| # 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 .tensor import Tensor, MemArea |
| from .nn_graph import TensorPurpose, PassPlacement |
| from .high_level_command_stream_generator import calc_allowed_ofm_ifm_overlap_for_cascaded_pass |
| |
| |
| class LiveRange: |
| def __init__(self, tens): |
| 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 = "" |
| |
| 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): |
| if op_time == -1: |
| return |
| op_time_start = op_time |
| op_time_end = op_time + 1 |
| |
| self.start_time = min(self.start_time, op_time_start) |
| self.end_time = max(self.end_time, op_time_end) |
| |
| 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 unaddressed tensors in LiveRange |
| for tens in self.tensors: |
| if tens.address == 0: |
| tens.address = address |
| # Also need to set the address to the tensor's cpu/npu clones |
| if tens.cpu_tensor != None: |
| tens.cpu_tensor.address = address |
| if tens.npu_tensor != None: |
| tens.npu_tensor.address = address |
| |
| def get_alignment(self): |
| # Get max alignment of LiveRange's tensors |
| if self.tensors: |
| alignment = 0 |
| for tens in self.tensors: |
| alignment = max(alignment, tens.alignment) |
| |
| return alignment |
| |
| return Tensor.AllocationQuantum |
| |
| |
| def merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area): |
| 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 the input or output tensor is tied to a Cpu tensor, i.e. a subgraph input |
| # or output, fuse the live-range with the Cpu tensors' live-range instead. |
| input_tensor = input_tensor.cpu_tensor if input_tensor.cpu_tensor != None else input_tensor |
| output_tensor = output_tensor.cpu_tensor if output_tensor.cpu_tensor != None else output_tensor |
| if not tensor_should_be_ignored(input_tensor, target_mem_area) and not tensor_should_be_ignored( |
| output_tensor, target_mem_area |
| ): |
| lr_graph.fuse_ranges(input_tensor, output_tensor) |
| |
| |
| class LiveRangeGraph: |
| def __init__(self): |
| self.ranges = {} # tens -> range |
| self.allowed_overlaps = {} # (tens,tens) -> overlap_int |
| self.ignore_tensors = set() |
| self.processed_subgraphs = set() |
| self.current_time = 0 |
| |
| def get_or_create_range(self, tens): |
| for rng in self.ranges.values(): |
| # Return the live range of the tensor (or it's cpu/npu clone) |
| if any(tensor in rng.tensors for tensor in [tens, tens.npu_tensor, tens.cpu_tensor]): |
| return rng |
| |
| # No live range found for the tensor, create a new one |
| rng = LiveRange(tens) |
| self.ranges[tens] = 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 extract_live_ranges_from_passes( |
| sg, |
| target_mem_area, |
| mark_output_tensors_overlapping_with_input_tensors=False, |
| 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) |
| |
| def tensor_should_be_ignored(tens, target_mem_area): |
| if tens.mem_area != target_mem_area: |
| 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 |
| |
| # Merge only memory operations in the NPU subgraphs |
| if sg.placement == PassPlacement.Npu: |
| merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area) |
| |
| for idx, ps in enumerate(sg.passes): |
| ps.time = 2 * idx |
| |
| time_for_pass = ps.time |
| |
| for tens in ps.inputs: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(time_for_pass) |
| |
| for tens in ps.intermediates: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(time_for_pass) |
| |
| for tens in ps.outputs: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| output_time = time_for_pass |
| if not mark_output_tensors_overlapping_with_input_tensors and ps.is_element_wise: |
| output_time += 1 |
| rng.mark_usage(output_time) |
| |
| end_time = len(sg.passes) * 2 |
| for tens in sg.output_tensors: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| 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, |
| mark_output_tensors_overlapping_with_input_tensors=False, |
| use_ifm_ofm_overlap=True, |
| ignore_subgraph_input_output_tensors=False, |
| lr_graph=None, |
| ): |
| if lr_graph == 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) |
| |
| def tensor_should_be_ignored(tens, target_mem_area): |
| if tens.mem_area != target_mem_area: |
| 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 |
| |
| # Merge only memory operations in the NPU subgraphs |
| if sg.placement == PassPlacement.Npu: |
| merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area) |
| |
| for cps in sg.cascaded_passes: |
| cps.time = lr_graph.current_time |
| |
| time_for_pass = cps.time |
| |
| is_element_wise = cps.is_element_wise |
| |
| for tens in cps.inputs: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(time_for_pass) |
| |
| cps_primary_op = cps.passes[0].primary_op |
| if cps_primary_op and cps_primary_op.type == "NpuOp" and target_mem_area in set((MemArea.Sram, MemArea.Dram)): |
| # 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. |
| npu_sg = cps_primary_op.attrs["subgraph"] |
| lr_graph = extract_live_ranges_from_cascaded_passes( |
| npu_sg, |
| target_mem_area, |
| mark_output_tensors_overlapping_with_input_tensors, |
| use_ifm_ofm_overlap, |
| False, |
| 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: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(time_for_pass) |
| |
| for tens in cps.outputs: |
| if tensor_should_be_ignored(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| output_time = time_for_pass |
| if not mark_output_tensors_overlapping_with_input_tensors and is_element_wise: |
| output_time += 1 |
| rng.mark_usage(output_time) |
| |
| if use_ifm_ofm_overlap: |
| # fill allowed overlap for ifm and ofm tensor |
| ifm_tensor = cps.passes[0].ifm_tensor |
| ofm_tensor = cps.passes[-1].ofm_tensor |
| if ( |
| ifm_tensor is not None |
| and ofm_tensor is not None |
| and not tensor_should_be_ignored(ifm_tensor, target_mem_area) |
| and not tensor_should_be_ignored(ofm_tensor, target_mem_area) |
| ): |
| lr_graph.allowed_overlaps[(ifm_tensor, ofm_tensor)] = calc_allowed_ofm_ifm_overlap_for_cascaded_pass( |
| cps |
| ) |
| |
| 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(tens, target_mem_area): |
| continue |
| rng = lr_graph.get_or_create_range(tens) |
| rng.mark_usage(end_time) |
| |
| # Add subgraph to set of processed subgraphs |
| lr_graph.processed_subgraphs.add(sg) |
| return lr_graph |