| # SPDX-FileCopyrightText: Copyright 2020-2022 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 write to a TensorFlow Lite format file. Supports adding in file identifiers. |
| import flatbuffers |
| import flatbuffers.number_types as N |
| import numpy as np |
| from flatbuffers import encode |
| from flatbuffers.builder import UOffsetTFlags |
| |
| from .errors import VelaError |
| from .nn_graph import PassPlacement |
| from .operation import Op |
| from .reader_util import align_inputs_indices |
| from .tensor import MemType |
| from .tensor import shape_num_elements |
| from .tensor import TensorPurpose |
| from .tflite import Buffer |
| from .tflite import Metadata |
| from .tflite import Model |
| from .tflite import Operator |
| from .tflite import OperatorCode |
| from .tflite import QuantizationParameters |
| from .tflite import SubGraph |
| from .tflite import Tensor |
| from .tflite_mapping import builtin_operator_inv_map |
| from .tflite_mapping import BuiltinOperator |
| from .tflite_mapping import datatype_inv_map |
| |
| # the python flatbuffer interface is missing a method to add in file identifier. patching it in here: |
| |
| tflite_version = 3 |
| tflite_file_identifier = "TFL" + str(tflite_version) |
| |
| |
| def FinishWithFileIdentifier(self, rootTable, fid): |
| if fid is None or len(fid) != 4: |
| raise VelaError("FileIdentifier 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: |
| |
| BUF_IDX_SCRATCH = 0 # Always assign scratch to buffer 0 |
| BUF_IDX_SCRATCH_FAST = 1 # Always assign scratch_fast to buffer 1 |
| BUF_IDX_START = 2 # Unique buffer id for every tensor in all subgraphs |
| |
| def __init__(self, nng): |
| self.builder = flatbuffers.Builder(0) |
| self.nng = nng |
| |
| self.buf_idx = TFLiteSerialiser.BUF_IDX_START |
| self.buffers_to_write = [] # have an empty array there |
| self.tensor_map_all = [] # Keep track of all subgraphs |
| self.tensor_map_sg = [] # Keep track of one subgraph |
| |
| self.ops_to_ignore = (Op.Const, Op.Placeholder, Op.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: |
| # swap from nng input indexing to TensorFlow Lite input indexing |
| self.align_nng_inputs_to_tflite(op) |
| all_ops.append(op) |
| if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op(): |
| # If values are None op has non-constant weights |
| if op.inputs[1].values is not None: |
| self.tensors_to_reshape[op.inputs[1]] = (3, 0, 1, 2) |
| if op.type == Op.FullyConnected: |
| # If values are None op has non-constant weights |
| if op.inputs[1].values is not None: |
| self.tensors_to_reshape[op.inputs[1]] = (1, 0) |
| |
| # list of tuple(Op, string); the custom code is only used for 3rd party custom operators |
| self.operator_codes = sorted(set((op.type, op.attrs.get("custom_code", "")) for op in all_ops)) |
| self.operator_code_map = {} |
| |
| def align_nng_inputs_to_tflite(self, op): |
| from_indices = op.type.info.indices |
| _, _, to_indices = builtin_operator_inv_map[op.type] |
| op.inputs = align_inputs_indices(from_indices, to_indices, op.inputs) |
| |
| 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() |
| |
| 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() |
| |
| 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() |
| |
| 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() |
| |
| 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() |
| |
| def assign_buffers_to_tensors(self, tensors, scratch_tensor): |
| if scratch_tensor is not None: |
| scratch_tensor_mem_area = scratch_tensor.mem_area |
| else: |
| scratch_tensor_mem_area = None # all tensors are initialised to MemArea.Unknown |
| |
| buffer_map = {} |
| |
| for tens in tensors: |
| # Set buffer ids depending on allocation |
| if tens.is_allocated_in_tensor_arena(scratch_tensor_mem_area): |
| buffer_map[tens] = TFLiteSerialiser.BUF_IDX_SCRATCH |
| elif tens.mem_type == MemType.Scratch_fast: |
| # For Scratch_fast when not co-allocated with scratch in the TensorArena: |
| buffer_map[tens] = TFLiteSerialiser.BUF_IDX_SCRATCH_FAST |
| else: |
| buffer_map[tens] = self.buf_idx |
| self.buf_idx += 1 |
| |
| # Initialize/extend buffers_to_write to a length equal to number of buffers so |
| # they can be appended at the correct index during tensor serialization |
| self.buffers_to_write += [None] * (self.buf_idx) |
| |
| return buffer_map |
| |
| def serialise_operator_code(self, idx, op_type, custom_code): |
| builder = self.builder |
| custom_code_offset = None |
| if op_type == Op.Custom: |
| tf_code, opt_serializer, _ = builtin_operator_inv_map[op_type] |
| custom_code_offset = builder.CreateString(custom_code) |
| else: |
| assert ( |
| op_type in builtin_operator_inv_map |
| ), "Vela does not contain a mapping to serialise {} operator to a TensorFlow Lite operator".format(op_type) |
| tf_code, opt_serializer, _ = builtin_operator_inv_map[op_type] |
| |
| if op_type == Op.CustomNpuOp: |
| assert ( |
| tf_code == BuiltinOperator.CUSTOM |
| ), "Vela only supports serialising NpuOp operators as TensorFlow Lite Custom operators" |
| custom_code_offset = builder.CreateString("ethos-u") |
| |
| # there can be multiple different types of 3rd party custom operators (i.e. non-"ethos-u" ones). therefore we |
| # need to add an extra level of indirection to this particular entry in the operator_code_map to allow for the |
| # correct lookup later on |
| if op_type == Op.Custom: |
| if op_type not in self.operator_code_map: |
| self.operator_code_map[op_type] = {} |
| self.operator_code_map[op_type][custom_code] = (idx, tf_code, opt_serializer) |
| else: |
| self.operator_code_map[op_type] = (idx, tf_code, opt_serializer) |
| |
| OperatorCode.OperatorCodeStart(builder) |
| OperatorCode.OperatorCodeAddDeprecatedBuiltinCode(builder, tf_code if tf_code < 127 else 127) |
| 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 |
| |
| qp = None |
| 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) |
| if quant.quant_dim is not None: |
| QuantizationParameters.QuantizationParametersAddQuantizedDimension(builder, quant.quant_dim) |
| qp = QuantizationParameters.QuantizationParametersEnd(builder) |
| |
| return qp |
| |
| def serialise_tensor(self, tens): |
| builder = self.builder |
| if shape_num_elements(tens.original_shape) != shape_num_elements(tens.shape): |
| # shapes have changed size, therefore assume that the latest (modified) shape is correct |
| tens_shape = tens.shape |
| else: |
| # shapes have not changed size, therefore the original shape is valid |
| tens_shape = tens.original_shape |
| 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) |
| |
| buf_id = self.buffer_map[tens] |
| 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) |
| if quant is not None: |
| Tensor.TensorAddQuantization(builder, quant) |
| Tensor.TensorAddIsVariable(builder, tens.is_variable) |
| |
| res = Tensor.TensorEnd(builder) |
| return res |
| |
| def serialise_operator(self, op): |
| builder = self.builder |
| |
| inputs_offset = self.write_int_vector( |
| [self.tensor_map_sg[tens] if tens in self.tensor_map_sg else -1 for tens in op.inputs] |
| ) |
| outputs_offset = self.write_int_vector( |
| [self.tensor_map_sg[tens] for tens in op.outputs if tens in self.tensor_map_sg] |
| ) |
| intermediates_offset = self.write_int_vector( |
| [self.tensor_map_sg[tens] for tens in op.intermediates if tens in self.tensor_map_sg] |
| ) |
| |
| if op.type == Op.Custom: |
| op_idx, tflop, opt_serializer = self.operator_code_map[op.type][op.attrs.get("custom_code", "")] |
| else: |
| 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"] |
| attrs["fused_activation_function"] = op.activation.op_type if op.activation is not None else None |
| |
| 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) |
| Operator.OperatorAddIntermediates(builder, intermediates_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, name): |
| builder = self.builder |
| all_ops = [] |
| placeholder_ops = [] |
| |
| for ps in sg.passes: |
| for op in ps.ops: |
| if op.type not in self.ops_to_ignore: |
| all_ops.append(op) |
| elif op.type == Op.Placeholder: |
| placeholder_ops.append(op) |
| |
| # Make sure all original tensors are written back, special case for Ops |
| # with connected subgraphs. Even though not all inputs are used, |
| # the reference kernel expects all inputs to be in the tflite file. |
| # Since we traverse the graph starting with all outputs they are |
| # always added but if an input is not referenced it will not be added |
| # to an op. |
| tensor_set = set(sg.original_inputs) |
| |
| # Add the tensors from all valid ops, as well as the tensors from placeholder ops |
| # This allows us to serialise tensors which arent attached to any specific ops, |
| # e.g. due to an empty graph containing no ops |
| for op in all_ops + placeholder_ops: |
| for tens in op.inputs + op.outputs + op.intermediates: |
| if tens is not None: |
| tensor_set.add(tens) |
| |
| all_tensors = [tens for nm, idx, tens in sorted((tens.name, idx, tens) for idx, tens in enumerate(tensor_set))] |
| |
| scratch_tensors = [tens for tens in all_tensors if tens.purpose is TensorPurpose.Scratch] |
| |
| if len(scratch_tensors) == 0: |
| scratch_tensor = None |
| else: |
| assert len(scratch_tensors) == 1, "Multiple scratch tensors" |
| scratch_tensor = scratch_tensors[0] |
| |
| self.tensor_map_sg = {tens: idx for idx, tens in enumerate(all_tensors)} |
| self.buffer_map = self.assign_buffers_to_tensors(all_tensors, scratch_tensor) |
| self.tensor_map_all.append(self.tensor_map_sg) |
| |
| tensors_offset = self.write_offset_vector([self.serialise_tensor(tens) for tens in all_tensors]) |
| |
| # Make sure the input_tensors haven't been modified |
| assert all(inp in sg.original_inputs for inp in sg.input_tensors) |
| inputs = [self.tensor_map_sg[tens] for tens in sg.original_inputs if tens in self.tensor_map_sg] |
| |
| inputs_offset = self.write_int_vector(inputs) |
| outputs_offset = self.write_int_vector( |
| [self.tensor_map_sg[tens] for tens in sg.output_tensors if tens in self.tensor_map_sg] |
| ) |
| |
| 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) |
| SubGraph.SubGraphAddName(builder, name) |
| |
| return SubGraph.SubGraphEnd(builder) |
| |
| def write_aligned_bytes(self, buf): |
| builder = self.builder |
| builder.assertNotNested() |
| builder.nested = True |
| data = bytes(buf) |
| length_bytes = UOffsetTFlags.py_type(len(data)) |
| builder.vectorNumElems = length_bytes |
| 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() |
| |
| 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, optype, code) for idx, (optype, code) in enumerate(self.operator_codes)] |
| ) |
| |
| description = builder.CreateString("Vela Optimised") |
| |
| subgraph_offset = self.write_offset_vector( |
| [self.serialise_subgraph(sg, builder.CreateString(sg.name)) for sg in self.subgraphs_to_write] |
| ) |
| |
| # Fill the metadata buffer |
| version = np.int32(0) |
| subgraph_idx = np.int32(len(self.subgraphs_to_write)) |
| |
| nbr_tensors_all = np.sum([len(tensor_map_sg) for tensor_map_sg in self.tensor_map_all], dtype=np.int32) |
| |
| offlineAlloc = [version, subgraph_idx, nbr_tensors_all] |
| |
| if not any([name == b"OfflineMemoryAllocation" for name, _ in self.nng.metadata]): |
| for tensor_map_sg in self.tensor_map_all: |
| nbr_tensors_sg = np.int32(len(tensor_map_sg)) |
| # An offset of -1 indicates that the tensor will be allocated online by Tensorflow Lite Micro |
| offsets = [np.int32(-1)] * nbr_tensors_sg |
| # Ensure that the order of the offsets match the order of the tensors |
| for tens, idx in tensor_map_sg.items(): |
| # Set offsets for tensor allocated in Tensor Arena or in the scratch_fast area |
| if tens.mem_type in (MemType.Scratch, MemType.Scratch_fast): |
| offsets[idx] = np.int32(tens.address) if tens.address is not None else np.int32(0) |
| |
| offlineAlloc += offsets |
| |
| self.nng.metadata.append(("OfflineMemoryAllocation", np.array(offlineAlloc))) |
| |
| metadata_list = [] |
| for name, buffer in self.nng.metadata: |
| self.buffers_to_write.append(buffer) |
| metadata_list.append((name, len(self.buffers_to_write) - 1)) |
| |
| buffers_offset = self.write_offset_vector([self.serialise_buffer(buf) for buf in self.buffers_to_write]) |
| 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) |