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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:
# Internal representation of a Neural Network Operation.
import enum
class NpuBlockType(enum.Enum):
Default = 0
ConvolutionMxN = 1
VectorProduct = 2
Pooling = 3
ConvolutionDepthWise = 4
ElementWise = 5
class Operation:
"""Class representing a Neural Network operation. Has a name, a type,
input and output tensors, as well as an attribute dictionary."""
__slots__ = "type", "name", "op_index", "attrs", "inputs", "outputs", "flops", "scheduled_pass", "run_on_npu"
def __init__(self, op_type, name):
self.type = op_type
self.name = name
self.attrs = {}
self.inputs = []
self.outputs = []
self.flops = 0
self.run_on_npu = True
self.scheduled_pass = None
self.op_index = None # input network operator index
def clone(self, suffix="_clone"):
res = Operation(self.type, self.name + suffix)
res.attrs = dict(self.attrs)
res.inputs = list(self.inputs)
res.outputs = list(self.outputs)
res.flops = self.flops
res.scheduled_pass = self.scheduled_pass
res.op_index = None # not relevant as not part of input network
return res
def __str__(self):
return "<nng.Operation '%s' type=%s>" % (self.name, self.type)
__repr__ = __str__
def get_ifm_ifm2_weight_bias_ofm_indices(self):
ifm_idx = -1
ifm2_idx = -1
weight_idx = -1
bias_idx = -1
ofm_idx = -1
npu_block_type = self.attrs.get("npu_block_type", NpuBlockType.Default)
if npu_block_type in set((NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise)):
ifm_idx = 0
weight_idx = 1
ofm_idx = 0
if self.type in set(("Conv2DBiasAct", "DepthwiseConv2dBiasAct", "TransposeConvAct")):
if len(self.inputs) >= 3:
bias_idx = 2
elif self.type == "Conv2DBackpropInputSwitchedBias":
bias_idx = 3
elif npu_block_type == NpuBlockType.Pooling:
ifm_idx = 0
ofm_idx = 0
elif npu_block_type == NpuBlockType.VectorProduct:
ifm_idx = 0
weight_idx = 1
ofm_idx = 0
if self.type in set(("FullyConnectedAct",)):
if len(self.inputs) >= 3:
bias_idx = 2
if self.type == "BlockLSTM":
ifm_idx = 3
weight_idx = 4
ofm_idx = 6
elif npu_block_type == NpuBlockType.ElementWise:
ifm_idx = 0
ifm2_idx = 1
ofm_idx = 0
# LeakyRelu and Abs have a single IFM
if self.type in set(("LeakyRelu", "Abs")):
ifm2_idx = -1
elif self.type == "Conv2DBackpropInput":
ifm_idx = 2
weight_idx = 1
ofm_idx = 0
elif self.type in set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims")):
ifm_idx = 0
ofm_idx = 0
elif self.is_split_op():
ifm_idx = 0
ofm_idx = 0
if self.type == "Split":
ifm_idx = 1
elif self.is_concat_op():
ifms, _ = self.get_concat_inputs_axis()
ifm_idx = self.inputs.index(ifms[0])
if len(ifms) > 1:
ifm2_idx = self.inputs.index(ifms[1])
ofm_idx = 0
return ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx
def get_ifm_ifm2_weights_ofm(self):
ifm_tensor = None
ifm2_tensor = None
weight_tensor = None
ofm_tensor = None
ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices()
if ifm_idx != -1:
ifm_tensor = self.inputs[ifm_idx]
if ifm2_idx != -1:
ifm2_tensor = self.inputs[ifm2_idx]
if weight_idx != -1:
weight_tensor = self.inputs[weight_idx]
if ofm_idx != -1:
ofm_tensor = self.outputs[ofm_idx]
return ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor
def get_ifm_weights_biases_ofm(self):
ifm_tensor = None
weight_tensor = None
bias_tensor = None
ofm_tensor = None
ifm_idx, _, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices()
if ifm_idx != -1:
ifm_tensor = self.inputs[ifm_idx]
if weight_idx != -1:
weight_tensor = self.inputs[weight_idx]
if bias_idx != -1:
bias_tensor = self.inputs[bias_idx]
if ofm_idx != -1:
ofm_tensor = self.outputs[ofm_idx]
return ifm_tensor, weight_tensor, bias_tensor, ofm_tensor
concat_ops = set(("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped"))
def is_concat_op(self):
return self.type in Operation.concat_ops
def get_concat_inputs_axis(self):
assert self.is_concat_op()
if self.type == "ConcatV2":
axis_tensor = self.inputs[-1]
inputs = self.inputs[:-1]
elif self.type == "Concat":
axis_tensor = self.inputs[0]
inputs = self.inputs[1:]
elif self.type == "QuantizedConcat":
axis_tensor = self.inputs[0]
inputs = self.inputs[1:]
inputs = inputs[: len(inputs) // 3] # Skip min/max
if self.type == "ConcatTFLite":
inputs = self.inputs
axis = self.attrs["axis"]
elif self.type == "PackReshaped":
# Requires fixup_pack_input to be called before this point
inputs = self.inputs
axis = self.attrs["axis"]
assert len(self.inputs) == self.attrs["values_count"]
else:
assert len(axis_tensor.ops) == 1 and axis_tensor.ops[0].type == "Const"
axis = int(axis_tensor.values)
return inputs, axis
def get_dilation_h_w(self):
_, dilation_h, dilation_w, _ = self.attrs.get("dilation", (1, 1, 1, 1))
return dilation_h, dilation_w
split_ops = set(("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped"))
def is_split_op(self):
return self.type in Operation.split_ops
def get_split_inputs_axis(self):
assert self.is_split_op()
offset_start = None
offset_end = None
axis = None
if self.type == "Split":
num_splits = self.attrs.get("num_splits")
axis_tens = self.inputs[0]
assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const"
axis = int(axis_tens.values)
input_tens = self.inputs[1]
outputs = self.outputs
assert num_splits == len(outputs)
elif self.type == "SplitV":
num_splits = self.attrs.get("num_splits")
input_tens = self.inputs[0]
size_tens = self.inputs[1]
assert len(size_tens.ops) == 1 and size_tens.ops[0].type == "Const"
sizes = size_tens.values
axis_tens = self.inputs[2]
assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const"
axis = int(axis_tens.values)
outputs = self.outputs
assert num_splits == len(outputs)
assert sum(sizes) == input_tens.shape[axis]
elif self.type == "Slice":
input_tens, begin_tens, size_tens = self.inputs
outputs = self.outputs
offset_start = [0] * len(input_tens.shape)
offset_end = [0] * len(input_tens.shape)
for idx in range(len(begin_tens.values)):
# Check if the op should slice in dimension idx
if size_tens.values[idx] != input_tens.shape[idx]:
offset_start[idx] = begin_tens.values[idx]
offset_end[idx] = size_tens.values[idx] + offset_start[idx]
elif self.type == "StridedSlice":
input_tens, begin_tens, end_tens, strides_tens = self.inputs
outputs = self.outputs
out_tens = outputs[0]
offset_start = [0] * len(outputs[0].shape)
offset_end = [0] * len(outputs[0].shape)
# Extract masks
begin_mask = self.attrs["begin_mask"]
ellipsis_mask = self.attrs["ellipsis_mask"]
end_mask = self.attrs["end_mask"]
new_axis_mask = self.attrs["new_axis_mask"]
shrink_axis_mask = self.attrs["shrink_axis_mask"]
# shrink_axis_mask/new_axis_mask/ellipsis_mask is not supported by the Operation class but the operation
# may have the attribute modified and handled in the graph optimization phase.
assert shrink_axis_mask == new_axis_mask == ellipsis_mask == 0
assert len(input_tens.shape) == len(out_tens.shape)
for idx in range(len(input_tens.shape)):
# Check if slicing is needed in this axis
if end_tens.values[idx] != input_tens.shape[idx] or (
end_tens.values[idx] == input_tens.shape[idx] and begin_tens.values[idx] != 0
):
# If the i:th bit in begin_mask is set then the value on begin[i] should be ignored
if (begin_mask & (1 << idx)) == 0:
offset_start[idx] = begin_tens.values[idx]
# If the i:th bit in end_mask is set then the value on end[i] should be ignored
if (end_mask & (1 << idx)) == 0:
offset_end[idx] = end_tens.values[idx]
elif self.type == "UnpackReshaped":
# Requires fixup_unpack_output to be called before this point
input_tens = self.inputs[0]
outputs = self.outputs
axis = self.attrs["axis"]
num_splits = self.attrs["num"]
# Number of outputs have to equal the value of the dimension to unpack
assert num_splits == len(outputs) == input_tens.shape[axis]
else:
assert False
return input_tens, outputs, axis, offset_start, offset_end
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