| # SPDX-FileCopyrightText: Copyright 2021-2024 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 TOSA format file. |
| import os.path |
| import struct |
| import sys |
| |
| import numpy as np |
| |
| from .nn_graph import Graph |
| from .nn_graph import Subgraph |
| from .operation import ExplicitScaling |
| 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 .shape4d import Shape4D |
| from .tensor import QuantizationParameters |
| from .tensor import shape_num_elements |
| from .tensor import Tensor |
| from .tflite_mapping import DataType |
| from .tosa.Op import Op as TosaOp |
| from .tosa.TosaGraph import TosaGraph as TG |
| from .tosa_mapping import datatype_map |
| from .tosa_mapping import datatype_map_numpy |
| from .tosa_mapping import tosa_operator_map |
| from .tosa_mapping import unsupported_tosa_operators |
| |
| |
| class TosaSubgraph: |
| def __init__(self, graph, block): |
| self.graph = graph |
| self.name = decode_str(block.Name()) |
| |
| self.tensors = [] |
| for idx in range(block.TensorsLength()): |
| self.tensors.append(self.parse_tensor(block.Tensors(idx))) |
| |
| for idx in range(block.OperatorsLength()): |
| self.parse_operator(idx, block.Operators(idx)) |
| |
| # Get the subgraph inputs and outputs |
| self.inputs = self.get_sg_inputs_remove_duplicates(block) |
| self.outputs = self.get_sg_outputs_remove_duplicates(block) |
| fixup_tensors(self.inputs, self.tensors) |
| |
| def get_sg_inputs_remove_duplicates(self, block): |
| inputs = [] |
| for idx in range(block.InputsLength()): |
| tens_data = block.Inputs(idx) |
| self.add_not_duplicate(tens_data, inputs, "input") |
| return inputs |
| |
| def get_sg_outputs_remove_duplicates(self, block): |
| outputs = [] |
| for idx in range(block.OutputsLength()): |
| tens_data = block.Outputs(idx) |
| self.add_not_duplicate(tens_data, outputs, "output") |
| return outputs |
| |
| def add_not_duplicate(self, tens_data, tensors, warning_str): |
| name = decode_str(tens_data) |
| tensor = self.get_tensor_by_name(name) |
| if tensor not in tensors: |
| tensors.append(tensor) |
| else: |
| print(f"Warning: Subgraph {warning_str} tensor ({tensor}) already seen. Removing the duplicate.") |
| |
| def get_tensor_by_name(self, name): |
| for tens in self.tensors: |
| if tens.name == name: |
| return tens |
| return None |
| |
| def parse_operator(self, op_index, op_data): |
| op_code = op_data.Op() |
| if op_code in unsupported_tosa_operators: |
| print("Unsupported Operator", op_code) |
| for opname in dir(TosaOp): |
| if op_code == getattr(TosaOp, opname): |
| print(f" {opname}") |
| return |
| |
| op_type, attr_serializer, quant_serializer, indices = tosa_operator_map[op_code] |
| inputs = [] |
| outputs = [] |
| for idx in range(op_data.InputsLength()): |
| input = decode_str(op_data.Inputs(idx)) |
| input_tens = self.get_tensor_by_name(input) |
| inputs.append(input_tens) |
| if input_tens is None: |
| print(f"could not find named input tensor {input}::{input_tens}") |
| assert input_tens is not None |
| |
| for idx in range(op_data.OutputsLength()): |
| output = decode_str(op_data.Outputs(idx)) |
| output_tens = self.get_tensor_by_name(output) |
| outputs.append(output_tens) |
| if output_tens is None: |
| print(f"could not find named output tensor {output}::{output_tens}") |
| assert output_tens is not None |
| |
| 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.inputs = inputs |
| op.outputs = outputs |
| |
| for out in op.outputs: |
| out.ops = [op] |
| |
| # TODO Transpose_conv and conv3d |
| if op.type.is_depthwise_conv2d_op() or op.type.is_conv2d_op() or op.type == Op.FullyConnected: |
| |
| def _remove_producing_identity_op(prod_op): |
| # find the producing op that is not an identity op and return it |
| while prod_op.type == Op.Identity: |
| prod_op = prod_op.inputs[0].ops[0] # get previous op |
| return prod_op |
| |
| def _check_and_get_connection(prod_op, tens): |
| # check weight producing op can be connected to the weight tensor |
| assert len(prod_op.outputs) == 1 |
| assert tens.shape == prod_op.outputs[0].shape |
| # only need to connect the current op connection as the tensor consuming connections haven't been |
| # initialised yet |
| return prod_op.outputs[0] |
| |
| # remove identity ops directly connected to the weight input of conv like ops |
| weights_producer_op = _remove_producing_identity_op(inputs[1].ops[0]) |
| inputs[1] = _check_and_get_connection(weights_producer_op, inputs[1]) # update connection |
| |
| if weights_producer_op.type == Op.Transpose: |
| # remove transpose op such that the weight op will a const op |
| transpose_op = weights_producer_op |
| # remove identity ops directly connected to the input of the transpose op |
| transpose_producer_op = _remove_producing_identity_op(transpose_op.inputs[0].ops[0]) |
| transpose_op.inputs[0] = _check_and_get_connection( |
| transpose_producer_op, transpose_op.inputs[0] |
| ) # update connection |
| |
| perms = transpose_op.attrs["perms"] |
| inputs[1] = clone_and_reshape_tensor(transpose_op.inputs[0], perms, False) |
| |
| if weights_producer_op.type == Op.Reshape: |
| # remove reshape op such that the weight op will a const op |
| reshape_op = weights_producer_op |
| # remove identity ops directly connected to the input of the reshape op |
| reshape_producer_op = _remove_producing_identity_op(reshape_op.inputs[0].ops[0]) |
| reshape_op.inputs[0] = _check_and_get_connection( |
| reshape_producer_op, reshape_op.inputs[0] |
| ) # update connection |
| |
| tens = reshape_op.inputs[0].clone("_reshape", False) |
| tens.values = np.reshape(tens.values, reshape_op.ofm.shape) |
| tens.shape = reshape_op.ofm.shape |
| tens._original_shape = tens.shape |
| tens.bandwidth_shape = tens.shape |
| tens.storage_shape = tens.shape |
| |
| tmp_op = Operation(Op.Const, tens.name) |
| tmp_op.set_output_tensor(tens) |
| inputs[1] = tens |
| |
| assert inputs[1].values is not None |
| |
| if op.type == Op.FullyConnected: |
| inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0), False) |
| elif op.type.is_conv2d_op(): |
| inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0), False) |
| elif op.type.is_depthwise_conv2d_op(): |
| inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 0, 3), 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], (0,), True) |
| |
| op.explicit_scaling = ExplicitScaling(False, [0], [1]) # no scaling |
| |
| if attr_serializer is not None: |
| op.attrs = attr_serializer.deserialize(op_data) |
| |
| if "pad" in op.attrs: |
| op.attrs["padding"] = op.attrs["pad"] # attribute was renamed to padding |
| padding = op.attrs["padding"] # [top, bottom, left, right] |
| op.attrs["explicit_padding"] = ( |
| padding[0], |
| padding[2], |
| padding[1], |
| padding[3], |
| ) # [top, left, bottom, right] |
| if "stride" in op.attrs: |
| stride = op.attrs["stride"] |
| if len(stride) == 2: |
| op.attrs["strides"] = (1, stride[0], stride[1], 1) |
| del op.attrs["stride"] |
| else: |
| # TODO CONV3D more to be done.... |
| print("Unsupported kernel dimensions: ", len(stride)) |
| assert False |
| if "dilation" in op.attrs: |
| dilation = op.attrs["dilation"] |
| if len(dilation) == 2: |
| op.attrs["dilation"] = (1, dilation[0], dilation[1], 1) |
| elif len(dilation) == 3: |
| # TODO CONV3D more to be done.... |
| op.attrs["dilation"] = (dilation[0], dilation[1], dilation[2], 1) |
| if "kernel" in op.attrs: |
| kernel = op.attrs["kernel"] |
| if len(kernel) == 2: |
| op.attrs["ksize"] = (1, kernel[0], kernel[1], 1) |
| else: |
| # TODO CONV3D more to be done.... |
| print("Unsupported kernel dimensions: ", len(kernel)) |
| assert False |
| if "shift" in op.attrs and op.type == Op.Mul: |
| shift = op.attrs["shift"] |
| if shift != 0: |
| op.explicit_scaling = ExplicitScaling(False, [shift], [1]) |
| if op.type.is_depthwise_conv2d_op(): |
| op.attrs["depth_multiplier"] = op.weights.shape[3] |
| if op.type == Op.SplitSliceRead: |
| op.read_offsets[0] = Shape4D.from_list(list(op.attrs["start"]), 0) |
| op.read_shapes[0] = op.attrs["size"] |
| |
| # TODO tensor zero points currently set here |
| # zero points part of Rescale operation, handled in tosa_graph_optimizer |
| if "input_zp" in op.attrs: |
| self.set_tensor_zp(op.ifm, op.attrs["input_zp"]) |
| if "weight_zp" in op.attrs: |
| self.set_tensor_zp(op.weights, op.attrs["weight_zp"]) |
| if "output_zp" in op.attrs: |
| self.set_tensor_zp(op.ofm, op.attrs["output_zp"]) |
| if "a_zp" in op.attrs: |
| self.set_tensor_zp(op.ifm, op.attrs["a_zp"]) |
| if "b_zp" in op.attrs: |
| self.set_tensor_zp(op.ifm2, op.attrs["b_zp"]) |
| |
| def parse_tensor(self, tens_data): |
| name = decode_str(tens_data.Name()) |
| np_shape = tens_data.ShapeAsNumpy() |
| shape = list(np_shape) if type(np_shape) is np.ndarray else [] |
| tens_dtype = tens_data.Type() |
| dtype = datatype_map[tens_dtype] |
| |
| tens = Tensor(shape, dtype, name) |
| |
| # Initialize quantization parameters |
| tens.quantization = QuantizationParameters() |
| |
| 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.int48): |
| tens.quantization.quant_min = -(1 << (dtype.bits - 1)) |
| tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1 |
| |
| tens.values = None |
| |
| data_length = tens_data.DataLength() |
| if data_length != 0: |
| data_as_numpy = tens_data.DataAsNumpy() |
| if tens_dtype in datatype_map_numpy: |
| np_dtype = datatype_map_numpy[tens_dtype] |
| |
| # TOSA pads the tensor data |
| shape_elements = shape_num_elements(shape) |
| values = np.array(data_as_numpy.view(np_dtype)) |
| values = values[0:shape_elements] |
| tens.values = values.reshape(shape) |
| else: |
| # int48 is only expected as an accumulated data/output format, int4 not supported |
| print(f"Error: unsupported/unexpected Tensor type {dtype}, with data") |
| assert False |
| |
| return tens |
| |
| def set_tensor_zp(self, tens, zp): |
| if tens.quantization.zero_point is None: |
| tens.quantization.zero_point = zp |
| elif tens.quantization.zero_point != zp: |
| print("Error: Setting tensor zp not possible, tensor already has different zero point") |
| assert False |
| |
| |
| class TosaGraph: |
| 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" |
| tosa_graph = TG.GetRootAsTosaGraph(buf, 0) |
| |
| parsing_step = "parsing version" |
| self.check_version(tosa_graph) |
| |
| parsing_step = "parsing single main region" |
| assert 1 == tosa_graph.RegionsLength() |
| assert b"main" == tosa_graph.Regions(0).Name() |
| |
| parsing_step = "parsing blocks length" |
| self.subgraphs = [] |
| for b_idx in range(tosa_graph.Regions(0).BlocksLength()): |
| parsing_step = f"parsing block {b_idx}" |
| self.subgraphs.append(TosaSubgraph(self, tosa_graph.Regions(0).Blocks(b_idx))) |
| |
| self.nng = Graph(self.name, self.batch_size) |
| for tosa_sg in self.subgraphs: |
| sg = Subgraph(tosa_sg.name) |
| sg.original_inputs = tosa_sg.inputs # Preserve the original input order |
| sg.output_tensors = tosa_sg.outputs |
| self.nng.subgraphs.append(sg) |
| |
| except (struct.error, TypeError, RuntimeError) as e: |
| print(f'Error: Invalid .tosa file. Got "{e}" while {parsing_step}.') |
| sys.exit(1) |
| |
| def check_version(self, tosa_graph): |
| version = tosa_graph.Version() |
| version_str = f"{version._Major()}.{version._Minor()}.{version._Patch()}" |
| if version_str not in ("0.80.0", "0.80.1"): |
| print(f"Unsupported TOSA version: {version_str}") |
| assert False |
| |
| |
| def read_tosa(filename, batch_size, feed_dict, output_node_names, initialisation_nodes): |
| tosa_graph = TosaGraph(filename, batch_size, feed_dict, output_node_names, initialisation_nodes) |
| nng = tosa_graph.nng |
| nng.refresh_after_modification() |
| return nng |