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# 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:
# Shared buffer allocation works out how to allocate the Ethos-U55 shared buffer for a given pass.
import numpy as np
from .architecture_features import ArchitectureFeatures
from .architecture_features import Block
from .architecture_features import Kernel
from .architecture_features import SharedBufferArea
from .architecture_features import SHRAMElements
from .errors import VelaError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .operation import NpuBlockType
from .operation import Op
from .range_set import MemoryRangeSet
from .tensor import MemArea
class SharedBufferAllocation:
def __init__(self, arch, ps):
self.arch = arch
self.bank_locations = np.zeros(SharedBufferArea.Size)
self.banks_required = np.zeros(SharedBufferArea.Size)
ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor = ps.get_primary_op_ifm_ifm2_weights_ofm()
tensors = [t for t in (ifm_tensor, ifm2_tensor, ofm_tensor) if t is not None]
scales = [t.quantization.scale_f32 for t in tensors if t.quantization is not None]
has_scale = len(tensors) == len(scales) and None not in scales
strides = (1, 1, 1, 1)
dilation = (1, 1, 1, 1)
self.kernel = Kernel(1, 1)
self.is_elementwise = ps.npu_block_type == NpuBlockType.ElementWise
self.uses_lut = False
self.ifm_count = 1
if ps.primary_op:
strides = ps.primary_op.attrs.get("strides", strides)
dilation = ps.primary_op.attrs.get("dilation", dilation)
k_h = 1
k_w = 1
if weight_tensor:
if ps.primary_op.type != Op.FullyConnected:
k_h = weight_tensor.shape[0]
k_w = weight_tensor.shape[1]
else:
k_h = ps.primary_op.attrs.get("filter_height", 1)
k_w = ps.primary_op.attrs.get("filter_width", 1)
self.kernel = Kernel(k_w, k_h, strides[2], strides[1], dilation[2], dilation[1])
self.uses_lut = ps.primary_op.activation_lut is not None
self.is_equal_depth_op = self.is_elementwise or ps.npu_block_type in (
NpuBlockType.ConvolutionDepthWise,
NpuBlockType.Pooling,
)
self.strides = strides
self.use_accumulator_element = SHRAMElements.Acc32
if self.is_elementwise:
self.use_ifm_element = SHRAMElements.IFM8_Elementwise
else:
self.use_ifm_element = SHRAMElements.IFM8
self.ifm_resampling_mode = resampling_mode.NONE
self.ifm_bits = 0
self.ifm_depth = 0
if ifm_tensor:
self.ifm_resampling_mode = ifm_tensor.resampling_mode
self.ifm_bits = ifm_tensor.dtype.size_in_bits()
if ifm_tensor.shape != []:
self.ifm_depth = ifm_tensor.shape[-1]
if self.is_elementwise:
self.ifm_count = 2
if ifm_tensor.shape == []: # Scalar in ifm1
assert ifm2_tensor
self.ifm_depth = ifm2_tensor.shape[-1]
self.ifm_count = 1
elif not ifm2_tensor or ifm2_tensor.shape == []: # Scalar in ifm2
self.ifm_count = 1
if self.ifm_bits == 16:
if ps.npu_block_type != NpuBlockType.Pooling and has_scale:
self.use_accumulator_element = SHRAMElements.Acc40
self.use_ifm_element = self.use_ifm_element + 1
assert (self.use_ifm_element == SHRAMElements.IFM16) or (
self.use_ifm_element == SHRAMElements.IFM16_Elementwise
)
elif self.ifm_bits == 32:
assert (
self.is_elementwise or ps.npu_block_type == NpuBlockType.ReduceSum
), "Unsupported 32-bit IFM operation"
self.use_ifm_element = SHRAMElements.IFM32
else:
assert self.ifm_bits == 8, "Unexpected IFM bitdepth"
self.ifm_block_depth = arch.calc_ifm_block_depth(self.ifm_depth, self.ifm_bits)
self.ofm_tensor = ofm_tensor
self.banks_required[SharedBufferArea.Weights] = arch.shram_reserved_weight_banks
self.banks_required[SharedBufferArea.OFM] = arch.shram_reserved_output_banks
def is_valid(self):
# Assign zero-based bank starts (first element remains zero)
self.bank_locations[1:] = np.cumsum(self.banks_required)[:-1]
# Accumulator area is measured from the end of the buffer
self.bank_locations[SharedBufferArea.Accumulators] = (
self.arch.available_shram_banks(self.uses_lut) - self.banks_required[SharedBufferArea.Accumulators]
)
ifm_end = self.bank_locations[SharedBufferArea.IFM] + self.banks_required[SharedBufferArea.IFM]
return ifm_end <= self.bank_locations[SharedBufferArea.Accumulators]
def try_block(self, ofm_block: Block):
# Get IFM block configuration
ifm_block_depth = ofm_block.depth if self.is_equal_depth_op else self.ifm_block_depth
ifm_block = self.arch.get_ifm_block_size(
ifm_block_depth, ofm_block, self.kernel, ifm_resampling_mode=self.ifm_resampling_mode
)
ifm_config = self.arch.get_block_config(ifm_block.width, ifm_block.height, ifm_block.depth)
if ifm_config is None:
return None
# Get OFM block configuration
ofm_config = self.arch.get_block_config(ofm_block.width, ofm_block.height, ofm_block.depth)
if ofm_config is None:
return None
acc_banks = ofm_config.banks[self.use_accumulator_element]
# Update bank counts for IFM and Accumulator
self.banks_required[SharedBufferArea.IFM] = ifm_config.banks[self.use_ifm_element] * self.ifm_count
self.banks_required[SharedBufferArea.Accumulators] = 0 if self.is_elementwise else acc_banks
# Validating calculates bank layout and returns validity
if not self.is_valid():
return None
return (ofm_block.height, ofm_block.width, ifm_block.depth, ofm_block.depth)
def generate_used_mask(self, active_set):
res = np.zeros(self.arch.shram_total_banks, dtype=np.int64)
for kind in active_set:
start = int(self.bank_locations[kind])
end = start + int(self.banks_required[kind])
res[start:end] = 1
return res
def is_compatible(first, second):
"""See if the bank allocations of two convolutions are compatible,
so that they can run back-to-back without a fence in between"""
first_set = set((SharedBufferArea.OFM, SharedBufferArea.Accumulators))
second_set = set((SharedBufferArea.IFM, SharedBufferArea.Weights))
first_mask = first.generate_used_mask(first_set)
second_mask = second.generate_used_mask(second_set)
if np.sum(first_mask & second_mask):
# overlap
return False
return True
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.arch.available_shram_banks(self.uses_lut) * self.arch.shram_bank_size
)
def shared_buffer_allocation_for_pass_and_block_config(arch, ps, block_config):
alloc = SharedBufferAllocation(arch, ps)
assert (alloc.ifm_block_depth == block_config[2]) or alloc.is_equal_depth_op
if alloc.try_block(Block(block_config[1], block_config[0], block_config[3])):
return alloc
return None
def find_block_configs_suitable_for_pass_and_shared_buffer(arch, ps):
alloc = SharedBufferAllocation(arch, ps)
if arch.override_block_config:
config = alloc.try_block(arch.override_block_config)
if config is None:
raise VelaError("Block config override '{0}' cannot be allocated".format(arch.override_block_config))
return [config]
# Constrain the search space if the OFM is smaller than the max block size
# - Add other block search constraints here if required
if len(alloc.ofm_tensor.shape) <= 2:
max_block_height = max_block_width = alloc.ofm_tensor.shape[0]
else:
max_block_width = alloc.ofm_tensor.shape[-2]
max_block_height = alloc.ofm_tensor.shape[-3]
# Common block depth
max_block_depth = alloc.ofm_tensor.shape[-1]
# Constrain to valid ranges before search
max_block_width = min(arch.ofm_block_max.width, max_block_width)
max_block_height = min(arch.ofm_block_max.height, max_block_height)
max_block_depth = min(arch.ofm_block_max.depth, max_block_depth)
valid_block_configs = []
# Try a range of block shapes against this pass
for w in range(arch.ofm_ublock.width, max_block_width + arch.ofm_ublock.width, arch.ofm_ublock.width):
for h in range(arch.ofm_ublock.height, max_block_height + arch.ofm_ublock.height, arch.ofm_ublock.height):
# Try valid OFM block depths
for c in range(arch.ofm_ublock.depth, max_block_depth + arch.ofm_ublock.depth, arch.ofm_ublock.depth):
# OFM block depth has the constraint that if it causes the OFM to be
# split, it must be a multiple of the OFM split size
if (c >= max_block_depth) or (c < max_block_depth and (c % ArchitectureFeatures.OFMSplitDepth) == 0):
config = alloc.try_block(Block(w, h, c))
if config:
valid_block_configs.append(config)
assert len(valid_block_configs) > 0
return valid_block_configs
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