_shape = ng_rhs->get_shape();
-
-  if (ng_lhs_shape[n_dims - 1] != ng_rhs_shape[0]) {
-    return errors::InvalidArgument(
-        "The last dimension of ng_lhs and the first dimension of ng_rhs "
-        "should have the same size");
-  }
-  if (n_dims == 2) {
-    SaveNgOp(ng_op_map, op->name(),
-             ConstructNgNode<ngraph::op::Dot>(op->name(), ng_lhs, ng_rhs));
-  } else {
-    auto output_shape = ng_lhs_shape;
-    output_shape[n_dims - 1] = ng_rhs_shape[1];
-    auto dot_output =
-        ConstructNgNode<ngraph::op::Dot>(op->name(), ng_lhs, ng_rhs);
-    size_t compound_size = 1;
-    for (int i = 0; i < out_axes.size(); i++) {
-      compound_size *= output_shape[i];
+  // Get the backend name, if the backend is CPU and n_dims >= 3
+  // then use the BatchMatMul op supported by nGraph
+  if (n_dims >= 3 && backend_name == "CPU") {
+    // Transpose X if AdjX = true
+    if (tf_adj_x) {
+      ng_lhs_axes.push_back(n_dims - 1);
+      ng_lhs_axes.push_back(n_dims - 2);
+      ng_lhs = ng::builder::numpy_transpose(ng_lhs, ng_lhs_axes);
+      ng_lhs_shape = ng_lhs->get_shape();
+    } else {
+      ng_lhs_axes.push_back(n_dims - 2);
+      ng_lhs_axes.push_back(n_dims - 1);
     }
-    auto dot_axes = out_axes;
-    dot_axes.push_back(n_dims - 2);
-    dot_axes.push_back(n_dims - 1);
-    for (int i = 0; i < out_axes.size(); i++) {
-      dot_axes.push_back(n_dims + i);
+    // Transpose Y if AdjY = true
+    if (tf_adj_y) {
+      ng_rhs_axes.push_back(n_dims - 1);
+      ng_rhs_axes.push_back(n_dims - 2);
+      ng_rhs = ng::builder::numpy_transpose(ng_rhs, ng_rhs_axes);
+      ng_rhs_shape = ng_rhs->get_shape();
+    } else {
+      ng_rhs_axes.push_back(n_dims - 2);
+      ng_rhs_axes.push_back(n_dims - 1);
     }
-    ng::Shape dot_shape = {compound_size, ng_lhs_shape[n_dims - 2],
-                           ng_rhs_shape[1], compound_size};
-    std::shared_ptr<ng::Node> dot_reshape;
+
     if (n_dims == 3) {
-      dot_reshape = dot_output;
+      SaveNgOp(ng_op_map, op->name(), ConstructNgNode<ngraph::op::BatchMatMul>(
+                                          op->name(), ng_lhs, ng_rhs));
     } else {
-      dot_reshape = ConstructNgNode<ngraph::op::Reshape>(op->name(), dot_output,
-                                                         dot_axes, dot_shape);
+      // Find the compound size for dim1 so as to reshape to 3D
+      size_t compound_size = 1;
+      for (int i = 0; i < out_axes.size(); i++) {
+        compound_size *= ng_lhs_shape[i];
+      }
+
+      ng::Shape tmp_lhs_shape = {compound_size, ng_lhs_shape[n_dims - 2],
+                                 ng_lhs_shape[n_dims - 1]};
+      ng::Shape tmp_rhs_shape = {compound_size, ng_rhs_shape[n_dims - 2],
+                                 ng_rhs_shape[n_dims - 1]};
+
+      auto output_shape = ng_lhs_shape;
+      output_shape[n_dims - 1] = ng_rhs_shape[n_dims - 1];
+      ng::AxisVector tmp_axes = {0, 1, 2};
+
+      std::shared_ptr<ng::Node> lhs_reshape =
+          ConstructNgNode<ngraph::op::Reshape>(op->name(), ng_lhs, ng_lhs_axes,
+                                               tmp_lhs_shape);
+      std::shared_ptr<ng::Node> rhs_reshape =
+          ConstructNgNode<ngraph::op::Reshape>(op->name(), ng_rhs, ng_rhs_axes,
+                                               tmp_rhs_shape);
+      std::shared_ptr<ng::Node> batchmatmul =
+          ConstructNgNode<ngraph::op::BatchMatMul>(op->name(), lhs_reshape,
+                                                   rhs_reshape);
+      SaveNgOp(ng_op_map, op->name(),
+               ConstructNgNode<ngraph::op::Reshape>(op->name(), batchmatmul,
+                                                    tmp_axes, output_shape));
     }
-    ng::Shape tmp_shape = {1, ng_lhs_shape[n_dims - 2], ng_rhs_shape[1]};
-    vector<shared_ptr<ngraph::Node>> tmp_tensors;
-    for (size_t i = 0; i < dot_shape[0]; i++) {
-      const std::vector<size_t> lower_bound{i, 0, 0, i};
-      const std::vector<size_t> upper_bound{i + 1, dot_shape[1], dot_shape[2],
-                                            i + 1};
-      auto slice_out = ConstructNgNode<ngraph::op::Slice>(
-          op->name(), dot_reshape, lower_bound, upper_bound);
-      auto reshape_out = ConstructNgNode<ngraph::op::Reshape>(
-          op->name(), slice_out, ng::AxisVector{0, 1, 2, 3}, tmp_shape);
-      tmp_tensors.push_back(reshape_out);
+  } else {
+    if (tf_adj_x) {
+      ng_lhs_axes.push_back(n_dims - 1);
+      ng_lhs_axes.push_back(n_dims - 2);
+      ng_lhs = ng::builder::numpy_transpose(ng_lhs, ng_lhs_axes);
     }
-    auto concat_op =
-        ConstructNgNode<ngraph::op::Concat>(op->name(), tmp_tensors, 0);
-    if (n_dims == 3) {
-      SaveNgOp(ng_op_map, op->name(), concat_op);
+    if (tf_adj_y) {
+      ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 2);
+      ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 1);
+      ng_rhs = ng::builder::numpy_transpose(ng_rhs, ng_rhs_axes);
+    } else {
+      ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 1);
+      ng_rhs_axes.insert(ng_rhs_axes.begin(), n_dims - 2);
+      ng_rhs = ng::builder::numpy_transpose(ng_rhs, ng_rhs_axes);
+    }
+
+    ng_lhs_shape = ng_lhs->get_shape();
+    ng_rhs_shape = ng_rhs->get_shape();
+
+    if (ng_lhs_shape[n_dims - 1] != ng_rhs_shape[0]) {
+      return errors::InvalidArgument(
+          "The last dimension of ng_lhs and the first dimension of ng_rhs "
+          "should have the same size");
+    }
+
+    if (n_dims == 2) {
+      SaveNgOp(ng_op_map, op->name(),
+               ConstructNgNode<ngraph::op::Dot>(op->name(), ng_lhs, ng_rhs));
     } else {
-      SaveNgOp(
-          ng_op_map, op->name(),
-          ConstructNgNode<ngraph::op::Reshape>(
-              op->name(), concat_op, ng::AxisVector{0, 1, 2}, output_shape));
+      auto output_shape = ng_lhs_shape;
+      output_shape[n_dims - 1] = ng_rhs_shape[1];
+      auto dot_output =
+          ConstructNgNode<ngraph::op::Dot>(op->name(), ng_lhs, ng_rhs);
+
+      size_t compound_size = 1;
+      for (int i = 0; i < out_axes.size(); i++) {
+        compound_size *= output_shape[i];
+      }
+      auto dot_axes = out_axes;
+      dot_axes.push_back(n_dims - 2);
+      dot_axes.push_back(n_dims - 1);
+      for (int i = 0; i < out_axes.size(); i++) {
+        dot_axes.push_back(n_dims + i);
+      }
+      ng::Shape dot_shape = {compound_size, ng_lhs_shape[n_dims - 2],
+                             ng_rhs_shape[1], compound_size};
+      std::shared_ptr<ng::Node> dot_reshape;
+      if (n_dims == 3) {
+        dot_reshape = dot_output;
+      } else {
+        dot_reshape = ConstructNgNode<ngraph::op::Reshape>(
+            op->name(), dot_output, dot_axes, dot_shape);
+      }
+      ng::Shape tmp_shape = {1, ng_lhs_shape[n_dims - 2], ng_rhs_shape[1]};
+      vector<shared_ptr<ngraph::Node>> tmp_tensors;
+      for (size_t i = 0; i < dot_shape[0]; i++) {
+        const std::vector<size_t> lower_bound{i, 0, 0, i};
+        const std::vector<size_t> upper_bound{i + 1, dot_shape[1], dot_shape[2],
+                                              i + 1};
+        auto slice_out = ConstructNgNode<ngraph::op::Slice>(
+            op->name(), dot_reshape, lower_bound, upper_bound);
+        auto reshape_out = ConstructNgNode<ngraph::op::Reshape>(
+            op->name(), slice_out, ng::AxisVector{0, 1, 2, 3}, tmp_shape);
+        tmp_tensors.push_back(reshape_out);
+      }
+      auto concat_op =
+          ConstructNgNode<ngraph::op::Concat>(op->name(), tmp_tensors, 0);
+      if (n_dims == 3) {
+        SaveNgOp(ng_op_map, op->name(), concat_op);
+      } else {
+        SaveNgOp(
+            ng_op_map, op->name(),
+            ConstructNgNode<ngraph::op::Reshape>(
+                op->name(), concat_op, ng::AxisVector{0, 1, 2}, output_shape));
+      }
     }
   }
   return Status::OK();

test/opexecuter.cpp

@@ -200,6 +200,29 @@ void OpExecuter::ExecuteOnNGraph(vector<Tensor>& ngraph_outputs,
 
   // Get Tensor input shapes and values from the const nodes
   int number_of_inputs = test_op->num_inputs();
+
+  // Create nGraph backend
+  // If NGRAPH_TF_BACKEND is set create that backend
+  // Else create backend of type ng_backend_name
+  string ng_backend_type = ng_backend_name;
+  const char* ng_backend_env_value = std::getenv("NGRAPH_TF_BACKEND");
+
+  if (ng_backend_env_value != nullptr) {
+    string backend_env = std::string(ng_backend_env_value);
+    bool valid_ngraph_tf_backend =
+        !backend_env.empty() && BackendManager::IsSupportedBackend(backend_env);
+    ASSERT_TRUE(valid_ngraph_tf_backend) << "NGRAPH_TF_BACKEND " << backend_env
+                                         << " is not a supported backend";
+    ng_backend_type = backend_env;
+  }
+
+  NGRAPH_VLOG(5) << " Creating NG Backend " << ng_backend_type;
+  BackendManager::CreateBackend(ng_backend_type);
+  auto backend = BackendManager::GetBackend(ng_backend_type);
+
+  // Add the _ngraph_backend attr to the node
+  test_op->AddAttr("_ngraph_backend", ng_backend_type);
+
   // TODO : Validate static_input_indexes < number_of_inputs
   vector<TensorShape> input_shapes;
   vector<DataType> input_dt;
@@ -328,24 +351,6 @@ void OpExecuter::ExecuteOnNGraph(vector<Tensor>& ngraph_outputs,
     NgraphSerialize("unit_test_" + test_op_type_ + ".json", ng_function);
   }
 
-  // Create nGraph backend
-  // If NGRAPH_TF_BACKEND is set create that backend
-  // Else create backend of type ng_backend_name
-  string ng_backend_type = ng_backend_name;
-  const char* ng_backend_env_value = std::getenv("NGRAPH_TF_BACKEND");
-  if (ng_backend_env_value != nullptr) {
-    string backend_env = std::string(ng_backend_env_value);
-    bool valid_ngraph_tf_backend =
-        !backend_env.empty() && BackendManager::IsSupportedBackend(backend_env);
-    ASSERT_TRUE(valid_ngraph_tf_backend) << "NGRAPH_TF_BACKEND " << backend_env
-                                         << " is not a supported backend";
-    ng_backend_type = backend_env;
-  }
-
-  NGRAPH_VLOG(5) << " Creating NG Backend " << ng_backend_type;
-  BackendManager::CreateBackend(ng_backend_type);
-  auto backend = BackendManager::GetBackend(ng_backend_type);
-
   // Allocate tensors for inputs
   vector<std::shared_ptr<ngraph::runtime::Tensor>> ng_ip_tensors;
   vector<std::shared_ptr<ngraph::runtime::Tensor>> ng_op_tensors;