| # 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: |
| # Build a live range graph for tensors in one or more subgraphs. Used for tensor allocation as well as in the scheduler. |
| # Can work with either a pass packed subgraph or a scheduled subgraph. |
| from typing import List |
| |
| import numpy as np |
| |
| from .nn_graph import PassPlacement |
| from .operation import Op |
| from .tensor import MemArea |
| from .tensor import MemType |
| from .tensor import Tensor |
| from .tensor import TensorPurpose |
| |
| |
| class LiveRange: |
| def __init__(self, tens, alignment): |
| self.tensors = [] # Tensors that are assigned to the same LiveRange will be allocated to the same address |
| self.start_time = 99999999999 |
| self.end_time = -1 |
| self.size = 0 |
| self.name = "" |
| self.alignment = alignment |
| self.mem_area = tens.mem_area if tens else MemArea.Unknown |
| |
| if tens: |
| self.add_tensor(tens) |
| |
| def __str__(self): |
| return "<live_range.LiveRange: '%s' start_time=%s, end_time=%s>" % (self.name, self.start_time, self.end_time) |
| |
| __repr__ = __str__ |
| |
| def add_tensor(self, tens): |
| if self.size == 0: |
| self.size = tens.storage_size() |
| self.name = tens.name # LiveRange will be named after the first tensor added |
| else: |
| assert ( |
| self.size >= tens.storage_size() |
| ), "Tensors assigned to the same LiveRange need to fit the size of the LiveRange." |
| |
| self.tensors.append(tens) |
| |
| def mark_usage(self, op_time, op_length=1): |
| op_time_start = max(op_time, 0) |
| op_time_end = op_time + op_length |
| if op_time_end <= op_time_start: |
| return |
| |
| self.start_time = min(self.start_time, op_time_start) |
| self.end_time = max(self.end_time, op_time_end) |
| |
| def set_buffer_size(self, buffer_size): |
| self.size = buffer_size |
| self.mem_area = MemArea.Sram |
| |
| def overlaps_ranges(self, other): |
| return max(self.start_time, other.start_time) < min(self.end_time, other.end_time) |
| |
| def overlaps_address(self, other): |
| # Returns the first pair of tensors in this LiveRange and 'other' which have |
| # overlapping addresses |
| for tens in self.tensors: |
| for other_tens in other.tensors: |
| if max(tens.address, other_tens.address) < min( |
| tens.address + self.size, other_tens.address + other.size |
| ): |
| return True, tens, other_tens |
| |
| return False, None, None |
| |
| def __lt__(self, other): |
| if self.start_time != other.start_time: |
| return self.start_time < other.start_time |
| if self.end_time != other.end_time: |
| return self.end_time < other.end_time |
| if self.size != other.size: |
| return self.size < other.size |
| return self.name < other.name |
| |
| def set_address(self, address): |
| # Set address of all tensors in LiveRange |
| for tens in self.tensors: |
| tens.address = address |
| |
| return address |
| |
| def get_alignment(self): |
| return self.alignment |
| |
| def set_alignment(self, alignment): |
| self.alignment = max(self.alignment, alignment) |
| |
| |
| class LiveRangeGraph: |
| def __init__(self): |
| self.lrs: List[LiveRange] = [] # List of all created ranges |
| self.ranges = {} # tens -> range |
| self.ignore_tensors = set() |
| self.processed_subgraphs = set() |
| self.current_time = 0 |
| self.end_time = None |
| |
| def get_or_create_range(self, tens, alignment=Tensor.AllocationQuantum): |
| # Return the live range of the tensor (or any of its clones) |
| for existing_tensor, rng in self.ranges.items(): |
| if tens.equivalent(existing_tensor): |
| rng.set_alignment(alignment) |
| return rng |
| |
| # No live range found for the tensor, create a new one |
| rng = LiveRange(tens, alignment) |
| self.ranges[tens] = rng |
| self.lrs.append(rng) |
| return rng |
| |
| def fuse_ranges(self, in_tens, out_tens): |
| live_range = self.get_or_create_range(in_tens) |
| assert out_tens not in self.ranges, out_tens |
| live_range.add_tensor(out_tens) |
| self.ranges[out_tens] = live_range |
| return live_range |
| |
| def update_endtime(self): |
| self.end_time = 0 |
| for rng in self.ranges.values(): |
| self.end_time = max(self.end_time, rng.end_time) |
| return self.end_time + 1 |
| |
| def get_temporal_memory_usage(self, target_mem_area): |
| if not self.end_time: |
| self.update_endtime() |
| usage = np.zeros(self.end_time, dtype=np.int32) |
| for rng in self.ranges.values(): |
| if rng.mem_area == target_mem_area: |
| # End time is inclusive |
| usage[rng.start_time : rng.end_time + 1] += rng.size |
| |
| return usage |
| |
| |
| def tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| if tens.mem_area != target_mem_area or tens.mem_type not in target_mem_type_set: |
| return True |
| if tens in lr_graph.ignore_tensors: |
| return True |
| if tens.name.endswith("reshape_shape_npu"): |
| # Reshape tensor, no need to allocate |
| lr_graph.ignore_tensors.add(tens) |
| return True |
| return False |
| |
| |
| # Tries merging of ifm/ofm live ranges for memory only ops and elementwise ops |
| def merge_op_ranges(sg, lr_graph, target_mem_area, target_mem_type_set): |
| for ps in sg.passes: |
| if ps.placement == PassPlacement.MemoryOnly: |
| # For memory only passes, e.g. Reshape. Add input and output tensor to the same LiveRange |
| input_tensor = ps.inputs[0] |
| output_tensor = ps.outputs[0] |
| if not tensor_should_be_ignored(lr_graph, input_tensor, target_mem_area, target_mem_type_set) and not ( |
| tensor_should_be_ignored(lr_graph, output_tensor, target_mem_area, target_mem_type_set) |
| ): |
| lr_graph.fuse_ranges(input_tensor, output_tensor) |
| elif ps.is_element_wise: |
| merge_elementwise_op_ranges(ps, lr_graph, target_mem_area, target_mem_type_set) |
| |
| |
| # Tries to merge ifm/ofm live of elementwise op |
| def merge_elementwise_op_ranges(ps, lr_graph, target_mem_area, target_mem_type_set): |
| elem_op = None |
| for op in ps.ops: |
| if op.type.is_elementwise_op(): |
| assert elem_op is None |
| elem_op = op |
| |
| if elem_op is not None and not tensor_should_be_ignored( |
| lr_graph, elem_op.ofm, target_mem_area, target_mem_type_set |
| ): |
| # Check if overwriting the inputs can be allowed |
| if elem_op.type not in (Op.SHL, Op.SHR): |
| inps = [] |
| if ( |
| elem_op.ifm is not None |
| and elem_op.ifm.shape != [] |
| and elem_op.ifm.mem_area == target_mem_area |
| and elem_op.ifm.mem_type in target_mem_type_set |
| ): |
| inps.append(elem_op.ifm) |
| if ( |
| elem_op.ifm2 is not None |
| and elem_op.ifm2.shape != [] |
| and elem_op.ifm2.mem_area == target_mem_area |
| and elem_op.ifm.mem_type in target_mem_type_set |
| ): |
| inps.append(elem_op.ifm2) |
| |
| if len(inps) > 0: |
| for i, inp in enumerate(inps): |
| # check input format, dtype, broadcasting or if there are more input consumers |
| if ( |
| inp.format == elem_op.ofm.format |
| and inp.dtype == elem_op.ofm.dtype |
| and elem_op.ifm_shapes[i] == elem_op.ofm_shapes[0] |
| and (len(inp.consumer_list) == 1 and len(inp.ops) == 1) |
| ): |
| lr_graph.fuse_ranges(inp, elem_op.ofm) |
| break |
| |
| |
| def extract_live_ranges_from_passes( |
| sg, target_mem_area, target_mem_type_set=None, ignore_subgraph_input_output_tensors=False, |
| ): |
| lr_graph = LiveRangeGraph() |
| |
| if ignore_subgraph_input_output_tensors: |
| lr_graph.ignore_tensors.update(sg.input_tensors) |
| lr_graph.ignore_tensors.update(sg.output_tensors) |
| |
| if target_mem_type_set is None: |
| target_mem_type_set = set((MemType.Scratch, MemType.Scratch_fast)) |
| |
| # Try to merge live ranges of operations in the NPU subgraphs |
| if sg.placement == PassPlacement.Npu: |
| merge_op_ranges(sg, lr_graph, target_mem_area, target_mem_type_set) |
| |
| for idx, ps in enumerate(sg.passes): |
| ps.time = 2 * idx |
| |
| time_for_pass = ps.time |
| |
| for tens in ps.inputs + ps.intermediates + ps.outputs: |
| if tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(time_for_pass) |
| |
| end_time = len(sg.passes) * 2 |
| for tens in sg.output_tensors: |
| if tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(end_time) |
| |
| return lr_graph |
| |
| |
| def extract_live_ranges_from_cascaded_passes( |
| sg, |
| target_mem_area, |
| target_mem_type_set, |
| ignore_subgraph_input_output_tensors=False, |
| lr_graph=None, |
| cpu_tensor_alignment=Tensor.AllocationQuantum, |
| ): |
| if lr_graph is None: |
| lr_graph = LiveRangeGraph() |
| |
| if sg in lr_graph.processed_subgraphs: |
| # if subgraph has been processed already, return the lr_graph as is |
| return lr_graph |
| |
| if ignore_subgraph_input_output_tensors: |
| lr_graph.ignore_tensors.update(sg.input_tensors) |
| lr_graph.ignore_tensors.update(sg.output_tensors) |
| |
| # Try to merge live ranges of operations in the NPU subgraphs |
| if sg.placement == PassPlacement.Npu: |
| merge_op_ranges(sg, lr_graph, target_mem_area, target_mem_type_set) |
| |
| for cps in sg.cascaded_passes: |
| cps.time = lr_graph.current_time |
| |
| time_for_pass = cps.time |
| |
| for tens in cps.inputs: |
| if tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| continue |
| rng = lr_graph.get_or_create_range(tens, cpu_tensor_alignment) |
| rng.mark_usage(time_for_pass) |
| |
| cps_primary_op = cps.passes[0].primary_op |
| |
| if ( |
| cps_primary_op |
| and cps_primary_op.type == Op.CustomNpuOp |
| and MemType.Permanent_CPU not in target_mem_type_set |
| ): |
| # If the primary-op is an NpuOp that means this is where an Npu subgraph |
| # is called. Go into said subgraph and extract live ranges before continuing. |
| # Use default allocation alignment of 16 for Npu tensors |
| npu_sg = cps_primary_op.attrs["subgraph"] |
| lr_graph = _extract_live_ranges_from_schedule(npu_sg, target_mem_area, target_mem_type_set, lr_graph) |
| # Set the new time after handling the Npu subgraph |
| time_for_pass = lr_graph.current_time |
| cps.time = time_for_pass |
| |
| for tens in cps.intermediates + cps.outputs: |
| if tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| continue |
| rng = lr_graph.get_or_create_range(tens, cpu_tensor_alignment) |
| rng.mark_usage(time_for_pass) |
| |
| lr_graph.current_time += 2 |
| |
| end_time = 0 |
| for rng in lr_graph.ranges.values(): |
| # Find the maximum end time of all live-ranges in the graph |
| end_time = max(end_time, rng.end_time) |
| |
| for tens in sg.output_tensors: |
| if tensor_should_be_ignored(lr_graph, tens, target_mem_area, target_mem_type_set): |
| continue |
| rng = lr_graph.get_or_create_range(tens, cpu_tensor_alignment) |
| rng.mark_usage(end_time) |
| |
| # Add subgraph to set of processed subgraphs |
| lr_graph.processed_subgraphs.add(sg) |
| return lr_graph |
| |
| |
| def create_linear_live_range_graph(sg, target_mem_area, target_mem_type_set, lr_graph): |
| assert lr_graph is not None |
| sg_time = lr_graph.current_time |
| for ps in sg.passes: |
| for tens in ps.inputs + ps.outputs + ps.intermediates: |
| if tens.purpose == TensorPurpose.Weights or tensor_should_be_ignored( |
| lr_graph, tens, target_mem_area, target_mem_type_set |
| ): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(sg_time) |
| |
| for sched_op, op_info in sg.schedule.cost_map.items(): |
| for tensor in [op_info.npu_weights_tensor, op_info.npu_scales_tensor]: |
| if tensor and not (tensor_should_be_ignored(lr_graph, tensor, target_mem_area, target_mem_type_set)): |
| rng = lr_graph.get_or_create_range(tensor) |
| rng.mark_usage(sg_time) |
| |
| lr_graph.current_time += 1 |
| return lr_graph |
| |
| |
| def _extract_live_ranges_from_schedule(sg, target_mem_area, target_mem_type_set, lr_graph): |
| time_for_cascade = {} |
| for sched_op in sg.sched_ops: |
| op_info = sg.schedule.cost_map[sched_op] |
| cascade = op_info.cascade |
| cascade_info = sg.schedule.cascades.get(cascade, None) |
| |
| time_to_set = time_for_cascade.get(cascade, lr_graph.current_time) |
| |
| op_info.time_index = time_to_set |
| |
| # Mark usage for all tensors related to this Pass |
| ps = sched_op.parent_ps |
| for tens in ps.inputs + ps.outputs + ps.intermediates: |
| if ( |
| target_mem_area == MemArea.Sram |
| and cascade_info |
| and tens == ps.ifm_tensor |
| and sched_op in cascade_info.buffers |
| ): |
| # This tensor is a rolling buffer in a cascade and the size of the LiveRange needs to be modified |
| # for enabling temporal memory snapshots without modifying the original Tensor |
| rng = lr_graph.get_or_create_range(tens) |
| rng.set_buffer_size(cascade_info.buffers[sched_op].elements() * sched_op.ifm.dtype.size_in_bytes()) |
| elif ( |
| tens.purpose == TensorPurpose.Weights |
| or tens.purpose == TensorPurpose.FSBias |
| or tens.mem_type not in target_mem_type_set |
| or tens.mem_area != target_mem_area |
| ): |
| continue |
| |
| else: |
| rng = lr_graph.get_or_create_range(tens) |
| |
| rng.mark_usage(time_to_set) |
| |
| weight_tens = op_info.buffered_weight_tensor |
| if weight_tens and weight_tens.mem_type in target_mem_type_set and weight_tens.mem_area == target_mem_area: |
| rng = lr_graph.get_or_create_range(weight_tens) |
| if weight_tens.pre_buffer: |
| rng.mark_usage(time_to_set - 1, 2) |
| else: |
| rng.mark_usage(time_to_set) |
| |
| if time_to_set == lr_graph.current_time: |
| lr_graph.current_time += 2 |
| |
| if cascade != 0: |
| time_for_cascade[cascade] = time_to_set |
| |
| end_time = lr_graph.update_endtime() |
| |
| for tens in sg.output_tensors: |
| if tens.mem_type not in target_mem_type_set or tens.mem_area != target_mem_area: |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(end_time) |
| |
| return lr_graph |