| # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| # |
| # 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 write to a TensorFlow Lite format file. Supports adding in file identifiers. |
| |
| import flatbuffers |
| |
| from .tflite import Tensor |
| from .tflite import QuantizationParameters |
| from .tflite import Model |
| from .tflite import SubGraph |
| from .tflite import OperatorCode |
| from .tflite import Operator |
| from .tflite import Buffer |
| from .tflite import Metadata |
| |
| import numpy as np |
| |
| from .tflite_mapping import datatype_inv_map, builtin_operator_inv_map, custom_prefix, BuiltinOperator |
| from .nn_graph import PassPlacement |
| from .tensor import TensorPurpose, MemArea |
| from flatbuffers.builder import UOffsetTFlags |
| |
| tflite_version = 3 |
| tflite_file_identifier = "TFL" + str(tflite_version) |
| |
| |
| import flatbuffers.number_types as N |
| from flatbuffers import encode |
| |
| |
| def FinishWithFileIdentifier(self, rootTable, fid): |
| if fid is None or len(fid) != 4: |
| raise Exception("fid must be 4 chars") |
| |
| flags = N.Uint8Flags |
| prepSize = 4 |
| self.Prep(self.minalign, prepSize + len(fid)) |
| for i in range(3, -1, -1): |
| self.head = self.head - flags.bytewidth |
| encode.Write(flags.packer_type, self.Bytes, self.Head(), ord(fid[i])) |
| |
| return self.Finish(rootTable) |
| |
| |
| flatbuffers.Builder.FinishWithFileIdentifier = FinishWithFileIdentifier |
| |
| |
| def make_vector(v): |
| try: |
| len(v) |
| return v |
| except TypeError: |
| return [v] |
| |
| |
| class TFLiteSerialiser: |
| def __init__(self, nng): |
| self.builder = flatbuffers.Builder(0) |
| self.nng = nng |
| |
| self.scratch_buf_id = 0 # Always assign scratch to buffer 0 |
| self.buffer_offsets_map = {} |
| self.buffers_to_write = [] # have an empty array there |
| |
| self.input_tensors = [] |
| self.ops_to_ignore = set(("Const", "Placeholder", "SubgraphInput")) |
| |
| self.tensors_to_reshape = {} |
| |
| self.subgraphs_to_write = [sg for sg in self.nng.subgraphs if sg.placement == PassPlacement.Cpu] |
| |
| all_ops = [] |
| for sg in self.subgraphs_to_write: |
| for ps in sg.passes: |
| for op in ps.ops: |
| if op.type not in self.ops_to_ignore: |
| all_ops.append(op) |
| if op.type.startswith("Conv2D") or op.type.startswith("DepthwiseConv2d"): |
| self.tensors_to_reshape[op.inputs[1]] = (3, 0, 1, 2) |
| if op.type.startswith("FullyConnected"): |
| self.tensors_to_reshape[op.inputs[1]] = (1, 0) |
| |
| self.operator_codes = list(sorted(set(op.type for op in all_ops))) |
| self.operator_code_map = {} |
| |
| def write_byte_vector(self, v, alignment=1): |
| builder = self.builder |
| builder.StartVector(1, len(v), alignment) |
| for e in v[::-1]: |
| builder.PrependByte(e) |
| return builder.EndVector(len(v)) |
| |
| def write_int_vector(self, v): |
| builder = self.builder |
| builder.StartVector(4, len(v), 4) |
| for e in v[::-1]: |
| builder.PrependInt32(e) |
| return builder.EndVector(len(v)) |
| |
| def write_long_vector(self, v): |
| builder = self.builder |
| builder.StartVector(8, len(v), 8) |
| for e in v[::-1]: |
| builder.PrependInt64(e) |
| return builder.EndVector(len(v)) |
| |
| def write_float_vector(self, v): |
| builder = self.builder |
| builder.StartVector(4, len(v), 4) |
| for e in v[::-1]: |
| builder.PrependFloat32(e) |
| return builder.EndVector(len(v)) |
| |
| def write_offset_vector(self, v): |
| builder = self.builder |
| builder.StartVector(4, len(v), 4) |
| for e in v[::-1]: |
| builder.PrependUOffsetTRelative(e) |
| return builder.EndVector(len(v)) |
| |
| def assign_buffers_to_tensors(self, tensors): |
| buffer_map = {} |
| scratch_tensor = [tens for tens in tensors if tens.purpose == TensorPurpose.Scratch][0] |
| buf_idx = 1 |
| |
| for tens in tensors: |
| if tens.mem_area == scratch_tensor.mem_area: |
| buffer_map[tens] = self.scratch_buf_id |
| else: |
| buffer_map[tens] = buf_idx |
| buf_idx += 1 |
| |
| # Initialize buffers_to_write to a length equal to numer of buffers so |
| # they can be appended at the correct index during tensor serialization |
| self.buffers_to_write = [None] * (buf_idx) |
| |
| return buffer_map |
| |
| def serialise_operator_code(self, idx, code): |
| builder = self.builder |
| custom_code_offset = None |
| if code.startswith(custom_prefix): |
| tf_code, opt_serializer = builtin_operator_inv_map[custom_prefix] |
| custom_code_offset = builder.CreateString(code[len(custom_prefix) :]) |
| else: |
| try: |
| tf_code, opt_serializer = builtin_operator_inv_map[code] |
| except KeyError: |
| print( |
| "Warning: Writing operation %s, which does not have a direct TensorFlow Lite mapping, as a custom operation" |
| % (code,) |
| ) |
| tf_code, opt_serializer = builtin_operator_inv_map[custom_prefix] |
| |
| if tf_code == BuiltinOperator.CUSTOM: |
| assert code == "NpuOp" # Currently only support serialising NPU operators as a custom op |
| custom_code_offset = builder.CreateString("ethos-u") |
| |
| self.operator_code_map[code] = (idx, tf_code, opt_serializer) |
| |
| OperatorCode.OperatorCodeStart(builder) |
| OperatorCode.OperatorCodeAddBuiltinCode(builder, tf_code) |
| if custom_code_offset is not None: |
| OperatorCode.OperatorCodeAddCustomCode(builder, custom_code_offset) |
| |
| return OperatorCode.OperatorCodeEnd(builder) |
| |
| def serialise_quantization_parameters(self, quant): |
| builder = self.builder |
| |
| min = None |
| max = None |
| scale = None |
| zero_point = None |
| if quant is not None: |
| if quant.min is not None: |
| min = self.write_float_vector(make_vector(quant.min)) |
| if quant.max is not None: |
| max = self.write_float_vector(make_vector(quant.max)) |
| if quant.scale_f32 is not None: |
| scale = self.write_float_vector(make_vector(quant.scale_f32)) |
| if quant.zero_point is not None: |
| zero_point = self.write_long_vector(make_vector(quant.zero_point)) |
| |
| QuantizationParameters.QuantizationParametersStart(builder) |
| if min is not None: |
| QuantizationParameters.QuantizationParametersAddMin(builder, min) |
| if max is not None: |
| QuantizationParameters.QuantizationParametersAddMax(builder, max) |
| if scale is not None: |
| QuantizationParameters.QuantizationParametersAddScale(builder, scale) |
| if zero_point is not None: |
| QuantizationParameters.QuantizationParametersAddZeroPoint(builder, zero_point) |
| return QuantizationParameters.QuantizationParametersEnd(builder) |
| |
| def serialise_tensor(self, tens): |
| builder = self.builder |
| tens_shape = tens.shape |
| values = tens.quant_values |
| if values is None: |
| values = tens.values |
| |
| if values is None: |
| values = np.empty(shape=(0), dtype=np.uint8) |
| |
| if tens in self.tensors_to_reshape: |
| reorder = self.tensors_to_reshape[tens] |
| tens_shape = [tens_shape[idx] for idx in reorder] |
| values = values.transpose(reorder) |
| |
| if tens.purpose == TensorPurpose.Scratch: |
| tens_shape = [0] |
| self.buffers_to_write[self.scratch_buf_id] = values.flatten().view(np.uint8) |
| |
| buf_id = self.buffer_map[tens] |
| if buf_id != self.scratch_buf_id: |
| self.buffers_to_write[buf_id] = values.flatten().view(np.uint8) |
| |
| shape = self.write_int_vector(tens_shape) |
| |
| name = builder.CreateString(tens.name) |
| quant = self.serialise_quantization_parameters(tens.quantization) |
| |
| Tensor.TensorStart(builder) |
| Tensor.TensorAddShape(builder, shape) |
| Tensor.TensorAddType(builder, datatype_inv_map[tens.dtype]) |
| # All tensors must have a valid backing buffer, even if it is empty. |
| # Empty buffers should be kept unique for TensorFlow Lite Micro |
| Tensor.TensorAddBuffer(builder, buf_id) |
| Tensor.TensorAddName(builder, name) |
| Tensor.TensorAddQuantization(builder, quant) |
| |
| res = Tensor.TensorEnd(builder) |
| return res |
| |
| def serialise_operator(self, op): |
| builder = self.builder |
| |
| inputs_offset = self.write_int_vector([self.tensor_map[tens] for tens in op.inputs]) |
| outputs_offset = self.write_int_vector([self.tensor_map[tens] for tens in op.outputs]) |
| |
| op_idx, tflop, opt_serializer = self.operator_code_map[op.type] |
| |
| builtin_opt_offset = None |
| custom_opt_offset = None |
| if opt_serializer is not None: |
| attrs = dict(op.attrs) |
| if "strides" in attrs: |
| attrs["stride_h"] = attrs["strides"][1] |
| attrs["stride_w"] = attrs["strides"][2] |
| if "ksize" in attrs: |
| attrs["filter_height"] = attrs["ksize"][1] |
| attrs["filter_width"] = attrs["ksize"][2] |
| if "dilation" in attrs: |
| attrs["dilation_h_factor"] = attrs["dilation"][1] |
| attrs["dilation_w_factor"] = attrs["dilation"][2] |
| if "channel_multiplier" in attrs: |
| attrs["depth_multiplier"] = attrs["channel_multiplier"] |
| |
| builtin_opt_offset, custom_opt_offset = opt_serializer.serialize(builder, attrs) |
| |
| mutating_variable_inputs_offset = self.write_byte_vector([]) |
| Operator.OperatorStart(builder) |
| Operator.OperatorAddOpcodeIndex(builder, op_idx) |
| Operator.OperatorAddInputs(builder, inputs_offset) |
| Operator.OperatorAddOutputs(builder, outputs_offset) |
| |
| if builtin_opt_offset is not None: |
| Operator.OperatorAddBuiltinOptionsType(builder, opt_serializer.builtin_opt_type) |
| Operator.OperatorAddBuiltinOptions(builder, builtin_opt_offset) |
| if custom_opt_offset is not None: |
| Operator.OperatorAddCustomOptions(builder, custom_opt_offset) |
| Operator.OperatorAddCustomOptionsFormat(builder, opt_serializer.custom_opt_format) |
| |
| Operator.OperatorAddMutatingVariableInputs(builder, mutating_variable_inputs_offset) |
| return Operator.OperatorEnd(builder) |
| |
| def serialise_subgraph(self, sg): |
| builder = self.builder |
| tensor_set = set() |
| |
| all_ops = [] |
| for ps in sg.passes: |
| for op in ps.ops: |
| if op.type not in self.ops_to_ignore: |
| all_ops.append(op) |
| |
| for op in all_ops: |
| for tens in op.inputs + op.outputs: |
| tensor_set.add(tens) |
| |
| all_tensors = [tens for nm, idx, tens in sorted((tens.name, idx, tens) for idx, tens in enumerate(tensor_set))] |
| |
| self.tensor_map = {tens: idx for idx, tens in enumerate(all_tensors)} |
| self.buffer_map = self.assign_buffers_to_tensors(all_tensors) |
| |
| tensors_offset = self.write_offset_vector([self.serialise_tensor(tens) for tens in all_tensors]) |
| |
| # Add the Scratch Tensor as input to the NPU subgraph to get it allocated by TensorFlow Lite Micro |
| scratch_tensor_idx = [v for k, v in self.tensor_map.items() if k.name.endswith("scratch")] |
| |
| # Make sure the input_tensors haven't been modified |
| assert all(inp in sg.original_inputs for inp in sg.input_tensors) |
| inputs_offset = self.write_int_vector( |
| [self.tensor_map[tens] for tens in sg.original_inputs] + scratch_tensor_idx |
| ) |
| outputs_offset = self.write_int_vector([self.tensor_map[tens] for tens in sg.output_tensors]) |
| |
| operators_offset = self.write_offset_vector([self.serialise_operator(op) for op in all_ops]) |
| |
| SubGraph.SubGraphStart(builder) |
| SubGraph.SubGraphAddTensors(builder, tensors_offset) |
| SubGraph.SubGraphAddInputs(builder, inputs_offset) |
| SubGraph.SubGraphAddOutputs(builder, outputs_offset) |
| |
| SubGraph.SubGraphAddOperators(builder, operators_offset) |
| |
| return SubGraph.SubGraphEnd(builder) |
| |
| def write_aligned_bytes(self, buf): |
| builder = self.builder |
| builder.nested = True |
| data = bytes(buf) |
| length_bytes = UOffsetTFlags.py_type(len(data)) |
| builder.Prep(16, length_bytes) # Reserve aligned storage |
| builder.head = UOffsetTFlags.py_type(builder.Head() - length_bytes) # Update FlatBuffer internal pointer |
| builder.Bytes[builder.Head() : builder.Head() + length_bytes] = data # Assign bytes to aligned area |
| return builder.EndVector(length_bytes) |
| |
| def serialise_buffer(self, buf): |
| builder = self.builder |
| data = None |
| if buf is not None: |
| data = self.write_aligned_bytes(buf) |
| Buffer.BufferStart(builder) |
| if data is not None: |
| Buffer.BufferAddData(builder, data) |
| return Buffer.BufferEnd(builder) |
| |
| def serialise_metadata(self, metadata): |
| builder = self.builder |
| name = builder.CreateString(metadata[0]) |
| |
| Metadata.MetadataStart(builder) |
| Metadata.MetadataAddName(builder, name) |
| Metadata.MetadataAddBuffer(builder, metadata[1]) |
| |
| return Metadata.MetadataEnd(builder) |
| |
| def serialise_model(self): |
| builder = self.builder |
| operator_code_offset = self.write_offset_vector( |
| [self.serialise_operator_code(idx, code) for idx, code in enumerate(self.operator_codes)] |
| ) |
| |
| description = builder.CreateString("Vela Optimised") |
| |
| subgraph_offset = self.write_offset_vector([self.serialise_subgraph(sg) for sg in self.subgraphs_to_write]) |
| |
| # Fill the metadata buffer |
| version = np.int32(0) |
| subgraph_idx = np.int32(len(self.subgraphs_to_write)) # Only 1 supported currently |
| nbr_tensors = np.int32(len(self.tensor_map)) |
| |
| # An offset of -1 indicates that the tensor will be allocated online by Tensorflow Lite Micro |
| offsets = [np.int32(-1)] * nbr_tensors |
| |
| # Ensure that the order of the offsets match the order of the tensors |
| for tens, idx in self.tensor_map.items(): |
| if tens.mem_area == MemArea.Sram: |
| offsets[idx] = np.int32(tens.address) |
| |
| metadata_buffer = np.array([version, subgraph_idx, nbr_tensors] + offsets) |
| self.buffers_to_write.append(metadata_buffer) |
| |
| buffers_offset = self.write_offset_vector([self.serialise_buffer(buf) for buf in self.buffers_to_write]) |
| |
| metadata_list = [("OfflineMemoryAllocation", len(self.buffers_to_write) - 1)] |
| metadata_offset = self.write_offset_vector([self.serialise_metadata(metadata) for metadata in metadata_list]) |
| |
| Model.ModelStart(builder) |
| Model.ModelAddVersion(builder, tflite_version) |
| Model.ModelAddOperatorCodes(builder, operator_code_offset) |
| Model.ModelAddSubgraphs(builder, subgraph_offset) |
| Model.ModelAddDescription(builder, description) |
| Model.ModelAddBuffers(builder, buffers_offset) |
| Model.ModelAddMetadata(builder, metadata_offset) |
| return Model.ModelEnd(builder) |
| |
| def serialise(self): |
| |
| model = self.serialise_model() |
| |
| self.builder.FinishWithFileIdentifier(model, tflite_file_identifier) |
| |
| return self.builder.Output() |
| |
| def write(self, filename): |
| with open(self.filename, "wb") as f: |
| f.write(self.serialised_buf) |
| |
| |
| def write_tflite(nng, filename): |
| writer = TFLiteSerialiser(nng) |
| buf = writer.serialise() |
| |
| with open(filename, "wb") as f: |
| f.write(buf) |