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Results 1 - 6 of 6 for resource_type (0.45 sec)
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tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
return op.emitOpError("requires input to have one resource"); auto resource_type = input_type.getElementType().cast<TF::ResourceType>(); auto subtypes = resource_type.getSubtypes(); switch (subtypes.size()) { case 1: return VerifyShapeOperandAndResult( op, resource_type.getSubtypes().front(), op.getType()); case 0:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
} TensorType resource_subtype = mlir::cast<TensorType>(value.getType()); ResourceType resource_type = ResourceType::get({resource_subtype}, op.getContext()); UnrankedTensorType new_resource_type = UnrankedTensorType::get(resource_type); Type refined_type = TypeMeet(resource.getType(), new_resource_type); if (refined_type == resource.getType()) continue;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/import_model.cc
auto etype, ConvertToMlirTensorType(shape_proto, dtype, &builder)); return mlir::UnrankedTensorType::get(mlir::TF::ResourceType::get( {mlir::cast<TensorType>(etype)}, builder.getContext())); } else { return mlir::UnrankedTensorType::get( mlir::TF::ResourceType::get(builder.getContext())); } } else if (auto shape = node.attrs().Find("_output_shapes")) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 183.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_export.cc
auto elementType = getElementTypeOrSelf(operand.getType()); if (mlir::isa<mlir::TF::ResourceType>(elementType)) { return true; } } for (const auto& result : op->getResults()) { auto elementType = getElementTypeOrSelf(result.getType()); if (mlir::isa<mlir::TF::ResourceType>(elementType)) { return true; } } return false; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:41:49 UTC 2024 - 164.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
This pass promotes resource accesses in function(s) (by default, the main) to input arguments and outputs of the function(s). Two types of resources are supported: (1) A function argument of TF::ResourceType type (this pass). (2) A VarHandleOp in the function (tf-promote-var-handles-to-args). After the pass, . The function will have an input argument for each resource that is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
for (auto ba : body_block.getArguments()) { int arg_no = ba.getArgNumber(); // Skip removing resources that are not read-only variables. if (getElementTypeOrSelf(ba.getType()).isa<TF::ResourceType>()) { bool has_read_only_variables = true; for (auto user : ba.getUsers()) { // Ternimator ops, for example, tfl::yield op, should be ignored since
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0)