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tensorflow/cc/framework/while_gradients.cc
return strings::StrCat(forward_frame_name, "_backprop"); } // Creates a loop that counts the number of iterations performed by the // while loop associated with `while_ctx`. The returned output yields the // iteration count. Status AddForwardLoopCounter(WhileContext* while_ctx, const Scope& scope, Output* count) { // Create while loop: // i = 0 // while forward loop predicate is true:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_variables.cc
// Add dequantize. builder.setInsertionPointAfter(read_variable_op); auto new_read_variable_op = builder.create<ReadVariableOp>(read_variable_op.getLoc(), ref_qtype, read_variable_op.getResourceId()); auto new_dq_op = builder.create<DequantizeOp>( read_variable_op.getLoc(), read_variable_op.getResult().getType(), new_read_variable_op.getResult());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.5K bytes - Viewed (0) -
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/tensorflow/transforms/constant_fold_utils.cc
// available during compilation or compilation only device for on demand // execution which may create a recursion if used for constant folding. std::string host_cpu = tensorflow::DeviceNameUtils::FullName( /*job=*/"localhost", /*replica=*/0, /*task=*/0, /*type=*/"CPU", /*id=*/0); absl::StatusOr<OpKernelRunner> runner = OpKernelRunner::Create( node_def->get()->op(), node_def->get()->name(), host_cpu, operands.size(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.3K 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/quantization/lite/tfl_to_std.cc
b.setInsertionPoint(op); if (auto dq = llvm::dyn_cast<DequantizeOp>(op)) { auto dcast = b.create<quantfork::DequantizeCastOp>( dq.getLoc(), dq.getOutput().getType(), dq.getInput()); dq.getOutput().replaceAllUsesWith(dcast); dq.erase(); } else if (auto q = llvm::dyn_cast<QuantizeOp>(op)) { auto qcast = b.create<quantfork::QuantizeCastOp>( q.getLoc(), q.getOutput().getType(), q.getInput());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 02:50:01 UTC 2024 - 3.5K bytes - Viewed (0)