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tensorflow/compiler/mlir/lite/quantization/lite/quantize_weights_test.cc
subgraph_idx++) { const auto quantized_graph = output_model->subgraphs()->Get(subgraph_idx); const auto float_graph = model_->subgraphs()->Get(subgraph_idx); ASSERT_EQ(quantized_graph->tensors()->size(), float_graph->tensors()->size()); std::vector<int> used_tensors; for (size_t i = 0; i < quantized_graph->tensors()->size(); i++) { const auto quant_tensor = quantized_graph->tensors()->Get(i);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 32.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/tftext_utils.cc
<< "output(s) when input has rank " << input_type.getRank(); } auto value_type = GetResultType(func, 0); if (!RankEquals(value_type, 1) || !mlir::isa<StringType>(value_type.getElementType())) { return func.emitError() << "1st output should be string tensor"; } if (func.getNumResults() > 1) { auto offset_type = GetResultType(func, 1); if (!RankEquals(offset_type, 1) ||
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_operator.h
// we're just returning empty vector template <> inline std::vector<bool> GetVector(DenseElementsAttr elements) { auto type = elements.getType(); auto elemType = type.getElementType(); if (elemType.isSignlessInteger(1)) { auto vec = llvm::to_vector( llvm::map_range(elements.getValues<bool>(), [&](bool value) -> uint8_t { return value ? 1 : 0; }));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 21:00:09 UTC 2024 - 11.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/unfuse_batch_norm_pass.cc
} auto scalar_type = RankedTensorType::get(/*shape=*/{}, fp_type); auto epsilon_tensor_attr = DenseElementsAttr::get( scalar_type, {mlir::cast<Attribute>(epsilon_attr)}); Value epsilon = b.create<mhlo::ConstantOp>(epsilon_tensor_attr); auto dims_type = RankedTensorType::get(/*shape=*/{0}, b.getIntegerType(64)); auto dims = DenseIntElementsAttr::get(dims_type, SmallVector<int64_t, 1>{});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf_communication.cc
if (flatten_tuple) { auto operands = llvm::to_vector(values); operands.push_back(token); return operands; } auto value = values[0]; // If value with token already exists, reuse it. auto it = rewritten_values.find(value); if (it != rewritten_values.end()) return {it->getSecond()}; auto create_tuple = [&](ArrayRef<Value> operands) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 40.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.cc
bool CanBeSymmetricallyQuantized(Value weight) { auto dq_op = weight.getDefiningOp<quantfork::DequantizeCastOp>(); if (!dq_op) return true; auto qtype = mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType(); if (auto uniform_type = llvm::dyn_cast_or_null<UniformQuantizedType>(qtype)) { return uniform_type.getZeroPoint() == 0; } else if (auto per_axis_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/perception_ops_utils_test.cc
func::FuncOp createMaxUnpoolingFunc( mlir::Builder* builder, const SmallVector<int64_t, 4>& input_shape, const SmallVector<int64_t, 4>& output_shape) { auto input_type = RankedTensorType::get(input_shape, builder->getF32Type()); auto indices_type = RankedTensorType::get(input_shape, builder->getI64Type()); auto output_type = RankedTensorType::get(output_shape, builder->getF32Type()); SmallVector<mlir::Type, 2> input_types{input_type, indices_type};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 29 21:02:21 UTC 2022 - 7.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/tpu_sharding_identification_pass.cc
Block& function_block = func.front(); for (auto sharding_and_arg : llvm::zip(sharding_for_args, function_block.getArguments())) { const auto& sharding = std::get<0>(sharding_and_arg); BlockArgument arg = std::get<1>(sharding_and_arg); if (failed(VerifySharding(arg.getType(), sharding))) return mlir::failure(); } Operation* terminator = function_block.getTerminator(); for (auto sharding_and_retval :
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 02:01:13 UTC 2024 - 28.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/hoist_replicate_invariant_resource_writes.cc
const auto num_replicas = replicate_op.getN(); auto return_op = llvm::dyn_cast<tf_device::ReturnOp>( replicate_op.getRegion().front().getTerminator()); // Get the new result types. // TODO(prakalps): Do not add a value to returned values if it is already // returned. auto new_result_types = llvm::to_vector<4>(replicate_op->getResultTypes()); for (auto assign : tail_assign_variable_ops) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 03 12:35:38 UTC 2022 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/merge_save_function_ops_to_main.cc
// exist. func::FuncOp GetMainFunction(ModuleOp module_op) { const auto main_func_id = StringAttr::get(module_op.getContext(), kImportModelDefaultGraphFuncName); auto func_ops = module_op.getOps<func::FuncOp>(); auto main_func_itr = absl::c_find_if(func_ops, [&main_func_id](auto func_op) { return func_op.getName() == main_func_id; }); if (main_func_itr == func_ops.end()) return {};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.7K bytes - Viewed (0)