TOSA: Support for TABLE operator (int8)

Added support to map TABLE operator to LUT.
Limitations:
-Only supported for int8
-TABLE input must be constant

This also adds the support for TFLite legalisation of
Tanh/Sigmoid (int8/uint8).

Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I1a95f61fb02fdd42c4a690494418cc0765c8b275

1 files changed, 31 insertions, 0 deletions

diff --git a/ethosu/vela/graph_optimiser_util.py b/ethosu/vela/graph_optimiser_util.py
index dafd2849..d2d3d833 100644
--- a/ethosu/vela/graph_optimiser_util.py
+++ b/ethosu/vela/graph_optimiser_util.py
@@ -19,6 +19,7 @@ from typing import Tuple
 
 import numpy as np
 
+from . import lut
 from .data_type import DataType
 from .debug_database import DebugDatabase
 from .errors import UnsupportedFeatureError
@@ -26,6 +27,8 @@ 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
 
 memory_only_ops = (
     Op.Reshape,
@@ -320,3 +323,31 @@ def convert_depthwise_to_conv(op, arch, nng):
             )
         DebugDatabase.add_optimised(op, op)
     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))  # 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
+    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