diff options
Diffstat (limited to 'ethosu/vela/graph_optimiser_util.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser_util.py | 39 |
1 files changed, 0 insertions, 39 deletions
diff --git a/ethosu/vela/graph_optimiser_util.py b/ethosu/vela/graph_optimiser_util.py index 82790364..da3fe138 100644 --- a/ethosu/vela/graph_optimiser_util.py +++ b/ethosu/vela/graph_optimiser_util.py @@ -20,7 +20,6 @@ from typing import Tuple import numpy as np -from . import lut from .architecture_features import Accelerator from .data_type import DataType from .debug_database import DebugDatabase @@ -29,8 +28,6 @@ from .errors import VelaError from .operation import Op from .operation_util import create_avgpool_nop from .shape4d import Shape4D -from .tensor import create_const_tensor -from .tensor import QuantizationParameters from .tensor import Tensor memory_only_ops = ( @@ -329,42 +326,6 @@ def convert_depthwise_to_conv(op, arch, nng): return op -def convert_to_lut(op, lut_values, lut_name): - # Rewrite the operation by Add with scalar 0 + LUT activation - ifm = op.ifm - ofm = op.ofm - 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(ifm.name + "_scalar0", [], ifm.dtype, [0], quantization=quantization) - op.add_input_tensor(tens) - op.ifm_shapes.append(Shape4D(tens.shape)) # TODO no 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 - - # the lut tensor datatype needs to match both; the ofm datatype, because these are the values output; and the - # datatype used to generate the lut values (which is probably the ifm datatype), because we want to avoid any - # potential overflow errors in create_lut_tensor() caused by converting Python int (which could represent a uint) - # to NumPy int. this can be guaranteed by checking that the ifm and ofm datatypes are the same - assert ifm.dtype == ofm.dtype - lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, ofm.dtype) - op.set_activation_lut(lut_tensor) - op.set_ifm_ofm_shapes() - DebugDatabase.add_optimised(op, op) - return op - - def create_avg_pool_for_concat(concat_op, name, ifm, ifm_shape: Shape4D, write_offset: Shape4D): """Creates an average pool for the given concat op/input feature map""" ofm = concat_op.ofm |