Louis Verhaard | ebf4af6 | 2021-01-27 15:57:57 +0100 | [diff] [blame] | 1 | # Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 2 | # |
| 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the License); you may |
| 6 | # not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 16 | # Description: |
| 17 | # Early optimisation of the network graph, using the rewrite_graph module to do the traversal of the graph. These are |
| 18 | # split into two parts optimise_graph_a and optimise_graph_b. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 19 | import math |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 20 | import uuid |
Louis Verhaard | ebf4af6 | 2021-01-27 15:57:57 +0100 | [diff] [blame] | 21 | from typing import Tuple |
Diego Russo | ea6111a | 2020-04-14 18:41:58 +0100 | [diff] [blame] | 22 | |
| 23 | import numpy as np |
| 24 | |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 25 | from . import fp_math |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 26 | from . import lut |
Diego Russo | ea6111a | 2020-04-14 18:41:58 +0100 | [diff] [blame] | 27 | from . import rewrite_graph |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 28 | from . import scaling |
Diego Russo | ea6111a | 2020-04-14 18:41:58 +0100 | [diff] [blame] | 29 | from .data_type import DataType |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 30 | from .debug_database import DebugDatabase |
Louis Verhaard | 7db7896 | 2020-05-25 15:05:26 +0200 | [diff] [blame] | 31 | from .errors import UnsupportedFeatureError |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 32 | from .errors import VelaError |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 33 | from .ethos_u55_regs.ethos_u55_regs import resampling_mode |
Louis Verhaard | 8912c53 | 2020-09-30 12:11:49 +0200 | [diff] [blame] | 34 | from .numeric_util import clamp_sigmoid |
Louis Verhaard | e0ef273 | 2020-06-03 08:56:44 +0200 | [diff] [blame] | 35 | from .numeric_util import full_shape |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 36 | from .numeric_util import round_away_zero |
Louis Verhaard | e8a5a78 | 2020-11-02 18:04:27 +0100 | [diff] [blame] | 37 | from .operation import create_activation_function |
Diego Russo | e8a1045 | 2020-04-21 17:39:10 +0100 | [diff] [blame] | 38 | from .operation import NpuBlockType |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 39 | from .operation import Op |
Diego Russo | e8a1045 | 2020-04-21 17:39:10 +0100 | [diff] [blame] | 40 | from .operation import Operation |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 41 | from .operation import Padding |
Fredrik Svedberg | d9c2c42 | 2020-12-01 16:33:45 +0100 | [diff] [blame] | 42 | from .operation_util import create_avgpool_nop |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 43 | from .shape4d import Shape4D |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 44 | from .softmax import SoftMax |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 45 | from .tensor import check_quantized_tens_scaling_equal |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 46 | from .tensor import create_const_tensor |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 47 | from .tensor import QuantizationParameters |
Diego Russo | e8a1045 | 2020-04-21 17:39:10 +0100 | [diff] [blame] | 48 | from .tensor import Tensor |
Michael McGeagh | 7a6f843 | 2020-12-02 15:29:22 +0000 | [diff] [blame] | 49 | from .tflite_mapping import optype_to_builtintype |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 50 | |
Michael McGeagh | f3e3ad7 | 2020-12-02 12:39:03 +0000 | [diff] [blame] | 51 | passthrough_nodes = (Op.Identity,) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 52 | |
Michael McGeagh | f3e3ad7 | 2020-12-02 12:39:03 +0000 | [diff] [blame] | 53 | memory_only_ops = (Op.Reshape,) |
Michael McGeagh | 11b0bdb | 2020-09-08 11:07:35 +0100 | [diff] [blame] | 54 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 55 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 56 | def remove_passthrough_tensor(tens, arch, nng): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 57 | if len(tens.ops) == 1 and tens.ops[0].type in passthrough_nodes: |
| 58 | assert len(tens.ops[0].inputs) == 1 |
| 59 | tens = tens.ops[0].inputs[0] |
| 60 | return tens |
| 61 | |
| 62 | |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 63 | def rewrite_concat_ops(op, arch): |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 64 | if not op.run_on_npu or not op.type.is_concat_op(): |
| 65 | return op |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 66 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 67 | axis_4D = 0 |
| 68 | ofm = op.ofm |
| 69 | ofm.ops = [] |
| 70 | offset = 0 |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 71 | |
Patrik Gustavsson | 7bada40 | 2021-01-28 15:46:21 +0100 | [diff] [blame] | 72 | unfuse_activation_function(op) |
| 73 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 74 | if op.type == Op.Pack: |
| 75 | # Pack is also referred to as Stack |
| 76 | axis = int(op.attrs["axis"]) |
| 77 | desired_shape = op.inputs[0].shape[:axis] + [1] + op.inputs[0].shape[axis:] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 78 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 79 | if axis >= 0: |
| 80 | axis_4D = axis + (4 - len(desired_shape)) |
| 81 | else: |
| 82 | axis_4D = axis |
| 83 | |
| 84 | for idx, inp in enumerate(op.inputs): |
| 85 | op.ifm_shapes[idx] = Shape4D(desired_shape) |
| 86 | if Shape4D(inp.shape) != op.ifm_shapes[idx]: |
| 87 | inp.avoid_NHCWB16 = True |
| 88 | op.type = Op.PackReshaped |
| 89 | |
| 90 | inputs, axis = op.get_concat_inputs_axis() |
| 91 | |
| 92 | for idx, inp in enumerate(inputs): |
| 93 | if op.type != Op.PackReshaped: |
| 94 | op.ifm_shapes[idx] = Shape4D(inp.shape) |
Patrik Gustavsson | 3d73717 | 2020-12-22 10:40:51 +0100 | [diff] [blame] | 95 | if axis >= 0: |
| 96 | axis_4D = axis + (4 - len(inp.shape)) |
| 97 | else: |
| 98 | axis_4D = axis |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 99 | avgpool_op = create_avgpool_nop(op.name + str(idx) + "_avgpool") |
| 100 | avgpool_op.inputs = [inp] |
| 101 | avgpool_op.outputs = [ofm] |
| 102 | avgpool_op.attrs["concat_axis"] = axis_4D |
| 103 | avgpool_op.attrs["concat_start"] = offset |
Tim Hall | 73e843f | 2021-02-04 22:47:46 +0000 | [diff] [blame] | 104 | offset += op.ifm_shapes[idx][axis_4D] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 105 | |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 106 | avgpool_op.attrs["concat_end"] = offset |
| 107 | avgpool_op.run_on_npu = True |
| 108 | ofm.ops.append(avgpool_op) |
| 109 | DebugDatabase.add_optimised(op, avgpool_op) |
| 110 | avgpool_op.ifm_shapes.append(op.ifm_shapes[idx]) |
| 111 | avgpool_op.ofm_shapes.append(op.ofm_shapes[0]) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 112 | assert ofm.shape[axis] == offset |
Patrik Gustavsson | 458a208 | 2020-08-13 13:41:05 +0200 | [diff] [blame] | 113 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 114 | # If axis corresponds to C-dimension, NHCWB16 can only be used in the output if all the concat_start's are a |
| 115 | # multiple of 16. This as, it is only then the address offset for the ofm, for all operations, will be 16 byte |
| 116 | # aligned. For other values of axis the address offsets will be 16 byte aligned, as they are all based on c = 0 |
| 117 | # and those addresses are always 16 byte aligned due to the NHCWB16 format. |
| 118 | if axis == -1 or axis == (len(ofm.shape) - 1): |
| 119 | for op in ofm.ops: |
| 120 | if op.attrs["concat_start"] % 16 != 0: |
| 121 | ofm.avoid_NHCWB16 = True |
| 122 | break |
| 123 | return op |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 124 | |
| 125 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 126 | def rewrite_split_ops(tens, arch, nng): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 127 | |
Patrik Gustavsson | 224e99b | 2021-01-14 10:55:43 +0100 | [diff] [blame] | 128 | if len(tens.ops) == 1 and tens.ops[0].type.is_split_op() and tens.ops[0].type != Op.Unpack: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 129 | split_op = tens.ops[0] |
| 130 | |
| 131 | # Not supported so leave it and run on CPU |
| 132 | if not split_op.run_on_npu: |
| 133 | return tens |
| 134 | |
| 135 | inp, outputs, axis, offset_start, offset_end = split_op.get_split_inputs_axis() |
| 136 | |
| 137 | tens.ops = [] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 138 | new_op = Operation(Op.SplitSliceRead, split_op.name) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 139 | new_op.inputs = [inp] |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 140 | ofm_shape_idx = 0 |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 141 | |
| 142 | # For Split the offset cannot be extracted from the tensor so it has to |
| 143 | # be calculated from the index of the output tensor |
Diego Russo | ea6111a | 2020-04-14 18:41:58 +0100 | [diff] [blame] | 144 | if axis is not None: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 145 | # Get the start and end of the split |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 146 | offset_start = [0] * 4 |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 147 | axis_4D_list = split_op.attrs.get("split_axis_4D", None) # Present for UnpackReshaped and some StridedSlice |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 148 | for idx, out in enumerate(outputs): |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 149 | if axis_4D_list is not None: |
| 150 | axis_4D = axis_4D_list[idx] |
Patrik Gustavsson | 3d73717 | 2020-12-22 10:40:51 +0100 | [diff] [blame] | 151 | else: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 152 | split_op.ofm_shapes[idx] = Shape4D(out.shape) |
| 153 | if axis >= 0: |
| 154 | axis_4D = axis + (4 - len(out.shape)) |
| 155 | else: |
| 156 | axis_4D = axis |
| 157 | |
| 158 | if out == tens: |
| 159 | ofm_shape_idx = idx |
| 160 | break |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 161 | |
Tim Hall | 73e843f | 2021-02-04 22:47:46 +0000 | [diff] [blame] | 162 | offset_start[axis_4D] += split_op.ofm_shapes[idx][axis_4D] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 163 | |
Patrik Gustavsson | eebb1c2 | 2020-08-18 15:03:04 +0200 | [diff] [blame] | 164 | # If start offset is not a multiple of 16 in the C-dimension, NHCWB16 need to be avoided in the input |
| 165 | if (offset_start[-1] % 16) != 0: |
| 166 | inp.avoid_NHCWB16 = True |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 167 | else: |
| 168 | offset_start = full_shape(4, offset_start, 0) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 169 | |
| 170 | new_op.attrs["split_start"] = offset_start |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 171 | new_op.run_on_npu = True |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 172 | new_op.set_output_tensor(tens) |
Patrik Gustavsson | 224e99b | 2021-01-14 10:55:43 +0100 | [diff] [blame] | 173 | new_op.ifm_shapes.append(Shape4D(inp.shape)) |
Tim Hall | 73e843f | 2021-02-04 22:47:46 +0000 | [diff] [blame] | 174 | new_op.ofm_shapes.append(split_op.ofm_shapes[ofm_shape_idx]) |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 175 | DebugDatabase.add_optimised(split_op, new_op) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 176 | |
| 177 | return tens |
| 178 | |
| 179 | |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 180 | def insert_copy_op_after_tens(tens): |
| 181 | tens_cons_list_copy = tens.consumer_list.copy() |
| 182 | |
| 183 | # Create a avg_pool nop op with ifm as input |
| 184 | copy_tens = tens.clone() |
| 185 | copy_op = create_avgpool_nop(tens.name + "_avgpool") |
| 186 | copy_op.add_input_tensor(tens) |
| 187 | copy_op.set_output_tensor(copy_tens) |
| 188 | copy_op.set_ifm_ofm_shapes() |
| 189 | copy_op.run_on_npu = True |
| 190 | |
| 191 | # Set copy_ifm consumers |
| 192 | for tens_cons in tens_cons_list_copy: |
| 193 | if tens_cons is not None: |
| 194 | for ifm_idx, cons_inp in enumerate(tens_cons.inputs): |
| 195 | if cons_inp == tens: |
| 196 | tens_cons.set_input_tensor(copy_tens, ifm_idx) |
| 197 | |
| 198 | DebugDatabase.add_optimised(tens.ops[0], copy_op) |
| 199 | |
| 200 | |
| 201 | def fix_sg_input_output(op, arch, nng): |
| 202 | if not op.run_on_npu or op.type != Op.Reshape: |
| 203 | return op |
| 204 | |
| 205 | # For the memory operators we want to remove, tensors are removed. |
| 206 | # But in order to to do this, they cannot be outputs of the sg, |
| 207 | # this need to be fixed prior to the removal. |
| 208 | # Solution is to add a avgpool NOP, to maintain the original tensor. |
| 209 | |
| 210 | # Check if operator ifm/ofm are sg ifm/ofm |
| 211 | ifm_is_sg_ifm = op.ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) |
| 212 | ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in op.ifm.consumer_list) |
| 213 | ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in op.ofm.consumer_list) |
| 214 | |
| 215 | if op.type == Op.Reshape and (ifm_is_sg_ofm or ifm_is_sg_ifm) and ofm_is_sg_ofm: |
| 216 | # Both ifm and ofm are sg outputs, only ifm need a copy, in order to remove the Reshape |
| 217 | insert_copy_op_after_tens(op.ifm) |
| 218 | |
| 219 | return op |
| 220 | |
| 221 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 222 | def needed_total_padding(input_size, stride, filter_size): |
| 223 | out_size = (input_size + stride - 1) // stride |
| 224 | needed_input = (out_size - 1) * stride + filter_size |
| 225 | total_padding = max(0, needed_input - input_size) |
| 226 | return total_padding |
| 227 | |
| 228 | |
Louis Verhaard | ebf4af6 | 2021-01-27 15:57:57 +0100 | [diff] [blame] | 229 | def calc_explicit_padding(input_size, stride, filter_size, pad_before, pad_after) -> Tuple[int, int]: |
| 230 | """ |
| 231 | Based on explicit padding provided in a PAD operation, returns the corresponding hardware padding |
| 232 | that provides equivalent results. |
| 233 | """ |
| 234 | total_padding = needed_total_padding(input_size, stride, filter_size) |
| 235 | # The top/left padding can be taken as is from the PAD |
| 236 | output_pad_before = pad_before |
| 237 | # The bottom/right padding might need downward adjustment depending on stride/input size |
| 238 | output_pad_after = pad_after |
| 239 | while output_pad_after > 0 and output_pad_after % stride != (total_padding - pad_before) % stride: |
| 240 | output_pad_after -= 1 |
| 241 | return output_pad_before, output_pad_after |
| 242 | |
| 243 | |
| 244 | def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding): |
| 245 | k_w, k_h = kernel.dilated_wh() |
| 246 | s_x, s_y = kernel.stride |
| 247 | ypad = needed_total_padding(int(input_shape.height), int(s_y), int(k_h)) |
| 248 | xpad = needed_total_padding(int(input_shape.width), int(s_x), int(k_w)) |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 249 | if padding_type == Padding.SAME: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 250 | left_pad = (xpad + 0) // 2 |
| 251 | right_pad = (xpad + 1) // 2 |
| 252 | top_pad = (ypad + 0) // 2 |
| 253 | bottom_pad = (ypad + 1) // 2 |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 254 | elif padding_type == Padding.VALID: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 255 | left_pad = 0 |
| 256 | right_pad = 0 |
| 257 | top_pad = 0 |
| 258 | bottom_pad = 0 |
Louis Verhaard | ae2d553 | 2020-12-11 17:19:54 +0100 | [diff] [blame] | 259 | elif padding_type == Padding.EXPLICIT: |
| 260 | # Padding is specified in a PAD operator which has been bypassed. |
Louis Verhaard | ebf4af6 | 2021-01-27 15:57:57 +0100 | [diff] [blame] | 261 | top, left, bottom, right = explicit_padding |
| 262 | top_pad, bottom_pad = calc_explicit_padding(int(input_shape.height), int(s_y), int(k_h), int(top), int(bottom)) |
| 263 | left_pad, right_pad = calc_explicit_padding(int(input_shape.width), int(s_x), int(k_w), int(left), int(right)) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 264 | else: |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 265 | raise UnsupportedFeatureError(f"Unknown padding") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 266 | padding = (top_pad, left_pad, bottom_pad, right_pad) |
| 267 | skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad) |
| 268 | return padding, skirt |
| 269 | |
Tim Hall | c30f495 | 2020-06-15 20:47:35 +0100 | [diff] [blame] | 270 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 271 | def calc_upscaled_padding_and_skirt(padding_type, kernel_size, stride, input_shape, upscaling_factor): |
Jacob Bohlin | 9b64ba0 | 2020-07-07 17:15:22 +0200 | [diff] [blame] | 272 | kernel_height, kernel_width = kernel_size[0], kernel_size[1] |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 273 | if padding_type == Padding.SAME: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 274 | ypad = needed_total_padding(int(input_shape.height) * upscaling_factor, int(stride[1]), int(kernel_height)) |
| 275 | xpad = needed_total_padding(int(input_shape.width) * upscaling_factor, int(stride[2]), int(kernel_width)) |
Jacob Bohlin | d47cc27 | 2020-08-24 11:42:14 +0200 | [diff] [blame] | 276 | right_pad = max(((xpad + 1) // upscaling_factor) - 1, 0) |
| 277 | bottom_pad = max(((ypad + 1) // upscaling_factor) - 1, 0) |
Jacob Bohlin | 9b64ba0 | 2020-07-07 17:15:22 +0200 | [diff] [blame] | 278 | left_pad = max(kernel_width - 1 - right_pad, 0) |
| 279 | top_pad = max(kernel_height - 1 - bottom_pad, 0) |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 280 | elif padding_type == Padding.VALID: |
Jacob Bohlin | 9b64ba0 | 2020-07-07 17:15:22 +0200 | [diff] [blame] | 281 | right_pad = max(kernel_width - 2, 0) |
| 282 | bottom_pad = max(kernel_height - 2, 0) |
| 283 | left_pad = kernel_width - 1 |
| 284 | top_pad = kernel_height - 1 |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 285 | else: |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 286 | raise UnsupportedFeatureError(f"Unknown padding") |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 287 | padding = (top_pad, left_pad, bottom_pad, right_pad) |
Jacob Bohlin | 9b64ba0 | 2020-07-07 17:15:22 +0200 | [diff] [blame] | 288 | skirt = padding |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 289 | return padding, skirt |
| 290 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 291 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 292 | def fixup_conv2d_backprop(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 293 | if op.type == Op.Conv2DBackpropInput: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 294 | # flip the inputs |
| 295 | op.inputs[0], op.inputs[2] = op.inputs[2], op.inputs[0] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 296 | op.type = Op.Conv2DBackpropInputSwitchedBias |
Louis Verhaard | 69b8480 | 2020-12-16 12:02:28 +0100 | [diff] [blame] | 297 | op.ifm.resampling_mode = resampling_mode.TRANSPOSE |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 298 | |
| 299 | # Update strides |
Tim Hall | c30f495 | 2020-06-15 20:47:35 +0100 | [diff] [blame] | 300 | op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)}) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 301 | |
| 302 | return op |
| 303 | |
| 304 | |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 305 | # Convert the op to an elementwise add |
| 306 | def convert_resizebilinear_1x1_to_add(op): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 307 | op.type = Op.Add |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 308 | op.name = op.name + "_add" |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 309 | op.attrs["resizebilinear"] = True |
| 310 | # Create an input tensor filled with zeros |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 311 | shape = op.ofm_shapes[0].as_list() |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 312 | tens = Tensor(shape, op.inputs[0].dtype, op.inputs[1].name + "_add") |
| 313 | tens.values = np.zeros(shape) |
| 314 | tens.quant_values = np.zeros(shape, np.uint8) |
| 315 | tens.quantization = QuantizationParameters(0.0, 255.0) |
| 316 | tens.quantization.scale_f32 = 1.0 |
| 317 | tens.quantization.zero_point = 0 |
| 318 | tens.consumer_list = [op] |
| 319 | tens_op = op.inputs[1].ops[0] |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 320 | tens_op.set_output_tensor(tens) |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 321 | # Set the add inputs |
| 322 | op.inputs[1] = op.inputs[0] |
| 323 | op.inputs[0] = tens |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 324 | op.set_ifm_ofm_shapes() |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 325 | |
| 326 | return op |
| 327 | |
| 328 | |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 329 | # Convert ResizeBilinear to a number of 2x2 pool ops |
| 330 | def convert_resizebilinear_to_2x2_pool(op): |
| 331 | count = 0 |
| 332 | pre_op = op |
| 333 | outputs = op.outputs |
| 334 | |
| 335 | op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 2, 2, 1)}) |
| 336 | if op.attrs["align_corners"]: |
| 337 | shape_modifier = 1 |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 338 | op.attrs["padding"] = Padding.VALID |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 339 | else: |
| 340 | shape_modifier = 0 |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 341 | op.attrs["padding"] = Padding.SAME |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 342 | op.inputs[0].resampling_mode = resampling_mode.NEAREST |
| 343 | |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 344 | upscaled_shape = np.array(op.ifm_shapes[0].get_hw_as_list()) |
| 345 | out_shape = np.array(op.ofm_shapes[0].get_hw_as_list()) |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 346 | if (upscaled_shape == upscaled_shape * 2 - shape_modifier).all(): |
| 347 | return op |
| 348 | |
| 349 | while (upscaled_shape < out_shape).all(): |
| 350 | if count == 0: |
| 351 | scaled_op = pre_op |
| 352 | else: |
| 353 | scaled_op = op.clone("_{}".format(count)) |
| 354 | scaled_op.inputs[0] = pre_op.outputs[0] |
| 355 | |
| 356 | upscaled_shape = upscaled_shape * 2 - shape_modifier |
| 357 | |
| 358 | if (upscaled_shape == out_shape).all(): |
| 359 | scaled_op.outputs = outputs |
| 360 | scaled_op.outputs[0].ops = [scaled_op] |
| 361 | else: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 362 | shape = op.ofm_shapes[0].as_list() |
| 363 | shape[1:3] = upscaled_shape |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 364 | out_tens = Tensor(shape, DataType.int16, "{}_{}".format(op.outputs[0].name, count)) |
| 365 | out_tens.quantization = op.outputs[0].quantization.clone() |
| 366 | out_tens.quantization.quant_min = np.iinfo(np.int16).min |
| 367 | out_tens.quantization.quant_max = np.iinfo(np.int16).max |
| 368 | scaled_op.set_output_tensor(out_tens) |
| 369 | pre_op = scaled_op |
| 370 | count += 1 |
| 371 | |
| 372 | # Setup the scale value |
| 373 | if scaled_op.inputs[0].dtype.bits == 8 and scaled_op.outputs[0].dtype.bits == 16: |
Fredrik Svedberg | e82be7c | 2021-01-18 15:21:03 +0100 | [diff] [blame] | 374 | scaled_op.rescale = 128 |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 375 | elif scaled_op.inputs[0].dtype.bits == 16 and scaled_op.outputs[0].dtype.bits == 8: |
Fredrik Svedberg | e82be7c | 2021-01-18 15:21:03 +0100 | [diff] [blame] | 376 | scaled_op.rescale = 1 / 128 |
| 377 | else: |
| 378 | scaled_op.rescale = None |
Patrik Gustavsson | cc6915c | 2020-12-22 09:16:50 +0100 | [diff] [blame] | 379 | scaled_op.set_ifm_ofm_shapes() |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 380 | |
| 381 | return op |
| 382 | |
| 383 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 384 | def fixup_resizebilinear(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 385 | if op.type == Op.ResizeBilinear and op.run_on_npu: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 386 | if op.ifm_shapes[0] == op.ofm_shapes[0]: |
Charles Xu | 36ffaf3 | 2020-08-05 15:40:44 +0200 | [diff] [blame] | 387 | # Bypass nop resizebilinear |
| 388 | op.inputs = op.inputs[:1] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 389 | op.type = Op.Identity |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 390 | elif op.ifm_shapes[0].height == 1 and op.ifm_shapes[0].width == 1: |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 391 | convert_resizebilinear_1x1_to_add(op) |
| 392 | else: |
| 393 | convert_resizebilinear_to_2x2_pool(op) |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 394 | |
| 395 | return op |
| 396 | |
| 397 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 398 | def convert_nop_split_to_identity(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 399 | if op.type == Op.Split and op.attrs.get("num_splits") == 1: |
Dwight Lidman | c3862c2 | 2020-09-14 15:22:33 +0200 | [diff] [blame] | 400 | # the list comprehension should return a list with a single tensor |
| 401 | # if it shouldn't, remove_passthrough_tensor will fail appropriately |
| 402 | op.inputs = [i for i in op.inputs if i.shape == op.outputs[0].shape] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 403 | op.type = Op.Identity |
Dwight Lidman | c3862c2 | 2020-09-14 15:22:33 +0200 | [diff] [blame] | 404 | return op |
| 405 | |
| 406 | |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 407 | def rewrite_fully_connected_input(op, arch, nng): |
| 408 | if op.type == Op.FullyConnected: |
| 409 | n_in_elems = op.weights.shape[-2] |
| 410 | elms = op.ifm.elements() |
| 411 | batch_size = elms // n_in_elems |
| 412 | assert batch_size * n_in_elems == elms |
| 413 | |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 414 | op.ifm_shapes[0] = Shape4D([batch_size, 1, 1, n_in_elems]) |
Patrik Gustavsson | da2b003 | 2021-02-04 16:28:29 +0100 | [diff] [blame] | 415 | if Shape4D(op.ifm.shape) != op.ifm_shapes[0]: |
| 416 | op.ifm.avoid_NHCWB16 = True |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 417 | return op |
| 418 | |
| 419 | |
Diqing Zhong | 94457b1 | 2020-12-09 15:22:40 +0100 | [diff] [blame] | 420 | def convert_batched_fc_shape(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 421 | if op.type == Op.FullyConnected: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 422 | # Check if the first dimension indicates batching |
| 423 | if op.ifm_shapes[0].batch > 1: |
Patrik Gustavsson | cb33704 | 2020-09-16 14:55:40 +0200 | [diff] [blame] | 424 | batching_split = {4: (2, 2), 8: (2, 4), 16: (4, 4)} |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 425 | n = op.ifm_shapes[0].batch |
Patrik Gustavsson | cb33704 | 2020-09-16 14:55:40 +0200 | [diff] [blame] | 426 | h, w = batching_split.get(n, (1, n)) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 427 | op.ifm_shapes[0] = Shape4D([1, h, w, op.ifm_shapes[0].depth]) |
Patrik Gustavsson | cb33704 | 2020-09-16 14:55:40 +0200 | [diff] [blame] | 428 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 429 | op.ifm.avoid_NHCWB16 = True |
Patrik Gustavsson | cb33704 | 2020-09-16 14:55:40 +0200 | [diff] [blame] | 430 | |
| 431 | # Reshape Weights to be 4D. IO becomes HWIO |
| 432 | weight_tensor = op.inputs[1] |
| 433 | weight_tensor.quant_values = np.expand_dims(np.expand_dims(weight_tensor.quant_values, axis=0), axis=0) |
| 434 | weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape)) |
| 435 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 436 | n = op.ofm_shapes[0].batch |
| 437 | h, w = batching_split.get(n, (1, n)) |
| 438 | op.ofm_shapes[0] = Shape4D([1, h, w, op.ofm_shapes[0].depth]) |
| 439 | op.ofm.avoid_NHCWB16 = True |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 440 | return op |
| 441 | |
| 442 | |
Patrik Gustavsson | 7bada40 | 2021-01-28 15:46:21 +0100 | [diff] [blame] | 443 | def unfuse_activation_function(op): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 444 | if op.type == Op.ConcatTFLite and op.run_on_npu and op.activation is not None: |
Louis Verhaard | e8a5a78 | 2020-11-02 18:04:27 +0100 | [diff] [blame] | 445 | act_op = Operation(op.activation.op_type, op.name + op.activation.op_type.name) |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 446 | op.activation = None |
Fredrik Svedberg | 0f98b36 | 2020-09-29 10:00:39 +0200 | [diff] [blame] | 447 | out_tens = op.outputs[0] |
| 448 | intermediate_tens = out_tens.clone("_act_intermediate") |
| 449 | act_op.set_output_tensor(out_tens) |
| 450 | act_op.add_input_tensor(intermediate_tens) |
| 451 | op.set_output_tensor(intermediate_tens) |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 452 | act_op.set_ifm_ofm_shapes() |
Fredrik Svedberg | 0f98b36 | 2020-09-29 10:00:39 +0200 | [diff] [blame] | 453 | |
Louis Verhaard | 8912c53 | 2020-09-30 12:11:49 +0200 | [diff] [blame] | 454 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 455 | def rewrite_stridedslice_output(op, arch, nng): |
| 456 | if not op.run_on_npu or op.type != Op.StridedSlice: |
| 457 | return op |
| 458 | |
| 459 | new_axis_mask = op.attrs["new_axis_mask"] |
| 460 | shrink_axis_mask = op.attrs["shrink_axis_mask"] |
| 461 | |
| 462 | if shrink_axis_mask == 0 and new_axis_mask == 0: |
| 463 | return op |
| 464 | |
| 465 | axis_4D = [0] * len(op.outputs) |
| 466 | for idx, out_tens in enumerate(op.outputs): |
| 467 | output_shape = list(out_tens.shape) |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 468 | |
Dwight Lidman | 73320a4 | 2020-11-05 10:34:41 +0100 | [diff] [blame] | 469 | if shrink_axis_mask != 0: |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 470 | n = 0 |
| 471 | axis = 0 |
| 472 | while shrink_axis_mask: |
| 473 | prev_mask = shrink_axis_mask |
| 474 | n += 1 |
| 475 | shrink_axis_mask &= shrink_axis_mask - 1 |
| 476 | axis = int(math.log2(prev_mask - shrink_axis_mask)) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 477 | output_shape = output_shape[:axis] + [1] + output_shape[axis:] |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 478 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 479 | assert len(out_tens.shape) == (len(op.inputs[0].shape) - n) |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 480 | op.attrs["shrink_axis_mask"] = 0 |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 481 | if axis >= 0: |
| 482 | axis_4D[idx] = axis + (4 - len(output_shape)) |
| 483 | else: |
| 484 | axis_4D[idx] = axis |
| 485 | op.ofm_shapes[idx] = Shape4D(output_shape) |
| 486 | |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 487 | elif new_axis_mask != 0: |
| 488 | n = 0 |
| 489 | axis = 0 |
| 490 | while new_axis_mask: |
| 491 | prev_mask = new_axis_mask |
| 492 | n += 1 |
| 493 | new_axis_mask &= new_axis_mask - 1 |
| 494 | axis = int(math.log2(prev_mask - new_axis_mask)) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 495 | output_shape = output_shape[:axis] + output_shape[(axis + 1) :] |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 496 | new_axis_mask >>= 1 |
| 497 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 498 | assert len(out_tens.shape) == (len(op.inputs[0].shape) + n) |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 499 | op.attrs["new_axis_mask"] = 0 |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 500 | if axis >= 0: |
| 501 | axis_4D[idx] = axis + (4 - len(output_shape)) |
| 502 | else: |
| 503 | axis_4D[idx] = axis |
| 504 | op.ofm_shapes[idx] = Shape4D(output_shape) |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 505 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 506 | if op.ofm_shapes[idx] != Shape4D(out_tens.shape): |
| 507 | out_tens.avoid_NHCWB16 = True |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 508 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 509 | op.attrs["split_axis_4D"] = axis_4D |
| 510 | return op |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 511 | |
| 512 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 513 | def rewrite_unpack_output(op, arch, nng): |
| 514 | tens = op.outputs[0] |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 515 | if op.run_on_npu and op.type == Op.Unpack: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 516 | # Unpack is also referred to as Unstack |
Diqing Zhong | c7c0b1b | 2020-10-26 11:45:25 +0100 | [diff] [blame] | 517 | axis = int(op.attrs["axis"]) |
| 518 | op.type = Op.UnpackReshaped |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 519 | desired_output_shape = tens.shape[:axis] + [1] + tens.shape[axis:] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 520 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 521 | if axis >= 0: |
| 522 | axis_4D = axis + (4 - len(desired_output_shape)) |
| 523 | else: |
| 524 | axis_4D = axis |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 525 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 526 | axis_4D_list = [0] * len(op.outputs) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 527 | for idx, out_tens in enumerate(op.outputs): |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 528 | op.ofm_shapes[idx] = Shape4D(desired_output_shape) |
| 529 | axis_4D_list[idx] = axis_4D |
| 530 | if op.ofm_shapes[idx] != Shape4D(out_tens.shape): |
| 531 | out_tens.avoid_NHCWB16 = True |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 532 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 533 | op.attrs["split_axis_4D"] = axis_4D_list |
| 534 | return op |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 535 | |
| 536 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 537 | def add_padding_fields(op, arch, nng): |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 538 | if op.run_on_npu: |
| 539 | if "padding" in op.attrs: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 540 | input_shape = op.ifm_shapes[0] |
| 541 | output_shape = op.ofm_shapes[0] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 542 | if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op(): |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 543 | kernel_size = op.inputs[1].shape[:2] |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 544 | elif op.type.is_pool_op() or op.type.npu_block_type == NpuBlockType.ReduceSum: |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 545 | kernel_size = op.attrs["ksize"][1:3] |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 546 | else: |
Michael McGeagh | 7a6f843 | 2020-12-02 15:29:22 +0000 | [diff] [blame] | 547 | raise UnsupportedFeatureError(f"Unknown operation that uses padding: {optype_to_builtintype(op.type)}") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 548 | |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 549 | if op.type == Op.Conv2DBackpropInputSwitchedBias: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 550 | upscaling_factor = output_shape.height // input_shape.height |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 551 | padding, skirt = calc_upscaled_padding_and_skirt( |
| 552 | op.attrs["padding"], kernel_size, op.attrs["strides"], input_shape, upscaling_factor |
| 553 | ) |
| 554 | else: |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 555 | padding, skirt = calc_padding_and_skirt( |
Louis Verhaard | ebf4af6 | 2021-01-27 15:57:57 +0100 | [diff] [blame] | 556 | op.attrs["padding"], op.kernel, input_shape, op.attrs.get("explicit_padding"), |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 557 | ) |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 558 | |
Jacob Bohlin | 90033f3 | 2020-08-28 15:45:44 +0200 | [diff] [blame] | 559 | op.attrs["explicit_padding"] = padding |
| 560 | op.attrs["skirt"] = skirt |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 561 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 562 | return op |
| 563 | |
| 564 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 565 | def convert_depthwise_to_conv(op, arch, nng): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 566 | # Depthwise is equivalent to a single conv2d if the ifm depth is 1 and |
| 567 | # the ofm depth equals the depth multipler. |
| 568 | # If those conditions are true, then we can perform a simple |
| 569 | # switch of the operator type (and weight order) |
| 570 | |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 571 | if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1): |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 572 | ifm_shape = op.ifm_shapes[0] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 573 | weight_tensor = op.inputs[1] |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 574 | ofm_shape = op.ofm_shapes[0] |
| 575 | if (ifm_shape.depth == 1) and (ofm_shape.depth == op.attrs["depth_multiplier"]): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 576 | # Change op type to Conv2d |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 577 | op.type = Op.Conv2DBias |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 578 | del op.attrs["channel_multiplier"] |
| 579 | del op.attrs["depth_multiplier"] |
| 580 | |
| 581 | weight_tensor.quant_values = np.transpose(weight_tensor.quant_values, (0, 1, 3, 2)) |
Michael McGeagh | 6a8d424 | 2020-07-28 12:17:59 +0100 | [diff] [blame] | 582 | weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape)) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 583 | else: |
Louis Verhaard | 7db7896 | 2020-05-25 15:05:26 +0200 | [diff] [blame] | 584 | raise UnsupportedFeatureError( |
Michael McGeagh | 7a6f843 | 2020-12-02 15:29:22 +0000 | [diff] [blame] | 585 | f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},", |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 586 | f" ifm channels = {ifm_shape.depth}, ofm channels = {ofm_shape.depth}", |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 587 | ) |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 588 | DebugDatabase.add_optimised(op, op) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 589 | return op |
| 590 | |
| 591 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 592 | def reorder_depthwise_weights(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 593 | if op.type.is_depthwise_conv2d_op(): |
Jacob Bohlin | e843d33 | 2020-06-23 12:12:56 +0200 | [diff] [blame] | 594 | weight_tensor = op.inputs[1] |
| 595 | weight_tensor.quant_values = np.transpose(weight_tensor.quant_values, (0, 1, 3, 2)) |
Michael McGeagh | 6a8d424 | 2020-07-28 12:17:59 +0100 | [diff] [blame] | 596 | weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape)) |
Jacob Bohlin | e843d33 | 2020-06-23 12:12:56 +0200 | [diff] [blame] | 597 | weight_tensor.weight_transpose_depthwise = True |
| 598 | |
| 599 | return op |
| 600 | |
| 601 | |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 602 | def optimise_strided_conv(op, arch, nng): |
| 603 | stride_x, stride_y = op.get_kernel_stride() |
| 604 | ifm_tensor, _, weight_tensor, _ = op.get_ifm_ifm2_weights_ofm() |
| 605 | |
| 606 | if ( |
| 607 | op.type == Op.Conv2DBias |
| 608 | and op.op_index == 0 |
| 609 | and stride_x == 2 |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 610 | and op.ifm_shapes[0].depth <= 4 |
| 611 | and op.ifm_shapes[0].width % 2 == 0 |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 612 | and weight_tensor is not None |
| 613 | and weight_tensor.shape[1] >= 2 |
| 614 | ): |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 615 | ifm_shape = op.ifm_shapes[0] |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 616 | # IFM |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 617 | op.ifm_shapes[0] = Shape4D([ifm_shape.batch, ifm_shape.height, ifm_shape.width // 2, ifm_shape.depth * 2]) |
| 618 | op.ifm.avoid_NHCWB16 = True |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 619 | |
| 620 | # Weights |
| 621 | weight_shape = weight_tensor.shape |
| 622 | if weight_shape[1] % 2 != 0: |
| 623 | weight_shape[1] = weight_shape[1] + 1 |
| 624 | padded_array = np.zeros(weight_shape) |
| 625 | for i in range(weight_shape[0]): |
| 626 | padded_array[i] = np.vstack( |
| 627 | [ |
| 628 | weight_tensor.quant_values[i], |
| 629 | np.full((1, weight_shape[2], weight_shape[3]), weight_tensor.quantization.zero_point), |
| 630 | ] |
| 631 | ) |
| 632 | weight_tensor.quant_values = padded_array |
| 633 | weight_shape[1] //= 2 |
| 634 | weight_shape[2] *= 2 |
| 635 | weight_tensor.quant_values = np.reshape(weight_tensor.quant_values, weight_shape) |
| 636 | weight_tensor.set_all_shapes(weight_shape) |
| 637 | # If multiple copies of the weights are used, we could avoid |
| 638 | # them having the same address by changing the value_id |
| 639 | weight_tensor.value_id = uuid.uuid4() |
| 640 | |
| 641 | # Strides |
| 642 | stride_x = 1 |
| 643 | op.attrs.update({"stride_w": stride_x, "stride_h": stride_y, "strides": (1, stride_y, stride_x, 1)}) |
| 644 | |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 645 | return op |
| 646 | |
| 647 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 648 | def convert_conv_to_fc(op, arch, nng): |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 649 | # Conv 1x1 can be equivalent to Fully Connected. |
| 650 | # By representing certain convs as fully connected layers, Vela can better determine wether or not to use |
| 651 | # caching/double buffering for the weights. |
| 652 | # (Weights dont need to be reloaded for convs when IFM H and W are 1) |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 653 | if op.type == Op.Conv2DBias: |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 654 | h = op.ifm_shapes[0].height |
| 655 | w = op.ifm_shapes[0].width |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 656 | kh, kw, _, _ = op.inputs[1].shape |
| 657 | if h == 1 and w == 1 and kh == 1 and kw == 1: |
| 658 | # Overwrite this op as a Fully Connected Op |
| 659 | op.name += "_fc" |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 660 | op.type = Op.FullyConnected |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 661 | op.attrs = { |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 662 | "weights_format": 0, |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 663 | } |
| 664 | # Reshape Weights to be 2D. HWIO becomes just IO (as H and W are 1, they can just be dropped) |
| 665 | weight_tensor = op.inputs[1] |
| 666 | weight_tensor.quant_values = weight_tensor.quant_values.squeeze(axis=(0, 1)) |
| 667 | weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape)) |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 668 | |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 669 | DebugDatabase.add_optimised(op, op) |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 670 | return op |
| 671 | |
| 672 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 673 | def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 674 | if op.run_on_npu and op.type.is_relu_op(): |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 675 | ifm = op.inputs[0] |
| 676 | ofm = op.outputs[0] |
| 677 | # Relu with differing IFM and OFM scaling cannot be fused with another primary op |
| 678 | # and requires its own to be inserted |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 679 | if not check_quantized_tens_scaling_equal(ifm, ofm): |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 680 | # Override this op with its own primary op (avgpool) |
| 681 | relu_fused_op = create_avgpool_nop(op.name + "_avgpool") |
| 682 | # And fuse the original activation function to it |
Louis Verhaard | e8a5a78 | 2020-11-02 18:04:27 +0100 | [diff] [blame] | 683 | relu_fused_op.activation = create_activation_function(op.type) |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 684 | # Tidy up and assign the ifm and ofm to the new op |
| 685 | ifm.consumer_list.remove(op) |
Andreas Nevalainen | f3d737e | 2020-09-25 14:12:43 +0200 | [diff] [blame] | 686 | |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 687 | relu_fused_op.add_input_tensor(ifm) |
| 688 | relu_fused_op.set_output_tensor(ofm) |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 689 | relu_fused_op.set_ifm_ofm_shapes() |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 690 | op = relu_fused_op |
| 691 | return op |
| 692 | |
| 693 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 694 | def fixup_elementwise_with_scalars(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 695 | if op.type.is_binary_elementwise_op(): |
Louis Verhaard | e0ef273 | 2020-06-03 08:56:44 +0200 | [diff] [blame] | 696 | ifm_tensor, ifm2_tensor, _, _ = op.get_ifm_ifm2_weights_ofm() |
Charles Xu | 7879222 | 2020-05-13 10:15:26 +0200 | [diff] [blame] | 697 | if ifm2_tensor.shape != [] and ifm_tensor.shape != []: |
| 698 | diff = len(ifm_tensor.shape) - len(ifm2_tensor.shape) |
| 699 | if diff > 0: |
| 700 | ifm2_tensor.shape = full_shape(len(ifm_tensor.shape), ifm2_tensor.shape, 1) |
| 701 | elif diff < 0: |
| 702 | ifm_tensor.shape = full_shape(len(ifm2_tensor.shape), ifm_tensor.shape, 1) |
Louis Verhaard | e0ef273 | 2020-06-03 08:56:44 +0200 | [diff] [blame] | 703 | elif ifm_tensor.shape == [] and ifm_tensor.quant_values is None: |
| 704 | # IFM is marked as a scalar, but is a result of an operation; change it to a shape of size 1 |
| 705 | ifm_tensor.shape = len(ifm2_tensor.shape) * [1] |
| 706 | ifm_tensor.storage_shape = ifm_tensor.shape |
| 707 | elif ifm2_tensor.shape == [] and ifm2_tensor.quant_values is None: |
| 708 | # IFM2 is marked as a scalar, but is a result of an operation; change it to a shape of size 1 |
| 709 | ifm2_tensor.shape = len(ifm_tensor.shape) * [1] |
| 710 | ifm2_tensor.storage_shape = ifm2_tensor.shape |
Charles Xu | 7879222 | 2020-05-13 10:15:26 +0200 | [diff] [blame] | 711 | return op |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 712 | |
Louis Verhaard | e0ef273 | 2020-06-03 08:56:44 +0200 | [diff] [blame] | 713 | |
Tim Hall | 4e12776 | 2020-05-15 16:05:49 +0100 | [diff] [blame] | 714 | # Set input/output tensor equivalence to the same id for memory operations |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 715 | def set_tensor_equivalence(op, arch, nng): |
Michael McGeagh | 11b0bdb | 2020-09-08 11:07:35 +0100 | [diff] [blame] | 716 | if op.type in memory_only_ops: |
Tim Hall | 4e12776 | 2020-05-15 16:05:49 +0100 | [diff] [blame] | 717 | eid = op.outputs[0].equivalence_id |
| 718 | for inp in op.inputs: |
| 719 | inp.equivalence_id = eid |
| 720 | return op |
| 721 | |
| 722 | |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 723 | def set_ifm_ofm_op_shapes(op, arch, nng): |
| 724 | if op.run_on_npu and op.type.needs_shapes(): |
| 725 | if op.ifm_shapes or op.ofm_shapes: |
| 726 | # Shapes already set |
| 727 | return op |
| 728 | op.set_ifm_ofm_shapes() |
| 729 | return op |
| 730 | |
| 731 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 732 | def convert_softmax(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 733 | if op.type == Op.Softmax and op.run_on_npu: |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 734 | softmax = SoftMax(op) |
| 735 | op = softmax.get_graph() |
| 736 | return op |
| 737 | |
| 738 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 739 | def convert_mul_max_to_abs_or_lrelu(op, arch, nng): |
Diego Russo | ea6111a | 2020-04-14 18:41:58 +0100 | [diff] [blame] | 740 | r"""Whenever there is a subgraph with this topology: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 741 | |
| 742 | Input X For X = -1 or X > 0 |
| 743 | | \ / This subgraph can be replaced with either |
| 744 | | Mul an Abs (if X = -1) or a LeakyReLU (if X > 0) |
| 745 | | / |
| 746 | Max |
| 747 | """ |
| 748 | |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 749 | if op.type == Op.Maximum: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 750 | # finds the Mul input(s) to the Max |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 751 | muls = [i for i in op.inputs if i.ops[0].type == Op.Mul] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 752 | if len(muls) == 1: |
| 753 | mul = muls[0].ops[0] |
| 754 | elif len(muls) == 2: |
| 755 | # In the case both inputs are Muls, find the one with the same input as the Max |
| 756 | mul = [m for m in muls if len(set(op.inputs + m.ops[0].inputs)) == 1][0].ops[0] |
| 757 | else: |
| 758 | # No Mul inputs |
| 759 | return op |
| 760 | |
| 761 | # make sure the Mul doesn't have any other consumers |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 762 | mul_ofm = mul.outputs[0] |
| 763 | if len(mul_ofm.consumers()) != 1: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 764 | return op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 765 | # make sure the Mul doesn't have a fused activation function |
| 766 | if mul.activation: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 767 | return op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 768 | ifm, ofm = op.get_ifm_ofm() |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 769 | if ifm is None or ofm is None: |
| 770 | return op |
| 771 | |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 772 | if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype: |
| 773 | return op |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 774 | if not check_quantized_tens_scaling_equal(ifm, ofm) or not check_quantized_tens_scaling_equal(ifm, mul_ofm): |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 775 | # rewrite to LeakyRelu currently only makes sense if the quantization is identical |
| 776 | return op |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 777 | |
| 778 | # finds the branched input that goes to both the Max and the Mul |
| 779 | shared = set(op.inputs) & set(mul.inputs) |
| 780 | if len(shared) == 1: |
| 781 | shared_in = shared.pop() |
| 782 | # find the constant scalar input to the Mul |
| 783 | const_tens = (set(mul.inputs) - {shared_in}).pop() |
| 784 | # check that it is a scalar |
| 785 | if const_tens.shape != []: |
| 786 | return op |
| 787 | const = const_tens.ops[0] |
| 788 | # check that it is a constant |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 789 | if const.type != Op.Const: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 790 | return op |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 791 | # Remove the Mul from the shared input's consumers |
| 792 | shared_in.consumer_list.remove(mul) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 793 | else: |
| 794 | return op |
| 795 | |
| 796 | val = const.outputs[0].values |
| 797 | if val >= 0: |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 798 | new_op = Op.LeakyRelu |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 799 | op.attrs["alpha"] = val |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 800 | # to produce bit exact results, the alpha is not enough; |
| 801 | # save additional scaling info in attr "alpha_scale", to be used as input |
| 802 | # to the LUT construction |
| 803 | alpha_scalar = const_tens.quant_values - const_tens.quantization.zero_point |
| 804 | mul_ifm_scale = np.double(ifm.quantization.scale_f32) |
| 805 | mul_ifm2_scale = np.double(const_tens.quantization.scale_f32) |
| 806 | mul_ofm_scale = np.double(mul_ofm.quantization.scale_f32) |
| 807 | alpha_scale, alpha_shift = scaling.elementwise_mul_scale(mul_ifm_scale, mul_ifm2_scale, mul_ofm_scale) |
| 808 | op.attrs["alpha_scaling"] = (alpha_scalar, alpha_scale, alpha_shift) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 809 | elif val == -1: |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 810 | new_op = Op.Abs |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 811 | else: |
| 812 | return op |
| 813 | |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 814 | op.type = new_op |
| 815 | op.name = op.name.replace("Maximum", new_op.name) |
| 816 | op.outputs[0].name = op.outputs[0].name.replace("Maximum", new_op.name) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 817 | op.inputs = [shared_in] |
Patrik Gustavsson | c509d33 | 2020-12-22 13:53:52 +0100 | [diff] [blame] | 818 | op.set_ifm_ofm_shapes() |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 819 | |
| 820 | # Record optimisation in debug database |
| 821 | DebugDatabase.add_optimised(op, op) |
| 822 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 823 | return op |
| 824 | |
| 825 | |
Diqing Zhong | 189f748 | 2021-01-26 12:12:51 +0100 | [diff] [blame] | 826 | def convert_hardswish_to_lut(op, arch, nng): |
| 827 | if op.type == Op.HardSwish: |
| 828 | ifm, ofm = op.get_ifm_ofm() |
| 829 | # Generate the LUT |
| 830 | ifm_scale = np.double(ifm.quantization.scale_f32) |
| 831 | ofm_scale = np.double(ofm.quantization.scale_f32) |
| 832 | zp_in = ifm.quantization.zero_point |
| 833 | zp_out = ofm.quantization.zero_point |
| 834 | ifm_scale_hires = (1 / 128) * ifm_scale |
| 835 | relu_multiplier = np.double(3 / 32768) |
| 836 | out_scale, out_shift = scaling.quantise_scale(ifm_scale_hires / ofm_scale) |
| 837 | relu_scale, relu_shift = scaling.quantise_scale(ifm_scale_hires / relu_multiplier) |
| 838 | # Use 16bit scale |
| 839 | out_scale_16 = fp_math.downscale_multiplier_int32_to_int16(out_scale) |
| 840 | relu_scale_16 = fp_math.downscale_multiplier_int32_to_int16(relu_scale) |
| 841 | |
| 842 | values = [] |
| 843 | ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128) |
| 844 | quantized_min = min(ix) |
| 845 | quantized_max = max(ix) |
| 846 | for x in ix: |
| 847 | input_value = x - zp_in |
| 848 | input_value_hires = input_value * 128 |
| 849 | # Compute the input value on essentially the output scale, not shifted yet |
| 850 | input_value_preshift = fp_math.saturating_rounding_mul16(input_value_hires, out_scale_16) |
| 851 | # Compute the "relu-ish multiplier". This matches the code in TensorFlow Lite Micro kernel |
| 852 | relu_value = np.int16(input_value_hires) |
| 853 | if relu_shift < 31: |
| 854 | relu_value = fp_math.shift_left16(relu_value, 30 - relu_shift) |
| 855 | |
| 856 | relu_value = fp_math.saturating_rounding_mul16(relu_value, relu_scale_16) |
| 857 | |
| 858 | if relu_shift < 31: |
| 859 | relu_value = fp_math.shift_left16(relu_value, 1) |
| 860 | |
| 861 | if relu_shift > 31: |
| 862 | relu_value = fp_math.rounding_divide_by_pot(relu_value, relu_shift - 31) |
| 863 | |
| 864 | # Rescaled the value into a 16bit fixedpoint relu_value in [-1, 1] |
| 865 | # Now convert that to a 16bit fixedpoint value in [0, 1] |
| 866 | relu_value = (relu_value + (1 << 15)) >> 1 |
| 867 | lut_result = fp_math.saturating_mul16(relu_value, input_value_preshift) |
| 868 | shift = 31 - out_shift |
| 869 | shift = -shift if shift < 0 else 0 |
| 870 | # Finally apply the output shift |
| 871 | lut_result = fp_math.rounding_divide_by_pot(lut_result, shift) + zp_out |
| 872 | lut_result = min(quantized_max, max(quantized_min, lut_result)) |
| 873 | values.append(lut_result) |
| 874 | return convert_to_lut(op, values, "hardswish") |
| 875 | return op |
| 876 | |
| 877 | |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 878 | def convert_lrelu_to_mul_max(op, arch): |
| 879 | # Converts LeakyRelu to Max(alpha * IFM, identity * IFM) |
| 880 | # (the opposite of convert_mul_max_to_abs_or_lrelu) |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 881 | ifm, ofm = op.get_ifm_ofm() |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 882 | if ifm is None or ofm is None: |
| 883 | return op |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 884 | |
| 885 | # Add multiplication with alpha |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 886 | mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha") |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 887 | mul_alpha.add_input_tensor(ifm) |
| 888 | # Create const tensor containing alpha as scalar |
| 889 | alpha = op.attrs["alpha"] |
| 890 | quantization = ifm.quantization.clone() |
| 891 | quantization.min = 0 |
| 892 | quantization.max = alpha * (quantization.quant_max - quantization.quant_min) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 893 | quantization.zero_point = 0 |
Louis Verhaard | ece4e65 | 2021-01-07 13:35:47 +0100 | [diff] [blame] | 894 | if np.isinf(1 / np.float32(alpha)): |
| 895 | # Handling of alpha near zero |
| 896 | quantization.scale_f32 = 1 |
| 897 | scalar = 0 |
| 898 | else: |
| 899 | quantization.scale_f32 = alpha |
| 900 | scalar = 1 |
| 901 | alpha_tens = create_const_tensor( |
| 902 | op.name + "_alpha_scalar", [], ifm.dtype, [scalar], np.int8, quantization=quantization |
| 903 | ) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 904 | mul_alpha.add_input_tensor(alpha_tens) |
| 905 | fm_alpha = ofm.clone(op.name + "_alpha") |
| 906 | mul_alpha.set_output_tensor(fm_alpha) |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 907 | mul_alpha.set_ifm_ofm_shapes() |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 908 | DebugDatabase.add_optimised(op, mul_alpha) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 909 | |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 910 | if check_quantized_tens_scaling_equal(ifm, ofm): |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 911 | # No identity multiplication is needed |
| 912 | fm_id = ifm |
| 913 | else: |
| 914 | # Add multiplication with identity |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 915 | mul_identity = Operation(Op.Mul, op.name + "_mul_identity") |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 916 | mul_identity.add_input_tensor(ifm) |
| 917 | # Create const tensor containing identity as scalar |
| 918 | quantization = ifm.quantization.clone() |
| 919 | quantization.min = 0 |
| 920 | quantization.max = quantization.quant_max - quantization.quant_min |
| 921 | quantization.scale_f32 = 1 |
| 922 | quantization.zero_point = 0 |
| 923 | identity_tens = create_const_tensor( |
| 924 | op.name + "_id_scalar", [], ifm.dtype, [1], np.uint8, quantization=quantization |
| 925 | ) |
| 926 | mul_identity.add_input_tensor(identity_tens) |
Louis Verhaard | ece4e65 | 2021-01-07 13:35:47 +0100 | [diff] [blame] | 927 | # Make sure that fm_id is allocated to a different address than fm_alpha |
| 928 | fm_id = ofm.clone(op.name + "_id", set_unique=True) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 929 | mul_identity.set_output_tensor(fm_id) |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 930 | mul_identity.set_ifm_ofm_shapes() |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 931 | DebugDatabase.add_optimised(op, mul_identity) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 932 | |
| 933 | # Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 934 | op.type = Op.Maximum |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 935 | op.name = op.name.replace("LeakyRelu", "Maximum") |
| 936 | op.inputs = [] |
| 937 | ifm.consumer_list.remove(op) |
| 938 | op.add_input_tensor(fm_alpha) |
| 939 | op.add_input_tensor(fm_id) |
Patrik Gustavsson | c509d33 | 2020-12-22 13:53:52 +0100 | [diff] [blame] | 940 | op.set_ifm_ofm_shapes() |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 941 | |
| 942 | DebugDatabase.add_optimised(op, op) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 943 | return op |
| 944 | |
| 945 | |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 946 | def convert_to_lut(op, lut_values, lut_name): |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 947 | # Rewrite the operation by Add with scalar 0 + LUT activation |
| 948 | ifm = op.inputs[0] |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 949 | if ifm is None: |
| 950 | return op |
Louis Verhaard | 58520b9 | 2020-08-24 16:45:38 +0200 | [diff] [blame] | 951 | assert ifm.dtype.size_in_bytes() == 1 |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 952 | op.type = Op.Add |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 953 | op.name = op.name + "_lut_" + lut_name |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 954 | # Mark as no-op to enable potential fusing optimizations |
| 955 | op.attrs["is_nop"] = True |
| 956 | # Create an input tensor containing scalar zero |
| 957 | quantization = QuantizationParameters(0.0, 255.0) |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 958 | quantization.scale_f32 = ifm.quantization.scale_f32 |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 959 | quantization.zero_point = 0 |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 960 | tens = create_const_tensor(op.inputs[0].name + "_scalar0", [], ifm.dtype, [0], np.uint8, quantization=quantization) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 961 | op.add_input_tensor(tens) |
patrik.gustavsson | eeb8515 | 2020-12-21 17:10:40 +0000 | [diff] [blame] | 962 | op.ifm_shapes.append(Shape4D(tens.shape)) |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 963 | |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 964 | # The LUT must be applied without any preceding rescaling (the LUT itself performs the rescale), |
| 965 | # so even if the OFM has a different scale than the IFM, the generated OFM scale instructions |
| 966 | # should be the same as the IFM |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 967 | op.forced_output_quantization = ifm.quantization |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 968 | lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, DataType.int8) |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 969 | op.set_activation_lut(lut_tensor) |
Patrik Gustavsson | c509d33 | 2020-12-22 13:53:52 +0100 | [diff] [blame] | 970 | op.set_ifm_ofm_shapes() |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 971 | return op |
| 972 | |
| 973 | |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 974 | def convert_to_lut8(op, fn, fn_name): |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 975 | # Converts op to a no-op + int8/uint8 LUT which is generated with the given function. |
| 976 | # fn is a function(real) -> real |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 977 | ifm, ofm = op.get_ifm_ofm() |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 978 | if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype: |
| 979 | return op |
| 980 | # Generate the LUT |
| 981 | ifm_scale = np.double(ifm.quantization.scale_f32) |
| 982 | ofm_scale = np.double(ofm.quantization.scale_f32) |
| 983 | zp_in = ifm.quantization.zero_point |
| 984 | zp_out = ofm.quantization.zero_point |
| 985 | values = [] |
| 986 | ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128) |
| 987 | quantized_min = min(ix) |
| 988 | quantized_max = max(ix) |
| 989 | for x in ix: |
| 990 | x_real = ifm_scale * (x - zp_in) |
| 991 | y_real = fn(x_real) |
| 992 | lut_result = round_away_zero(zp_out + y_real / ofm_scale) |
| 993 | lut_result = min(quantized_max, max(quantized_min, lut_result)) |
| 994 | values.append(lut_result) |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 995 | return convert_to_lut(op, values, fn_name) |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 996 | |
| 997 | |
| 998 | def convert_lrelu_to_lut(op, arch): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 999 | ifm, ofm = op.get_ifm_ofm() |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1000 | # Generate the LUT |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 1001 | alpha = op.attrs["alpha"] |
| 1002 | ifm_scale = np.double(ifm.quantization.scale_f32) |
| 1003 | ofm_scale = np.double(ofm.quantization.scale_f32) |
| 1004 | zp_in = ifm.quantization.zero_point |
| 1005 | zp_out = ofm.quantization.zero_point |
| 1006 | identity_scale, identity_shift = scaling.elementwise_mul_scale(ifm_scale, 1, ofm_scale) |
| 1007 | alpha_scalar = 1 |
| 1008 | alpha_scale, alpha_shift = scaling.elementwise_mul_scale(ifm_scale, alpha, ofm_scale) |
| 1009 | if "alpha_scaling" in op.attrs: |
| 1010 | # The LeakyRelu was the result from convert_mul_max_to_abs_or_lrelu |
| 1011 | alpha_scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"] |
| 1012 | values = [] |
Louis Verhaard | 58520b9 | 2020-08-24 16:45:38 +0200 | [diff] [blame] | 1013 | ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128) |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 1014 | quantized_min = min(ix) |
| 1015 | quantized_max = max(ix) |
| 1016 | for x in ix: |
| 1017 | if x < zp_in: |
| 1018 | lut_result = zp_out + fp_math.multiply_by_quantized_multiplier( |
| 1019 | alpha_scalar * (x - zp_in), alpha_scale, alpha_shift |
| 1020 | ) |
| 1021 | else: |
| 1022 | lut_result = zp_out + fp_math.multiply_by_quantized_multiplier(x - zp_in, identity_scale, identity_shift) |
| 1023 | lut_result = min(quantized_max, max(quantized_min, lut_result)) |
| 1024 | values.append(lut_result) |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 1025 | return convert_to_lut(op, values, "lrelu") |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1026 | |
| 1027 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1028 | def convert_lrelu(op, arch, nng): |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1029 | # Converts LeakyRelu to a LUT based solution if possible, otherwise a mul + max |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1030 | if op.type != Op.LeakyRelu: |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1031 | return op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1032 | ifm, ofm = op.get_ifm_ofm() |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 1033 | if ifm is None or ofm is None: |
| 1034 | return op |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 1035 | if ifm.dtype in (DataType.uint8, DataType.int8) and ifm.dtype == ofm.dtype: |
| 1036 | # use LUT for int8/uint8 |
| 1037 | return convert_lrelu_to_lut(op, arch) |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 1038 | if check_quantized_tens_scaling_equal(ifm, ofm) and ifm.dtype == ofm.dtype == DataType.int16: |
Louis Verhaard | d7911c4 | 2020-08-25 13:36:41 +0200 | [diff] [blame] | 1039 | # use LeakyRelu unmodified for int16 with equal input/output scaling |
| 1040 | return op |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1041 | return convert_lrelu_to_mul_max(op, arch) |
| 1042 | |
| 1043 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1044 | def convert_tanh_sigmoid_to_lut(op, arch, nng): |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 1045 | # Converts int8/uint8 Sigmoid and Tanh to a LUT based solution |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1046 | if op.type == Op.Sigmoid: |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 1047 | return convert_to_lut8(op, clamp_sigmoid, "sigmoid") |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1048 | elif op.type == Op.Tanh: |
Louis Verhaard | 2e186c7 | 2020-10-09 10:47:04 +0200 | [diff] [blame] | 1049 | return convert_to_lut8(op, math.tanh, "tanh") |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 1050 | return op |
| 1051 | |
| 1052 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1053 | def remove_reshapes(op, arch): |
| 1054 | if op.run_on_npu and op.type == Op.Reshape: |
| 1055 | ofm = op.ofm |
| 1056 | ifm = op.ifm |
Patrik Gustavsson | fa4cb29 | 2020-09-10 08:19:36 +0200 | [diff] [blame] | 1057 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1058 | # Check if quantization is the same in the input and output for the reshape ops |
| 1059 | if not check_quantized_tens_scaling_equal(ifm, ofm): |
| 1060 | # TODO Both tensors are needed, since quantisation properties currently are linked to Tensors. |
| 1061 | # In order to remove this reshape either quantization properties need to be moved to Operator, |
| 1062 | # or the reshape need to be replace with a NOP. |
| 1063 | return |
Patrik Gustavsson | fa4cb29 | 2020-09-10 08:19:36 +0200 | [diff] [blame] | 1064 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1065 | # Check if Reshape ifm/ofm are network ifm/ofm |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 1066 | ifm_is_sg_ifm = ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1067 | ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in ifm.consumer_list) |
| 1068 | ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in ofm.consumer_list) |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 1069 | # This case should be handled prior to this function |
| 1070 | assert not ((ifm_is_sg_ifm or ifm_is_sg_ofm) and ofm_is_sg_ofm) |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1071 | |
| 1072 | if ofm_is_sg_ofm: |
| 1073 | # Bypassed by replacing ifm with ofm |
| 1074 | ofm.ops = [] |
| 1075 | for prev_op in ifm.ops: |
| 1076 | prev_op.outputs = [ofm] |
| 1077 | ofm.ops.append(prev_op) |
| 1078 | |
| 1079 | # All ifm consumers need to use ofm as input |
| 1080 | for ifm_cons in ifm.consumer_list: |
| 1081 | for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs): |
| 1082 | if cons_ifm == ifm: |
| 1083 | ifm_cons.set_input_tensor(ofm, ifm_idx) |
| 1084 | if op.ifm_shapes[0] != op.ofm_shapes[0]: |
| 1085 | ofm.avoid_NHCWB16 = True |
| 1086 | else: |
| 1087 | # Bypassed Reshape by replacing ofm with ifm |
| 1088 | for cons in ofm.consumer_list: |
| 1089 | for ifm_idx, cons_ifm in enumerate(cons.inputs): |
| 1090 | if cons_ifm == ofm: |
| 1091 | cons.set_input_tensor(ifm, ifm_idx) |
| 1092 | if op.ifm_shapes[0] != op.ofm_shapes[0]: |
| 1093 | ifm.avoid_NHCWB16 = True |
| 1094 | |
| 1095 | |
| 1096 | def check_reshapes(op, arch): |
| 1097 | if op.run_on_npu and op.type == Op.Reshape: |
| 1098 | ofm = op.ofm |
| 1099 | |
| 1100 | if check_quantized_tens_scaling_equal(op.ifm, ofm): |
| 1101 | # Reshape should have been removed |
| 1102 | raise VelaError(f"Reshape op {op} expected to have been removed, still remains") |
Patrik Gustavsson | fa4cb29 | 2020-09-10 08:19:36 +0200 | [diff] [blame] | 1103 | |
| 1104 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1105 | def fuse_activation_function_with_prev(op, arch, nng): |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1106 | # if op is a no-op: attempts to move the activation function to the preceding op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1107 | if not op.attrs.get("is_nop", False) or op.activation is None: |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1108 | return op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1109 | ifm, ofm = op.get_ifm_ofm() |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 1110 | if ifm is None or ofm is None: |
| 1111 | return op |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1112 | # finds the input(s) to the operation |
| 1113 | prev_op = ifm.ops[0] |
| 1114 | # Note: the below checks on prev_op require that a first optimize pass on the full graph has been performed |
| 1115 | fuse = ( |
| 1116 | prev_op.run_on_npu |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1117 | and prev_op.type.npu_block_type != NpuBlockType.Default |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1118 | and len(ifm.ops) == 1 |
| 1119 | and len(prev_op.outputs[0].consumers()) == 1 |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1120 | and prev_op.activation is None |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1121 | ) |
| 1122 | if op.activation_lut is not None and arch.shram_reserved_unused_banks == 0: |
| 1123 | # TODO: if SHRAM LUT space is shared with SHRAM ACC (32, 64 MAC), |
| 1124 | # LUT currently only works correctly for elementwise ops |
| 1125 | fuse = False |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1126 | if not fuse: |
| 1127 | return op |
| 1128 | # Move the fused activation function + corresponding info to prev_op |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1129 | prev_op.activation = op.activation |
| 1130 | prev_op.forced_output_quantization = op.forced_output_quantization |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1131 | if op.activation_lut is not None: |
| 1132 | prev_op.set_activation_lut(op.activation_lut) |
| 1133 | # Bypass op |
Louis Verhaard | 98a3499 | 2020-09-01 10:39:04 +0200 | [diff] [blame] | 1134 | prev_op.set_output_tensor(ofm) |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 1135 | DebugDatabase.add_optimised(op, prev_op) |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1136 | return op |
| 1137 | |
| 1138 | |
Louis Verhaard | ae2d553 | 2020-12-11 17:19:54 +0100 | [diff] [blame] | 1139 | def optimise_pad(op, arch, nng): |
| 1140 | """ |
| 1141 | Converts tens1 -> PAD -> tens2 -> CONV to tens1 -> CONV |
| 1142 | if both operations can be run on the NPU. |
| 1143 | """ |
| 1144 | if ( |
| 1145 | (op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op()) |
| 1146 | and op.run_on_npu |
| 1147 | and op.attrs["padding"] == Padding.VALID |
| 1148 | ): |
| 1149 | pad_op = op.ifm.ops[0] |
| 1150 | if pad_op.type != Op.Pad or not pad_op.run_on_npu: |
| 1151 | return op |
| 1152 | # Bypass the PAD operator |
| 1153 | op.set_input_tensor(pad_op.ifm, 0) |
| 1154 | # Adjust the padding attributes of the convolution operator |
| 1155 | op.attrs["padding"] = Padding.EXPLICIT |
| 1156 | padding = pad_op.inputs[1].values # 4x2 tensor, first dimension is N, H, W, C |
| 1157 | top, left, bottom, right = (padding[1][0], padding[2][0], padding[1][1], padding[2][1]) |
| 1158 | op.attrs["explicit_padding"] = (top, left, bottom, right) |
| 1159 | op.set_ifm_ofm_shapes() |
| 1160 | return op |
| 1161 | |
| 1162 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1163 | def add_attrs_to_resizebilinear(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1164 | if op.type == Op.ResizeBilinear and op.run_on_npu: |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1165 | input_tensor = op.inputs[0] |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1166 | input_shape = op.ifm_shapes[0] |
| 1167 | upscaled_height = input_shape.height * 2 |
| 1168 | upscaled_width = input_shape.width * 2 |
| 1169 | out_shape = op.ofm_shapes[0] |
| 1170 | if not op.attrs["align_corners"] and out_shape.height == upscaled_height and out_shape.width == upscaled_width: |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1171 | # this means the output is supposed to be a x2 upscale, |
| 1172 | # so we need to do SAME padding |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 1173 | op.attrs["padding"] = Padding.SAME |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1174 | elif ( |
| 1175 | op.attrs["align_corners"] |
| 1176 | and out_shape.height == (upscaled_height - 1) |
| 1177 | and out_shape.width == (upscaled_width - 1) |
| 1178 | ): |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1179 | # here we can just run the avg pool without padding and |
| 1180 | # produce a (M * 2 - 1, N * 2 - 1) sized output |
Michael McGeagh | 1689548 | 2020-12-14 15:51:20 +0000 | [diff] [blame] | 1181 | op.attrs["padding"] = Padding.VALID |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1182 | else: |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 1183 | return op |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1184 | input_tensor.resampling_mode = resampling_mode.NEAREST |
Tim Hall | c30f495 | 2020-06-15 20:47:35 +0100 | [diff] [blame] | 1185 | op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 2, 2, 1)}) |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 1186 | return op |
| 1187 | |
| 1188 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1189 | def fixup_bias_tensors(op, arch, nng): |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 1190 | if op.type.needs_bias() and op.bias is None: |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 1191 | # Op has no bias, add bias tensor filled with zeros |
| 1192 | nr_biases = op.inputs[1].shape[-1] |
| 1193 | bias_values = [0] * nr_biases |
| 1194 | bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], DataType.int32, bias_values) |
| 1195 | bias_tensor.quant_values = bias_tensor.values |
| 1196 | op.set_input_tensor(bias_tensor, -1) |
Jacob Bohlin | 67e0d8f | 2020-08-20 10:53:02 +0200 | [diff] [blame] | 1197 | |
| 1198 | return op |
| 1199 | |
| 1200 | |
Patrik Gustavsson | 3010d9b | 2020-10-01 08:22:10 +0200 | [diff] [blame] | 1201 | def supported_operator_check(op, arch, nng): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1202 | op.run_on_npu = arch.supported_operators.is_operator_supported(op) |
| 1203 | return op |
| 1204 | |
| 1205 | |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 1206 | def _record_optimised(op, arch): |
| 1207 | if op.type != Op.Const: |
| 1208 | DebugDatabase.add_optimised(op, op) |
| 1209 | |
| 1210 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1211 | def optimise_graph_a(nng, arch, verbose_graph=False): |
| 1212 | if verbose_graph: |
| 1213 | nng.print_graph() |
| 1214 | |
Patrik Gustavsson | 2349d42 | 2020-12-01 16:02:29 +0100 | [diff] [blame] | 1215 | pre_process_list = [ |
| 1216 | supported_operator_check, |
| 1217 | set_ifm_ofm_op_shapes, |
| 1218 | # TODO: memory-only Op removal |
| 1219 | ] |
| 1220 | |
| 1221 | for idx, sg in enumerate(nng.subgraphs): |
| 1222 | # rewrite graph pass |
| 1223 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| 1224 | nng, sg, arch, [], pre_process_list, rewrite_unsupported=False, |
| 1225 | ) |
| 1226 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1227 | # Handle Concat Ops |
| 1228 | for idx, sg in enumerate(nng.subgraphs): |
| 1229 | # rewrite graph pass |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 1230 | rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [rewrite_concat_ops]) |
| 1231 | sg.refresh_after_modification() |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1232 | |
| 1233 | # Handle Split Ops |
| 1234 | for idx, sg in enumerate(nng.subgraphs): |
| 1235 | # rewrite graph pass |
| 1236 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| 1237 | nng, |
| 1238 | sg, |
| 1239 | arch, |
| 1240 | [], |
| 1241 | [rewrite_unpack_output, rewrite_stridedslice_output, convert_nop_split_to_identity], |
| 1242 | rewrite_unsupported=False, |
| 1243 | ) |
| 1244 | |
| 1245 | for idx, sg in enumerate(nng.subgraphs): |
| 1246 | # rewrite graph pass |
| 1247 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| 1248 | nng, sg, arch, [rewrite_split_ops], [], rewrite_unsupported=False, |
| 1249 | ) |
| 1250 | |
Patrik Gustavsson | 138d47f | 2021-02-08 10:13:48 +0100 | [diff] [blame^] | 1251 | # Handle sg input output |
| 1252 | for idx, sg in enumerate(nng.subgraphs): |
| 1253 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| 1254 | nng, sg, arch, [], [fix_sg_input_output], rewrite_unsupported=False, |
| 1255 | ) |
| 1256 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1257 | # Removal of reshapes |
| 1258 | for sg in nng.subgraphs: |
| 1259 | rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_reshapes]) |
| 1260 | sg.refresh_after_modification() |
| 1261 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1262 | op_rewrite_list = [ |
Tim Hall | 4e12776 | 2020-05-15 16:05:49 +0100 | [diff] [blame] | 1263 | set_tensor_equivalence, |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1264 | convert_depthwise_to_conv, |
Michael McGeagh | 8d939c0 | 2020-07-29 13:11:43 +0100 | [diff] [blame] | 1265 | convert_conv_to_fc, |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 1266 | convert_softmax, |
Diqing Zhong | 016b827 | 2020-12-16 16:46:06 +0100 | [diff] [blame] | 1267 | optimise_strided_conv, |
Diqing Zhong | 189f748 | 2021-01-26 12:12:51 +0100 | [diff] [blame] | 1268 | convert_hardswish_to_lut, |
Patrik Gustavsson | 2c2522d | 2021-01-29 11:51:31 +0100 | [diff] [blame] | 1269 | rewrite_fully_connected_input, |
Diqing Zhong | 94457b1 | 2020-12-09 15:22:40 +0100 | [diff] [blame] | 1270 | convert_batched_fc_shape, |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1271 | fixup_conv2d_backprop, |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame] | 1272 | fixup_relus_with_differing_ifm_ofm_scaling, |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1273 | fixup_elementwise_with_scalars, # TODO Move to early stage? |
Jacob Bohlin | e843d33 | 2020-06-23 12:12:56 +0200 | [diff] [blame] | 1274 | reorder_depthwise_weights, |
Charles Xu | 9a03fdf | 2020-07-02 15:12:40 +0200 | [diff] [blame] | 1275 | fixup_resizebilinear, |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 1276 | fixup_bias_tensors, |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1277 | convert_mul_max_to_abs_or_lrelu, |
| 1278 | convert_lrelu, |
Louis Verhaard | f03bad3 | 2020-09-25 08:30:44 +0200 | [diff] [blame] | 1279 | convert_tanh_sigmoid_to_lut, |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1280 | ] |
| 1281 | |
| 1282 | for idx, sg in enumerate(nng.subgraphs): |
| 1283 | # rewrite graph pass |
| 1284 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
Dwight Lidman | 73320a4 | 2020-11-05 10:34:41 +0100 | [diff] [blame] | 1285 | nng, sg, arch, [], op_rewrite_list, rewrite_unsupported=False, |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1286 | ) |
| 1287 | |
| 1288 | for idx, sg in enumerate(nng.subgraphs): |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1289 | # remove passthrough tensors and attempt further optimizations |
| 1290 | nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
Louis Verhaard | ae2d553 | 2020-12-11 17:19:54 +0100 | [diff] [blame] | 1291 | nng, |
| 1292 | sg, |
| 1293 | arch, |
| 1294 | [remove_passthrough_tensor], |
| 1295 | [fuse_activation_function_with_prev, optimise_pad, add_padding_fields], |
Louis Verhaard | b9fc33c | 2020-08-13 11:47:36 +0200 | [diff] [blame] | 1296 | ) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1297 | |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1298 | # Post-optimisation operator debug tracing, and checking that no undesired reshapes are left in the graph |
Tim Hall | e6ccd87 | 2020-11-09 16:46:37 +0000 | [diff] [blame] | 1299 | for sg in nng.subgraphs: |
Patrik Gustavsson | 3a26920 | 2021-01-21 08:28:55 +0100 | [diff] [blame] | 1300 | rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [check_reshapes, _record_optimised]) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1301 | |
| 1302 | if verbose_graph: |
| 1303 | nng.print_graph() |
| 1304 | return nng |