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tensorflow/compiler/mlir/tensorflow/transforms/prepare_tpu_computation_for_tf_export.cc
RankedTensorType::get({3}, rewriter.getType<TF::StringType>()); auto dynamic_key = rewriter.create<TF::_XlaCompileMlirPlaceholderProgramKeyOp>( func.getLoc(), /*program=*/result_type, llvm::ArrayRef<Value>{}); auto recv_at_host = rewriter.create<TF::_XlaRecvAtHostOp>( func.getLoc(), op.getOperandTypes(), /*dynamic_key=*/dynamic_key, op.getSendKeyAttr(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/saved_model/saved_model_test.cc
auto runtime = tensorflow::tfrt_stub::Runtime::Create(/*num_inter_op_threads=*/1); tfrt_stub::GraphExecutionOptions options(runtime.get()); options.compile_options.device_target = TfrtDeviceInfraTarget::kGpu; TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr<tfrt_stub::FallbackState> fallback_state, tfrt_stub::FallbackState::Create(SessionOptions(), FunctionDefLibrary()));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Oct 13 01:17:29 UTC 2023 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/odml_converter/folders.cc
namespace { // Helper class for parsing operands to a foldable operation. class FoldAdaptor { public: // Returns std::nullopt if the operation cannot be folded. static std::optional<FoldAdaptor> Create(Operation* operation) { auto foldable_opr = [](Value val) -> bool { return !llvm::isa<BlockArgument>(val) && llvm::isa<stablehlo::ConstantOp>(val.getDefiningOp()); };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 06:11:55 UTC 2024 - 4.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/push_transpose_through_ewise.cc
auto new_out_type = RankedTensorType::get( PermuteShape(current_out_type.getShape(), inverse_perm_arr), current_out_type.getElementType()); // Create new ewise op to appear before the tranpose. auto *new_ewise_op = rewriter.create(op->getLoc(), op->getName().getIdentifier(), {tpose_arg1.getOperand(0), tpose_arg2.getOperand(0)}, new_out_type, op->getAttrs());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 12.5K bytes - Viewed (0) -
tensorflow/c/experimental/saved_model/core/saved_variable_loading_test.cc
TensorShape shape(shape_vector); // Create the variable. Status status; std::unique_ptr<Variable> var; TF_EXPECT_OK(Variable::CreateUninitialized(context(), dtype, shape, absl::nullopt, nullptr, {}, &var)); // Create a TensorHandle ImmediateTensorHandlePtr expected_handle =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Feb 27 09:34:33 UTC 2024 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/add_functions_for_exported_names.cc
f.setName(StringAttr::get(f->getContext(), f.getName() + "_internal")); for (StringRef name : llvm::reverse(exported_names)) { // Create a "trampoline" function with the given name. So given // func bar(...) {exported_names = ["foo"]} // we create // func foo(...) { // return bar(...) // } func::FuncOp other = f.cloneWithoutRegions(); other.setName(name);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Dec 19 08:06:04 UTC 2023 - 4.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_stablehlo_to_vhlo.cc
std::optional<Value> MaterializeIllegalCast(OpBuilder &builder, Type type, ValueRange inputs, Location loc) { return builder.create<UnrealizedConversionCastOp>(loc, type, inputs) ->getResult(0); } class StablehloToOdmlTypeConverter : public vhlo::VhloTypeConverter { public: StablehloToOdmlTypeConverter() : vhlo::VhloTypeConverter() {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 15 19:48:51 UTC 2024 - 12.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/post_quantize.cc
TFL::populateWithGenerated(patterns); patterns.add<RemoveVolatileOps<kPreserveNone>>(ctx); (void)applyPatternsAndFoldGreedily(func, std::move(patterns)); } } // namespace // Creates an instance of the TensorFlow Lite dialect PostQuantize pass. std::unique_ptr<OperationPass<func::FuncOp>> CreatePostQuantizePass( bool emit_quant_adaptor_ops, const quant::CustomOpMap& custom_op_map) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.1K bytes - Viewed (0) -
tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc
*library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float"}, {"c_0_retval:float"}, {}, { {{"C"}, "UnaryTest", {"a_0_arg"}}, }, {{"c_0_retval", "C:o:0"}}); *library_expected.add_function() = FunctionDefHelper::Create( "F2", {"b_0_arg:float", "c_0_arg:float"}, {"d_0_retval:float"}, {}, {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 08:47:20 UTC 2024 - 113.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/add_dump_tensor_op.cc
const StringAttr &new_ref_func_name) { // Create PartitionedCallOp to the copied composite function. This // PartitionedCallOp does not have kQuantTraitAttrName, and therefore won't // get quantized. auto new_call_op = rewriter.create<TF::PartitionedCallOp>( call_op.getLoc(), call_op.getResultTypes(), call_op.getOperands(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 13K bytes - Viewed (0)