# Copyright (C) 2020-2021 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. from collections import namedtuple from typing import Tuple import numpy as np from .api import NpuBlockTraversal 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[int, int], ifm_bitdepth: int, ofm_block_depth: int, is_depthwise: bool, block_traversal: NpuBlockTraversal, ): """ Internal implementation of the public facing API to use weight encoding. :param accelerator: architecture_features.Accelerator enum to pick the correct Ethos-U 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 Ethos-U processing :param is_depthwise: a boolean indicating these weights are used for a depthwise traversal :param block_traversal: indicates how these weights are traversed on sub-kernel basis :return: a tuple with a bytearray of encoded weights and the size of the unencoded 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(block_traversal, NpuBlockTraversal) # 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 block_traversal == NpuBlockTraversal.PART_KERNEL_FIRST ), "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 decomp_h = ArchitectureFeatures.SubKernelMax.height // dilation_xy[0] decomp_w = ArchitectureFeatures.SubKernelMax.width // dilation_xy[1] return mlw_codec.reorder_encode( ifm_ublock.depth, ofm_ublock.depth, weights_volume, ofm_block_depth, is_depthwise, block_traversal == NpuBlockTraversal.PART_KERNEL_FIRST, ifm_bitdepth, decomp_h, decomp_w, ) def encode_bias(bias: np.int64, scale: int, shift: int): """ Internal implementation of public facing API to pack bias and scale values as required by the Ethos-U :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 has_tensor_with_same_compression(self, wcc): return self.cache.get(wcc) is not None def get_tensor_with_same_compression(self, wcc): cache_obj = self.cache.get(wcc) return cache_obj[0] if cache_obj else None def get_unencoded_size_with_same_compression(self, wcc): cache_obj = self.cache.get(wcc) return cache_obj[1] if cache_obj else None def add(self, tens, unencoded_size): # 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, unencoded_size) 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 nng.weight_cache.get_unencoded_size_with_same_compression(wcc) # 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 unencoded_size = 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 if tens.block_traversal == TensorBlockTraversal.PartKernelFirst: block_traversal = NpuBlockTraversal.PART_KERNEL_FIRST else: block_traversal = NpuBlockTraversal.DEPTH_FIRST 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, raw_stream_size = 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, block_traversal=block_traversal, ) unencoded_size += raw_stream_size 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, unencoded_size) return unencoded_size 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.get_input_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.get_input_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: # for some cases of the Mean operator, the scale must be calculated differently to match reference if first_consumer_op.low_precision_scaling: scales = [ np.double(np.single(ifm_scale) / (np.single(weight_scale) * np.single(ofm_scale))) for weight_scale in weight_scales ] else: 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(f"Compression of {ifm_dtype} is not implemented; Tensor: '{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(f"Compression of {ifm_dtype} is not implemented; Tensor: '{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] nng.total_npu_weights += compress_weights( arch, nng, tens, op.type.npu_block_type, ps.block_config[-1], ofm_depth_step, op.get_dilation_h_w() ) nng.total_npu_encoded_weights += tens.weight_compressed_offsets[-1] nng.total_original_weights += int(tens.elements() * tens.element_size()) # 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 if (ps.ops[-1].type in (Op.Sigmoid, Op.Tanh)) 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)