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tensorflow/compiler/mlir/quantization/tensorflow/passes/propagate_quantize_type.cc
if (failed(applyPatternsAndFoldGreedily(func, frozen_patterns))) { func.emitError() << "quant-propagate-quantize-type failed."; signalPassFailure(); } } } } // namespace // Creates an instance of the TensorFlow dialect PropagateQuantizeType pass. std::unique_ptr<OperationPass<ModuleOp>> CreatePropagateQuantizeTypePass() { return std::make_unique<PropagateQuantizeType>(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_batch_matmul.cc
if (constant.getType().getRank() != 2) return failure(); // Create a tfl.transpose op that performs ZX transpose on `input`. auto create_z_x_transpose_op = [&](Value input) -> Value { RankedTensorType input_type = mlir::cast<RankedTensorType>(input.getType()); const int input_rank = input_type.getRank(); // Create a 1D I32 tensor for representing the dimension permutation.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.6K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_compile_util.cc
// _Arg nodes, and let CompileGraph walk it. This could be optimized. std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global())); // First create the actual node we care about computing. TF_ASSIGN_OR_RETURN(Node * main_node, graph->AddNode(node_def)); // Create dummy _Arg nodes. Link these to `node` and also via a control // dependency edge to the _SOURCE node. for (int64_t i = 0, end = args.size(); i < end; ++i) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 09:53:30 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc
Value input = op->getOperand(0); const TensorType new_input_tensor_type = GetTransposedTensorType( mlir::cast<TensorType>(input.getType()), kNchwToNhwcPermutation); auto input_transpose_op = rewriter.create<mlir::stablehlo::TransposeOp>( op.getLoc(), /*resultType0=*/new_input_tensor_type, /*operand=*/input, rewriter.getDenseI64ArrayAttr(kNchwToNhwcPermutation));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/decompose_hybrid_quantization.cc
if (QuantizedType::getQuantizedElementType(operand.getType())) { auto newTy = QuantizedType::castToExpressedType(operand.getType()); newOperands.push_back( rewriter.create<TFL::DequantizeOp>(loc, newTy, operand)); continue; } newOperands.push_back(operand); } SmallVector<Type> newResultTys; for (auto result : op->getResults()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_globals_to_ml_program.cc
if (globalTensor.getValue()) { initial_value = *globalTensor.getValue(); } else { initial_value = mlir::Attribute(); } opToName[globalTensor] = name; auto variableOp = globalBuilder.create<ml_program::GlobalOp>( globalTensor.getLoc(), name, globalTensor.getType(), globalTensor.getIsMutable(), initial_value, /*visibility=*/globalBuilder.getStringAttr("private"));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/debug/debug_test.cc
return registry; }()) { context_.loadAllAvailableDialects(); mlir::OpBuilder builder(&context_); module_ = builder.create<mlir::ModuleOp>(builder.getUnknownLoc()); builder.setInsertionPointToStart(module_->getBody()); auto func = builder.create<mlir::func::FuncOp>( // builder.getUnknownLoc(), "main", builder.getFunctionType({}, {})); func->setAttr("tfl.func", builder.getUnitAttr());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 17 11:15:16 UTC 2024 - 9.7K 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/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/jit/device_compilation_profiler.cc
bool used_persistent_cache) { metrics::UpdateXlaCompilationTime(compile_time_us); const std::string& function_name = function.name(); mutex_lock lock(mu_); // Create a stats entry if it doesn't already exist. auto it = cluster_compile_stats_.emplace(function.name(), ClusterCompileStats{}) .first; const uint64 compile_time_s = compile_time_us / 1.0e6;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 06:59:07 UTC 2024 - 8.5K bytes - Viewed (0)