| # 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. |
| import time |
| |
| from . import extract_npu_subgraphs |
| from . import graph_optimiser |
| from . import high_level_command_stream_generator |
| from . import insert_dma |
| from . import live_range |
| from . import lut |
| from . import mark_tensors |
| from . import npu_performance |
| from . import npu_serialisation |
| from . import pass_packing |
| from . import register_command_stream_generator |
| from . import scheduler |
| from . import tensor_allocation |
| from . import weight_compressor |
| from .debug_database import DebugDatabase |
| from .errors import VelaError |
| from .nn_graph import PassPlacement |
| from .nn_graph import TensorAllocator |
| from .operation import Op |
| from .rewrite_graph import verify_graph_health |
| from .rewrite_graph import visit_graph_post_order |
| from .tensor import MemType |
| from .tensor import Tensor |
| |
| |
| 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", |
| allocation_alignment=Tensor.AllocationQuantum, |
| ): |
| |
| 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 |
| self.allocation_alignment = allocation_alignment |
| |
| def __str__(self): |
| return type(self).__name__ + ": " + str(self.__dict__) |
| |
| __repr__ = __str__ |
| |
| |
| def next_sram_factor(alloc_results): |
| # Bisects to find the max SRAM usage that successfully can be fitted with the tensor allocator. |
| # Returns tuple (factor, dry_test), with factor is None (stop) or 0 <= factor <= 1 (next SRAM factor to try), |
| # dry_test is True while still bisecting. |
| upper = 1.0 |
| lower = 0.7 |
| MAX_ITERATIONS = 8 |
| if len(alloc_results) == 0: |
| # First iteration, try max SRAM, keep the result if it succeeds |
| return (upper, False) |
| elif len(alloc_results) == 1: |
| if alloc_results[0]: |
| # The allocator succeeded at first try; stop |
| return (None, False) |
| else: |
| # Start bisecting, try lowerbound SRAM |
| return (lower, True) |
| elif len(alloc_results) > MAX_ITERATIONS: |
| # Stop |
| return (None, False) |
| if not alloc_results[1]: |
| # Allocation at lower failed; search interval 0 - lower |
| upper = lower |
| lower = 0 |
| best = lower |
| for success in alloc_results[2:]: |
| middle = (lower + upper) / 2 |
| if success: |
| best = max(best, middle) |
| lower = middle |
| else: |
| upper = middle |
| if len(alloc_results) == MAX_ITERATIONS: |
| # Done bisecting; repeat the best match, but not as dry test |
| return (best, False) |
| # Next try; run only as dry test |
| return ((lower + upper) / 2, True) |
| |
| |
| def _record_operator(op, arch): |
| if op.type != Op.Const: |
| DebugDatabase.add_source(op) |
| |
| |
| def compiler_driver(nng, arch, options, scheduler_options): |
| assert verify_graph_health(nng) |
| |
| # Pre-optimisation operator tracking |
| for sg in nng.subgraphs: |
| visit_graph_post_order(sg.output_tensors, arch, [], [_record_operator]) |
| |
| 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) |
| |
| 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) |
| |
| # 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 |
| scratch_fast_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, |
| MemType.Permanent_NPU, |
| ignore_subgraph_input_output_tensors=True, |
| lr_graph=lr_graph_flash, |
| ) |
| |
| if len(nng.subgraphs) > 1: |
| # 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, |
| set((MemType.Permanent_NPU,)), |
| tensor_allocator=TensorAllocator.LinearAlloc, |
| verbose_allocation=options.verbose_allocation, |
| show_minimum_possible_allocation=options.show_minimum_possible_allocation, |
| lr_graph=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. |
| # The non-constant tensors are stored either in arch.feature_map_storage_mem_area or |
| # arch.fast_storage_mem_area. |
| # When these memory areas are the same, all non-constant tensors are allocated together. |
| # Otherwise they are allocated separately. |
| |
| root_sg = nng.get_root_subgraph() |
| |
| alloc_list = [] |
| feature_maps_in_fast_storage = arch.feature_map_storage_mem_area == arch.fast_storage_mem_area |
| if feature_maps_in_fast_storage: |
| mem_alloc_scratch = (arch.feature_map_storage_mem_area, set((MemType.Scratch, MemType.Scratch_fast))) |
| alloc_list.append(mem_alloc_scratch) |
| else: |
| mem_alloc_scratch_fast = (arch.fast_storage_mem_area, set((MemType.Scratch_fast,))) |
| mem_alloc_scratch = (arch.feature_map_storage_mem_area, set((MemType.Scratch,))) |
| # Order is important |
| alloc_list.append(mem_alloc_scratch_fast) |
| alloc_list.append(mem_alloc_scratch) |
| |
| for mem_area, mem_type_set in alloc_list: |
| if feature_maps_in_fast_storage or mem_area != arch.fast_storage_mem_area: |
| tensor_allocation.allocate_tensors( |
| nng, |
| root_sg, |
| arch, |
| mem_area, |
| mem_type_set, |
| tensor_allocator=options.tensor_allocator, |
| verbose_allocation=options.verbose_allocation, |
| show_minimum_possible_allocation=options.show_minimum_possible_allocation, |
| allocation_alignment=options.allocation_alignment, |
| ) |
| else: |
| # For the case where scratch_fast != scratch: attempt to place feature maps used between |
| # cascaded passes in fast storage. Bisection is used to find the max possible usage of SRAM. |
| alloc_results = [] |
| while True: |
| assert len(alloc_results) < 10, "Infinite allocator loop" |
| sram_factor, dry_test = next_sram_factor(alloc_results) |
| if sram_factor is None: |
| break |
| # Try to move as many feature maps as possible to SRAM before allocating |
| sram_limit = sram_factor * arch.sram_size |
| for sg in nng.subgraphs: |
| scheduler.use_fast_storage_for_feature_maps(sg, sram_limit, arch) |
| alloc_success = tensor_allocation.allocate_tensors( |
| nng, |
| root_sg, |
| arch, |
| mem_area, |
| mem_type_set, |
| max_size=arch.sram_size, |
| dry_test=dry_test, |
| tensor_allocator=options.tensor_allocator, |
| verbose_allocation=options.verbose_allocation, |
| show_minimum_possible_allocation=options.show_minimum_possible_allocation, |
| allocation_alignment=options.allocation_alignment, |
| ) |
| if dry_test or not alloc_success: |
| for sg in nng.subgraphs: |
| scheduler.undo_use_fast_storage(sg, arch) |
| alloc_results.append(alloc_success) |
| if not alloc_results[-1]: |
| raise VelaError( |
| "Sram limit {} bytes, has been exceeded by the scratch fast tensor. " |
| "Increasing the value of --weight-estimation-scaling may help to resolve the issue. " |
| "See OPTIONS.md for more information.".format(arch.sram_size) |
| ) |
| |
| # 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 |
| ) |
| lut.optimize_high_level_cmd_stream(sg, arch) |
| register_command_stream_generator.generate_register_command_stream_for_sg( |
| nng, sg, arch, options.verbose_register_command_stream |
| ) |
| scratch_tens, scratch_fast_tens, flash_tens = npu_serialisation.serialise_npu_subgraph_into_tensors( |
| nng, sg, arch, scratch_tens, scratch_fast_tens, flash_tens |
| ) |
| |
| npu_serialisation.rewrite_npu_call_ops(nng, root_sg, arch) |
| |
| # Set Scratch and Fast_scratch Tensor size |
| if scratch_tens is not None: |
| scratch_tens.set_all_shapes([root_sg.memory_used_per_type.get(MemType.Scratch, 0)]) |
| if scratch_fast_tens is not None: |
| scratch_fast_tens.set_all_shapes([root_sg.memory_used_per_type.get(MemType.Scratch_fast, 0)]) |
| |
| # 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, |
| set((MemType.Permanent_CPU,)), |
| tensor_allocator=TensorAllocator.LinearAlloc, |
| verbose_allocation=options.verbose_allocation, |
| show_minimum_possible_allocation=options.show_minimum_possible_allocation, |
| allocation_alignment=options.allocation_alignment, |
| ) |
| |
| npu_performance.calc_performance_for_network(nng, arch) |