ONE - On-device Neural Engine
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constant.py
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#!/usr/bin/env python
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# Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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class
CONSTANT
:
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__slots__ = ()
# This prevents access via __dict__.
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# Basic optimization passes
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# These passes do not change the execution result of the model
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O1 = (
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# Constant folding
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'fold_add_v2'
,
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'fold_cast'
,
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'fold_densify'
,
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'fold_dequantize'
,
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'fold_dwconv'
,
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'fold_fully_connected'
,
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'fold_gather'
,
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'fold_mul'
,
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'fold_reshape'
,
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'fold_shape'
,
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'fold_sparse_to_dense'
,
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'fold_squeeze'
,
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# Operator fusion
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'fuse_add_to_fullyconnected_bias'
,
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'fuse_add_with_conv'
,
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'fuse_add_with_tconv'
,
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'fuse_add_with_fully_connected'
,
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'fuse_batchnorm_with_conv'
,
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'fuse_batchnorm_with_dwconv'
,
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'fuse_batchnorm_with_tconv'
,
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'fuse_activation_function'
,
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'fuse_mul_to_fullyconnected_weights'
,
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'fuse_instnorm'
,
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'fuse_prelu'
,
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'fuse_gelu'
,
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'fuse_rsqrt'
,
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'fuse_mean_with_mean'
,
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'fuse_mul_with_conv'
,
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'fuse_mul_with_div'
,
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'fuse_mul_with_fullyconnected'
,
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'fuse_transpose_with_mean'
,
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'fuse_slice_with_tconv'
,
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'fuse_horizontal_fc_layers'
,
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'transform_min_max_to_relu6'
,
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'transform_min_relu_to_relu6'
,
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# Remove redundant operators
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'remove_redundant_reshape'
,
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'remove_redundant_transpose'
,
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'remove_unnecessary_add'
,
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'remove_unnecessary_cast'
,
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'remove_unnecessary_reshape'
,
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'remove_unnecessary_slice'
,
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'remove_unnecessary_strided_slice'
,
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'remove_unnecessary_split'
,
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'remove_unnecessary_transpose'
,
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'common_subexpression_elimination'
,
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# Canonicalization
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# (passes to help further optimization)
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'resolve_customop_add'
,
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'resolve_customop_batchmatmul'
,
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'resolve_customop_matmul'
,
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'resolve_customop_max_pool_with_argmax'
,
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'resolve_customop_splitv'
,
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'substitute_expand_dims_to_reshape'
,
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'substitute_pack_to_reshape'
,
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'substitute_padv2_to_pad'
,
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'substitute_splitv_to_split'
,
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'substitute_squeeze_to_reshape'
,
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'substitute_strided_slice_to_reshape'
,
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'substitute_transpose_to_reshape'
,
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'forward_reshape_to_unaryop'
,
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'forward_transpose_op'
,
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'replace_non_const_fc_with_batch_matmul'
,
# For quantization
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'replace_with_fc_gelu_fc'
,
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'transform_sqrt_div_to_rsqrt_mul'
,
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)
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OPTIMIZATION_OPTS = (
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# (OPTION_NAME, HELP_MESSAGE)
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(
'convert_nchw_to_nhwc'
,
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'Experimental: This will convert NCHW operators to NHWC under the assumption that input model is NCHW.'
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),
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(
'common_subexpression_elimination'
,
'perform common subexpression elimination'
),
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(
'expand_broadcast_const'
,
'expand broadcastable constant node inputs'
),
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(
'nchw_to_nhwc_input_shape'
,
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'convert the input shape of the model (argument for convert_nchw_to_nhwc)'
),
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(
'nchw_to_nhwc_output_shape'
,
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'convert the output shape of the model (argument for convert_nchw_to_nhwc)'
),
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(
'fold_add_v2'
,
'fold AddV2 op with constant inputs'
),
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(
'fold_cast'
,
'fold Cast op with constant input'
),
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(
'fold_densify'
,
'fold Densify op with sparse constant input'
),
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(
'fold_dequantize'
,
'fold Dequantize op'
),
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(
'fold_dwconv'
,
'fold Depthwise Convolution op with constant inputs'
),
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(
'fold_fully_connected'
,
'fold FullyConnected op with constant inputs'
),
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(
'fold_gather'
,
'fold Gather op'
),
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(
'fold_mul'
,
'fold Mul Op'
),
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(
'fold_reshape'
,
'fold Reshape op'
),
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(
'fold_shape'
,
'fold Shape op'
),
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(
'fold_sparse_to_dense'
,
'fold SparseToDense op'
),
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(
'fold_squeeze'
,
'fold Squeeze op'
),
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(
'forward_reshape_to_unaryop'
,
'Forward Reshape op'
),
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(
'forward_transpose_op'
,
'Forward Transpose op'
),
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(
'fuse_add_to_fullyconnected_bias'
,
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'Fuse Add op to following FullyConnected op bias'
),
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(
'fuse_add_with_conv'
,
'fuse Add op to Convolution op'
),
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(
'fuse_add_with_tconv'
,
'fuse Add op to Transposed'
),
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(
'fuse_add_with_fully_connected'
,
'fuse Add op to FullyConnected op'
),
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(
'fuse_batchnorm_with_conv'
,
'fuse BatchNorm op to Convolution op'
),
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(
'fuse_batchnorm_with_dwconv'
,
'fuse BatchNorm op to Depthwise Convolution op'
),
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(
'fuse_batchnorm_with_tconv'
,
'fuse BatchNorm op to Transposed Convolution op'
),
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(
'fuse_mul_to_fullyconnected_weights'
,
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'fuse Mul op to following FullyConnected op weights'
),
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(
'fuse_slice_with_tconv'
,
'fuse Slice op to Transposed Convolution op'
),
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(
'fuse_bcq'
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'apply Binary Coded Quantization'
),
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(
'fuse_preactivation_batchnorm'
,
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'fuse BatchNorm operators of pre-activations to Convolution op'
),
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(
'fuse_mean_with_mean'
,
'fuse two consecutive Mean ops'
),
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(
'fuse_mul_with_conv'
,
'fuse Mul op to Convolution op'
),
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(
'fuse_mul_with_div'
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'fuse Mul with Div as Div'
),
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(
'fuse_mul_with_fullyconnected'
,
'fuse Mul op to FullyConnected op'
),
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(
'fuse_transpose_with_mean'
,
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'fuse Mean with a preceding Transpose under certain conditions'
),
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(
'fuse_horizontal_fc_layers'
,
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'fuse horizontal FullyConnected layers under certain conditions'
),
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(
'make_batchnorm_gamma_positive'
,
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'make negative gamma of BatchNorm to a small positive value (1e-10).'
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' Note that this pass can change the execution result of the model.'
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' So, use it only when the impact is known to be acceptable.'
),
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(
'fuse_activation_function'
,
'fuse Activation function to a preceding operator'
),
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(
'fuse_instnorm'
,
'fuse ops to InstanceNorm operator'
),
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(
'fuse_prelu'
,
'fuse ops to PReLU operator'
),
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(
'fuse_gelu'
,
'fuse ops to GeLU operator'
),
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(
'fuse_rmsnorm'
,
'fuse ops to RmsNorm operator'
),
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(
'fuse_rsqrt'
,
'fuse ops to Rsqrt operator'
),
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(
'replace_cw_mul_add_with_depthwise_conv'
,
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'replace channel-wise Mul/Add with DepthwiseConv2D'
),
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(
'remove_fakequant'
,
'remove FakeQuant ops'
),
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(
'remove_gather_guard'
,
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'remove Add/FloorMod guards of Gather indices with certain conditions. '
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'CAUTION: user must guarantee that indices are all non-negative values.'
),
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(
'remove_quantdequant'
,
'remove Quantize-Dequantize sequence'
),
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(
'remove_redundant_quantize'
,
'remove redundant Quantize ops'
),
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(
'remove_redundant_reshape'
,
'fuse or remove subsequent Reshape ops'
),
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(
'remove_redundant_transpose'
,
'fuse or remove subsequent Transpose ops'
),
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(
'remove_unnecessary_add'
,
'remove unnecessary add ops'
),
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(
'remove_unnecessary_cast'
,
'remove unnecessary cast ops'
),
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(
'remove_unnecessary_reshape'
,
'remove unnecessary reshape ops'
),
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(
'remove_unnecessary_slice'
,
'remove unnecessary slice ops'
),
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(
'remove_unnecessary_strided_slice'
,
'remove unnecessary strided slice ops'
),
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(
'remove_unnecessary_split'
,
'remove unnecessary split ops'
),
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(
'remove_unnecessary_transpose'
,
'remove unnecessary transpose ops'
),
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(
'replace_non_const_fc_with_batch_matmul'
,
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'replace FullyConnected op with non-const weights to BatchMatMul op'
),
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(
'replace_sub_with_add'
,
'replace Sub op with Add op'
),
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(
'replace_with_fc_gelu_fc'
,
'replace a certain pattern with FC-Gelu-FC ops'
),
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(
'resolve_customop_add'
,
'convert Custom(Add) op to Add op'
),
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(
'resolve_customop_batchmatmul'
,
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'convert Custom(BatchMatmul) op to BatchMatmul op'
),
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(
'resolve_customop_matmul'
,
'convert Custom(Matmul) op to Matmul op'
),
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(
'resolve_customop_max_pool_with_argmax'
,
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'convert Custom(MaxPoolWithArgmax) to net of builtin operators'
),
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(
'resolve_customop_splitv'
,
'convert Custom(SplitV) op to SplitV op'
),
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(
'shuffle_weight_to_16x1float32'
,
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'convert weight format of FullyConnected op to SHUFFLED16x1FLOAT32.'
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' Note that it only converts weights whose row is a multiple of 16'
),
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(
'substitute_expand_dims_to_reshape'
,
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'convert ExpandDims with constant axis to Reshape op'
),
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(
'substitute_pack_to_reshape'
,
'convert single input Pack op to Reshape op'
),
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(
'substitute_padv2_to_pad'
,
'convert certain condition PadV2 to Pad'
),
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(
'substitute_splitv_to_split'
,
'convert certain condition SplitV to Split'
),
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(
'substitute_squeeze_to_reshape'
,
'convert certain condition Squeeze to Reshape'
),
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(
'substitute_strided_slice_to_reshape'
,
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'convert certain condition StridedSlice to Reshape'
),
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(
'substitute_transpose_to_reshape'
,
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'convert certain condition Transpose to Reshape'
),
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(
'transform_min_max_to_relu6'
,
'transform Minimum-Maximum pattern to Relu6 op'
),
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(
'transform_min_relu_to_relu6'
,
'transform Minimum(6)-Relu pattern to Relu6 op'
),
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(
'transform_sqrt_div_to_rsqrt_mul'
,
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'transform Sqrt-Div pattern to Rsqrt-Mul ops'
),
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(
'decompose_hardswish'
,
'decompose the HardSwish op to Add, Mul and Relu6 ops'
),
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(
'decompose_softmax'
,
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'decompose the Softmax op to Max, Sub, Exp, Sum, Div and optionally Mul ops'
),
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(
'unroll_unidirseqlstm'
,
'unroll UnidirectionalSequenceLSTM op'
),
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(
'dynamic_batch_to_single_batch'
,
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'convert dynamic batch size (first dimension) of inputs to 1'
))
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CONSTANT =
CONSTANT
()
constant.CONSTANT
Definition
constant.py:18
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constant.py
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