def get_bn(scale, bias):
norm_shift, norm_mul = 15, scale
return {
'norm_mul': tools.signed_to_hex(norm_mul, 24),
'norm_add': tools.signed_to_hex(bias, 32),
'norm_shift': norm_shift
}
def gen_weights_code(dlayer, idx, eight_bit_mode, prefix):
weights = dlayer[0]['kernel_load_cfg']['para_start_addr']
weights_data = ', '.join([
('\n ' if i % 64 == 0 else '') +
tools.signed_to_hex(item, 8 if eight_bit_mode else 16)
for item, i in zip(weights, range(len(weights)))
])
para_type = 'static uint8_t' if eight_bit_mode else 'static uint16_t'
return para_type + \
' {prefix}para_start_addr_{idx}[] __attribute__((aligned(128))) = {{{data}}};'\
.format(idx=idx, data=weights_data, prefix=prefix)
def gen_act_code(dlayer, idx, prefix):
act_list = dlayer[0]['kernel_calc_type_cfg']['active_addr']
active_para = ' .activate_para = {\n' + ',\n'.join([
' {{.data = {{.shift_number={dxs}, .y_mul={dy}, .x_start={x} }}}}'.format(
dxs=item['dxs'], dy=int(item['dy']), x=tools.signed_to_hex(item['x'], 36)
)
for item in act_list
]) + '\n }'
bias_list = [int(item['y']) for item in act_list]
active_para_bias0 = (
' .activate_para_bias0.data = {{\n .result_bias = {{{},{},{},{},{},{},{},{}}}\n }}'
).format(*(bias_list[:8]))
active_para_bias1 = (
' .activate_para_bias1.data = {{\n .result_bias = {{{},{},{},{},{},{},{},{}}}\n }}'
).format(*(bias_list[8:]))
return 'static kpu_activate_table_t ' + prefix + 'active_addr_' + str(idx) + ' __attribute__((aligned(128))) = {\n' + \
',\n'.join([active_para, active_para_bias0, active_para_bias1]) + \
'\n};'
def gen_layer_struct(klayer: k210_layer.K210Layer, idx: int):
reserved = 0
set_to_zero = 0
img_ram_size = 2 * 1024 * 1024
conv_arg = klayer.conv and klayer.conv.to_k210() or default_conv_arg
bn_arg = klayer.bn and klayer.bn.to_k210(conv_arg['swsx']) or default_bn_arg
act_arg = klayer.act and klayer.act.to_k210(bn_arg['post_scale']) or default_act_arg
pool_arg = klayer.pool and klayer.pool.to_k210() or default_pool_arg
io_arg = klayer.to_k210()
mino, maxo = klayer.act.min_y, klayer.act.max_y
output_scale, output_bias = tools.min_max_to_scale_bias(mino, maxo)
img_input_size = int(math.ceil(io_arg['i_ch_num'] / conv_arg['coef_group']) * 64 * conv_arg['channel_switch_addr'])
img_output_size = int(math.ceil(io_arg['o_ch_num'] / io_arg['wb_group']) * 64 * io_arg['wb_channel_switch_addr'])
assert (img_input_size + img_output_size <= img_ram_size)
interrupt_enabe = {
'int_en': set_to_zero,
'ram_flag': reserved,
'full_add': set_to_zero,
'depth_wise_layer': conv_arg['depth_wise_layer']
}
image_addr = {
'image_src_addr': '(uint64_t)' + hex(int((0 if not idx & 1 else (img_ram_size - img_input_size)) / 64)),
'image_dst_addr': '(uint64_t)' + hex(int((0 if idx & 1 else (img_ram_size - img_output_size)) / 64))
}
image_channel_num = {
'i_ch_num': hex(io_arg['i_ch_num'] - 1),
'o_ch_num': hex(io_arg['o_ch_num'] - 1),
'o_ch_num_coef': hex(conv_arg['o_ch_num_coef'] - 1),
}
image_size = {
'i_row_wid': hex(conv_arg['i_row_wid'] - 1),
'i_col_high': hex(conv_arg['i_col_high'] - 1),
'o_row_wid': hex(io_arg['o_row_wid'] - 1),
'o_col_high': hex(io_arg['o_col_high'] - 1),
}
kernel_pool_type_cfg = {
'kernel_type': conv_arg['kernel_type'],
'pad_type': conv_arg['pad_type'],
'pool_type': pool_arg['pool_type'],
'first_stride': conv_arg['first_stride'],
'bypass_conv': 0 if klayer.conv else 1,
'load_para': bn_arg['load_para'],
'dma_burst_size': io_arg['dma_burst_size'],
'pad_value': tools.signed_to_hex(conv_arg['pad_value'], 8),
'bwsx_base_addr': bn_arg['bwsx_base_addr'],
}
kernel_load_cfg = {
'load_coor': conv_arg['load_coor'],
'load_time': conv_arg['load_time'] - 1,
'para_size': conv_arg['para_size'],
'para_start_addr': conv_arg['para_start_addr'],
}
kernel_offset = {
'coef_column_offset': set_to_zero,
'coef_row_offset': set_to_zero,
}
kernel_calc_type_cfg = {
'channel_switch_addr': hex(conv_arg['channel_switch_addr']),
'row_switch_addr': hex(conv_arg['row_switch_addr']),
'coef_size': reserved,
'coef_group': conv_arg['coef_group'],
'load_act': 1 if klayer.act else 0,
'active_addr': act_arg['active_addr']
}
write_back_cfg = {
'wb_channel_switch_addr': hex(io_arg['wb_channel_switch_addr']),
'wb_row_switch_addr': hex(io_arg['wb_row_switch_addr']),
'wb_group': io_arg['wb_group']
}
conv_value = {
'shr_w': conv_arg['shr_w'],
'shr_x': conv_arg['shr_x'],
'arg_w': tools.signed_to_hex(conv_arg['arg_w'], 24),
'arg_x': tools.signed_to_hex(conv_arg['arg_x'], 24),
}
conv_value2 = {
'arg_add': int(round(conv_arg['arg_add'])),
}
dma_parameter = {
'send_data_out': io_arg['send_data_out'],
'channel_byte_num': io_arg['channel_byte_num'] - 1,
'dma_total_byte': io_arg['dma_total_byte'] - 1,
}
return {
'interrupt_enabe': interrupt_enabe,
'image_addr': image_addr,
'image_channel_num': image_channel_num,
'image_size': image_size,
'kernel_pool_type_cfg': kernel_pool_type_cfg,
'kernel_load_cfg': kernel_load_cfg,
'kernel_offset': kernel_offset,
'kernel_calc_type_cfg': kernel_calc_type_cfg,
'write_back_cfg': write_back_cfg,
'conv_value': conv_value,
'conv_value2': conv_value2,
'dma_parameter': dma_parameter
}, (output_scale, output_bias)