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# 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:
# Packs a subgraph with Neural Network Operations into Passes. Each Pass has one or more Operations.
import collections
import enum
from .debug_database import DebugDatabase
from .nn_graph import Pass
from .nn_graph import PassPlacement
from .operation import NpuBlockType
from .operation import Op
from .operation_util import create_avgpool_nop
from .tensor import TensorPurpose
class PassFlags(enum.Flag):
Empty = 0
Main = 1
Post = 2
Mac = 4
ElementWise = 8
Npu = 16
Cpu = 32
StartupInit = 64
MemoryOnly = 128
PostFusingLimited = 256
mac_main_ops = set(
(
# convolutions
Op.Conv2DBias,
Op.Conv2D,
Op.QuantizedConv2D,
Op.Conv2DBackpropInputSwitchedBias,
# depth-wise convolutions
Op.DepthwiseConv2DBias,
# FC layers
Op.QuantizedMatMul,
Op.MatMul,
Op.FullyConnected,
# RNN/LSTM/GRU
Op.BlockLSTM,
# pooling
Op.QuantizedMaxPool,
Op.QuantizedAvgPool,
Op.AvgPool,
Op.MaxPool,
Op.ReduceSum,
# deconvolution
Op.ResizeBilinear,
)
)
binary_elem_wise_main_ops = Op.op_set(Op.is_binary_elementwise_op)
unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op)
elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
activation_ops = Op.op_set(Op.is_relu_op)
npu_post_ops = activation_ops
npu_post_fuse_limited_ops = set(
# Set of post operators that should not be fused with main/elementwise ops
(Op.Sigmoid, Op.Tanh, Op.Quantize)
)
elem_wise_ops = elem_wise_main_ops | activation_ops | set((Op.Sigmoid, Op.Tanh))
quantization_ops = set((Op.Dequantize, Op.Max, Op.Min))
cpu_ops = set((Op.Softmax, Op.LRN, Op.Shape, Op.Pad, Op.AddN)) | quantization_ops
startup_init_ops = set((Op.Const, Op.Placeholder, Op.SubgraphInput))
memory_only_ops = set(
(
Op.Squeeze,
Op.Reshape,
Op.QuantizedReshape,
Op.ExpandDims,
)
)
test_sequence = [
(
# ops_set
npu_post_ops,
# incompatible_pack_flags
PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.Main,
# flags_to_set
PassFlags.Npu | PassFlags.Post,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
npu_post_fuse_limited_ops,
# incompatible_pack_flags
PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.Main | PassFlags.PostFusingLimited,
# flags_to_set
PassFlags.Npu | PassFlags.PostFusingLimited,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
mac_main_ops,
# incompatible_pack_flags
PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.ElementWise | PassFlags.Main | PassFlags.PostFusingLimited,
# flags_to_set
PassFlags.Npu | PassFlags.Mac | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
elem_wise_main_ops,
# incompatible_pack_flags
PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.Mac | PassFlags.Main | PassFlags.PostFusingLimited,
# flags_to_set
PassFlags.Npu | PassFlags.ElementWise | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
startup_init_ops,
# incompatible_pack_flags
PassFlags.Npu | PassFlags.Cpu | PassFlags.MemoryOnly,
# flags_to_set
PassFlags.StartupInit | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
memory_only_ops,
# incompatible_pack_flags
PassFlags.Npu | PassFlags.Cpu,
# flags_to_set
PassFlags.MemoryOnly | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
(
# ops_set
cpu_ops,
# incompatible_pack_flags
PassFlags.Npu | PassFlags.MemoryOnly | PassFlags.Main,
# flags_to_set
PassFlags.Cpu | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
( # This last one is a fallback for unrecognised operations
# ops_set
None,
# incompatible_pack_flags
PassFlags.Npu | PassFlags.MemoryOnly | PassFlags.Main,
# flags_to_set
PassFlags.Cpu | PassFlags.Main,
# flags_to_clear
PassFlags.Empty,
),
]
# Some sanity checking
for (operation_set, incompatible_pack_flags, flags_to_set, flags_to_clear) in test_sequence:
assert not flags_to_clear & flags_to_set
def pack_into_passes(nng, arch, verbose_packing=False):
def visit_op(op, ignored):
visit_op_refcount[op] += 1
if visit_op_refcount[op] == 1: # First-time visit, go and fix up unused output tensors
for tens in op.outputs:
if len(tens.consumers()) == 0:
visit_op_refcount[op] += 1
assert visit_op_refcount[op] <= len(op.outputs)
if visit_op_refcount[op] == len(op.outputs):
if op.type in startup_init_ops:
startup_list.append(op)
else:
ofm_tensor = op.ofm
if ofm_tensor is None:
ofm_tensor = op.outputs[0]
ofm_shape = op.ofm_shapes[0] if op.run_on_npu else None
build_pass((op,), ofm_tensor, ofm_shape)
def build_pass(start_ops_to_process, ofm_tensor=None, ofm_shape=None):
reverse_ops_list = []
curr_flags = PassFlags.Empty
npu_block_type = NpuBlockType.Default
reverse_intermediates = []
input_set = set()
ifm_tensor = None
primary_op = None
ifm_shapes = None
to_process = collections.deque()
for start_op in start_ops_to_process:
to_process.append((start_op, None))
while to_process:
curr_op, tens = to_process.popleft()
if curr_op in reverse_ops_list:
continue
for operation_set, incompatible_pack_flags, flags_to_set, flags_to_clear in test_sequence:
if operation_set is None or curr_op.type in operation_set:
if not (curr_flags & incompatible_pack_flags):
if flags_to_set & PassFlags.Npu:
if not curr_op.run_on_npu:
continue
reverse_ops_list.append(curr_op)
new_block_type = curr_op.type.npu_block_type
if new_block_type != NpuBlockType.Default:
assert npu_block_type == NpuBlockType.Default
npu_block_type = new_block_type # Only one major block type per pass
assert primary_op is None
primary_op = curr_op
curr_flags &= ~flags_to_clear
curr_flags |= flags_to_set
if flags_to_set & PassFlags.Npu:
if flags_to_set & (
PassFlags.Mac | PassFlags.ElementWise | PassFlags.Post | PassFlags.PostFusingLimited
):
assert len(curr_op.inputs) >= 1
ifm_tensor = curr_op.ifm
ifm_shapes = curr_op.ifm_shapes.copy()
assert ifm_tensor is not None, "IFM missing in {}".format(curr_op)
assert ifm_tensor.purpose == TensorPurpose.FeatureMap
if operation_set is None:
print("Warning:", curr_op.type, "operation is unknown or unsupported, placing on CPU")
for inp in reversed(curr_op.inputs):
if inp is None:
continue
if can_pack(inp, curr_op):
to_process.append((inp.ops[0], inp))
else:
input_set.add(inp)
break
else:
# This operation is not compatible with already packed operations, just register the tensor as an input
assert tens is not None
input_set.add(tens)
if curr_flags & PassFlags.Npu and not curr_flags & (PassFlags.ElementWise | PassFlags.Mac):
# Make the choice that if we don't have a mac operation, the ambidextrous operations go on the
# element wise unit
curr_flags |= PassFlags.ElementWise
is_element_wise = True
for op in reverse_ops_list:
if op.type not in elem_wise_ops and op.type:
is_element_wise = False
break
placement = PassPlacement.Unknown
if curr_flags & PassFlags.Npu:
assert placement == PassPlacement.Unknown
placement = PassPlacement.Npu
if curr_flags & PassFlags.Cpu:
assert placement == PassPlacement.Unknown
placement = PassPlacement.Cpu
if curr_flags & PassFlags.MemoryOnly:
assert placement == PassPlacement.Unknown
placement = PassPlacement.MemoryOnly
if curr_flags & PassFlags.StartupInit:
assert placement == PassPlacement.Unknown
placement = PassPlacement.StartupInit
assert placement != PassPlacement.Unknown
ops_list = list(reversed(reverse_ops_list))
intermediates = list(reversed(reverse_intermediates))
if primary_op is None:
primary_op = create_primary_op(ops_list)
if primary_op is not None:
visit_tensor_refcount[primary_op.inputs[0]] += 1
npu_block_type = primary_op.type.npu_block_type
for input_tens in primary_op.inputs:
if input_tens not in input_set:
input_set.add(input_tens)
ordered_input_list = []
# Keep LUT-s in a separate list and add as inputs at the end
# to avoid that they would accidentally be assigned as ifm or ifm2
lut_list = []
input_refcounts = collections.defaultdict(int)
input_ops_list = ops_list.copy()
# Check primary_op first
if primary_op is not None:
for inp in primary_op.inputs:
if inp is None:
continue
add_input_list(inp, input_set, input_refcounts, lut_list, ordered_input_list)
input_ops_list.remove(primary_op)
# Check rest of the list
for op in input_ops_list:
for inp in op.inputs:
add_input_list(inp, input_set, input_refcounts, lut_list, ordered_input_list)
name = ops_list[0].name
ps = Pass(name, placement, is_element_wise, npu_block_type)
ps.ops = ops_list
ps.primary_op = primary_op
ps.inputs = ordered_input_list
ps.intermediates = intermediates
ps.outputs = list(ops_list[-1].outputs)
# ElementWise operation, 2 IFMs
if ps.primary_op and ps.primary_op.type in binary_elem_wise_main_ops:
ps.ifm_tensor = ps.inputs[0]
ps.ifm2_tensor = ps.inputs[-1]
if len(ps.inputs) > 2:
ps.ifm_tensor = ps.inputs[-2]
# Get the corresponding ifm_shapes
for op in input_ops_list + [primary_op]:
if op.run_on_npu:
if ps.ifm_tensor == op.ifm:
ps.ifm_shapes.append(op.ifm_shapes[0])
elif ps.ifm_tensor == op.ifm2:
ps.ifm_shapes.append(op.ifm_shapes[1])
if ps.ifm2_tensor == op.ifm:
ps.ifm_shapes.append(op.ifm_shapes[0])
elif ps.ifm2_tensor == op.ifm2:
ps.ifm_shapes.append(op.ifm_shapes[1])
else:
ps.ifm_tensor = ifm_tensor
ps.ifm2_tensor = None
if ps.primary_op is not None and ps.primary_op.run_on_npu:
ps.ifm_shapes.append(ifm_shapes[0])
ps.ofm_tensor = ofm_tensor
ps.ofm_shapes.append(ofm_shape)
assert ps.placement != PassPlacement.Npu or ps.ofm_tensor is not None
ps.weight_tensor = ps.get_primary_op_ifm_weights()[1]
ps.scale_tensor = ps.get_primary_op_ifm_weights_biases_ofm()[2]
ps.lut_tensor = ps.get_primary_op_lut()
ps.inputs.extend(lut_list)
for op in ps.ops:
op.scheduled_pass = ps
reverse_pass_list.append(ps)
for inp, refcount in input_refcounts.items():
for _ in range(refcount):
visit_tensor(inp)
return ps
def visit_tensor(tens):
visit_tensor_refcount[tens] += 1
assert visit_tensor_refcount[tens] <= len(tens.consumers())
if visit_tensor_refcount[tens] == len(tens.consumers()):
for op in reversed(tens.ops):
visit_op(op, tens)
def create_primary_op(op_list):
if any(op.type in (npu_post_ops | npu_post_fuse_limited_ops) and op.run_on_npu for op in op_list):
# Configure a 1x1 AvgPool and attach the op onto it
op = op_list[0]
inp = op.inputs[0]
avgpool_op = create_avgpool_nop(op.name + "_avgpool")
avgpool_op.add_input_tensor(inp)
avgpool_out = inp.clone("_avgpooled")
avgpool_out.consumer_list.append(op)
avgpool_op.set_output_tensor(avgpool_out)
avgpool_op.ifm_shapes = op.ifm_shapes.copy()
avgpool_op.ofm_shapes = op.ofm_shapes.copy()
avgpool_op.read_offsets = op.read_offsets.copy()
avgpool_op.read_shapes = op.read_shapes.copy()
op.inputs[0] = avgpool_out
op_list.insert(0, avgpool_op)
DebugDatabase.add_optimised(op, avgpool_op)
return avgpool_op
return None
def can_pack(inp, curr_op):
if len(inp.ops) == 1:
next_op = inp.ops[0]
for outp in next_op.outputs:
consumers = outp.consumers()
if len(consumers) > 1 or (len(consumers) == 1 and consumers[0] != curr_op):
return False
# There cannot be any reshaping between next_op ofm and corresponding curr_op ifm
if len(curr_op.ifm_shapes) != 0 and len(next_op.ofm_shapes) != 0:
if inp == curr_op.ifm and next_op.ofm_shapes[0] != curr_op.ifm_shapes[0]:
return False
elif (
curr_op.ifm2 is not None and inp == curr_op.ifm2 and next_op.ofm_shapes[0] != curr_op.ifm_shapes[1]
):
return False
else:
return False
return True
def add_input_list(inp_to_add, inp_set, inp_refcnts, lut_list, ordered_inp_list):
if inp_to_add in inp_set:
if inp_refcnts[inp_to_add] == 0:
if inp_to_add.purpose == TensorPurpose.LUT:
lut_list.append(inp_to_add)
else:
ordered_inp_list.append(inp_to_add)
inp_refcnts[inp_to_add] += 1
for sg in nng.subgraphs:
reverse_pass_list = []
visit_op_refcount = collections.defaultdict(int)
visit_tensor_refcount = collections.defaultdict(int)
startup_list = []
for tens in sg.output_tensors:
visit_tensor(tens)
if startup_list:
startup_ps = build_pass(startup_list)
startup_ps.outputs = [op.outputs[0] for op in startup_list] # Need to fixup the outputs
startup_ps.name = "startup_weight_initialisation"
# Graphs with both CPU and NPU ops might not have an optimal order in
# the pass list due to how the graph is traversed (depth first search).
# This can result in more context switching between CPU and NPU.
# Try to optmize this by moving/grouping CPU ops where that is possible.
# Criteria for CPU pass to be moved:
#
# 1) CPU passes that only depends on sg.input_tensor can be
# moved to the top of the list.
#
# 2) A CPU pass X is allowed to be grouped together with CPU pass Y
# if there is no NPU pass between pass X and pass Y that depends
# on output from pass X or a MemoryOnly pass.
#
# Criteria 2 will try to move as many CPU passes towards the bottom of
# the list.
pass_list_top = []
pass_list = []
# Filter out early passes from the rest
for ps in list(reversed(reverse_pass_list)):
if startup_ps == ps:
# startup pass belongs in the top
pass_list_top.insert(0, ps)
continue
if (
ps.placement == PassPlacement.Cpu
and ps.ops[0].ifm in sg.input_tensors
and (ps.ops[0].ifm2 in sg.input_tensors or ps.ops[0].ifm2 is None)
):
# This CPU pass only depends on sg.input_tensors
pass_list_top.append(ps)
else:
# Add pass to the list that will be sorted in the next step
pass_list.append(ps)
# Sort the rest of the list based on critera 2.
# Search from bottom of list and when a CPU pass is found
# search forward in the list and see if it is possible to join another CPU pass.
last_idx = len(pass_list) - 1
for cpu_ps in reversed(pass_list):
if cpu_ps.placement != PassPlacement.Cpu:
continue
# CPU pass found, search forward and move pass if possible
idx = pass_list.index(cpu_ps)
for next_ps in pass_list[idx + 1 :]:
if next_ps.placement == PassPlacement.Cpu:
# It is possible to move the CPU pass
pass_list.remove(cpu_ps)
insert_index = pass_list.index(next_ps)
pass_list.insert(insert_index, cpu_ps)
break
if (
cpu_ps.ops[0].ofm in [next_ps.ops[0].ifm, next_ps.ops[0].ifm2]
or next_ps.placement == PassPlacement.MemoryOnly
):
# Not possible to move
break
if pass_list.index(next_ps) == last_idx:
# Last element, ok to move the CPU pass
pass_list.remove(cpu_ps)
pass_list.append(cpu_ps)
break
pass_list_top.extend(pass_list)
sg.passes = pass_list_top
sg.build_pass_links()
if verbose_packing:
nng.print_passes()
return nng
|