diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 30 |
1 files changed, 1 insertions, 29 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index b48cc7af..cf211de4 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -22,7 +22,6 @@ import uuid import numpy as np from . import fp_math -from . import lut from . import rewrite_graph from . import scaling from .api import NpuRoundingMode @@ -33,6 +32,7 @@ from .ethos_u55_regs.ethos_u55_regs import resampling_mode 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 convert_to_lut 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 @@ -858,34 +858,6 @@ def convert_lrelu_to_mul_max(op, arch): return op -def convert_to_lut(op, lut_values, lut_name): - # Rewrite the operation by Add with scalar 0 + LUT activation - ifm = op.inputs[0] - if ifm is None: - return op - assert ifm.dtype.size_in_bytes() == 1 - op.type = Op.Add - op.name = op.name + "_lut_" + lut_name - # Mark as no-op to enable potential fusing optimizations - op.attrs["is_nop"] = True - # Create an input tensor containing scalar zero - quantization = QuantizationParameters(0.0, 255.0) - quantization.scale_f32 = ifm.quantization.scale_f32 - quantization.zero_point = 0 - tens = create_const_tensor(op.inputs[0].name + "_scalar0", [], ifm.dtype, [0], np.uint8, quantization=quantization) - op.add_input_tensor(tens) - op.ifm_shapes.append(Shape4D(tens.shape)) - - # The LUT must be applied without any preceding rescaling (the LUT itself performs the rescale), - # so even if the OFM has a different scale than the IFM, the generated OFM scale instructions - # should be the same as the IFM - op.forced_output_quantization = ifm.quantization - lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, DataType.int8) - op.set_activation_lut(lut_tensor) - op.set_ifm_ofm_shapes() - return op - - def convert_to_lut8(op, fn, fn_name): # Converts op to a no-op + int8/uint8 LUT which is generated with the given function. # fn is a function(real) -> real |