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# SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
#
# 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 collections import OrderedDict
from typing import Dict
from typing import Optional
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 .numeric_util import round_up
from .operation import NpuBlockType
from .operation import Op
from .scaling import quantise_scale
from .scaling import reduced_quantise_scale
from .tensor import Tensor
from .tensor import TensorFormat
from .tensor import TensorPurpose
# Handle any errors thrown by NumPy while importing mlw_codec module
try:
from ethosu import mlw_codec
except RuntimeError as ex:
if "mlw_codec error: module compiled against API version" in str(ex):
# Extract API versions from error message
matches = [s for s in str(ex).split() if "0x" in s]
if len(matches) == 2:
# Raise new exception with more detailed message
raise ImportError( # pylint: disable=W0707
"NumPy C API version mismatch "
f"(Build-time version: {matches[0]}, "
f"Run-time version: {matches[1]})"
"\nThis is a known issue most likely caused by a change in the API "
"version in NumPy after installing ethos-u-vela.\nYou can find more "
"information about the issue and possible solutions in the "
"'Known Issues' section at https://review.mlplatform.org/"
"plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/heads/main/"
"README.md#known-issues"
)
raise
# 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", "weight_value_id"],
)
ScaleCompressionConfig = namedtuple("ScaleCompressionConfig", ["scale_value_id", "ifm_scale", "ofm_scale"])
WeightKey = namedtuple("WeightKey", ["core", "depth"])
class WeightRange:
def __init__(self):
self.offset = 0
self.scale_bytes = 0
self.weight_offset = 0
self.weight_bytes = 0
self.index = 0
@property
def total_bytes(self):
return self.scale_bytes + self.weight_bytes
class NpuWeightTensor(Tensor):
def __init__(self, name):
Tensor.__init__(self, None, None, name + "_npu_encoded_weights")
self.buffer = []
self.double_buffer_sizes = [0, 0] # Required sizes if double buffering is used
self.encoded_ranges = OrderedDict()
self.hw_traversal = NpuBlockTraversal.DEPTH_FIRST
self.dtype = DataType.uint8
self.scale_compression_config = None
def max_range_bytes(self):
return max(self.double_buffer_sizes)
def double_buffer_size(self):
"""Return total required size for double buffering"""
return sum(self.double_buffer_sizes)
class CompressedWeightCache:
"""Global tensor weight compression cache"""
cache: Dict[WeightCompressionConfig, Tensor] = {}
@staticmethod
def get_tensor_with_same_compression(wcc):
return CompressedWeightCache.cache.get(wcc)
@staticmethod
def add(tens):
# Adds the compressed weights from the tensor to the cache
wcc = tens.weight_compression_config
CompressedWeightCache.cache[wcc] = tens
@staticmethod
def has_tensor_with_same_compression(wcc):
return wcc in CompressedWeightCache.cache
@staticmethod
def get_unencoded_size_with_same_compression(wcc):
cache_obj = CompressedWeightCache.cache.get(wcc)
return cache_obj[1] if cache_obj else None
def create_weight_compression_config(weight_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, weight_tens.values.shape[-1])
return WeightCompressionConfig(npu_block_type, block_depth, ofm_depth_step, dilation, weight_tens.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[1]
decomp_w = ArchitectureFeatures.SubKernelMax.width // dilation_xy[0]
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 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]
def _prepare_scale_and_bias(arch, tens, rescale_for_faf, explicit_scaling):
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.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 as well as
# for FullyConnected operators
if not rescale_for_faf:
if ifm_dtype == DataType.uint8 or first_consumer_op.type == Op.FullyConnected:
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}'")
if explicit_scaling:
assert len(explicit_scaling.shift) == len(explicit_scaling.multiplier)
quantised_scales = [(int(m), int(s)) for s, m in zip(explicit_scaling.shift, explicit_scaling.multiplier)]
else:
# quantise all of the weight scales into (scale_factor, shift)
if ifm_dtype == DataType.int16 and bias_tens.dtype == DataType.int64:
# Reference uses reduced scaling for int16 with int64 bias
quantised_scales = [reduced_quantise_scale(scale) for scale in scales]
else:
quantised_scales = [quantise_scale(scale) for scale in scales]
# Check the output quantisation to see if the scale value needs increasing to the next one
if first_consumer_op.get_output_quantization().next_after:
for i, quant_scale in enumerate(quantised_scales):
q_scale, q_shift = quant_scale
quantised_scales[i] = (q_scale + 1, q_shift)
# If only 1 quantised scale is used, repeat that value for the length of the biases
if len(quantised_scales) == 1:
quantised_scales = [quantised_scales[0]] * len(biases)
return quantised_scales, biases
def encode_weight_and_scale_tensor(
arch, op, weight_tens, scale_tens, kernel, block_config, depth_offsets, rescale_for_faf=False
) -> Tuple[Optional[NpuWeightTensor], Optional[NpuWeightTensor]]:
npu_block_type = op.type.npu_block_type
ifm_scale = scale_tens and scale_tens.consumer_list[0].get_input_quantization().scale_f32
ofm_scale = scale_tens and scale_tens.consumer_list[0].get_output_quantization().scale_f32
wcc = create_weight_compression_config(
weight_tens, npu_block_type, block_config.ofm_block.depth, hash(str(depth_offsets)), kernel.dilation
)
scc = ScaleCompressionConfig(scale_tens and scale_tens.value_id, ifm_scale, ofm_scale)
tens_cached = CompressedWeightCache.get_tensor_with_same_compression(wcc)
if tens_cached is not None:
if tens_cached.scale_compression_config == scc:
return tens_cached, None
npu_tensor = NpuWeightTensor(scale_tens.name)
do_weights = False
do_scales = True
else:
npu_tensor = NpuWeightTensor(weight_tens.name)
do_weights = True
do_scales = True
npu_tensor.weight_compression_config = wcc
npu_tensor.scale_compression_config = scc
# Ensure depth offsets are terminated at end of OFM shape
assert len(depth_offsets) > 1, "Require closed depth ranges"
ifm_bitdepth = op.inputs[0].dtype.size_in_bits()
# No cache hit, need to perform the encoding
if do_weights:
assert weight_tens.quantization is not None
assert weight_tens.quantization.scale_f32 is not None or op.explicit_scaling
assert weight_tens.quantization.zero_point is not None
# Early zero-point correction
quant_buf = weight_tens.values.astype(np.int16)
# the zero point can be either a native or numpy type
if isinstance(weight_tens.quantization.zero_point, (int, float)):
zero_point = np.int16(weight_tens.quantization.zero_point)
else:
zero_point = weight_tens.quantization.zero_point.astype(np.int16)
weights = quant_buf - zero_point
if len(weights.shape) == 2:
weights = np.expand_dims(np.expand_dims(weights, axis=0), axis=0)
# Expect this (undilated) equivalence
assert kernel.height == weights.shape[0]
assert kernel.width == weights.shape[1]
ifm_depth = weights.shape[-2]
# Default HW traversal
npu_tensor.hw_traversal = NpuBlockTraversal.DEPTH_FIRST
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
npu_tensor.hw_traversal = NpuBlockTraversal.PART_KERNEL_FIRST
if op.type == Op.Conv2DBackpropInputSwitchedBias:
# Transpose Convoluion, reverse weights in H and W axes
weights = np.flip(weights, axis=(0, 1))
encoded_stream = bytearray()
double_buffer_sizes = [0, 0]
is_depthwise = npu_block_type == NpuBlockType.ConvolutionDepthWise
# Bias & scale
if do_scales:
quantised_scales, biases = _prepare_scale_and_bias(arch, scale_tens, rescale_for_faf, op.explicit_scaling)
scale_tens.element_size_bytes = 10
# Slice the weight stream up depth-ways into bricks and compress
full_ofm_depth = weight_tens.values.shape[-1]
ofm_block_depth = block_config.ofm_block.depth
weight_range_index = 0
for idx, depth_offset in enumerate(depth_offsets[:-1]):
# Do not generate for offsets outside the OFM
assert depth_offset >= 0 and depth_offset < full_ofm_depth
depth_length = depth_offsets[idx + 1] - depth_offset
# Get the weights necessary for this brick
if do_weights:
brick_weights = weights[:, :, :, depth_offset : depth_offset + depth_length]
buffer_start_offset = len(encoded_stream)
# For each core, deinterleave weights/scales from the larger volume
# and generate separate compressed streams.
for core in range(0, min(arch.ncores, full_ofm_depth)):
core_block_depth = int((ofm_block_depth + arch.ncores - 1 - core) // arch.ncores)
if core_block_depth != 0:
key = WeightKey(core, depth_offset)
weight_range = WeightRange()
weight_range.offset = len(encoded_stream)
weight_range.index = weight_range_index
weight_range_index += 1
# Scales & biases
if do_scales:
scale_stream = []
core_scales = quantised_scales[
depth_offset + core : depth_offset + core + depth_length : arch.ncores
]
core_biases = biases[depth_offset + core : depth_offset + core + depth_length : arch.ncores]
for j, core_bias in enumerate(core_biases):
scale_stream.extend(encode_bias(np.int64(core_bias), *core_scales[j]))
weight_range.scale_bytes = len(scale_stream)
encoded_stream.extend(scale_stream)
# Align to 16 for start of next substream
remainder = len(encoded_stream) % 16
if remainder > 0:
encoded_stream.extend(bytearray(16 - remainder))
# Weights
if do_weights:
core_weights = core_deinterleave(brick_weights, core, arch.ncores)
encoded_substream, _ = encode_weights(
accelerator=arch.accelerator_config,
weights_volume=core_weights,
dilation_xy=kernel.dilation,
ifm_bitdepth=ifm_bitdepth,
ofm_block_depth=core_block_depth,
is_depthwise=is_depthwise,
block_traversal=npu_tensor.hw_traversal,
)
weight_range.weight_offset = len(encoded_stream) - weight_range.offset
weight_range.weight_bytes = len(encoded_substream)
# Append encoded section
encoded_stream.extend(encoded_substream)
assert len(encoded_stream) % 16 == 0
# Record encoded range in tensor
npu_tensor.encoded_ranges[key] = weight_range
# Remember maximum encoded length for DoubleBuffering
double_buffer_sizes[idx % 2] = max(double_buffer_sizes[idx % 2], len(encoded_stream) - buffer_start_offset)
# Attach buffer to tensor
npu_tensor.buffer = encoded_stream
npu_tensor.double_buffer_sizes = double_buffer_sizes
npu_tensor.set_all_shapes([1, 1, 1, len(encoded_stream)])
npu_tensor.format = TensorFormat.WeightsCompressed
# Scale only tensor
if not do_weights:
npu_tensor.weight_compression_config = None
npu_tensor.purpose = TensorPurpose.FSBias
npu_tensor.mem_area = scale_tens.mem_area
npu_tensor.mem_type = scale_tens.mem_type
weights_tensor = tens_cached
scale_tensor = npu_tensor
else:
npu_tensor.purpose = TensorPurpose.Weights
npu_tensor.mem_area = weight_tens.mem_area
npu_tensor.mem_type = weight_tens.mem_type
weights_tensor = npu_tensor
scale_tensor = None
CompressedWeightCache.add(weights_tensor)
return weights_tensor, scale_tensor
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