| # 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: |
| # The scheduler costs various strategies for scheduling the network in order to select the block configuration. |
| import copy |
| import enum |
| from functools import lru_cache |
| |
| import numpy as np |
| |
| from . import live_range |
| from . import npu_performance |
| from . import stats_writer |
| from .high_level_command_stream_generator import calc_allowed_ofm_ifm_overlap_for_pass_list |
| from .nn_graph import CascadedPass |
| from .nn_graph import PassPlacement |
| from .nn_graph import SchedulerRewrite |
| from .nn_graph import SchedulingStrategy |
| from .npu_performance import make_bandwidth_array |
| from .npu_performance import make_cycles_array |
| from .npu_performance import make_macs_array |
| from .npu_performance import make_metrics_arrays |
| from .npu_performance import PassCycles |
| from .operation import NpuBlockType |
| from .shared_buffer_allocation import find_block_configs_suitable_for_pass_and_shared_buffer |
| from .shared_buffer_allocation import shared_buffer_allocation_for_pass_and_block_config |
| from .tensor import MemArea |
| from .tensor import MemType |
| from .tensor import TensorFormat |
| from .tensor import TensorPurpose |
| from .tensor import TensorSubPurpose |
| from .numeric_util import full_shape |
| |
| class ParetoMetric(enum.Enum): |
| BwCycMem = 1 |
| BwCycMemBlkH = 2 |
| |
| def __str__(self): |
| return self.name |
| |
| |
| class SchedulerOptions: |
| def __init__( |
| self, |
| use_cascading=True, |
| use_ifm_ofm_overlap=True, |
| verbose_schedule=False, |
| verbose_pareto_frontier_schedules=False, |
| use_ifm_streaming=True, |
| pareto_metric=ParetoMetric.BwCycMem, |
| use_nhcwb16_between_cascaded_passes=True, |
| ): |
| self.use_cascading = use_cascading |
| self.use_ifm_ofm_overlap = use_ifm_ofm_overlap |
| self.verbose_schedule = verbose_schedule |
| self.verbose_pareto_frontier_schedules = verbose_pareto_frontier_schedules |
| self.use_ifm_streaming = use_ifm_streaming |
| self.pareto_metric = pareto_metric |
| self.use_nhcwb16_between_cascaded_passes = use_nhcwb16_between_cascaded_passes |
| |
| def __str__(self): |
| return type(self).__name__ + ": " + str(self.__dict__) |
| |
| __repr__ = __str__ |
| |
| |
| class Strategy: |
| __slots__ = "strat", "param", "passes", "block_configs", "rewrite_list", "bws", "macs", "cycles", "sram_used" |
| |
| def __init__(self, strat, param, passes, block_configs, rewrite_list, bws, macs, cycles, sram_used): |
| self.strat = strat |
| self.param = param |
| self.passes = passes |
| self.block_configs = block_configs |
| self.rewrite_list = ( |
| rewrite_list # list of (SchedulerRewrite, Tensor, new sub purpose, purpose param a, purpose param b, pass) |
| ) |
| self.bws = bws |
| self.macs = macs |
| self.cycles = cycles |
| self.sram_used = sram_used |
| |
| def __eq__(self, other): |
| if self.strat != other.strat: |
| return False |
| if self.param != other.param: |
| return False |
| if self.block_configs != other.block_configs: |
| return False |
| if self.passes != other.passes: |
| return False |
| if (self.bws != other.bws).any(): |
| return False |
| if (self.macs != other.macs).any(): |
| return False |
| if (self.cycles != other.cycles).any(): |
| return False |
| if self.sram_used != other.sram_used: |
| return False |
| return True |
| |
| def empty(self): |
| return not self.passes |
| |
| def key(self): |
| return self.passes[-1] |
| |
| def clone(self): |
| return Strategy( |
| self.strat, |
| self.param, |
| self.passes, |
| self.block_configs, |
| self.rewrite_list, |
| self.bws, |
| self.macs, |
| self.cycles, |
| self.sram_used, |
| ) |
| |
| def __str__(self): |
| return "<scheduler.Strategy: %s %s %s %s %s %s %s>" % ( |
| self.strat, |
| self.passes, |
| self.rewrite_list, |
| self.bws, |
| self.macs, |
| self.cycles, |
| self.sram_used, |
| ) |
| |
| __repr__ = __str__ |
| |
| |
| class StrategySet: |
| __slots__ = "strats", "bws", "macs", "cycles", "max_sram_used", "total_sram_used" |
| |
| def __init__(self, strats=None): |
| if strats is None: |
| strats = dict() |
| self.strats = strats # final pass in packed pass -> Strategy |
| self.bws, self.macs, self.cycles = make_metrics_arrays() |
| self.max_sram_used = 0 |
| self.total_sram_used = 0 |
| |
| def update_statistics(self): |
| self.bws = make_bandwidth_array() |
| self.max_sram_used = 0 |
| for ps, strat in self.strats.items(): |
| self.bws += strat.bws |
| self.macs += strat.macs |
| self.cycles += strat.cycles |
| self.max_sram_used = max(self.max_sram_used, strat.sram_used) |
| self.total_sram_used += strat.sram_used |
| |
| def clone_add_strategy(self, new_strat): |
| key = new_strat.key() |
| if key in self.strats: |
| assert new_strat == self.strats[key] |
| return self |
| else: |
| new_strats = dict(self.strats) |
| new_strats[key] = new_strat |
| new_set = StrategySet(new_strats) |
| new_set.bws = self.bws + new_strat.bws |
| new_set.macs = self.macs + new_strat.macs |
| new_set.cycles = self.cycles + new_strat.cycles |
| new_set.max_sram_used = max(self.max_sram_used, new_strat.sram_used) |
| new_set.total_sram_used = self.total_sram_used + new_strat.sram_used |
| return new_set |
| |
| def __eq__(self, other): |
| if (self.bws != other.bws).any(): |
| return False |
| if (self.macs != other.macs).any(): |
| return False |
| if (self.cycles != other.cycles).any(): |
| return False |
| if self.max_sram_used != other.max_sram_used: |
| return False |
| if self.total_sram_used != other.total_sram_used: |
| return False |
| if self.strats != other.strats: |
| return False |
| return True |
| |
| def __str__(self): |
| return "<scheduler.StrategySet: max_sram_used=%s passes_covered=%s>" % ( |
| self.max_sram_used, |
| list(ps.name for ps in self.strats), |
| ) |
| |
| __repr__ = __str__ |
| |
| |
| empty_strategy = Strategy( |
| SchedulingStrategy.Unknown, None, [], [], [], make_bandwidth_array(), make_macs_array(), make_cycles_array(), 0 |
| ) |
| INFINITY = 1e30 |
| |
| ABORT_SEARCH = [] |
| |
| |
| def flatten_list_of_lists(lstlst): |
| lst = [] |
| for v in lstlst: |
| lst.extend(v) |
| return lst |
| |
| |
| class DynamicProgrammingScheduler: |
| def __init__(self, nng, sg, arch, sram_limit, options: SchedulerOptions): |
| self.nng = nng |
| self.sg = sg |
| self.arch = arch |
| self.sram_limit = sram_limit |
| self.options = copy.copy(options) |
| self.use_cascading = options.use_cascading |
| |
| if self.arch.feature_map_storage_mem_area != MemArea.Sram: |
| self.use_ifm_ofm_overlap = False # force off IFM/OFM overlap if IFMs and OFMs are not in the SRAM |
| else: |
| self.use_ifm_ofm_overlap = options.use_ifm_ofm_overlap |
| |
| self.verbose_schedule = options.verbose_schedule |
| self.verbose_pareto_frontier_schedules = options.verbose_pareto_frontier_schedules |
| self.mem_area = MemArea.Sram |
| |
| self.bandwidth_weights = arch.bandwidth_weights |
| self.cycles_weight = arch.cycles_weight |
| self.max_sram_used_weight = arch.max_sram_used_weight |
| |
| self.n_combinations_searched = 0 |
| |
| self.feature_maps_not_in_fast_storage = ( |
| arch.tensor_storage_mem_area[TensorPurpose.FeatureMap] != arch.fast_storage_mem_area |
| ) |
| |
| self.pareto_max_candidates = 16 |
| |
| self.ifm_stream_npu_blocks = set( |
| (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise, NpuBlockType.Pooling,) |
| ) |
| |
| num_pareto_metrics = 4 |
| view_values = ",".join(["d"] * num_pareto_metrics) |
| order_values = ["f%d" % (idx,) for idx in range(num_pareto_metrics)] |
| |
| def pareto_metric(self, candidate): |
| strat, strat_set = candidate |
| total_cycles = strat.cycles[PassCycles.Total] + strat_set.cycles[PassCycles.Total] |
| bws = strat.bws + strat_set.bws |
| last_block_height = 0 |
| if self.options.pareto_metric == ParetoMetric.BwCycMemBlkH and len(strat.block_configs) > 0: |
| last_block_height = strat.block_configs[-1][0] |
| |
| return ( |
| np.tensordot(bws, self.bandwidth_weights, axes=3) + total_cycles * self.cycles_weight, |
| strat_set.max_sram_used, |
| strat.sram_used, |
| last_block_height, |
| ) |
| |
| def filter_pareto_frontier(self, candidates, remove_equally_good_candidates): |
| |
| candidates = [cand for cand in candidates if max(cand[0].sram_used, cand[1].max_sram_used) <= self.sram_limit] |
| |
| if len(candidates) <= 1: |
| return candidates |
| assert remove_equally_good_candidates |
| pareto_vals = np.zeros((len(candidates), DynamicProgrammingScheduler.num_pareto_metrics)) |
| ids = np.arange(len(candidates), dtype=np.int32) |
| for idx, cand in enumerate(candidates): |
| pareto_vals[idx] = self.pareto_metric(cand) |
| |
| sort_order = np.argsort( |
| pareto_vals.view(DynamicProgrammingScheduler.view_values), |
| order=DynamicProgrammingScheduler.order_values, |
| axis=0, |
| kind="stable", |
| ).flatten() |
| pareto_vals = pareto_vals[sort_order] |
| ids = ids[sort_order] |
| |
| pareto_frontier = [] |
| while len(ids) > 0: |
| pareto_frontier.append(candidates[ids[0]]) |
| not_dominated_by_first = (pareto_vals < pareto_vals[0]).any(axis=1) |
| ids = ids[not_dominated_by_first] |
| pareto_vals = pareto_vals[not_dominated_by_first] |
| |
| if len(pareto_frontier) > self.pareto_max_candidates: |
| pareto_frontier = self.sort_by_candidate_metric(pareto_frontier) |
| pareto_frontier = pareto_frontier[: self.pareto_max_candidates] |
| |
| return pareto_frontier |
| |
| def candidate_metric(self, candidate): |
| strat, strat_set = candidate |
| max_sram_used = max(strat_set.max_sram_used, strat.sram_used) |
| bws = strat.bws + strat_set.bws |
| total_cycles = strat.cycles[PassCycles.Total] + strat_set.cycles[PassCycles.Total] |
| |
| return ( |
| max_sram_used * self.max_sram_used_weight |
| + np.tensordot(bws, self.bandwidth_weights, axes=3) |
| + total_cycles * self.cycles_weight |
| ) |
| |
| def sort_by_candidate_metric(self, candidate_list): |
| sorted_list = list(sorted(candidate_list, key=self.candidate_metric)) |
| return sorted_list |
| |
| def best_candidate(self, candidate_list): |
| if len(candidate_list) == 0: |
| return ABORT_SEARCH |
| if len(candidate_list) == 1: |
| return candidate_list[0] |
| sorted_list = self.sort_by_candidate_metric(candidate_list) |
| return sorted_list[0] |
| |
| def graduate_strat(self, strat_type, sram_used, old_strat_data): |
| res = [] |
| for old_strat, old_strat_set in old_strat_data: |
| if old_strat.sram_used + sram_used > self.sram_limit: |
| continue # This strategy is bad, drop it |
| if old_strat_set.max_sram_used > self.sram_limit: |
| continue # This strategy is bad, drop it |
| assert old_strat.strat == SchedulingStrategy.Unknown |
| |
| new_strat = old_strat.clone() |
| new_strat.strat = strat_type |
| new_strat.sram_used = old_strat.sram_used + sram_used |
| |
| if self.use_ifm_ofm_overlap: |
| overlap = calc_allowed_ofm_ifm_overlap_for_pass_list( |
| new_strat.strat, new_strat.passes, new_strat.block_configs |
| ) |
| new_strat.sram_used -= overlap |
| |
| new_strat_set = old_strat_set.clone_add_strategy(new_strat) |
| res.append((empty_strategy, new_strat_set)) |
| return self.filter_pareto_frontier(res, remove_equally_good_candidates=True) |
| |
| def append_sram(self, sram_used, old_strat_data): |
| res = [] |
| for old_strat, strat_set in old_strat_data: |
| assert old_strat.strat == SchedulingStrategy.Unknown |
| assert old_strat.sram_used == 0 |
| new_strat = old_strat.clone() |
| new_strat.sram_used = old_strat.sram_used + sram_used |
| |
| res.append((new_strat, strat_set)) |
| return res |
| |
| def append_sram_block_config_performance_metrics(self, sram_used, block_config, metrics, old_strat_data): |
| res = [] |
| for old_strat, strat_set in old_strat_data: |
| assert old_strat.strat == SchedulingStrategy.Unknown |
| new_strat = old_strat.clone() |
| bws, macs, cycles = metrics[:3] |
| |
| new_strat.sram_used = old_strat.sram_used + sram_used |
| new_strat.block_configs = old_strat.block_configs + [block_config] |
| new_strat.bws = old_strat.bws + bws |
| new_strat.macs = old_strat.macs + macs |
| new_strat.cycles = old_strat.cycles + cycles |
| new_strat.bws, new_strat.macs, new_strat.cycles = npu_performance.collate_stats_for_cascaded_pass( |
| self.arch, new_strat.bws, new_strat.macs, new_strat.cycles |
| ) |
| |
| res.append((new_strat, strat_set)) |
| return res |
| |
| def append_sram_pass_block_config_performance_metrics_rewrite_list( |
| self, sram_used, new_pass, block_config, metrics, rewrite_list, old_strat_data |
| ): |
| res = [] |
| for old_strat, strat_set in old_strat_data: |
| assert old_strat.strat == SchedulingStrategy.Unknown |
| new_strat = old_strat.clone() |
| bws, macs, cycles = metrics[:3] |
| new_strat.sram_used = old_strat.sram_used + sram_used |
| new_strat.block_configs = old_strat.block_configs + [block_config] |
| new_strat.bws = old_strat.bws + bws |
| new_strat.macs = old_strat.macs + macs |
| new_strat.cycles = old_strat.cycles + cycles |
| new_strat.passes = old_strat.passes + [new_pass] |
| new_strat.bws, new_strat.macs, new_strat.cycles = npu_performance.collate_stats_for_cascaded_pass( |
| self.arch, new_strat.bws, new_strat.macs, new_strat.cycles |
| ) |
| new_strat.rewrite_list = old_strat.rewrite_list + rewrite_list |
| res.append((new_strat, strat_set)) |
| return res |
| |
| def append_sram_rewrite_list(self, sram_used, rewrite_list, old_strat_data): |
| res = [] |
| for old_strat, strat_set in old_strat_data: |
| assert old_strat.strat == SchedulingStrategy.Unknown |
| new_strat = old_strat.clone() |
| new_strat.sram_used = old_strat.sram_used + sram_used |
| new_strat.rewrite_list = old_strat.rewrite_list + rewrite_list |
| res.append((new_strat, strat_set)) |
| return res |
| |
| def pass_to_strat(self, strat_data): |
| res = {} |
| for strat in strat_data[1].strats.values(): |
| for ps in strat.passes: |
| res[ps] = strat |
| return res |
| |
| def compatible_strats(self, a, b): |
| intersection = a.keys() & b.keys() |
| for k in intersection: |
| if a[k] != b[k]: |
| return False |
| return True |
| |
| def collate_strats_for_passes(self, all_passes): |
| if len(all_passes) == 0: |
| return [(empty_strategy, StrategySet(dict()))] |
| if len(all_passes) == 1: |
| return all_passes[0] # save some space in the common case |
| all_strands = [[self.pass_to_strat(strat_data) for strat_data in strand] for strand in all_passes] |
| prev_combos = [dict()] |
| for j, strand in enumerate(all_strands): |
| new_combos = [] |
| for i, alt in enumerate(strand): |
| for prev in prev_combos: |
| if self.compatible_strats(prev, alt): |
| cmb = dict(prev) |
| cmb.update(all_passes[j][i][1].strats) |
| new_combos.append(cmb) |
| prev_combos = new_combos |
| |
| res = [] |
| for d in prev_combos: |
| s = StrategySet(d) |
| s.update_statistics() |
| res.append((empty_strategy, s)) |
| return res |
| |
| def search_all_but_one_predecessor(self, ps, pred_pass, pred_pass_data): |
| # get the rest of the predecessors |
| other_predecessors = [pred for pred in ps.dag_predecessors if pred != pred_pass] |
| other_predecessor_data = self.search_pass_list(other_predecessors) |
| |
| # pred strat data has an incomplete strategy, which we need |
| # to continue on, whereas the other ones have completed strategies. |
| # we need to merge these, but keep the incomplete strategy too. |
| |
| res = [] |
| for pred_pass_strat, pred_pass_strat_set in pred_pass_data: |
| all_strats = [ |
| [(empty_strategy, pred_pass_strat_set)], # pred strat data but with a dummy empty strategy |
| other_predecessor_data, # this one is fine to use as-is |
| ] |
| collated_strat_data = self.collate_strats_for_passes(all_strats) |
| strat_data = [(pred_pass_strat, strat_set) for _, strat_set in collated_strat_data] |
| res.extend(strat_data) |
| return res |
| |
| def calc_non_local_mem_usage(self): |
| ignore_subgraph_input_output_tensors = self.sg.placement == PassPlacement.Cpu |
| range_set = live_range.extract_live_ranges_from_passes( |
| self.sg, |
| self.mem_area, |
| mark_output_tensors_overlapping_with_input_tensors=True, |
| ignore_subgraph_input_output_tensors=ignore_subgraph_input_output_tensors, |
| ) |
| range_dict = range_set.ranges |
| |
| # find which ranges overlap passes but aren't input/outputs of the passes. |
| # these won't be counted by the dynamic programming search and must be counted in manually. |
| end_pos = max(ps.time for ps in self.sg.passes) + 2 |
| mem_usage = np.zeros(end_pos) + self.sg.base_sram_used |
| non_local_mem_usage = np.zeros(end_pos, dtype=np.int64) |
| |
| for tens, rng in range_dict.items(): |
| storage_size = tens.storage_size() |
| assert tens.mem_area == self.mem_area |
| mem_usage[rng.start_time : rng.end_time] += storage_size |
| |
| for ps in self.sg.passes: |
| local_mem_usage = 0 |
| for tens in ps.inputs + ps.outputs + ps.intermediates: |
| if tens.mem_area != self.mem_area: |
| continue |
| |
| local_mem_usage += tens.storage_size() |
| |
| non_local_mem_usage[ps.time] = mem_usage[ps.time] - local_mem_usage |
| |
| self.non_local_mem_usage = non_local_mem_usage |
| |
| def search(self): |
| self.calc_non_local_mem_usage() |
| starting_passes = [ps for ps in self.sg.passes if not ps.successors] |
| strat_data = self.search_pass_list(starting_passes) |
| |
| _, best_set = self.best_candidate(strat_data) |
| |
| if self.verbose_pareto_frontier_schedules: |
| print( |
| "Scheduler searched %d combinations and found %d candidate schedules along the pareto frontier" |
| % (self.n_combinations_searched, len(strat_data,)) |
| ) |
| for idx, (_, strat_set) in enumerate(strat_data): |
| extra = "" |
| if strat_set == best_set: |
| extra = "(Best candidate)" |
| print("Candidate", idx, extra) |
| memory_used = {MemArea.Sram: strat_set.max_sram_used} |
| stats_writer.print_performance_metrics_for_strat( |
| self.arch, |
| "", |
| strat_set.cycles, |
| strat_set.macs, |
| strat_set.bws, |
| self.nng.batch_size, |
| memory_used, |
| len(self.sg.passes), |
| len(strat_set.strats), |
| ) |
| |
| return best_set |
| |
| def search_pass_list(self, pass_list): |
| all_strats = [] |
| for ps in pass_list: |
| strat = self.search_output(ps) |
| all_strats.append(strat) |
| strat_data = self.collate_strats_for_passes(all_strats) |
| for strd in strat_data: |
| for ps in pass_list: |
| assert ps in strd[1].strats # should have strategies for everything we asked to search |
| return strat_data |
| |
| def search_predecessors(self, ps): |
| |
| # protect against graphs with loops. collate_strats_for_passes will sort this out later so that |
| # we have strats for all passes |
| |
| pass_list = ps.dag_predecessors |
| strat_data = self.search_pass_list(pass_list) |
| |
| return strat_data |
| |
| @lru_cache(maxsize=None) |
| def search_output(self, ps): |
| |
| assert ps in self.sg.passes |
| candidate_list = [] |
| |
| candidate_list.extend(self.search_weight_streaming_output(ps)) |
| |
| if self.options.use_ifm_streaming: |
| candidate_list.extend(self.search_ifm_streaming_output(ps)) |
| |
| best = self.filter_pareto_frontier(candidate_list, remove_equally_good_candidates=True) |
| |
| if not best: |
| print( |
| "Warning: Dynamic search programming algorithm failed for pass %s, invoking fallback strategy" |
| % (ps.name,) |
| ) |
| return self.search_predecessors(ps) |
| |
| return best |
| |
| def search_ifm_streaming_output(self, ps): |
| if ps.placement != PassPlacement.Npu: |
| return ABORT_SEARCH |
| if ps.npu_block_type not in self.ifm_stream_npu_blocks: |
| return ABORT_SEARCH |
| strat_data = self.search_ifm_streaming_body(ps, False) |
| |
| sram_used = self.non_local_mem_usage[ps.time] |
| for tens in ps.outputs: |
| if tens.mem_area == self.mem_area: |
| sram_used += tens.storage_size() |
| |
| return self.graduate_strat(SchedulingStrategy.IfmStream, sram_used, strat_data) |
| |
| @lru_cache(maxsize=None) |
| def search_ifm_streaming_body(self, ps, force_outputs_to_fast_storage): |
| if ps.placement != PassPlacement.Npu: |
| return ABORT_SEARCH |
| if ps.npu_block_type not in self.ifm_stream_npu_blocks: |
| return ABORT_SEARCH |
| ifm_input_search_resuls = self.search_ifm_streaming_input(ps) |
| res = [] |
| |
| base_sram_used = 0 |
| for tens in ps.intermediates: |
| if tens.mem_area == self.mem_area: |
| if tens.purpose == TensorPurpose.Weights: |
| base_sram_used = tens.storage_size(self.arch.weight_estimation_scaling) |
| else: |
| base_sram_used += tens.storage_size() |
| |
| all_block_configs = self.get_block_configs(ps) |
| for block_config in all_block_configs: |
| all_strats = [] |
| |
| if self.use_cascading: |
| all_strats.extend(self.search_ifm_streaming_partial(ps, block_config)) |
| |
| all_strats.extend(ifm_input_search_resuls) |
| |
| rewrite_list = [] |
| sram_used = base_sram_used |
| |
| metrics = npu_performance.performance_metrics_for_pass( |
| self.arch, |
| ps, |
| block_config, |
| rewrite_list=rewrite_list, |
| force_outputs_to_fast_storage=force_outputs_to_fast_storage, |
| ) |
| |
| res.extend( |
| self.append_sram_pass_block_config_performance_metrics_rewrite_list( |
| sram_used, ps, block_config, metrics, rewrite_list, all_strats |
| ) |
| ) |
| |
| self.n_combinations_searched += len(res) |
| res = self.filter_pareto_frontier(res, remove_equally_good_candidates=True) |
| return res |
| |
| def avoid_for_spilling(self, pred_candidate): |
| if self.arch.feature_map_storage_mem_area == self.arch.fast_storage_mem_area: |
| return False |
| |
| # For SRAM spilling, concat op is avoided as predecessor |
| for op in pred_candidate.ops: |
| if op.type == "ConcatSliceWrite": |
| return True |
| return False |
| |
| def search_ifm_streaming_partial(self, ps, block_config): |
| if ps.placement != PassPlacement.Npu: |
| return ABORT_SEARCH |
| |
| if len(ps.inputs) < 1: |
| return ABORT_SEARCH |
| |
| ifm_tensor = ps.ifm_tensor |
| |
| if ifm_tensor is None: |
| return ABORT_SEARCH |
| if ifm_tensor.purpose != TensorPurpose.FeatureMap: |
| return ABORT_SEARCH |
| if not ifm_tensor.storage_shape or len(ifm_tensor.storage_shape) != 4: |
| return ABORT_SEARCH |
| |
| pred_pass_list = [] |
| for pred_candidate in ps.dag_predecessors: |
| if len(pred_candidate.outputs) == 1 and pred_candidate.outputs[0] == ifm_tensor: |
| # we found a predecessor that produces this IFM tensor |
| if not ifm_tensor.avoid_NHCWB16: |
| # and NHCWB16 format is not to be avoided |
| if len(pred_candidate.successors) == 1 and pred_candidate.successors[0] == ps: |
| # and it only has one successor, namely us |
| if pred_candidate.placement == PassPlacement.Npu: |
| if pred_candidate.npu_block_type in self.ifm_stream_npu_blocks: |
| # and it is on the Npu |
| if not self.avoid_for_spilling(pred_candidate): |
| # and fusable - it's a candidate |
| pred_pass_list.append(pred_candidate) |
| |
| if not pred_pass_list: |
| return ABORT_SEARCH |
| |
| all_candidates = [] |
| for pred_pass in pred_pass_list: |
| # recurse into the next pass |
| ifm_strat_data = self.search_ifm_streaming_body(pred_pass, self.feature_maps_not_in_fast_storage) |
| |
| strat_data = self.search_all_but_one_predecessor(ps, pred_pass, ifm_strat_data) |
| for strat_opt in strat_data: |
| |
| pred_pass_block_config = strat_opt[0].block_configs[-1] |
| rolling_buffer_dims = npu_performance.rolling_buffer_dims_from_passes( |
| self.arch, pred_pass, pred_pass_block_config, ps, block_config |
| ) |
| if rolling_buffer_dims is None: |
| continue # this does not pack properly, skip it. |
| |
| sram_used = 0 |
| for tens in ps.inputs: |
| if tens != ifm_tensor: |
| if tens.mem_area == self.mem_area: |
| sram_used += tens.storage_size() |
| |
| rolling_buffer_y, rolling_buffer_x = rolling_buffer_dims |
| |
| rewrite_list = [ |
| ( |
| SchedulerRewrite.ChangeTensorSubPurpose, |
| ifm_tensor, |
| TensorSubPurpose.RollingBufferY, |
| rolling_buffer_y, |
| None, |
| ps, |
| ) |
| ] |
| sram_used += ifm_tensor.storage_size_for_sub_purpose( |
| self.arch, TensorSubPurpose.RollingBufferY, rolling_buffer_y, None |
| ) |
| |
| all_candidates.extend(self.append_sram_rewrite_list(sram_used, rewrite_list, [strat_opt])) |
| |
| self.n_combinations_searched += len(all_candidates) |
| return all_candidates |
| |
| def get_block_configs(self, ps): |
| if ps.placement != PassPlacement.Npu: |
| return [(1, 1, 1, 1)] # default |
| |
| block_configs = find_block_configs_suitable_for_pass_and_shared_buffer(self.arch, ps) |
| |
| # Take a limited number of the largest blocks |
| if self.arch.block_config_limit > 0: |
| # Sort by block area, followed by depth |
| block_configs.sort(key=lambda cfg: (cfg[0] * cfg[1]) << 8 | cfg[3], reverse=True) |
| bound = min(len(block_configs), self.arch.block_config_limit) |
| # We take 'n' from the fat end of the list, and 'n' from the thin end of the list. |
| tmp = block_configs[:bound] |
| tmp.extend(block_configs[max(bound, len(block_configs) - bound) :]) |
| block_configs = tmp |
| |
| return block_configs |
| |
| def search_ifm_streaming_input(self, ps): |
| sram_used = 0 |
| for tens in ps.inputs: |
| if tens.mem_area == self.mem_area: |
| sram_used += tens.storage_size() |
| |
| return self.append_sram(sram_used, self.search_predecessors(ps)) |
| |
| def search_weight_streaming_output(self, ps): |
| strat_data = self.search_weight_streaming_body(ps) |
| |
| sram_used = self.non_local_mem_usage[ps.time] |
| for tens in ps.outputs: |
| if tens.mem_area == self.mem_area: |
| sram_used += tens.storage_size() |
| |
| return self.graduate_strat(SchedulingStrategy.WeightStream, sram_used, strat_data) |
| |
| @lru_cache(maxsize=None) |
| def search_weight_streaming_body(self, ps): |
| |
| strat_data = self.search_weight_streaming_input(ps) |
| |
| res = [] |
| |
| all_block_configs = self.get_block_configs(ps) |
| |
| for block_config in all_block_configs: |
| |
| sram_used = 0 |
| rewrite_list = [] |
| |
| for tens in ps.intermediates: |
| if tens.mem_area == self.mem_area: |
| if tens.purpose == TensorPurpose.Weights: |
| sram_used += tens.storage_size_for_sub_purpose( |
| self.arch, TensorSubPurpose.DoubleBuffer, block_config[3] |
| ) |
| rewrite_list.append( |
| ( |
| SchedulerRewrite.ChangeTensorSubPurpose, |
| tens, |
| TensorSubPurpose.DoubleBuffer, |
| block_config[3], |
| None, |
| ps, |
| ) |
| ) |
| else: |
| sram_used += tens.storage_size() |
| |
| metrics = npu_performance.performance_metrics_for_pass( |
| self.arch, ps, block_config, rewrite_list=rewrite_list |
| ) |
| |
| res.extend( |
| self.append_sram_pass_block_config_performance_metrics_rewrite_list( |
| sram_used, ps, block_config, metrics, rewrite_list, strat_data |
| ) |
| ) |
| |
| self.n_combinations_searched += len(res) |
| res = self.filter_pareto_frontier(res, remove_equally_good_candidates=True) |
| return res |
| |
| def search_weight_streaming_input(self, ps): |
| sram_used = 0 |
| for tens in ps.inputs: |
| if tens.mem_area == self.mem_area: |
| sram_used += tens.storage_size() |
| |
| return self.append_sram(sram_used, self.search_predecessors(ps)) |
| |
| def apply_result(self, strat_set, arch): |
| pass_to_cascaded_pass = dict() |
| for _, strat in strat_set.strats.items(): |
| # rewrite the tensors that need this first. e.g. make rolling buffers |
| inputs = [] |
| intermediates = [] |
| outputs = [] |
| |
| for ps in strat.passes: |
| inputs += ps.inputs |
| intermediates += ps.intermediates |
| outputs += ps.outputs |
| |
| for tens in set(inputs) & set(outputs): |
| # tensors that are in both sets are intermediates |
| |
| # find pass with input/output tensor, and check if they are both placed on NPU |
| input_placement = None |
| output_placement = None |
| for ps in strat.passes: |
| if tens in ps.inputs: |
| input_placement = ps.placement |
| if tens in ps.outputs: |
| output_placement = ps.placement |
| if input_placement == output_placement == PassPlacement.Npu: |
| tens.set_format(TensorFormat.NHCWB16, arch) |
| |
| intermediates.append(tens) |
| inputs.remove(tens) |
| outputs.remove(tens) |
| |
| for rewrite_op, tens, sub_purpose, param_a, param_b, ps in strat.rewrite_list: |
| if rewrite_op == SchedulerRewrite.ChangeTensorSubPurpose: |
| tens.mem_area = self.arch.fast_storage_mem_area |
| tens.mem_type = MemType.Scratch_fast |
| tens.set_new_sub_purpose(sub_purpose, param_a, param_b) |
| else: |
| assert 0, "unknown rewrite_op " + str(rewrite_op) |
| |
| is_element_wise = True |
| for ps in strat.passes: |
| assert ps.placement == strat.passes[0].placement |
| if not ps.is_element_wise: |
| is_element_wise = False |
| break |
| |
| cascaded_pass = CascadedPass( |
| strat.passes[0].name, |
| strat.strat, |
| inputs, |
| intermediates, |
| outputs, |
| strat.passes, |
| strat.passes[0].placement, |
| is_element_wise, |
| ) |
| assert strat.sram_used >= 0 |
| cascaded_pass.sram_used = strat.sram_used |
| |
| for idx, ps in enumerate(strat.passes): |
| assert ps not in pass_to_cascaded_pass |
| pass_to_cascaded_pass[ps] = cascaded_pass |
| ps.cascade = cascaded_pass |
| ps.block_config = strat.block_configs[idx] |
| |
| if ps.placement == PassPlacement.Npu: |
| ps.shared_buffer = shared_buffer_allocation_for_pass_and_block_config( |
| self.arch, ps, ps.block_config |
| ) |
| assert ps.shared_buffer is not None |
| |
| for op in ps.ops: |
| subgraph = op.attrs.get("subgraph") |
| if subgraph: |
| subgraph.base_sram_used = cascaded_pass.sram_used |
| |
| # all passes should have a cascaded pass now |
| if len(pass_to_cascaded_pass) != len(self.sg.passes): |
| print( |
| "mismatch: we have %d passes, but only %d have cascaded passes associated" |
| % (len(self.sg.passes), len(pass_to_cascaded_pass)) |
| ) |
| for ps in self.sg.passes: |
| if ps not in pass_to_cascaded_pass: |
| print("%3d pass missing cascaded pass %s" % (ps.time, ps)) |
| |
| assert len(pass_to_cascaded_pass) == len(self.sg.passes) |
| |
| cascaded_passes = [] |
| if self.sg.placement == PassPlacement.Cpu: |
| # Retain the pass order for CPU subgraph |
| cascaded_passes = [ps.cascade for ps in self.sg.passes] |
| else: |
| # we have all the passes, but we need to put them in order and build predecessor/successor links. |
| visit_pass_set = set() |
| |
| def visit_pass(ps): |
| if ps in visit_pass_set: |
| return |
| visit_pass_set.add(ps) |
| |
| cps = ps.cascade |
| dont_traverse = set(cps.passes) |
| |
| for ps in cps.passes: |
| for pred in ps.predecessors: |
| if pred in dont_traverse: |
| continue |
| visit_pass(pred) |
| |
| cascaded_passes.append(cps) |
| |
| starting_passes = [ps for ps in self.sg.passes if not ps.successors] |
| for ps in starting_passes: |
| visit_pass(ps) |
| |
| # reorder so startup init cascaded passes come first |
| def is_startup_cascaded_pass(cps): |
| if not cps.passes: |
| return False |
| return cps.placement == PassPlacement.StartupInit |
| |
| cascaded_passes = [cps for cps in cascaded_passes if is_startup_cascaded_pass(cps)] + [ |
| cps for cps in cascaded_passes if not is_startup_cascaded_pass(cps) |
| ] |
| |
| self.sg.cascaded_passes = cascaded_passes |
| self.sg.build_cascaded_pass_links() |
| |
| if self.options.use_nhcwb16_between_cascaded_passes: |
| # Check if NHCWB16 can be used in between cascaded passes |
| # (NHCWB16 within cascaded passes has been handled earlier in this function) |
| if self.sg.placement == PassPlacement.Npu: |
| for ps in self.sg.cascaded_passes: |
| if ps.placement != PassPlacement.Npu: |
| continue |
| for output in ps.outputs: |
| if output.purpose != TensorPurpose.FeatureMap or output.avoid_NHCWB16: |
| continue |
| |
| use_NHCWB16 = True |
| rewrites = [] |
| for op in output.consumer_list: |
| if op is None: |
| use_NHCWB16 = False |
| elif op.type == "Reshape": |
| # Detect no-op reshapes by comparing their full input and output tensor shapes. |
| inshape = full_shape(4, op.inputs[0].shape, 1) |
| outshape = full_shape(4, op.outputs[0].shape, 1) |
| # Using NHCWB16 format for a no-op reshape is only an option if subsequent |
| # consumers do not also need to perform a reshape or if the OFM is going to |
| # be processed by CPU operations. No-op reshape consumers with empty lists |
| # (those that have no consumers, or null-consumers used as list terminators) |
| # must use normal NHWC output. |
| incompatible_consumers = [ (not consumer.run_on_npu or consumer.type == "Reshape") for consumer in op.outputs[0].consumer_list |
| if consumer is not None ] |
| if (outshape == inshape) and incompatible_consumers and not any(incompatible_consumers): |
| rewrites.append(op) |
| else: |
| use_NHCWB16 = False |
| else: |
| use_NHCWB16 &= op.run_on_npu |
| |
| if use_NHCWB16: |
| output.set_format(TensorFormat.NHCWB16, arch) |
| for rewrite_op in rewrites: |
| rewrite_op.outputs[0].set_format(TensorFormat.NHCWB16, arch) |
| |
| |
| def schedule_passes(nng, arch, options: SchedulerOptions): |
| |
| for sg in nng.subgraphs: |
| sg.base_sram_used = 0 |
| |
| for sg in nng.subgraphs: |
| # re-entering the same nodes from different contexts requires us to |
| # build a simplified directed acyclic (DAG) version of the graph to |
| # use for traversal, rather than using a visit dictionary. this avoids |
| # recursing infinitely due to loops. |
| sg.build_pass_dag_predecessors() |
| |
| dps = DynamicProgrammingScheduler(nng, sg, arch, arch.sram_size, options) |
| |
| strat_set = dps.search() |
| |
| dps.apply_result(strat_set, arch) |
| |
| if options.verbose_schedule: |
| sg.print_cascaded_passes() |