| # 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: |
| # Contains the main sequencing of the compiler. |
| |
| from . import graph_optimiser |
| from . import mark_tensors |
| from . import insert_dma |
| from . import pass_packing |
| from . import scheduler |
| from . import tensor_allocation |
| from . import npu_performance |
| import time |
| |
| from . import high_level_command_stream |
| from . import high_level_command_stream_generator |
| from . import register_command_stream_generator |
| from . import extract_npu_subgraphs |
| from . import npu_serialisation |
| from . import weight_compressor |
| from . import live_range |
| from .tensor import MemArea |
| from .nn_graph import TensorAllocator, PassPlacement |
| from .rewrite_graph import verify_graph_health, verify_subgraph_health |
| |
| |
| class CompilerOptions: |
| """Set of options to change compiler behaviour - verbosity, targets, turning off passes. |
| |
| Note the difference between ArchitectureFeatures and CompilerOptions |
| - ArchitectureFeatures is for changing the Ethos-U55 and system architecture |
| - CompilerOptions is for changing the behaviour of the compiler |
| """ |
| |
| def __init__( |
| self, |
| verbose_graph=False, |
| verbose_quantization=False, |
| verbose_packing=False, |
| verbose_tensor_purpose=False, |
| verbose_tensor_format=False, |
| verbose_allocation=False, |
| verbose_high_level_command_stream=False, |
| verbose_register_command_stream=False, |
| verbose_operators=False, |
| show_minimum_possible_allocation=False, |
| show_cpu_operations=False, |
| tensor_allocator=TensorAllocator.Greedy, |
| timing=False, |
| output_dir="outputs", |
| ): |
| |
| self.verbose_graph = verbose_graph |
| self.verbose_quantization = verbose_quantization |
| self.verbose_packing = verbose_packing |
| self.verbose_tensor_purpose = verbose_tensor_purpose |
| self.verbose_tensor_format = verbose_tensor_format |
| self.verbose_allocation = verbose_allocation |
| self.verbose_high_level_command_stream = verbose_high_level_command_stream |
| self.verbose_register_command_stream = verbose_register_command_stream |
| self.verbose_operators = verbose_operators |
| self.show_minimum_possible_allocation = show_minimum_possible_allocation |
| self.show_cpu_operations = show_cpu_operations |
| self.tensor_allocator = tensor_allocator |
| self.timing = timing |
| self.output_dir = output_dir |
| |
| def __str__(self): |
| return type(self).__name__ + ": " + str(self.__dict__) |
| |
| __repr__ = __str__ |
| |
| |
| def compiler_driver(nng, arch, options, scheduler_options): |
| assert verify_graph_health(nng) |
| nng = graph_optimiser.optimise_graph_a(nng, arch, options.verbose_graph) |
| assert verify_graph_health(nng) |
| |
| if options.verbose_quantization: |
| nng.print_graph_with_tensor_quantization() |
| |
| nng = graph_optimiser.optimise_graph_b(nng, arch, options.verbose_graph) |
| assert verify_graph_health(nng) |
| |
| nng = mark_tensors.mark_tensor_purpose(nng, arch, options.verbose_tensor_purpose) |
| assert verify_graph_health(nng) |
| nng = insert_dma.insert_dma_commands(nng, arch, options.verbose_graph) |
| assert verify_graph_health(nng) |
| pass_packing.pack_into_passes(nng, arch, options.verbose_packing) |
| assert verify_graph_health(nng) |
| |
| extract_npu_subgraphs.extract_npu_subgraphs(nng, arch) |
| |
| mark_tensors.mark_tensor_format(nng, arch, options.verbose_tensor_format) |
| assert verify_graph_health(nng) |
| if options.timing: |
| start = time.time() |
| |
| # Run the scheduler |
| scheduler.schedule_passes(nng, arch, scheduler_options) |
| |
| if options.timing: |
| stop = time.time() |
| print("Scheduling took %f s" % (stop - start)) |
| start = time.time() |
| |
| # Update the compressed weights now that we have determined the |
| # block config, and calc and pack the scales and biases |
| weight_compressor.update_pass_weight_and_scale_tensors(nng, arch) |
| |
| # Memory area for all non-constant tensors (Cpu and Npu) |
| non_const_mem_area = MemArea.Sram |
| |
| # LiveRanges for constant tensors for all Npu subgraphs |
| permanent_storage = arch.permanent_storage_mem_area |
| lr_graph_flash = live_range.LiveRangeGraph() |
| |
| # Placeholders for scratch and flash tensors that are common for all Npu subgraphs |
| scratch_tens = None |
| flash_tens = None |
| |
| # Calculate live ranges for all constant Npu tensors, in permanent storage |
| for sg in nng.subgraphs: |
| if sg.placement == PassPlacement.Npu: |
| lr_graph_flash = live_range.extract_live_ranges_from_cascaded_passes( |
| sg, permanent_storage, ignore_subgraph_input_output_tensors=True, lr_graph=lr_graph_flash |
| ) |
| |
| # Allocate all Npu constant tensors to the first Npu subgraph since it is |
| # processed first during serialization into tensors |
| first_npu_sg = nng.subgraphs[1] |
| assert first_npu_sg.placement == PassPlacement.Npu |
| tensor_allocation.allocate_tensors( |
| nng, |
| first_npu_sg, |
| arch, |
| permanent_storage, |
| scheduler_options.use_ifm_ofm_overlap, |
| options.tensor_allocator, |
| options.verbose_allocation, |
| options.show_minimum_possible_allocation, |
| lr_graph_flash, |
| ) |
| |
| # Allocate all non-constant tensors to the root, i.e. Cpu, subgraph. This step |
| # will start at the root subgraph's input and traverse from top to bottom. When |
| # it comes across an Npu-op it will extract live ranges for it's corresponding |
| # Npu subgraph and add them to the root's live range graph. Finally, all of the |
| # non-constant tensors are allocated together |
| root_sg = nng.get_root_subgraph() |
| tensor_allocation.allocate_tensors( |
| nng, |
| root_sg, |
| arch, |
| non_const_mem_area, |
| scheduler_options.use_ifm_ofm_overlap, |
| options.tensor_allocator, |
| options.verbose_allocation, |
| options.show_minimum_possible_allocation, |
| ) |
| |
| # Generate command streams and serialise Npu-ops into tensors |
| for sg in nng.subgraphs: |
| high_level_command_stream_generator.generate_high_level_command_stream( |
| nng, sg, arch, options.verbose_high_level_command_stream |
| ) |
| register_command_stream_generator.generate_register_command_stream( |
| nng, sg, arch, options.verbose_register_command_stream |
| ) |
| scratch_tens, flash_tens = npu_serialisation.serialise_npu_subgraph_into_tensors( |
| nng, sg, arch, scratch_tens, flash_tens |
| ) |
| |
| npu_serialisation.rewrite_npu_call_ops(nng, root_sg, arch) |
| |
| # Allocate all Cpu constant tensors, this is done last because the Npu-ops |
| # have to be serialized into flash and scratch tensors first |
| tensor_allocation.allocate_tensors( |
| nng, |
| root_sg, |
| arch, |
| permanent_storage, |
| scheduler_options.use_ifm_ofm_overlap, |
| options.tensor_allocator, |
| options.verbose_allocation, |
| options.show_minimum_possible_allocation, |
| ) |
| |
| npu_performance.calc_performance_for_network(nng, arch) |