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Diffstat (limited to 'ethosu/vela/live_range.py')
| -rw-r--r-- | ethosu/vela/live_range.py | 324 |
1 files changed, 324 insertions, 0 deletions
diff --git a/ethosu/vela/live_range.py b/ethosu/vela/live_range.py new file mode 100644 index 00000000..24f1f64c --- /dev/null +++ b/ethosu/vela/live_range.py @@ -0,0 +1,324 @@ +# 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 |