| # SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| # Description: |
| # Functions used to read from a TensorFlow Lite format file. |
| import os.path |
| import struct |
| import sys |
| |
| import numpy as np |
| |
| from .errors import InputFileError |
| from .nn_graph import Graph |
| from .nn_graph import Subgraph |
| from .operation import create_activation_function |
| from .operation import Op |
| from .operation import Operation |
| from .reader_util import align_tensor_indices_to_nng |
| from .reader_util import clone_and_reshape_tensor |
| from .reader_util import decode_str |
| from .reader_util import fixup_tensors |
| from .tensor import create_virtual_tensor |
| from .tensor import QuantizationParameters |
| from .tensor import Tensor |
| from .tflite.BuiltinOperator import BuiltinOperator |
| from .tflite.Model import Model |
| from .tflite_mapping import builtin_operator_map |
| from .tflite_mapping import DataType |
| from .tflite_mapping import datatype_map |
| from .tflite_mapping import datatype_map_numpy |
| from .tflite_mapping import optype_to_builtintype |
| |
| |
| class TFLiteSubgraph: |
| def __init__(self, graph, subgraph): |
| self.graph = graph |
| self.name = decode_str(subgraph.Name()) |
| |
| self.tensors = [] |
| for idx in range(subgraph.TensorsLength()): |
| self.tensors.append(self.parse_tensor(subgraph.Tensors(idx))) |
| |
| self.virtual_outputs = [] |
| for idx in range(subgraph.OperatorsLength()): |
| self.parse_operator(idx, subgraph.Operators(idx)) |
| |
| self.outputs = self.get_tensors_from_indices_remove_duplicates(subgraph.OutputsAsNumpy(), "output") |
| self.inputs = self.get_tensors_from_indices_remove_duplicates(subgraph.InputsAsNumpy(), "input") |
| fixup_tensors(self.inputs, self.tensors) |
| |
| self.outputs.extend(self.virtual_outputs) |
| |
| def get_tensors_from_indices_remove_duplicates(self, indices, warning_str): |
| tensors = [] |
| for idx in indices: |
| tensor = self.tensors[idx] |
| if tensor not in tensors: |
| tensors.append(tensor) |
| else: |
| print( |
| "Warning: Subgraph {0} tensor ({1}) with idx = {2} already seen. Removing the duplicate.".format( |
| warning_str, tensor, idx |
| ) |
| ) |
| |
| return tensors |
| |
| def parse_tensor(self, tens_data): |
| np_shape = tens_data.ShapeAsNumpy() |
| shape = list(np_shape) if type(np_shape) is np.ndarray else [] |
| name = decode_str(tens_data.Name()) |
| tens_dtype = tens_data.Type() |
| dtype = datatype_map[tens_dtype] |
| tens = Tensor(shape, dtype, name) |
| quant = tens_data.Quantization() |
| tens.is_variable = tens_data.IsVariable() |
| |
| tens.quantization = QuantizationParameters() |
| if quant is not None: |
| tens.quantization.min = self.len1_array_to_scalar(quant.MinAsNumpy()) |
| tens.quantization.max = self.len1_array_to_scalar(quant.MaxAsNumpy()) |
| tens.quantization.scale_f32 = self.len1_array_to_scalar(quant.ScaleAsNumpy()) |
| tens.quantization.zero_point = self.len1_array_to_scalar(quant.ZeroPointAsNumpy()) |
| tens.quantization.quant_dim = quant.QuantizedDimension() |
| |
| if dtype == DataType.uint8: |
| tens.quantization.quant_min = 0 |
| tens.quantization.quant_max = (1 << dtype.bits) - 1 |
| elif dtype in (DataType.int8, DataType.int16, DataType.int32, DataType.int64): |
| tens.quantization.quant_min = -(1 << (dtype.bits - 1)) |
| tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1 |
| |
| if tens.quantization.scale_f32 is None and tens.quantization.zero_point is None: |
| tens.quantization = None |
| |
| tens.values = None |
| buf = self.graph.buffers[tens_data.Buffer()] |
| if buf is not None: |
| np_dtype = datatype_map_numpy[tens_dtype] |
| if dtype == DataType.string: |
| tens.values = np.array(buf.view(np_dtype)) |
| else: |
| tens.values = np.array(buf.view(np_dtype).reshape(shape)) |
| return tens |
| |
| def parse_operator(self, op_index, op_data): |
| op_type, opt_serializer, custom_code, indices, version = self.graph.operator_codes[op_data.OpcodeIndex()] |
| inputs = [self.tensors[idx] if idx != -1 else None for idx in op_data.InputsAsNumpy()] |
| outputs = [self.tensors[idx] if idx != -1 else None for idx in op_data.OutputsAsNumpy()] |
| intermediates = [] |
| if op_data.IntermediatesLength(): |
| intermediates = [self.tensors[idx] if idx != -1 else None for idx in op_data.IntermediatesAsNumpy()] |
| |
| name = "unknown_op_name" |
| if len(outputs): |
| name = outputs[0].name |
| inputs = align_tensor_indices_to_nng(op_type, indices, inputs) |
| op = Operation(op_type, name) |
| op.op_index = op_index |
| op.version = version |
| op.inputs = inputs |
| op.outputs = outputs |
| op.intermediates = intermediates |
| for out in op.outputs: |
| out.ops = [op] |
| |
| if op_type in (Op.AssignVariable, Op.CallOnce): |
| # All graph traversals are based on depth-first and the starting |
| # points are the subgraph output tensors. Because of this, operators |
| # like AssignVariable and CallOnce will not be visit when the |
| # graph is traversed and the ops are never handled. In order to |
| # fix that, the code base will have to be changed in several places. |
| # Until then this workaround is applied. A virtual output is added |
| # both to the operator and to the subgraph. By doing this the full |
| # graph is traversed correctly. The tensor is not used for anything |
| # else. |
| op.name = f"{op_type}_{op_index}" |
| tens = create_virtual_tensor(op.name) |
| op.set_output_tensor(tens) |
| self.virtual_outputs.append(tens) |
| |
| if op.type.is_depthwise_conv2d_op() or op.type.is_conv2d_op() or op.type == Op.FullyConnected: |
| # Reshape and add bias for ops with constant weights |
| # Do not modify ops with dynamic data since they will run on CPU |
| if inputs[1].values is not None: |
| if op.type == Op.FullyConnected: |
| inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0), False) |
| else: |
| inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0), False) |
| |
| if op.type.needs_bias() and len(inputs) <= op_type.info.indices.biases[0]: |
| # No Bias tensor |
| inputs.append(None) |
| if inputs[-1] and inputs[-1].values is not None: |
| # Since bias tensor is used for both bias and scale, |
| # a clone with a unique equivalence_id is needed. |
| inputs[-1] = clone_and_reshape_tensor(inputs[-1], None, True) |
| |
| if opt_serializer is not None: |
| op.attrs = opt_serializer.deserialize(op_data) |
| |
| if op_type == Op.While: |
| # Attach the actual nng subgraphs to the op |
| cond_subgraph_index = op.attrs["cond_subgraph_index"] |
| body_subgraph_index = op.attrs["body_subgraph_index"] |
| op.attrs["subgraph"] = ( |
| self.graph.nng.subgraphs[cond_subgraph_index], |
| self.graph.nng.subgraphs[body_subgraph_index], |
| ) |
| if op_type == Op.CallOnce: |
| # Attach the actual nng subgraphs to the op |
| init_subgraph_index = op.attrs["init_subgraph_index"] |
| op.attrs["subgraph"] = (self.graph.nng.subgraphs[init_subgraph_index],) |
| |
| if op_type == Op.Reshape: |
| if "new_shape" in op.attrs["attribute_read_error"] and len(inputs) > 1: |
| # the "new_shape" attribute is optional if the new_shape tensor (inputs[1]) is specified. therefore, |
| # remove the attribute read error |
| op.attrs["attribute_read_error"].remove("new_shape") |
| |
| if op_type == Op.Cast: |
| # Cast op should have "in/out_data_type" attribs add if missing |
| if "in_data_type" not in op.attrs: |
| op.attrs["in_data_type"] = inputs[0].dtype |
| if "out_data_type" not in op.attrs: |
| op.attrs["out_data_type"] = outputs[0].dtype |
| |
| if "stride_w" in op.attrs: |
| op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1) |
| if "filter_width" in op.attrs: |
| op.attrs["ksize"] = (1, op.attrs["filter_height"], op.attrs["filter_width"], 1) |
| if "dilation_w_factor" in op.attrs: |
| op.attrs["dilation"] = (1, op.attrs["dilation_h_factor"], op.attrs["dilation_w_factor"], 1) |
| if "depth_multiplier" in op.attrs: |
| op.attrs["channel_multiplier"] = op.attrs["depth_multiplier"] |
| |
| if op_type == Op.DepthwiseConv2DBias and op.attrs["depth_multiplier"] == 0: |
| # The depth multiplier is implicit and is calculated as weight channels / ifm channels |
| # Note however that the weights have been reshaped above. |
| # The original value is cached above in channel_multiplier |
| op.attrs["depth_multiplier"] = op.weights.shape[2] // op.ifm.shape[-1] |
| |
| # The fused_activation_function attribute needs to be retained so that the |
| # tflite_writer can correctly pass through operators that run on the CPU. |
| # This is because the operator activation attribute is later converted to an |
| # NpuActivation which treats None and ReLU the same, thereby making it difficult |
| # for the tflite_writer to recover the original activation function. |
| faf = op.attrs.get("fused_activation_function", None) |
| if faf is not None: |
| op.activation = create_activation_function(faf) |
| if custom_code is not None: |
| op.attrs["custom_code"] = custom_code |
| |
| # finally, report any missing attributes that could not be read during deserialize() |
| attribute_read_error = op.attrs["attribute_read_error"] |
| if len(attribute_read_error) != 0: |
| print( |
| f"Warning: Could not read the following attributes from {optype_to_builtintype(op.type)}" |
| f" '{op.name}' {opt_serializer.name} field: {', '.join(attribute_read_error)}" |
| ) |
| |
| @staticmethod |
| def len1_array_to_scalar(arr): |
| # The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in |
| # the input buffer. This is represented in Vela by using None. |
| # Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays |
| # are converted to scalars |
| if isinstance(arr, int) and arr == 0: |
| return None |
| if len(arr) == 1: |
| return arr[0] |
| return arr |
| |
| |
| class TFLiteGraph: |
| def __init__(self, filename, batch_size, feed_dict, output_node_names, initialisation_nodes): |
| |
| self.op_times = {} |
| if batch_size is None: |
| batch_size = 1 |
| self.batch_size = batch_size |
| self.name = os.path.splitext(os.path.basename(filename))[0] |
| self.initialisation_nodes = initialisation_nodes |
| |
| with open(filename, "rb") as f: |
| buf = bytearray(f.read()) |
| |
| try: |
| parsing_step = "parsing root" |
| model = Model.GetRootAsModel(buf, 0) |
| |
| parsing_step = "parsing buffers length" |
| self.buffers = [] |
| if not model.BuffersIsNone(): |
| for idx in range(model.BuffersLength()): |
| parsing_step = f"parsing buffer {idx}" |
| buffer = model.Buffers(idx) |
| buffer_data = self.parse_buffer(buffer) |
| # buffers can be either; empty, or contain no data (zero length), or contain data (non-zero length). |
| # when a buffer is None it means that it is either empty or zero length, and an empty buffer |
| # will have DataIsNone() equal to true. |
| # we should detect zero length buffers and report a warning because the TFLite semantics for these |
| # types of buffers changed in TensorFlow 2.11, whereby they could result in runtime errors |
| if buffer_data is None and not buffer.DataIsNone(): |
| print( |
| f"Warning: Input TensorFlow Lite network contains a zero length buffer (index = {idx})" |
| f" which is semantically not empty. However, it will be treated as an empty buffer." |
| ) |
| |
| self.buffers.append(buffer_data) |
| |
| parsing_step = "parsing operator codes length" |
| self.operator_codes = [] |
| for idx in range(model.OperatorCodesLength()): |
| parsing_step = f"parsing operator code {idx}" |
| self.operator_codes.append(self.parse_operator_code(model.OperatorCodes(idx))) |
| |
| parsing_step = "parsing subgraphs length" |
| self.subgraphs = [] |
| |
| # Pre-allocate nng subgraphs - needed when parsing an operator and the operator |
| # has subgraph attributes. |
| self.nng = Graph(self.name, self.batch_size) |
| for idx in range(model.SubgraphsLength()): |
| sg = Subgraph() |
| self.nng.subgraphs.append(sg) |
| |
| for idx in range(model.SubgraphsLength()): |
| parsing_step = f"parsing subgraph {idx}" |
| self.subgraphs.append(TFLiteSubgraph(self, model.Subgraphs(idx))) |
| |
| for idx, tflite_sg in enumerate(self.subgraphs): |
| sg = self.nng.subgraphs[idx] |
| sg.name = tflite_sg.name |
| sg.original_inputs = tflite_sg.inputs # Preserve the original input order |
| sg.output_tensors = tflite_sg.outputs |
| sg.virtual_outputs = tflite_sg.virtual_outputs |
| |
| parsing_step = "parsing metadata length" |
| # Preserve the original metadata |
| for idx in range(model.MetadataLength()): |
| parsing_step = f"parsing metadata {idx}" |
| meta = model.Metadata(idx) |
| parsing_step = f"parsing metadata name of metadata {idx}" |
| name = meta.Name() |
| if name is not None: |
| parsing_step = f"parsing metadata {idx} ({name})" |
| buf_data = self.buffers[meta.Buffer()] |
| self.nng.metadata.append((name, buf_data)) |
| except (struct.error, TypeError, RuntimeError) as e: |
| print(f'Error: Invalid tflite file. Got "{e}" while {parsing_step}.') |
| sys.exit(1) |
| |
| def parse_buffer(self, buf_data): |
| if buf_data.DataLength() == 0: |
| return None |
| data = buf_data.DataAsNumpy() |
| return data |
| |
| def parse_operator_code(self, code): |
| c = code.BuiltinCode() |
| if c == 0: |
| c = code.DeprecatedBuiltinCode() |
| if c not in builtin_operator_map: |
| raise InputFileError( |
| self.name, f"The input file contains operator code '{c}' which is currently not supported" |
| ) |
| op_type, ser, indices = builtin_operator_map[c] |
| custom_code = None |
| if c == BuiltinOperator.CUSTOM: |
| custom_code = decode_str(code.CustomCode()) |
| return op_type, ser, custom_code, indices, code.Version() |
| |
| |
| def read_tflite(filename, batch_size, feed_dict, output_node_names, initialisation_nodes): |
| tflite_graph = TFLiteGraph(filename, batch_size, feed_dict, output_node_names, initialisation_nodes) |
| nng = tflite_graph.nng |
| nng.refresh_after_modification() |
| return nng |