# 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: # Compresses and pads the weigths. It also calculates the scales and packs with the biases. import math from collections import namedtuple import numpy as np from .architecture_features import Accelerator from .architecture_features import ArchitectureFeatures from .data_type import DataType from .errors import UnsupportedFeatureError from .nn_graph import SchedulingStrategy from .numeric_util import round_up from .numeric_util import round_up_divide from .operation import NpuBlockType from .operation import Op from .scaling import quantise_scale from .scaling import reduced_quantise_scale from .tensor import create_equivalence_id from .tensor import TensorBlockTraversal from .tensor import TensorFormat from .tensor import TensorPurpose from .tensor import TensorSubPurpose from ethosu import mlw_codec # Contains meta info for a weight compression. If two tensors have identical weight compression config, # then they also will have identical compressed weights. WeightCompressionConfig = namedtuple( "WeightCompressionConfig", ["npu_block_type", "ofm_block_depth", "ofm_depth_step", "dilation", "value_id"] ) def encode_weights( accelerator: Accelerator, weights_volume: np.ndarray, dilation_xy: tuple, ifm_bitdepth: int, ofm_block_depth: int, is_depthwise: bool, is_partkernel: bool, ): """ Public facing API to use the ethosu weight encoding. :param accelerator: architecture_features.Accelerator enum to pick the correct ethosu accelerator :param weights_volume: numpy.ndarray in OHWI layout with a shape of four :param dilation_xy: a two element tuple of dilation attributes in x,y dimension :param ifm_bitdepth: the bitdepth of input feature map :param ofm_block_depth: the depth of blocks for ethosu processing :param is_depthwise: a boolean indicating these weights are used for a depthwise traversal :param is_partkernel: a boolean indicating these weights are traversed on sub-kernal basis :return: a bytearray of compressed weights """ # Check arg types assert isinstance(accelerator, Accelerator) assert isinstance(weights_volume, np.ndarray) assert isinstance(dilation_xy, tuple) assert isinstance(ifm_bitdepth, int) assert isinstance(ofm_block_depth, int) assert isinstance(is_depthwise, bool) assert isinstance(is_partkernel, bool) # Checks for weight layout assert len(weights_volume.shape) == 4, "weights ndarray should have a shape of 4" # It cannot be both partkernel and depthwise assert not (is_depthwise and is_partkernel), "encode_weights :: partkernel and depthwise are mutually exclusive" # Check valid values for dilation assert dilation_xy[0] in (1, 2), "encode_weights :: dilation x should be 1 or 2 not {}".format(dilation_xy[0]) assert dilation_xy[1] in (1, 2), "encode_weights :: dilation y should be 1 or 2 not {}".format(dilation_xy[1]) ifm_ublock = ArchitectureFeatures.accelerator_configs[accelerator].ifm_ublock ofm_ublock = ArchitectureFeatures.accelerator_configs[accelerator].ofm_ublock raw_stream = generate_brick( ifm_ublock=ifm_ublock, ofm_ublock=ofm_ublock, brick_weights=weights_volume, ofm_block_depth=ofm_block_depth, is_depthwise=is_depthwise, is_partkernel=is_partkernel, ifm_bitdepth=ifm_bitdepth, dilation=dilation_xy, ) encoded_stream = encode(raw_stream) return encoded_stream def encode_bias(bias: np.int64, scale: int, shift: int): """ Public facing API to pack bias and scale values as required by the hardware :param bias: 64bit signed number that includes 40bit signed bias :param scale: 32bit scale value :param shift: 6bit shift value :return: packed 80bit [0(2-bits),shift(6-bits),scale(32-bits),bias(40-bits)] """ # Check arg types assert isinstance(bias, np.int64) assert isinstance(scale, int) assert isinstance(shift, int) assert -(1 << (40 - 1)) <= bias < (1 << (40 - 1)) # signed 40-bit range assert 0 <= scale < (1 << 32) # unsigned 32-bit range assert 0 <= shift < (1 << 6) # unsigned 6-bit range data = bytearray(10) data[0] = (bias >> (0 * 8)) & 0xFF data[1] = (bias >> (1 * 8)) & 0xFF data[2] = (bias >> (2 * 8)) & 0xFF data[3] = (bias >> (3 * 8)) & 0xFF data[4] = (bias >> (4 * 8)) & 0xFF data[5] = (scale >> (0 * 8)) & 0xFF data[6] = (scale >> (1 * 8)) & 0xFF data[7] = (scale >> (2 * 8)) & 0xFF data[8] = (scale >> (3 * 8)) & 0xFF data[9] = shift & 0x3F return data def create_weight_compression_config(tens, npu_block_type, ofm_block_depth, ofm_depth_step, dilation): # Note: for an ofm block only its depth is used in weight compression. # And block depth > ofm depth gives same result as block depth == ofm depth block_depth = min(ofm_block_depth, tens.quant_values.shape[-1]) return WeightCompressionConfig(npu_block_type, block_depth, ofm_depth_step, dilation, tens.value_id) def set_storage_shape(tens): # Sets the storage shape depending on the tensor's sub purpose if tens.sub_purpose == TensorSubPurpose.DoubleBuffer and len(tens.compressed_values) > 2: offset = 2 * np.amax([len(x) for x in tens.compressed_values]) assert offset % 16 == 0 else: offset = tens.weight_compressed_offsets[-1] tens.storage_shape = [1, 1, 1, offset] class CompressedWeightCache: # Contains weight compressions for all weight tensors in a graph def __init__(self): self.cache = {} # maps from WeightCompressionConfig to a tensor clone containing compressed weights def get_tensor_with_same_compression(self, wcc): return self.cache.get(wcc) def add(self, tens): # Adds the compressed weights from the tensor to the cache wcc = tens.weight_compression_config # Clone the tensor to make sure that nothing related to the weight compression is modified tens_clone = tens.clone("_weights{}_{}".format(wcc.ofm_block_depth, wcc.ofm_depth_step)) self.cache[wcc] = tens_clone def encode(weight_stream): if len(weight_stream) == 0: return [] assert np.amin(weight_stream) >= -255 assert np.amax(weight_stream) <= 255 # Encode flattened signed weight stream compressed = mlw_codec.encode(weight_stream) # pad with 0xFF as needed so the length of the weight stream # is a multiple of 16 while (len(compressed) % 16) != 0: compressed.append(0xFF) return compressed def generate_brick( ifm_ublock, ofm_ublock, brick_weights, ofm_block_depth, is_depthwise, is_partkernel, ifm_bitdepth, dilation ): decomp_h = ArchitectureFeatures.SubKernelMax.height // dilation[0] decomp_w = ArchitectureFeatures.SubKernelMax.width // dilation[1] # Expect weights formatted OHWI ofm_depth = brick_weights.shape[-4] ifm_depth = brick_weights.shape[-1] kernel_width = brick_weights.shape[-2] kernel_height = brick_weights.shape[-3] # IFM block depth if is_partkernel or (ifm_bitdepth == 16): # IFM block depth is always 16 for part-kernel-first ifm_block_depth = 16 elif ifm_bitdepth == 8: ifm_block_depth = 32 else: assert False stream = [] # Top level striping - OFM blocks in the entire brick's depth for ofm_block_z in range(0, ofm_depth, ofm_block_depth): clipped_ofm_block_depth = min(ofm_block_depth, ofm_depth - ofm_block_z) # IFM blocks required for the brick for ifm_block_z in range(0, (1 if is_depthwise else ifm_depth), ifm_block_depth): if is_depthwise: clipped_ifm_block_depth = ifm_ublock.depth else: clipped_ifm_block_depth = ( min(ifm_block_depth, ifm_depth - ifm_block_z) if is_partkernel else ifm_block_depth ) # Weight decomposition # Subkernel Splitting (H) for subkernel_y in range(0, kernel_height, decomp_h): sub_height = min(kernel_height - subkernel_y, decomp_h) # Subkernel splitting (W) for subkernel_x in range(0, kernel_width, decomp_w): sub_width = min(kernel_width - subkernel_x, decomp_w) subkernel_elements = sub_width * sub_height # Part kernel first works across the kernel H/W and needs padding if is_partkernel: if ifm_bitdepth == 16 and subkernel_elements % 2 != 0: subkernel_elements = int(math.ceil(subkernel_elements / 2) * 2) elif ifm_bitdepth == 8 and subkernel_elements % 4 != 0: subkernel_elements = int(math.ceil(subkernel_elements / 4) * 4) # Depthwise Conv requires multiple of 4 kernel elements in its weight block # this is different from normal conv which is considered "weights depth-first" elif is_depthwise: subkernel_elements = int(math.ceil(subkernel_elements / 4.0) * 4) ifm_block_depth_outer = clipped_ifm_block_depth if is_partkernel else 1 ifm_block_depth_inner = 1 if is_partkernel else clipped_ifm_block_depth # IFM Ublocks in IFM-block over depth for part-kernel-first mode # For depth-first IFM Ublocks are traversed after subkernel elements so this loop is ignored. for ifm_ublk_outer in range(0, ifm_block_depth_outer, ifm_ublock.depth): # OFM Ublocks in OFM-block over depth for ofm_ublk in range(0, clipped_ofm_block_depth, ofm_ublock.depth): # HW Kernel element traversal - cannot be a H/W loop due to element # padding requirement on depthwise/part-kernel configurations for element in range(subkernel_elements): kx = element % sub_width ky = element // sub_width # IFM Ublocks in IFM-block over depth (only 1 ublock if depthwise) # In case of part-kernel-first IFM Ublock traversal have already been handled # and this loop is ignored. for ifm_ublk_inner in range(0, ifm_block_depth_inner, ifm_ublock.depth): # Feed OFM ublock elements for ofm_ublock_z in range(ofm_ublock.depth): # Source IFM ublock elements (only 1 element deep if depthwise) for ifm_ublock_z in range(1 if is_depthwise else ifm_ublock.depth): # Source position within the current subkernel wx = subkernel_x + kx wy = subkernel_y + ky # Source IFM/OFM slices ifm_ublk = ifm_ublk_inner + ifm_ublk_outer ifm_z = ifm_block_z + ifm_ublk + ifm_ublock_z ofm_z = ofm_block_z + ofm_ublk + ofm_ublock_z if (ifm_z >= ifm_depth) or (ofm_z >= ofm_depth) or (ky >= sub_height): stream.append(0) else: stream.append(brick_weights[ofm_z][wy][wx][ifm_z]) return stream def core_deinterleave(hwio, core, ncores): # Put weights back into OHWI ohwi = np.transpose(hwio, (3, 0, 1, 2)) return ohwi[core : ohwi.shape[0] : ncores] # Compress the weights def compress_weights(arch, nng, tens, npu_block_type, ofm_block_depth, ofm_depth_step, dilation): assert tens.purpose == TensorPurpose.Weights # Check the weight cache if nng.weight_cache is None: nng.weight_cache = CompressedWeightCache() wcc = create_weight_compression_config(tens, npu_block_type, ofm_block_depth, ofm_depth_step, dilation) tens.weight_compression_config = wcc # Reassign equivalence id such that tensors with same weight compression get identical equivalence ids, # but tensors with the same values but different compression get different equivalence ids tens.equivalence_id = create_equivalence_id(wcc) tens_cached = nng.weight_cache.get_tensor_with_same_compression(wcc) if tens_cached is not None: # Cache hit, copy weights from the cache tens.copy_compressed_weight_info(tens_cached) set_storage_shape(tens) return # No cache hit, perform the compression assert tens.quantization is not None assert tens.quantization.scale_f32 is not None assert tens.quantization.zero_point is not None zero_point = tens.quantization.zero_point quant_buf = tens.quant_values.astype(np.int64) # Early zero-point correction weights = quant_buf - zero_point if len(weights.shape) == 2: weights = np.expand_dims(np.expand_dims(weights, axis=0), axis=0) compression_scales = [] compressed_offsets = [] encoded_streams = [] encoded_streams_substream_offsets = [] offset = 0 max_single_buffer_len = 0 ifm_bitdepth = tens.consumer_list[0].inputs[0].dtype.size_in_bits() ifm_depth = weights.shape[-2] if npu_block_type == NpuBlockType.ConvolutionDepthWise: tens.block_traversal = TensorBlockTraversal.DepthWise if npu_block_type == NpuBlockType.ConvolutionMxN: # Determine which block traversal strategy has better DPU utilization kernel_size = weights.shape[0] * weights.shape[1] depth_utilization = weights.shape[2] / round_up(weights.shape[2], 32 if ifm_bitdepth == 8 else 16) part_kernel_utilization = (weights.shape[2] / round_up(weights.shape[2], 8)) * ( kernel_size / round_up(kernel_size, 4 if ifm_bitdepth == 8 else 2) ) if part_kernel_utilization >= depth_utilization or ifm_depth <= 8: # Part-kernel first is always better for ifm depths <= 8 tens.block_traversal = TensorBlockTraversal.PartKernelFirst else: tens.block_traversal = TensorBlockTraversal.DepthFirst is_depthwise = tens.block_traversal == TensorBlockTraversal.DepthWise is_partkernel = tens.block_traversal == TensorBlockTraversal.PartKernelFirst if tens.consumer_list[0].type == Op.Conv2DBackpropInputSwitchedBias: # Transpose Convoluion, reverse weights in H and W axes weights = np.flip(weights, axis=(0, 1)) # Calculate brick size brick_size = (weights.shape[0], weights.shape[1], weights.shape[2], min(tens.shape[-1], ofm_depth_step)) elements_in_brick = np.prod(brick_size) # Slice weight stream up depth-ways into bricks and compress full_ofm_depth = quant_buf.shape[-1] for idx in range(0, full_ofm_depth, ofm_depth_step): # Get the weights necessary for this brick count = min(full_ofm_depth - idx, ofm_depth_step) brick_weights = weights[:, :, :, idx : idx + count] substream_offsets = [0] encoded_stream = [] # For each core, deinterleave weights from the larger volume # and generate separate compressed streams. for core in range(0, min(arch.ncores, full_ofm_depth)): core_weights = core_deinterleave(brick_weights, core, arch.ncores) block_depth = (ofm_block_depth + arch.ncores - 1 - core) // arch.ncores encoded_substream = [] if block_depth != 0: encoded_substream = encode_weights( accelerator=arch.accelerator_config, weights_volume=core_weights, dilation_xy=dilation, ifm_bitdepth=ifm_bitdepth, ofm_block_depth=block_depth, is_depthwise=is_depthwise, is_partkernel=is_partkernel, ) encoded_stream.extend(encoded_substream) substream_offsets.append(len(encoded_stream)) encoded_streams.append(encoded_stream) encoded_streams_substream_offsets.append(substream_offsets) # Remember maximum encoded length for DoubleBuffering max_single_buffer_len = max(max_single_buffer_len, len(encoded_stream)) # Remember where we put it for linear addressing compressed_offsets.append(offset) offset += len(encoded_stream) assert offset % 16 == 0 # Compression scale tracking compression_scales.append(len(encoded_stream) / elements_in_brick) # Track total length as last element of the offsets array compressed_offsets.append(offset) tens.weight_compression_scales = compression_scales tens.weight_compressed_offsets = compressed_offsets tens.compression_scale_for_worst_weight_stream = np.amax(compression_scales) tens.storage_compression_scale = tens.bandwidth_compression_scale = np.average(compression_scales) tens.compressed_values = encoded_streams tens.compressed_values_substream_offsets = encoded_streams_substream_offsets tens.brick_size = brick_size set_storage_shape(tens) nng.weight_cache.add(tens) def calc_scales_and_pack_biases(tens, arch, ofm_depth_step, rescale_for_faf=False): assert tens.purpose in [TensorPurpose.FeatureMap, TensorPurpose.FSBias] assert tens.format == TensorFormat.NHWC # the connected operator should expect a bias input unless it is a FullyConnected assert tens.consumer_list[0].type.needs_bias() # the input bias tensor is the same as that connected to the operator bias_tens = tens.consumer_list[0].bias assert tens is bias_tens # the operator should only have a single output assert len(tens.consumer_list[0].outputs) == 1 biases = tens.quant_values first_consumer_op = tens.consumer_list[0] ifm_dtype = first_consumer_op.inputs[0].dtype ifm_scale = first_consumer_op.inputs[0].quantization.scale_f32 ofm_scale = first_consumer_op.get_output_quantization().scale_f32 weight_scales = first_consumer_op.inputs[1].quantization.scale_f32 # biases can have multiple consumers for rnn cells. if so, then check that they are all the same for op in tens.consumer_list[1:]: assert ifm_scale == op.inputs[0].quantization.scale_f32 assert ofm_scale == op.get_output_quantization().scale_f32 assert weight_scales == op.inputs[1].quantization.scale_f32 if not hasattr(weight_scales, "__iter__"): # If weight_scales is not already an iterable make it into a list weight_scales = [weight_scales] # Convert scales to np.double (from np.float32) to conform to TensorFlow Lite which # uses double during scaling calculations # TensorFlow Lite casts the scales slightly differently for uint8 and int8 if not rescale_for_faf: if ifm_dtype == DataType.uint8: scales = [np.double(ifm_scale * weight_scale) / np.double(ofm_scale) for weight_scale in weight_scales] elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16: scales = [ (np.double(ifm_scale) * np.double(weight_scale)) / np.double(ofm_scale) for weight_scale in weight_scales ] else: raise UnsupportedFeatureError( "Compression of {} is not implemented; tensor: {}".format(ifm_dtype, tens.name) ) else: if ifm_dtype == DataType.uint8: scales = [np.double(ifm_scale * weight_scale * 0x3000) for weight_scale in weight_scales] elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16: scales = [(np.double(ifm_scale * 0x3000) * np.double(weight_scale)) for weight_scale in weight_scales] else: raise UnsupportedFeatureError( "Compression of {} is not implemented; tensor: {}".format(ifm_dtype, tens.name) ) # quantise all of the weight scales into (scale_factor, shift) if ifm_dtype == DataType.int16: quantised_scales = [reduced_quantise_scale(scale) for scale in scales] else: quantised_scales = [quantise_scale(scale) for scale in scales] # pack the biases and scales if len(quantised_scales) == 1: # If only 1 quantised scale is used, repeat that value for the length of the biases quantised_scales = [quantised_scales[0]] * len(biases) assert len(quantised_scales) == len(biases) tens.element_size_bytes = 10 tens.compressed_values = [] tens.compressed_values_substream_offsets = [] total_elements = len(quantised_scales) alignment_bytes = 0 for i in range(0, total_elements, ofm_depth_step): # Extract streams from brick to generate substreams for each core stream = bytearray() substream_offsets = [0] max_len = min(ofm_depth_step, total_elements - i) for core in range(0, min(arch.ncores, max_len)): core_scales = quantised_scales[i + core : i + core + max_len : arch.ncores] core_biases = biases[i + core : i + core + max_len : arch.ncores] for j, core_bias in enumerate(core_biases): stream.extend(encode_bias(np.int64(core_bias), *core_scales[j])) # Align to 16 for start for next substream remainder = (len(stream)) % 16 if remainder > 0: stream.extend(bytearray(16 - remainder)) alignment_bytes += 16 - remainder substream_offsets.append(len(stream)) # Add to compressed values with their substream offset lists to the tensor tens.compressed_values.append(stream) tens.compressed_values_substream_offsets.append(substream_offsets) tens.storage_shape = [total_elements + round_up_divide(alignment_bytes, tens.element_size_bytes)] def update_pass_weight_and_scale_tensors(nng, arch): for sg in nng.subgraphs: for ps in sg.passes: tens = ps.weight_tensor if tens is not None: op = tens.find_npu_op() if op is None: continue needs_dma = tens.needs_dma() if ps.cascade.strategy == SchedulingStrategy.WeightStream and needs_dma: ofm_depth_step = ps.block_config[-1] else: ofm_depth_step = tens.shape[-1] compress_weights( arch, nng, tens, op.type.npu_block_type, ps.block_config[-1], ofm_depth_step, op.get_dilation_h_w() ) # Update source tensor if needs_dma: src_tens = tens.get_dma_src_tensor() src_tens.shape = tens.shape src_tens.quant_values = tens.quant_values src_tens.copy_compressed_weight_info(tens) set_storage_shape(src_tens) if ps.scale_tensor is not None: rescale_for_faf = False activation_ops = set((Op.Sigmoid, Op.Tanh)) if (ps.ops[-1].type in activation_ops) and (ps.npu_block_type != NpuBlockType.ElementWise): rescale_for_faf = True calc_scales_and_pack_biases(ps.scale_tensor, arch, ofm_depth_step, rescale_for_faf) if ps.scale_tensor.ops[0].type == Op.DMA: src_tens = ps.scale_tensor.get_dma_src_tensor() src_tens.shape = ps.scale_tensor.shape src_tens.quant_values = ps.scale_tensor.quant_values src_tens.element_size_bytes = ps.scale_tensor.element_size_bytes src_tens.copy_compressed_weight_info(ps.scale_tensor)