diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 86 |
1 files changed, 4 insertions, 82 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 255a1f5e..ef39aea3 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -30,7 +30,10 @@ 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 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 needed_total_padding from .graph_optimiser_util import set_ifm_ofm_op_shapes from .graph_optimiser_util import set_tensor_equivalence @@ -244,32 +247,6 @@ def insert_copy_op_after_tens(tens): DebugDatabase.add_optimised(tens.ops[0], copy_op) -def fix_sg_input_output(op, arch, nng): - if not op.run_on_npu or op.type not in (Op.Reshape, Op.Squeeze): - return op - - # For the Reshape/Squeeze operators we want to remove, tensors are removed. - # But in order to to do this, they cannot be outputs of the sg, - # this need to be fixed prior to the removal. - # Solution is to add a avgpool NOP, to maintain the original tensor. - # This is also valid when reshape ifm/ofm is produced respectively - # consumed by CPU - - # Check if operator ifm/ofm are sg ifm/ofm - ifm_is_sg_ifm = op.ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) - ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in op.ifm.consumer_list) - ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in op.ofm.consumer_list) - # Check if ifm/ofm is produced respectively consumed by CPU - ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops) - ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list) - - if (ifm_is_sg_ofm or ifm_is_sg_ifm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed): - # Both ifm and ofm need to persist, but only ifm need a copy, in order to remove the Reshape/Squeeze - insert_copy_op_after_tens(op.ifm) - - return 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 @@ -576,33 +553,6 @@ def add_padding_fields(op, arch, nng): return op -def convert_depthwise_to_conv(op, arch, nng): - # Depthwise is equivalent to a single conv2d if the ifm depth is 1 and - # the ofm depth equals the depth multipler. - # If those conditions are true, then we can perform a simple - # switch of the operator type (and weight order) - - if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1): - ifm_shape = op.ifm_shapes[0] - weight_tensor = op.inputs[1] - ofm_shape = op.ofm_shapes[0] - if (ifm_shape.depth == 1) and (ofm_shape.depth == op.attrs["depth_multiplier"]): - # Change op type to Conv2d - op.type = Op.Conv2DBias - del op.attrs["channel_multiplier"] - del op.attrs["depth_multiplier"] - - weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2)) - weight_tensor.set_all_shapes(list(weight_tensor.values.shape)) - else: - raise UnsupportedFeatureError( - f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},", - f" ifm channels = {ifm_shape.depth}, ofm channels = {ofm_shape.depth}", - ) - DebugDatabase.add_optimised(op, op) - return op - - def reorder_depthwise_weights(op, arch, nng): if op.type.is_depthwise_conv2d_op(): weight_tensor = op.inputs[1] @@ -1058,35 +1008,7 @@ def remove_reshape_and_squeeze_ops(op, arch): # or the reshape need to be replace with a NOP. return - # Check if ifm/ofm are network ifm/ofm - ifm_is_sg_ifm = ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) - ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in ifm.consumer_list) - ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in ofm.consumer_list) - # Check if ifm/ofm is produced respectively consumed by CPU - ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops) - ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list) - - # This case should be handled prior to this function - assert not ((ifm_is_sg_ifm or ifm_is_sg_ofm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed)) - - if ofm_is_sg_ofm or ofm_is_cpu_consumed: - # Bypassed by replacing ifm with ofm - ofm.ops = [] - for prev_op in ifm.ops: - prev_op.outputs = [ofm] - ofm.ops.append(prev_op) - - # All ifm consumers need to use ofm as input - for ifm_cons in ifm.consumer_list: - for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs): - if cons_ifm == ifm: - ifm_cons.set_input_tensor(ofm, ifm_idx) - else: - # Bypassed by replacing ofm with ifm - for cons in ofm.consumer_list: - for ifm_idx, cons_ifm in enumerate(cons.inputs): - if cons_ifm == ofm: - cons.set_input_tensor(ifm, ifm_idx) + bypass_reshape_and_squeeze_ops(op) def fuse_activation_function_with_prev(op, arch, nng): |