| 126 | } |
| 127 | |
| 128 | bool RecursiveCompilabilityChecker::HasXLAKernel(const Node& node) const { |
| 129 | // There is a SymbolicGradient kernel on the XLA_JIT device, but the gradient |
| 130 | // is really a kind of function call and will be handled by |
| 131 | // IsCompilableCall(). |
| 132 | if (node.type_string() == "SymbolicGradient") return false; |
| 133 | if (node.type_string() == "Const") { |
| 134 | // Skip Const op with type DT_STRING, since XLA doesn't support it, but the |
| 135 | // registered Const KernelDef says that it does, to support no-op Assert for |
| 136 | // tfcompile. |
| 137 | const AttrValue* attr = node.attrs().Find("dtype"); |
| 138 | if (attr != nullptr && attr->type() == DT_STRING) { |
| 139 | VLOG(2) << "Rejecting " << node.name() << ": Const with STRING attribute."; |
| 140 | return false; |
| 141 | } |
| 142 | } |
| 143 | |
| 144 | // XLA does not offer guaranteed aliasing between the input and output of the |
| 145 | // XLA cluster so it can't implement the forward-tensor-ref semantic. Leave |
| 146 | // such nodes out of XLA clusters. |
| 147 | if (HasForwardedRefInput(node)) { |
| 148 | VLOG(2) << "Rejecting " << node.name() << ": Identity with unsafe cast."; |
| 149 | return false; |
| 150 | } |
| 151 | Status stat = FindKernelDef(jit_device_type_, node.def(), nullptr, nullptr); |
| 152 | if (!stat.ok()) { |
| 153 | VLOG(3) << "Rejecting " << node.name() << ": Can't find a matching kernel. " << stat.error_message(); |
| 154 | } |
| 155 | return stat.ok(); |
| 156 | } |
| 157 | |
| 158 | // Tests whether 'if_node' is compilable. Every operator in the then_branch and |
| 159 | // else_branch functions must be compilable for 'if_node' to be compilable. |
nothing calls this directly
no test coverage detected