MLBEDSW-2688: LUT calculation with different in/out scale
Enables LUT for LeakyRelu with int8/uint8 even if input scale
is different from the output scale.
Fusing LUT with a previous operator for this situation
requires further work.
Change-Id: I9eddfe36f457e763d44eb3e05fbe240eac7cfec9
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py
index 8d9f918..609fcc6 100644
--- a/ethosu/vela/register_command_stream_generator.py
+++ b/ethosu/vela/register_command_stream_generator.py
@@ -442,6 +442,13 @@
fmf = primary_op.attrs.get("fused_memory_function", None)
faf = primary_op.attrs.get("fused_activation_function", None)
fused_quantize = any(op.type == "Quantize" for op in ps.ops)
+ # Force output scale, used in operations with fused LUT
+ # Note: with current LUT support, forced_ofm_quantization is always equal to cmd.ofm_tensor.quantization
+ # except when primary_op is AddAct + 0 (no-op) + LUT
+ forced_ofm_quantization = primary_op.attrs.get("forced_output_quantization", None)
+ ofm_quant = cmd.ofm_tensor.quantization
+ if forced_ofm_quantization is not None:
+ ofm_quant = forced_ofm_quantization
# Specifies which operand to apply scaling to in bitexact elementwise ADD/SUB
op_to_scale = 0
@@ -476,7 +483,7 @@
if primary_op.type in set(("AddAct", "MulAct", "SubAct",)):
input_scale = cmd.ifm_tensor.quantization.scale_f32
input2_scale = cmd.ifm2_tensor.quantization.scale_f32
- output_scale = cmd.ofm_tensor.quantization.scale_f32
+ output_scale = ofm_quant.scale_f32
use_global_scale = True
if output_scale is not None and faf in ("Sigmoid", "Tanh"):
@@ -491,7 +498,7 @@
emit.cmd1_with_offset(cmd1.NPU_SET_OFM_SCALE, ofm_scale, shift)
else: # AddAct/SubAct
# Force output scale same as the input scale for
- # resizebiliner 1x1 that is converted to add
+ # resizebilinear 1x1 that is converted to add
if "resizebilinear" in primary_op.attrs:
output_scale = input2_scale
@@ -529,7 +536,7 @@
emit.cmd1_with_offset(cmd1.NPU_SET_OFM_SCALE, ofm_scale, shift)
elif primary_op.type in set(("LeakyRelu", "Abs",)):
- output_scale = cmd.ofm_tensor.quantization.scale_f32
+ output_scale = ofm_quant.scale_f32
use_global_scale = True
if primary_op.type == "LeakyRelu":
@@ -664,7 +671,7 @@
elif fused_quantize:
# Quantize op requires different scaling
ifm_scale_f64 = np.double(cmd.ifm_tensor.quantization.scale_f32)
- ofm_scale_f64 = np.double(cmd.ofm_tensor.quantization.scale_f32)
+ ofm_scale_f64 = np.double(ofm_quant.scale_f32)
scale, shift = scaling.quantise_scale(ifm_scale_f64 / ofm_scale_f64)
elif primary_op.type == "ResizeBilinear" and "rescale" in primary_op.attrs:
rescale = primary_op.attrs["rescale"]
@@ -676,11 +683,8 @@
# k_height == k_width == 1 is allways true in this case
# Normally the scale is maximised, to get maximum precision, which means that
# if rescale != 1, scale need to consider the number of bits needed for rescaling
- if None not in (
- cmd.ofm_tensor.quantization.scale_f32,
- cmd.ifm_tensor.quantization.scale_f32,
- ):
- rescale = cmd.ifm_tensor.quantization.scale_f32 / cmd.ofm_tensor.quantization.scale_f32
+ if None not in (ofm_quant.scale_f32, cmd.ifm_tensor.quantization.scale_f32,):
+ rescale = cmd.ifm_tensor.quantization.scale_f32 / ofm_quant.scale_f32
rescale_bits = 0
if k_height == k_width == 1:
if fmf == "ConcatSliceWrite":
@@ -797,9 +801,8 @@
scale_region = base_ptr_idx_map[cmd.scale_tensor.mem_type]
emit.cmd0_with_param(cmd0.NPU_SET_SCALE_REGION, scale_region)
- ofm_quant = cmd.ofm_tensor.quantization
- ofm_quant_qmin = cmd.ofm_tensor.quantization.quant_min
- ofm_quant_qmax = cmd.ofm_tensor.quantization.quant_max
+ ofm_quant_qmin = ofm_quant.quant_min
+ ofm_quant_qmax = ofm_quant.quant_max
ifm_min = cmd.ifm_tensor.quantization.min
ifm_max = cmd.ifm_tensor.quantization.max
@@ -912,13 +915,15 @@
emit.cmd0_with_param(zero_point_op, 0)
else:
assert tens.quantization.zero_point is not None, "need an actual zero point set"
- if (
+ if cmd0.NPU_SET_OFM_ZERO_POINT == zero_point_op and forced_ofm_quantization is not None:
+ zero_point = forced_ofm_quantization.zero_point
+ elif (
"resizebilinear" in primary_op.attrs
and primary_op.type == "AddAct"
and cmd0.NPU_SET_OFM_ZERO_POINT == zero_point_op
):
# Force output zero point same as the input zero point
- # for resizebiliner 1x1 that is converted to add
+ # for resizebilinear 1x1 that is converted to add
zero_point = cmd.ifm2_tensor.quantization.zero_point
else:
zero_point = tens.quantization.zero_point