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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:
# The SupportedOperators class which is a collection of all supported operators and parameter checks.
from .data_type import BaseType
class SupportedOperators:
def __init__(self):
# Categorised lists of supported operators
self.npu_pre_ops = set(("QuantizedResizeBilinear", "SplitSliceRead"))
self.convolution_ops = set(("Conv2DBiasAct", "Conv2D", "QuantizedConv2D", "Conv2DBackpropInputSwitched"))
self.depthwise_convolution_ops = set(
("DepthwiseConv2dBiasAct", "DepthwiseConv2dNative", "QuantizedDepthwiseConv2D")
)
self.max_pooling_ops = set(("QuantizedMaxPool", "MaxPool", "MaxPoolAct"))
self.avg_pooling_ops = set(("QuantizedAvgPool", "AvgPool", "AvgPoolAct"))
self.pooling_ops = self.max_pooling_ops | self.avg_pooling_ops
self.fc_vector_products = set(("QuantizedMatMul", "MatMul", "FullyConnectedAct"))
self.mac_main_ops = (
# convolutions
self.convolution_ops
# depth-wise convolutions
| self.depthwise_convolution_ops
# pooling
| self.pooling_ops
# FC layers
| self.fc_vector_products
# RNN/LSTM/GRU
| set(("BlockLSTM"))
)
self.elem_wise_main_ops = set(
(
# element-wise
"AddAct",
"MulAct",
"SubAct",
"QuantizedAdd",
"QuantizedSub",
"QuantizedMul",
"Mul",
"Add",
"Sub",
"Minimum",
"Maximum",
)
)
self.activation_ops = set(
("QuantizedRelu", "QuantizedRelu1", "QuantizedRelu6", "Relu", "Relu6", "ReluN1To1", "Sigmoid", "Tanh")
)
self.npu_post_ops = (
# activation functions
self.activation_ops
# concatenation write direction
| set(("ConcatSliceWrite"))
# bias add and batch norm
| set(("QuantizedBiasAdd", "Requantize", "QuantizedBatchNorm", "BiasAdd", "FusedBatchNorm"))
)
self.split_ops = set(("Split", "StridedSlice", "Slice", "UnpackReshaped", "Unpack"))
self.concat_ops = set(("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped", "Pack"))
self.memory_only_ops = (
set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims")) | self.concat_ops | self.split_ops
)
self.supported_fused_activations = set(("Relu", "Relu6", "ReluN1To1", "Tanh", "Sigmoid"))
self.supported_operators = (
self.npu_pre_ops | self.mac_main_ops | self.elem_wise_main_ops | self.npu_post_ops | self.memory_only_ops
)
# Setup supported operator restriction checkers
self.supported_operator_restrictions = {}
self.supported_operator_restrictions.update(
{op: self.check_convolution_restrictions for op in self.convolution_ops}
)
self.supported_operator_restrictions.update(
{op: self.check_depthwise_convolution_restrictions for op in self.depthwise_convolution_ops}
)
self.supported_operator_restrictions.update({op: self.check_pooling_restrictions for op in self.pooling_ops})
self.supported_operator_restrictions.update(
{op: self.check_vector_product_restrictions for op in self.fc_vector_products}
)
self.supported_operator_restrictions.update(
{op: self.check_element_wise_restrictions for op in self.elem_wise_main_ops}
)
self.supported_operator_restrictions.update(
{op: self.check_memory_only_restrictions for op in self.memory_only_ops}
)
def is_operator_supported(self, op):
if op.type not in self.supported_operators:
return False
if not self.check_generic_restrictions(op):
return False
if op.type in self.supported_operator_restrictions:
return self.supported_operator_restrictions[op.type](op)
return True
def check_generic_restrictions(self, op):
# check fully defined shapes
for t in op.inputs + op.outputs:
if not t.has_fully_defined_shape():
print("Warning:", op, "has inputs/outputs of undefined shape, placing on CPU")
return False
# check data type
tensors = [t for t in op.get_ifm_ifm2_weights_ofm() if t is not None]
if not tensors:
tensors = op.inputs
for t in tensors:
if not (t.dtype.type & BaseType.Int):
return False
if t.element_size() > 2 and op.type != "Requantize":
return False
# check size
if any(dim > 65536 for dim in t.shape):
return False
# check fused activations
if (
"fused_activation_function" in op.attrs
and op.attrs["fused_activation_function"] is not None
and op.attrs["fused_activation_function"] not in self.supported_fused_activations
):
return False
return True
def check_convolution_restrictions(self, op):
# check stride
if op.attrs["stride_w"] > 2 or op.attrs["stride_h"] > 2:
return False
# check dilation
dilation_w_factor = op.attrs.get("dilation_w_factor", 1)
dilation_h_factor = op.attrs.get("dilation_h_factor", 1)
if dilation_w_factor > 2 or dilation_h_factor > 2:
return False
# check data type
ifm_tensor, _, weight_tensor, _ = op.get_ifm_ifm2_weights_ofm()
if weight_tensor.element_size() > 1:
return False
# check kernel size
dilated_weight_w = weight_tensor.shape[0] + (weight_tensor.shape[0] - 1) * (dilation_w_factor - 1)
dilated_weight_h = weight_tensor.shape[1] + (weight_tensor.shape[1] - 1) * (dilation_h_factor - 1)
if (
dilated_weight_w > 64
or dilated_weight_h > 64
or dilated_weight_w * dilated_weight_h * weight_tensor.shape[2] > 127 * 65536
):
return False
# check batch size
if ifm_tensor.shape[0] != 1:
return False
return True
def check_depthwise_convolution_restrictions(self, op):
# check depth
ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm()
if op.attrs["depth_multiplier"] > 1 and not (
(ifm_tensor.shape[3] == 1) and (ofm_tensor.shape[3] == op.attrs["depth_multiplier"])
):
return False
return self.check_convolution_restrictions(op)
def check_pooling_restrictions(self, op):
# check stride
if op.attrs["stride_w"] > 2 or op.attrs["stride_h"] > 2:
return False
# check data type
ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm()
if ifm_tensor.dtype != ofm_tensor.dtype:
return False
# check batch size
if ifm_tensor.shape[0] != 1:
return False
if op.type in self.avg_pooling_ops:
# check kernel size
if op.attrs["padding"] == b"SAME" and (op.attrs["filter_width"] > 8 or op.attrs["filter_height"] > 8):
return False
if op.attrs["padding"] == b"VALID" and (op.attrs["filter_width"] > 256 or op.attrs["filter_height"] > 256):
return False
if op.type in self.max_pooling_ops:
# check data type
if not ifm_tensor.dtype == ofm_tensor.dtype:
return False
# check kernel size
if op.attrs["filter_width"] > 256 or op.attrs["filter_height"] > 256: # any padding
return False
return True
def check_vector_product_restrictions(self, op):
# check data type
ifm_tensor, _, weight_tensor, _ = op.get_ifm_ifm2_weights_ofm()
if weight_tensor.element_size() > 1:
return False
return True
def check_element_wise_restrictions(self, op):
# check data type
ifm_tensor, ifm2_tensor, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm()
if op.type in ("Minimum", "Maximum") and ifm_tensor.dtype != ofm_tensor.dtype:
return False
# check batch size
if (len(ifm_tensor.shape) > 2 and ifm_tensor.shape[0] != 1) or (
len(ifm2_tensor.shape) > 2 and ifm2_tensor.shape[0] != 1
):
return False
# check scalar size
if (hasattr(ifm_tensor.values, "__len__") and len(ifm_tensor.values) > 1) or (
hasattr(ifm2_tensor.values, "__len__") and len(ifm2_tensor.values) > 1
):
return False
return True
def check_memory_only_restrictions(self, op):
# check stride size
if op.type == "StridedSlice":
if len(op.inputs) > 3 and any(stride != 1 for stride in op.inputs[3].values):
return False
return True
|
