diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 46 |
1 files changed, 8 insertions, 38 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 15b82c7e..b48cc7af 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -30,10 +30,11 @@ from .data_type import DataType from .debug_database import DebugDatabase from .errors import UnsupportedFeatureError from .ethos_u55_regs.ethos_u55_regs import resampling_mode -from .graph_optimiser_util import bypass_reshape_and_squeeze_ops +from .graph_optimiser_util import bypass_memory_only_ops from .graph_optimiser_util import calc_explicit_padding from .graph_optimiser_util import convert_depthwise_to_conv from .graph_optimiser_util import fix_sg_input_output +from .graph_optimiser_util import memory_only_ops from .graph_optimiser_util import move_splitsliceread_to_consumer from .graph_optimiser_util import needed_total_padding from .graph_optimiser_util import set_ifm_ofm_op_shapes @@ -190,7 +191,7 @@ def remove_SplitSliceRead(op, arch): len(op.ofm.consumer_list) == 1 and op.ofm.consumer_list[0] is not None and op.ofm.consumer_list[0].run_on_npu - and op.ofm.consumer_list[0].type not in (Op.Reshape, Op.Squeeze) + and op.ofm.consumer_list[0].type not in memory_only_ops and op.ofm_shapes[0] == Shape4D.from_list(op.ofm.shape) ): # SplitSliceRead can be performed by tensor consumer @@ -211,27 +212,6 @@ def remove_SplitSliceRead(op, arch): DebugDatabase.add_optimised(op, avgpool_op) -def insert_copy_op_after_tens(tens): - tens_cons_list_copy = tens.consumer_list.copy() - - # Create a avg_pool nop op with ifm as input - copy_tens = tens.clone() - copy_op = create_avgpool_nop(tens.name + "_avgpool") - copy_op.add_input_tensor(tens) - copy_op.set_output_tensor(copy_tens) - copy_op.set_ifm_ofm_shapes() - copy_op.run_on_npu = True - - # Set copy_ifm consumers - for tens_cons in tens_cons_list_copy: - if tens_cons is not None: - for ifm_idx, cons_inp in enumerate(tens_cons.inputs): - if cons_inp == tens: - tens_cons.set_input_tensor(copy_tens, ifm_idx) - - DebugDatabase.add_optimised(tens.ops[0], copy_op) - - def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding): k_w, k_h = kernel.dilated_wh() s_x, s_y = kernel.stride @@ -985,19 +965,9 @@ def convert_tanh_sigmoid_to_lut(op, arch, nng): return op -def remove_reshape_and_squeeze_ops(op, arch): - if op.run_on_npu and op.type in (Op.Reshape, Op.Squeeze): - ofm = op.ofm - ifm = op.ifm - - # Check if quantization is the same in the input and output for the reshape ops - if not check_quantized_tens_scaling_equal(ifm, ofm): - # TODO Both tensors are needed, since quantisation properties currently are linked to Tensors. - # In order to remove this reshape either quantization properties need to be moved to Operator, - # or the reshape need to be replace with a NOP. - return - - bypass_reshape_and_squeeze_ops(op) +def remove_memory_only_ops(op, arch): + if op.run_on_npu and op.type in memory_only_ops: + bypass_memory_only_ops(op) def fuse_activation_function_with_prev(op, arch, nng): @@ -1463,9 +1433,9 @@ def tflite_optimise_graph(nng, arch): nng, sg, arch, [], [fix_sg_input_output], rewrite_unsupported=False, ) - # Removal of reshapes and squeeze + # Removal of memory only operators for sg in nng.subgraphs: - rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_reshape_and_squeeze_ops]) + rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_memory_only_ops]) sg.refresh_after_modification() # Rewrite of operators |