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# Copyright (C) 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: Architecture SHRAM allocator
import enum
import math
from typing import Dict
from typing import Optional
from typing import Tuple
from typing import Union
from .architecture_features import ArchitectureFeatures
from .architecture_features import Block
from .architecture_features import SHRAMConfig
from .architecture_features import SHRAMElements
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 Kernel
from .operation import NpuBlockType
from .range_set import MemoryRangeSet
from .shape4d import Shape4D
from .tensor import MemArea
class SHRAMLayout:
def __init__(self):
self.ib_start = 0
self.ib_end = 0
self.ib_start2 = 0
self.ab_start = 0
self.lut_start = 0
class ArchitectureBlockConfig:
def __init__(self):
self.layout = SHRAMLayout()
self.ifm_block = Shape4D()
self.ofm_block = Shape4D() # non-1D-optimised block
self.acc_type = SHRAMElements.Acc32
self.is_partkernel = False
self.bank_size = 0
def get_shram_memory_access_range(self):
# Returns the SHRAM memory access range used by this shared buffer,
# excluding access to LUT
return MemoryRangeSet(MemArea.Shram, 0, self.layout.lut_start * self.bank_size)
def old_style_representation(self):
return [self.ofm_block.height, self.ofm_block.width, self.ifm_block.depth, self.ofm_block.depth]
def __str__(self):
return str(self.old_style_representation())
_AccumulatorBits = {SHRAMElements.Acc16: 16, SHRAMElements.Acc32: 32, SHRAMElements.Acc40: 40}
class ElementwiseUsage(enum.IntEnum):
No = 0
Full = 1
Scalar = 2
def _try_block_config(
shram: SHRAMConfig,
ew_usage: ElementwiseUsage,
ofm_block: Union[Shape4D, Block],
ifm_block: Union[Shape4D, Block],
ifm_bits: int,
ifm_granule: int,
acc_bits: int,
acc_granule: int,
lut_banks: int,
) -> Union[SHRAMLayout, None]:
assert (acc_bits > 0) and (acc_granule > 0)
assert (ifm_bits >= 8) and ((ifm_bits % 8) == 0) and (ifm_granule > 0)
# Aways need IFM space
ifm_bytes = ifm_block.elements_wh() * round_up((ifm_block.depth * ifm_bits) / 8, 8)
ifm_banks = round_up_divide(ifm_bytes, shram.bank_size_bytes) * 2
ifm_banks = round_up(ifm_banks, ifm_granule)
# Calculate SHRAM boundaries of the IFM and Accumulators
lut_start = shram.total_banks - lut_banks
ifm_end = shram.reserved_output_banks + ifm_banks
ifm2_start = ifm_end
acc_start = lut_start
# If not elementwise then we need accumulator space
if ew_usage == ElementwiseUsage.No:
acc_bytes = (ofm_block.elements_wh() * round_up(ofm_block.depth, 8) * acc_bits) // 8
acc_banks = round_up_divide(acc_bytes, shram.bank_size_bytes) * 2
acc_banks = round_up(acc_banks, acc_granule)
acc_start = acc_start - acc_banks
else:
ifm2_banks = ifm_banks if ew_usage == ElementwiseUsage.Full else 0
if ifm2_start + ifm2_banks > acc_start:
return None
ifm_end = acc_start
# IFM must still fit before accumulators
if ifm_end > acc_start:
return None
# Should all fit, so return this layout
layout = SHRAMLayout()
layout.ib_start = shram.reserved_output_banks
layout.ib_start2 = ifm2_start
layout.ib_end = ifm_end
layout.ab_start = acc_start
layout.lut_start = lut_start
return layout
def _choose_kernel_method(ifm_shape: Shape4D, ifm_bits: int, kernel: Kernel) -> bool:
if ifm_shape.depth <= 8:
return True
# Compare part-kernel to depth-kernel and choose the one with best utilisation
kernel_elements = kernel.elements_wh()
depth_utilisation = ifm_shape.depth / round_up(ifm_shape.depth, 32 if ifm_bits == 8 else 16)
part_utilisation = (
ifm_shape.depth
* kernel_elements
/ (round_up(ifm_shape.depth, 8) * round_up(kernel_elements, 4 if ifm_bits == 8 else 2))
)
return part_utilisation > depth_utilisation
def _ew_usage(npu_op_type: NpuBlockType, uses_scalar: bool) -> ElementwiseUsage:
ew_usage = ElementwiseUsage.No
if npu_op_type == NpuBlockType.ElementWise:
ew_usage = ElementwiseUsage.Full
if uses_scalar:
ew_usage = ElementwiseUsage.Scalar
return ew_usage
def _acc_type(npu_op_type: NpuBlockType, ifm_bits: int, scaled: bool) -> int:
"""Returns accumulator type"""
acc_type = SHRAMElements.Acc32
if (ifm_bits == 16) and npu_op_type != NpuBlockType.Pooling and scaled:
acc_type = SHRAMElements.Acc40
return acc_type
def is_nearest(ifm_resampling: resampling_mode) -> bool:
return ifm_resampling == resampling_mode.NEAREST
def to_upscale(ifm_resampling: resampling_mode) -> int:
# Upscaling depending on resampling mode
return 1 if ifm_resampling == resampling_mode.NONE else 2
def _ifm_blockdepth(arch, ifm_shape: Union[Shape4D, Block], ifm_bits: int, is_partkernel: bool):
if ifm_bits == 16:
ifm_blockdepth = round_up(min(ifm_shape.depth, 16), 4)
else:
ifm_blockdepth = round_up(min(ifm_shape.depth, 16 if is_partkernel else 32), arch.ifm_ublock.depth)
return ifm_blockdepth
def _required_size(value: int, stride: int, border: int, upscale: int, nearest: bool) -> int:
return int(math.ceil(((value - 1) * stride + border + nearest) / upscale))
def get_ifm_area_required(
ofm_shape: Union[Shape4D, Block], kernel: Kernel, resampling_mode: resampling_mode
) -> Tuple[int, int]:
upscale = to_upscale(resampling_mode)
nearest = is_nearest(resampling_mode)
h1 = _required_size(ofm_shape.height, kernel.stride.y, kernel.area_height(), upscale, nearest)
w1 = _required_size(ofm_shape.width, kernel.stride.x, kernel.area_width(), upscale, nearest)
return (w1, h1)
def _get_ifm_blocksize(
ofm_block: Union[Shape4D, Block], kernel: Kernel, ublock: Block, subkernel_limit: Block, upscale: int, nearest: bool
) -> Shape4D:
# IFM block height
h1 = _required_size(
ofm_block.height, kernel.stride.y, min(kernel.area_height(), subkernel_limit.height), upscale, nearest
)
h2 = h1
height = round_up(min(h1, h2), ublock.height)
# IFM block width
w1 = _required_size(
ofm_block.width, kernel.stride.x, min(kernel.area_width(), subkernel_limit.width), upscale, nearest
)
w2 = w1
width = round_up(min(w1, w2), ublock.width)
return Shape4D(1, height, width, ofm_block.depth)
def fit_block_for_ofm(
arch: ArchitectureFeatures, ofm_shape: Union[Shape4D, Block], kernel: Kernel, block: Union[Shape4D, Block]
):
# 256/512 Conv1D optimisation (ratio of IFM:Accumulators changes) This is a specific
# interpretation of a more general constraint that can't be applied because the
# find_block_config function must return block configs that can be applied to any OFM shape.
if (ofm_shape.height == 1) and (kernel.height == 1) and (arch.ofm_ublock.height == 2):
return Shape4D(1, min(block.height, ofm_shape.height), block.width, block.depth)
return block
def find_block_config(
arch: ArchitectureFeatures,
npu_op_type: NpuBlockType,
ofm_shape: Shape4D,
ifm_shape: Shape4D,
ifm2_shape: Optional[Shape4D],
uses_scalar: bool,
ifm_bits: int,
kernel: Kernel,
lut_banks: int,
scaled: bool,
ifm_resampling: resampling_mode,
) -> Optional[ArchitectureBlockConfig]:
SplitDepth = ArchitectureFeatures.OFMSplitDepth
# Elementwise larger-volume correction
if ifm2_shape is not None and ifm2_shape.elements() > ifm_shape.elements():
ifm_shape = ifm2_shape
# Figure out if SHRAM should be portioned for elementwise
ew_usage = _ew_usage(npu_op_type, uses_scalar)
# Operator typing help
is_pooling = npu_op_type == NpuBlockType.Pooling
is_depthwise = npu_op_type == NpuBlockType.ConvolutionDepthWise
is_equal_depth_op = (ew_usage != ElementwiseUsage.No) or is_pooling or is_depthwise
is_convolution = (npu_op_type == NpuBlockType.ConvolutionMxN) or is_depthwise
# Block config to be returned
config = ArchitectureBlockConfig()
config.is_partkernel = is_convolution and _choose_kernel_method(ifm_shape, ifm_bits, kernel)
# Accumulator & granule settings
config.acc_type = _acc_type(npu_op_type, ifm_bits, scaled)
# Memory rounding granules
acc_granule = arch.accumulator_granules[config.acc_type]
acc_bits = _AccumulatorBits[config.acc_type]
if ew_usage != ElementwiseUsage.No:
ifm_granule = arch.ifm_ew_bank_granules[ifm_bits]
else:
ifm_granule = arch.ifm_bank_granules[ifm_bits]
lut_banks = max(lut_banks, arch.shram.reserved_end_banks)
upscale = to_upscale(ifm_resampling)
nearest = is_nearest(ifm_resampling)
# Subkernel repeats of the IFM
ifm_repeats = round_up_divide(kernel.area_width(), arch.SubKernelMax.width) * round_up_divide(
kernel.area_height(), arch.SubKernelMax.height
)
ifm_blockdepth = _ifm_blockdepth(arch, ifm_shape, ifm_bits, config.is_partkernel)
# Weights fetch (for operators that have them)
weight_fetch_wh = (kernel.area_width() * kernel.area_height()) if is_convolution else 0
search_space = Shape4D.min(ofm_shape, Shape4D(arch.ofm_block_max.to_hwc()))
search_space = Shape4D.round_up(search_space, Shape4D(arch.ofm_ublock.to_hwc()))
# Block WHC search, loops across the search space looking for best efficiency
best_cost = math.inf
best_coverage = math.inf
depth = max(arch.ofm_ublock.depth, min(search_space.depth, SplitDepth))
if depth < ofm_shape.depth:
depth = round_up(depth, SplitDepth)
while depth <= search_space.depth:
wont_fit: Dict[Tuple[int, int], bool] = {}
for height in range(arch.ofm_ublock.height, search_space.height + 1, arch.ofm_ublock.height):
for width in range(arch.ofm_ublock.width, search_space.width + 1, arch.ofm_ublock.width):
# Avoid checking W/H transposed blocks that already didn't fit. i.e. if 8x4x16 didn't
# fit, then 4x8x16 won't either.
if wont_fit.get((height, width), False):
continue
# Calculate the IFM block dimensions required to feed this OFM block
ofm_block = Shape4D(1, height, width, depth)
ifm_block = _get_ifm_blocksize(ofm_block, kernel, arch.ofm_ublock, arch.SubKernelMax, upscale, nearest)
if not is_equal_depth_op:
ifm_block = ifm_block.with_depth(ifm_blockdepth)
# Test if the IFM/OFM blocks fit into SHRAM
ofm_block = fit_block_for_ofm(arch, ofm_shape, kernel, ofm_block)
layout = _try_block_config(
arch.shram,
ew_usage,
Block(ofm_block.width, ofm_block.height, ofm_block.depth),
Block(ifm_block.width, ifm_block.height, ifm_block.depth),
ifm_bits,
ifm_granule,
acc_bits,
acc_granule,
lut_banks,
)
if layout:
full_blocks = Shape4D.div_round_up(ofm_shape, ofm_block)
blocks = ofm_shape / ofm_block
# Weights fetching
weight_fetch = weight_fetch_wh * ifm_shape.depth * full_blocks.elements_wh()
if not is_depthwise:
weight_fetch *= ofm_block.depth * blocks.depth
# IFM fetching
ifm_fetch = ifm_block.elements_wh() * ifm_shape.depth * ifm_repeats * blocks.elements_wh()
if not is_equal_depth_op:
ifm_fetch *= full_blocks.depth
# Scale relative to every output OFM element
if npu_op_type == NpuBlockType.ElementWise:
relative_cost = max(ofm_shape.elements() / (height * width * depth), 1)
else:
relative_cost = (ifm_fetch + weight_fetch) / ofm_shape.elements()
# If the entire IFM can be encompassed by both buffers, bias to prefer this configuration
if ifm_shape.elements() < ifm_block.elements() * 2:
relative_cost = relative_cost / 2
# Choose based on relative minimum cost or larger IFM area (if equal cost)
if relative_cost <= best_cost:
choose_this = False
# Check IFM coverage only when it's equal best_cost and small OFM
if relative_cost == best_cost:
coverage_shape = Shape4D.min(ifm_shape, ifm_block)
coverage = ifm_shape.elements_wh() / coverage_shape.elements_wh()
# Small 4x4 IFM constraint found through analysis of networks
if coverage <= best_coverage and (height <= 4 and width <= 4):
best_coverage = coverage
choose_this = True
else:
best_coverage = math.inf
choose_this = True
if choose_this:
best_cost = relative_cost
config.layout = layout
config.bank_size = arch.shram_bank_size
config.ifm_block = ifm_block
config.ofm_block = Shape4D(1, height, width, depth)
else:
wont_fit[(width, height)] = True
depth = depth + arch.ofm_ublock.depth
if depth < ofm_shape.depth:
depth = round_up(depth, SplitDepth)
if best_cost != math.inf:
return config
return None
def try_block_config(
block_config: Block,
arch: ArchitectureFeatures,
npu_op_type: NpuBlockType,
ofm_shape: Union[Shape4D, Block],
ifm_shape: Union[Shape4D, Block],
ifm2_shape: Optional[Union[Shape4D, Block]],
uses_scalar: bool,
ifm_bits: int,
is_partkernel: bool,
kernel: Kernel,
lut_banks: int,
scaled: bool,
ifm_resampling: resampling_mode,
) -> Optional[ArchitectureBlockConfig]:
"""
Given a block_config, returns a corresponding ArchitectureBlockConfig.
Returns None if the block_config does not fit or is invalid.
"""
# Check block config validity
if not all(
blk > 0 and blk <= blk_max and blk % ublk == 0
for blk, blk_max, ublk in zip(block_config.as_list(), arch.ofm_block_max.as_list(), arch.ofm_ublock.as_list())
):
return None
# Elementwise larger-volume correction
if ifm2_shape is not None and ifm2_shape.elements() > ifm_shape.elements():
ifm_shape = ifm2_shape
ew_usage = _ew_usage(npu_op_type, uses_scalar)
# Operator typing help
is_pooling = npu_op_type == NpuBlockType.Pooling
is_depthwise = npu_op_type == NpuBlockType.ConvolutionDepthWise
is_equal_depth_op = (ew_usage != ElementwiseUsage.No) or is_pooling or is_depthwise
# Block config to be returned
config = ArchitectureBlockConfig()
config.is_partkernel = is_partkernel
# Accumulator & granule settings
config.acc_type = _acc_type(npu_op_type, ifm_bits, scaled)
# Memory rounding granules
acc_granule = arch.accumulator_granules[config.acc_type]
acc_bits = _AccumulatorBits[config.acc_type]
if ew_usage != ElementwiseUsage.No:
ifm_granule = arch.ifm_ew_bank_granules[ifm_bits]
else:
ifm_granule = arch.ifm_bank_granules[ifm_bits]
lut_banks = max(lut_banks, arch.shram.reserved_end_banks)
upscale = to_upscale(ifm_resampling)
nearest = is_nearest(ifm_resampling)
ifm_blockdepth = _ifm_blockdepth(arch, ifm_shape, ifm_bits, is_partkernel)
ifm_block = _get_ifm_blocksize(block_config, kernel, arch.ofm_ublock, arch.SubKernelMax, upscale, nearest)
if not is_equal_depth_op:
ifm_block = ifm_block.with_depth(ifm_blockdepth)
# 256/512 Conv1D optimisation (ratio of IFM:Accumulators changes)
block_config_opt = fit_block_for_ofm(arch, ofm_shape, kernel, block_config)
layout = _try_block_config(
arch.shram, ew_usage, block_config_opt, ifm_block, ifm_bits, ifm_granule, acc_bits, acc_granule, lut_banks
)
if layout is None:
return None
config.layout = layout
config.bank_size = arch.shram_bank_size
config.ifm_block = ifm_block
config.ofm_block = block_config
return config
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