# 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: # Holds a container for Ethos-U55/System architecture parameters. import enum from collections import namedtuple from configparser import ConfigParser import numpy as np from .errors import OptionError from .ethos_u55_regs.ethos_u55_regs import resampling_mode from .numeric_util import round_up from .numeric_util import round_up_divide from .operation import NpuBlockType from .supported_operators import SupportedOperators from .tensor import MemArea from .tensor import TensorFormat from .tensor import TensorPurpose PointXY = namedtuple("PointXY", "x y") PointXYZ = namedtuple("PointXYZ", "x y z") class Block: def __init__(self, w, h, d): self.width = w self.height = h self.depth = d def __eq__(self, other): if self.width == other.width and self.height == other.height and self.depth == other.depth: return True else: return False def __repr__(self): return "".format(self.width, self.height, self.depth) @classmethod def from_string(cls, s): w, h, c = (int(v) for v in s.split("x")) return cls(w, h, c) class Rect: def __init__(self, x, y, z, x2, y2, z2): self.x = x self.y = y self.z = z self.x2 = x2 self.y2 = y2 self.z2 = z2 def start(self): return PointXYZ(self.x, self.y, self.z) def end(self): return PointXYZ(self.x2, self.y2, self.z2) def size(self): return Block(self.x2 - self.x + 1, self.y2 - self.y + 1, self.z2 - self.z + 1) def __repr__(self): return "".format(self.x, self.y, self.z, self.x2, self.y2, self.z2) class Kernel: def __init__(self, w, h, sx=1, sy=1, dx=1, dy=1): assert sx > 0 and sy > 0 assert dx > 0 and dy > 0 self.width = w self.height = h self.stride = PointXY(sx, sy) self.dilation = PointXY(dx, dy) class SHRAMElements: IFM8 = 0 IFM16 = 1 IFM8_Elementwise = 2 IFM16_Elementwise = 3 Acc16 = 4 Acc32 = 5 Acc40 = 6 Last = Acc40 BitSizes = np.array([8, 16, 8, 16, 16, 32, 40], np.int32) ByteSizes = BitSizes // 8 PostAlign = np.array([8, 8, 8, 8, 1, 1, 1], np.int32) PreAlign = np.array([1, 1, 1, 1, 8, 8, 8], np.int32) class SHRAMBlockConfig: def __init__(self, sizes, banks): assert len(banks) == SHRAMElements.Last + 1 self.sizes = sizes self.banks = banks # Area indices must match Ethos-U55 SHRAM layout spec class SharedBufferArea(enum.IntEnum): OFM = 0 Weights = 1 IFM = 2 Accumulators = 3 Size = Accumulators + 1 class ArchitectureFeatures: """This class is a container for various parameters of the Ethos-U55 core and system configuration that can be tuned, either by command line parameters or by the Ethos-U55 architects. The class is often passed around to passes that need to do architecture-dependent actions. 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 """ ArchitectureConfig = namedtuple( "ArchitectureConfig", "macs cores ofm_ublock ifm_ublock shram_banks shram_granules elem_units" ) accelerator_configs = { "ethos-u55-256": ArchitectureConfig(256, 1, Block(2, 2, 8), Block(2, 2, 8), 48, [8, 8, 8, 8, 8, 16, 20], 8), "ethos-u55-128": ArchitectureConfig(128, 1, Block(2, 1, 8), Block(2, 2, 8), 24, [4, 4, 4, 4, 4, 8, 12], 4), "ethos-u55-64": ArchitectureConfig(64, 1, Block(1, 1, 8), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 8], 2), "ethos-u55-32": ArchitectureConfig(32, 1, Block(1, 1, 4), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 4], 1), } OFMSplitDepth = 16 def __init__( self, vela_config: ConfigParser, accelerator_config, system_config, permanent_storage, inter_pass_cycle_delay, dram_bandwidth, override_block_config, block_config_limit, global_memory_clock_scale, max_blockdep, ): accelerator_config = accelerator_config.lower() self.vela_config = vela_config self.accelerator_config = accelerator_config if self.accelerator_config not in ArchitectureFeatures.accelerator_configs: raise OptionError("--accelerator-config", self.accelerator_config, "Unknown accelerator configuration") accel_config = ArchitectureFeatures.accelerator_configs[self.accelerator_config] self.config = accel_config self.system_config = system_config is_yoda_system = "yoda-" in self.accelerator_config if is_yoda_system: self.sram_size = 256 * 1024 else: self.sram_size = 200 * 1024 * 1024 self.ncores = accel_config.cores self.ofm_ublock = accel_config.ofm_ublock self.ifm_ublock = accel_config.ifm_ublock self.subkernel_max = Block(8, 8, 65536) self.ofm_block_max = Block(64, 32, 128) self.override_block_config = override_block_config self.block_config_limit = block_config_limit self.global_memory_clock_scale = global_memory_clock_scale if self.global_memory_clock_scale <= 0.0 or self.global_memory_clock_scale > 1.0: raise Exception( "Invalid global_memory_clock_scale = " + str(self.global_memory_clock_scale) + " (must be > 0.0 and <= 1.0)" ) self.max_blockdep = max_blockdep dpu_min_height = accel_config.ofm_ublock.height dpu_min_width = accel_config.ofm_ublock.width dpu_dot_product_width = 8 dpu_min_ofm_channels = accel_config.ofm_ublock.depth self.num_elem_wise_units = accel_config.elem_units self.num_macs_per_cycle = dpu_min_height * dpu_min_width * dpu_dot_product_width * dpu_min_ofm_channels self.memory_clock_scales = np.zeros(MemArea.Size) self.memory_port_widths = np.zeros(MemArea.Size) # Get system configuration self.__read_sys_config() # apply the global memory clock scales to the individual ones from the system config for mem in MemArea.all(): self.memory_clock_scales[mem] *= self.global_memory_clock_scale self.memory_clocks = self.memory_clock_scales * self.npu_clock self.memory_bandwidths_per_cycle = self.memory_port_widths * self.memory_clock_scales / 8 if dram_bandwidth != 0: self.memory_bandwidths_per_cycle[MemArea.Dram] = dram_bandwidth * 1e9 / self.npu_clock self.memory_bandwidths_per_second = self.memory_bandwidths_per_cycle * self.npu_clock # sizes as N x H x W x C. we need to round up to these when allocating storage self.storage_rounding_quantums = { TensorFormat.Unknown: (1, 1, 1, 1), TensorFormat.WeightsCompressed: (1, 1, 1, 1), TensorFormat.NHWC: (1, 1, 1, 1), TensorFormat.NHCWB16: (1, 1, 1, 16), } # brick sizes as N x H x W x C. We have to fetch whole bricks at a time self.brick_sizes = { TensorFormat.Unknown: (1, 1, 1, 1), TensorFormat.WeightsCompressed: (1, 1, 1, 1), TensorFormat.NHWC: (1, 1, 1, 1), TensorFormat.NHCWB16: (1, 1, 1, 16), } self.inter_pass_cycle_delay = inter_pass_cycle_delay self.default_weight_format = TensorFormat.WeightsCompressed self.default_feature_map_format = TensorFormat.NHWC if permanent_storage != MemArea.OffChipFlash: self.permanent_storage_mem_area = permanent_storage self.tensor_storage_mem_area = { # permanent mem_area TensorPurpose.Weights: self.permanent_storage_mem_area, TensorPurpose.FeatureMap: self.feature_map_storage_mem_area, } self.tensor_load_mem_area = dict(self.tensor_storage_mem_area) if self.tensor_storage_mem_area[TensorPurpose.Weights] in (MemArea.OffChipFlash,): self.tensor_load_mem_area[TensorPurpose.Weights] = MemArea.Sram self.min_block_sizes = { NpuBlockType.Default: (dpu_min_height, dpu_min_width), NpuBlockType.VectorProduct: (1, 1), NpuBlockType.ConvolutionMxN: (dpu_min_height, dpu_min_width), NpuBlockType.Pooling: (dpu_min_height, dpu_min_width), NpuBlockType.ConvolutionDepthWise: (dpu_min_height, dpu_min_width), NpuBlockType.ElementWise: (1, 1), } self.sub_kernel_limits = { NpuBlockType.Default: (8, 8), NpuBlockType.VectorProduct: (1, 1), NpuBlockType.ConvolutionMxN: (8, 8), NpuBlockType.Pooling: (8, 8), NpuBlockType.ConvolutionDepthWise: (8, 8), NpuBlockType.ElementWise: (1, 1), } # weights for scheduler search from .npu_performance import make_bandwidth_array self.bandwidth_weights = make_bandwidth_array() self.bandwidth_weights[MemArea.Sram] = 1.0 self.bandwidth_weights[MemArea.Dram] = 10.0 self.bandwidth_weights[MemArea.OnChipFlash] = 2.0 self.bandwidth_weights[MemArea.OffChipFlash] = 20.0 self.cycles_weight = 40 self.max_sram_used_weight = 1000 if is_yoda_system: self.max_sram_used_weight = 0 # Shared Buffer Block allocations self.shram_bank_size = 1024 # bytes self.shram_size_bytes = accel_config.shram_banks * self.shram_bank_size self.shram_reserved_output_banks = 2 self.shram_reserved_weight_banks = 0 self.shram_reserved_unused_banks = 2 if accel_config.shram_banks > 16 else 0 self.shram_total_banks = accel_config.shram_banks - self.shram_reserved_unused_banks self.shram_bank_granules = np.array(accel_config.shram_granules, np.int32) # Build a map of acceptable IFM/OFM block configurations up to the maximum # IFM/OFM block size. ifm_block_max = self.get_ifm_block_size(32, self.ofm_block_max, Kernel(8, 8)) self.block_config_map = dict() self.generate_block_config_map(Block(ifm_block_max.width, ifm_block_max.height, 128)) # Setup supported operators and restriction checkers class self.supported_operators = SupportedOperators() # Calculate block configuration for ALL known IFM operations and # accumulator sizes. Consumers will need to select their preferred # operation and bit-width at read-time. def generate_block_config(self, width, height, depth): # Number of bytes required for any SHRAM element for a FM of given dimensions. # For IFM: size = H*W*Align(D*BYTE_WIDTH, 8) # For ACC: size = H*W*Align(D,8)*BYTE_WIDTH d1 = round_up(depth, SHRAMElements.PreAlign) d2 = round_up(d1 * SHRAMElements.ByteSizes, SHRAMElements.PostAlign) size_bytes = (height * width) * d2 # Convert byte size (rounded) to size in banks size_banks = round_up_divide(size_bytes, self.shram_bank_size) size_banks *= 2 # Double buffer the IFM/Acc (need twice as many banks) # Round bank requirement to bank granularity required_banks = round_up(size_banks, self.shram_bank_granules) return SHRAMBlockConfig(size_bytes, required_banks) @staticmethod def make_block_config_key(width, height, depth): return (int(height), int(width), int(depth)) def get_block_config(self, width, height, depth): assert depth <= self.ofm_block_max.depth key = ArchitectureFeatures.make_block_config_key(width, height, depth) config = self.block_config_map.get(key, None) return config # Generate a key:value map of possible block configurations, where the # key is compounded from the block dimensions: 0x00HHWWCC def generate_block_config_map(self, block: Block): for h in range(1, block.height + 1): for w in range(1, block.width + 1): # All possible IFM/OFM depth values for c in [4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]: key = ArchitectureFeatures.make_block_config_key(w, h, c) self.block_config_map[key] = self.generate_block_config(w, h, c) def calc_ifm_block_depth(self, ifm_depth, ifm_bits): assert ifm_bits == 8 or ifm_bits == 16 assert ifm_depth > 0 ifm_depth = round_up(ifm_depth, self.ifm_ublock.depth) max_block_depth = 32 if ifm_bits == 8 else 16 return min(max_block_depth, ifm_depth) # Calculate the size of the IFM block given a depth, target OFM block and a kernel def get_ifm_block_size(self, ifm_block_depth, ofm_block: Block, kernel: Kernel, subkernel: Block = Block(8, 8, 65536), ifm_resampling_mode=resampling_mode.NONE): upscaling = 1 if ifm_resampling_mode == resampling_mode.NONE else 2 # Height ifm_odd_2x_height_enable = 0 dilated_kernel_height = ((kernel.height - 1) * kernel.dilation.y) + 1 ifm_block_height = ( (ofm_block.height - 1) * kernel.stride.y + min(subkernel.height, dilated_kernel_height) + ifm_odd_2x_height_enable ) // upscaling ifm_block_height = round_up(ifm_block_height, self.ofm_ublock.height) # Width ifm_odd_2x_width_enable = 0 dilated_kernel_width = ((kernel.width - 1) * kernel.dilation.x) + 1 ifm_block_width = ( (ofm_block.width - 1) * kernel.stride.x + min(subkernel.width, dilated_kernel_width) + ifm_odd_2x_width_enable ) // upscaling ifm_block_width = round_up(ifm_block_width, self.ofm_ublock.width) return Block(ifm_block_width, ifm_block_height, ifm_block_depth) @staticmethod def intersects(start_a, end_a, start_b, end_b): start_x = max(start_a[0], start_b[0]) end_x = min(end_a[0], end_b[0]) start_y = max(start_a[1], start_b[1]) end_y = min(end_a[1], end_b[1]) start_z = max(start_a[2], start_b[2]) end_z = min(end_a[2], end_b[2]) return ((end_x - start_x) > 0) and ((end_y - start_y) > 0) and ((end_z - start_z) > 0) # Block job dependency: # Does the VOLUME of IFMs for block job B(0) overlap with VOLUME of OFMs block jobs A(8,9,10) # # A | B # ----------------------+------------------ # .... 3,4,5,6,7,8,9,10 | 0,1,2,3,4,5,6,8 10 < JOB NUMBER # |<------->| dependency offset # MAX_BLOCKDEP = 3 # Get the coordinates of a block offset from either the end (negative) # or the start (zero or positive) of the given 3d area def get_offset_block_coords(self, area: Rect, block: Block, offset): size = area.size() # Dimensions of the region, in blocks width_blocks = round_up_divide(size.width, block.width) height_blocks = round_up_divide(size.height, block.height) depth_blocks = round_up_divide(size.depth, block.depth) total_blocks = width_blocks * height_blocks * depth_blocks if offset < 0: index = total_blocks + offset else: index = offset if index >= total_blocks: return None # Coordinates of the indexed block coord_z = block.depth * (index % depth_blocks) coord_y = block.height * (index // (depth_blocks * width_blocks)) coord_x = block.width * ((index // depth_blocks) % width_blocks) return (coord_x + area.x, coord_y + area.y, coord_z + area.z) def get_first_job_input_volume( self, ifm: Rect, ofm: Rect, ifm_block_depth, ofm_block: Block, kernel: Kernel, padLT, block_offset ): # Get ifm block size (jobs are invisibly decomposed into subkernels) ifm_block = self.get_ifm_block_size(ifm_block_depth, ofm_block, kernel, self.ofm_block_max) ifm_depth_blocks = round_up_divide(ifm.size().depth, ifm_block_depth) # Which OFM block are we calculating ofm_coord = self.get_offset_block_coords(ofm, ofm_block, block_offset // ifm_depth_blocks) if ofm_coord is None: return None # Coordinate of the source IFM block ifm_coord_x = max(0, ofm_coord[0] * kernel.stride.x - padLT[0]) ifm_coord_y = max(0, ofm_coord[1] * kernel.stride.y - padLT[1]) ifm_coord_z = ifm.z + (block_offset % ifm_depth_blocks) * ifm_block.depth # IFM block that will be sampled for the FIRST+block_offset job in the next operator's OFM start_coord = (ifm_coord_x, ifm_coord_y, ifm_coord_z) end_coord = ( start_coord[0] + ifm_block.width, start_coord[1] + ifm_block.height, start_coord[2] + ifm_block.depth, ) return (start_coord, end_coord, 1) # start, end, total jobs def get_prev_job_output_volume( self, ifm: Block, ofm: Rect, ifm_block_depth, ofm_block: Block, kernel: Kernel, block_offset ): assert block_offset >= 0 # Get OFM block's volume coordinates start_coord = self.get_offset_block_coords(ofm, ofm_block, -1 - block_offset) if start_coord is None: return None end_coord = ( start_coord[0] + ofm_block.width, start_coord[1] + ofm_block.height, start_coord[2] + ofm_block.depth, ) # Calculate how many IFM blocks this OFM block requires (i.e how many jobs) ifm_depth_blocks = round_up_divide(ifm.size().depth, ifm_block_depth) ifm_depth_blocks = 1 # Overwrite with 1 to force OFM block dependency, not IFM return (start_coord, end_coord, ifm_depth_blocks) # start, end, total jobs for this OFM block def calc_block_dep( self, prev_ifm: Block, prev_ofm: Block, prev_ifm_block_depth, prev_ofm_block: Block, prev_kernel: Kernel, ifm: Block, ofm: Block, ifm_block_depth, ofm_block: Block, kernel: Kernel, padLT, ): blockdep = ArchitectureFeatures.MAX_BLOCKDEP # Iterate over the next BLOCKDEP inputs, checking to see if a sliding window # of IFM area overlaps with any previous OFM block generation. elapsed_jobs = 0 for forward_offset in range(ArchitectureFeatures.MAX_BLOCKDEP): # This is the IFM block we want to sample from in_area = self.get_first_job_input_volume( ifm, ofm, ifm_block_depth, ofm_block, kernel, padLT, forward_offset ) if in_area is None: break # Try several previous-OFM blocks in the past (they still might comprise multiple IFM jobs) outstanding_jobs = 0 for block_offset in range(ArchitectureFeatures.MAX_BLOCKDEP): # This is the OFM block being generated by the previous op out_area = self.get_prev_job_output_volume( prev_ifm, prev_ofm, prev_ifm_block_depth, prev_ofm_block, prev_kernel, block_offset ) if out_area is None: break # Block dependency is the max number of allowed outstanding jobs # in the pipeline. Selected by determining how many jobs occur # in between two operators' overlapping OFM->IFM block volumes if ArchitectureFeatures.intersects(in_area[0], in_area[1], out_area[0], out_area[1]): break # Early exit if no intersections and we've seen enough jobs in the pipeline elif outstanding_jobs > ArchitectureFeatures.MAX_BLOCKDEP: break # This OFM had this many jobs (accumulate over multiple OFM blocks) outstanding_jobs += out_area[2] blockdep = min(blockdep, elapsed_jobs + outstanding_jobs) elapsed_jobs += in_area[2] # Early exit if no intersections and we've seen enough jobs in the pipeline if elapsed_jobs > ArchitectureFeatures.MAX_BLOCKDEP: break return blockdep def cpu_cycle_estimate(self, op): """ Gets estimated performance of a CPU operation, based on a linear model of intercept, slope, specified in the vela config file, in ConfigParser file format (.ini file). Example configuration snippet: [CpuPerformance.MyOperationType] Cortex-Mx.intercept= Cortex-Mx.slope= """ section = "CpuPerformance." + op.type if self.vela_config is not None and section in self.vela_config: op_config = self.vela_config[section] try: intercept = float(op_config.get(self.cpu_config + ".intercept", op_config["default.intercept"])) slope = float(op_config.get(self.cpu_config + ".slope", op_config["default.slope"])) n_elements = op.inputs[0].elements() cycles = intercept + n_elements * slope return cycles except Exception: print("Error: Reading CPU cycle estimate in vela configuration file, section {}".format(section)) raise print("Warning: No configured CPU performance estimate for", op.type) return 0 def __read_sys_config(self): """ Gets the system configuration with the given name from the vela configuration file Example configuration snippet: [SysConfig.MyConfigName] npu_freq= cpu=Cortex-Mx ... """ # Get system configuration from the vela configuration file if self.vela_config is None: print("Warning: Using default values for system configuration") else: section_key = "SysConfig." + self.system_config if section_key not in self.vela_config: raise OptionError("--system-config", self.system_config, "Unknown system configuration") try: self.npu_clock = float(self.__sys_config("npu_freq", "500e6")) self.cpu_config = self.__sys_config("cpu", "Cortex-M7") self.memory_clock_scales[MemArea.Sram] = float(self.__sys_config("Sram_clock_scale", "1")) self.memory_port_widths[MemArea.Sram] = int(self.__sys_config("Sram_port_width", "64")) self.memory_clock_scales[MemArea.OnChipFlash] = float(self.__sys_config("OnChipFlash_clock_scale", "1")) self.memory_port_widths[MemArea.OnChipFlash] = int(self.__sys_config("OnChipFlash_port_width", "64")) self.memory_clock_scales[MemArea.OffChipFlash] = float( self.__sys_config("OffChipFlash_clock_scale", "0.25") ) self.memory_port_widths[MemArea.OffChipFlash] = int(self.__sys_config("OffChipFlash_port_width", "32")) self.memory_clock_scales[MemArea.Dram] = float(self.__sys_config("Dram_clock_scale", "1")) self.memory_port_widths[MemArea.Dram] = int(self.__sys_config("Dram_port_width", "32")) self.fast_storage_mem_area = MemArea[self.__sys_config("fast_storage_mem_area", "Sram")] self.feature_map_storage_mem_area = MemArea[self.__sys_config("feature_map_storage_mem_area", "Sram")] self.permanent_storage_mem_area = MemArea[self.__sys_config("permanent_storage_mem_area", "OffChipFlash")] if self.permanent_storage_mem_area not in set((MemArea.OnChipFlash, MemArea.OffChipFlash)): raise Exception( "Invalid permanent_storage_mem_area = " + str(self.permanent_storage_mem_area) + " (must be 'OnChipFlash' or 'OffChipFlash'). To store the weights and other constant data in SRAM" " select 'OnChipFlash'" ) except Exception: print("Error: Reading System Configuration in vela configuration file, section {}".format(section_key)) raise def __sys_config(self, key, default_value): """ Gets the system configuration value with the given key from the vela config file. """ if self.vela_config is None: return default_value section = "SysConfig." + self.system_config result = self.vela_config[section].get(key, None) if result is None: raise Exception("Error: System Configuration Missing key {} in section [{}] ".format(key, section)) return result