MLBEDSW-839: Code generation using external API
Added external API to generate register command streams.
Existing code generation has been refactored to make
use of this API.
Change-Id: Ibb4c2b167809869f16470b14da24f08a65c82b7b
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py
index efd91a3..01146ee 100644
--- a/ethosu/vela/softmax.py
+++ b/ethosu/vela/softmax.py
@@ -24,8 +24,10 @@
from . import fp_math
from . import scaling
+from .api import NpuRoundingMode
from .data_type import DataType
from .debug_database import DebugDatabase
+from .operation import ActivationFunction
from .operation import Op
from .operation import Operation
from .tensor import create_const_tensor
@@ -227,6 +229,12 @@
no_scale_quant = ifm.quantization.clone()
no_scale_quant.scale_f32 = None
no_scale_quant.zero_point = 0
+ activation = ActivationFunction(Op.Clip)
+ activation.min = ifm.quantization.quant_min
+ activation.max = ifm.quantization.quant_max
+ activation2 = activation.clone()
+ activation2.min = 2 * ifm.quantization.quant_min
+ activation2.max = 2 * ifm.quantization.quant_max
one_scale_quant = ifm.quantization.clone()
one_scale_quant.scale_f32 = 1.0
one_scale_quant.zero_point = 0
@@ -263,20 +271,23 @@
ifm_exp = Tensor(ifm.shape, DataType.int32, sub_op.name + "_0")
ifm_exp.quantization = one_scale_quant.clone()
ifm_exp.quantization.zero_point = 127
- ifm_exp.quantization.quant_min = -128
- ifm_exp.quantization.quant_max = 127
+ sub_op.activation = ActivationFunction(Op.LUT)
+ # Note: activation.min/max are non-quantized values
+ sub_op.activation.min = -128 - ifm_exp.quantization.zero_point
+ sub_op.activation.max = 127 - ifm_exp.quantization.zero_point
sub_op.set_output_tensor(ifm_exp)
DebugDatabase.add_optimised(self.op, sub_op)
# PASS 2 - SHR
shr2_op = Operation(Op.SHR, self.op.name + "_shr2")
- shr2_op.attrs["rounding_mode"] = b"NATURAL"
+ shr2_op.attrs["rounding_mode"] = NpuRoundingMode.NATURAL
shr2_op.add_input_tensor(ifm_exp)
shr2_op.add_input_tensor(
create_const_tensor(
shr2_op.name + "_const", [1, 1, 1, 1], DataType.int32, [12], np.int32, quantization=no_scale_quant
),
)
+ shr2_op.activation = activation.clone()
rescaled_exp = Tensor(ifm.shape, ifm_exp.dtype, shr2_op.name + "_0")
rescaled_exp.quantization = no_scale_quant
shr2_op.set_output_tensor(rescaled_exp)
@@ -292,6 +303,7 @@
reduce_sum_op.attrs["strides"] = [1, reduce_sum_op.attrs["stride_h"], reduce_sum_op.attrs["stride_w"], 1]
reduce_sum_op.attrs["ksize"] = [1, reduce_sum_op.attrs["filter_height"], reduce_sum_op.attrs["filter_width"], 1]
reduce_sum_op.add_input_tensor(rescaled_exp)
+ reduce_sum_op.activation = activation.clone()
reduce_sum_shape = [1, rescaled_exp.shape[1], rescaled_exp.shape[2], 1]
sum_of_exp = Tensor(reduce_sum_shape, DataType.int32, reduce_sum_op.name + "_0")
@@ -302,6 +314,7 @@
# PASS 4 - CLZ
clz_op = Operation(Op.CLZ, self.op.name + "_clz4")
clz_op.add_input_tensor(sum_of_exp)
+ clz_op.activation = activation.clone()
headroom_plus_one = Tensor(reduce_sum_shape, DataType.int32, clz_op.name + "_0")
headroom_plus_one.quantization = no_scale_quant
clz_op.set_output_tensor(headroom_plus_one)
@@ -320,6 +333,7 @@
),
)
sub5_op.add_input_tensor(headroom_plus_one)
+ sub5_op.activation = activation.clone()
right_shift = Tensor(reduce_sum_shape, DataType.int32, sub5_op.name + "_0")
right_shift.quantization = no_scale_quant
sub5_op.set_output_tensor(right_shift)
@@ -330,6 +344,7 @@
sub6_op = Operation(Op.Sub, self.op.name + "_sub6")
sub6_op.add_input_tensor(headroom_plus_one)
sub6_op.add_input_tensor(one)
+ sub6_op.activation = activation.clone()
headroom = Tensor(reduce_sum_shape, DataType.int32, sub6_op.name + "_0")
headroom.quantization = no_scale_quant
sub6_op.set_output_tensor(headroom)
@@ -339,8 +354,10 @@
shl7_op = Operation(Op.SHL, self.op.name + "_shl7")
shl7_op.add_input_tensor(sum_of_exp)
shl7_op.add_input_tensor(headroom)
+ shl7_op.activation = activation.clone()
shifted_sum = Tensor(reduce_sum_shape, DataType.int32, shl7_op.name + "_0")
shifted_sum.quantization = no_scale_quant
+
shl7_op.set_output_tensor(shifted_sum)
DebugDatabase.add_optimised(self.op, shl7_op)
@@ -352,6 +369,7 @@
"shifted_one_const", [1, 1, 1, 1], DataType.int32, [1 << 30], np.int32, quantization=no_scale_quant
),
)
+ sub8_op.activation = activation.clone()
shifted_sum_minus_one = Tensor(reduce_sum_shape, DataType.int32, sub8_op.name + "_0")
shifted_sum_minus_one.quantization = no_scale_quant
sub8_op.set_output_tensor(shifted_sum_minus_one)
@@ -361,6 +379,7 @@
shl9_op = Operation(Op.SHL, self.op.name + "_shl9")
shl9_op.add_input_tensor(shifted_sum_minus_one)
shl9_op.add_input_tensor(one)
+ shl9_op.activation = activation.clone()
shifted_sum_minus_one = Tensor(reduce_sum_shape, DataType.int32, shl9_op.name + "_0")
shifted_sum_minus_one.quantization = no_scale_quant
shl9_op.set_output_tensor(shifted_sum_minus_one)
@@ -374,7 +393,8 @@
),
)
add10_op.add_input_tensor(shifted_sum_minus_one)
- add10_op.attrs["rescale"] = [1, 1]
+ add10_op.activation = activation.clone()
+ add10_op.attrs["rescale"] = (1, 1)
half_denominator = Tensor(reduce_sum_shape, DataType.int32, add10_op.name + "_0")
half_denominator.quantization = one_scale_quant
add10_op.set_output_tensor(half_denominator)
@@ -396,6 +416,7 @@
rescaled = Tensor(reduce_sum_shape, DataType.int32, mul11_op.name + "_0")
rescaled.quantization = one_scale_quant.clone()
rescaled.quantization.scale_f32 = 2.0
+ mul11_op.activation = activation2.clone()
mul11_op.set_output_tensor(rescaled)
DebugDatabase.add_optimised(self.op, mul11_op)
@@ -407,6 +428,7 @@
"48_over_17_const", [1, 1, 1, 1], DataType.int32, [1515870810], np.int32, quantization=no_scale_quant
),
)
+ add12_op.activation = activation.clone()
rescale_w_offset = Tensor(reduce_sum_shape, DataType.int32, add12_op.name + "_0")
rescale_w_offset.quantization = one_scale_quant
add12_op.set_output_tensor(rescale_w_offset)
@@ -424,6 +446,7 @@
mul_op = Operation(Op.Mul, self.op.name + "_mul%d" % (13 + i * 5))
mul_op.add_input_tensor(nr_x)
mul_op.add_input_tensor(half_denominator)
+ mul_op.activation = activation2.clone()
half_denominator_times_x = Tensor(reduce_sum_shape, DataType.int32, mul_op.name + "_0")
half_denominator_times_x.quantization = one_scale_quant.clone()
half_denominator_times_x.quantization.scale_f32 = 2.0
@@ -433,6 +456,7 @@
sub_op = Operation(Op.Sub, self.op.name + "_sub%d" % (14 + i * 5))
sub_op.add_input_tensor(F2_one)
sub_op.add_input_tensor(half_denominator_times_x)
+ sub_op.activation = activation.clone()
one_minus_half_denominator_times_x = Tensor(reduce_sum_shape, DataType.int32, sub_op.name + "_0")
one_minus_half_denominator_times_x.quantization = one_scale_quant
sub_op.set_output_tensor(one_minus_half_denominator_times_x)
@@ -441,6 +465,7 @@
mul_op = Operation(Op.Mul, self.op.name + "_mul%d" % (15 + i * 5))
mul_op.add_input_tensor(nr_x)
mul_op.add_input_tensor(one_minus_half_denominator_times_x)
+ mul_op.activation = activation2.clone()
to_rescale = Tensor(reduce_sum_shape, DataType.int32, mul_op.name + "_0")
to_rescale.quantization = one_scale_quant.clone()
to_rescale.quantization.scale_f32 = 2.0
@@ -450,6 +475,7 @@
shl_op = Operation(Op.Mul, self.op.name + "_mul%d" % (16 + i * 5))
shl_op.add_input_tensor(to_rescale)
shl_op.add_input_tensor(four)
+ shl_op.activation = activation.clone()
to_add = Tensor(reduce_sum_shape, DataType.int32, shl_op.name + "_0")
to_add.quantization = no_scale_quant
shl_op.set_output_tensor(to_add)
@@ -458,6 +484,7 @@
add_op = Operation(Op.Add, self.op.name + "_add%d" % (17 + i * 5))
add_op.add_input_tensor(nr_x)
add_op.add_input_tensor(to_add)
+ add_op.activation = activation.clone()
nr_x = Tensor(reduce_sum_shape, DataType.int32, add_op.name + "_0")
nr_x.quantization = one_scale_quant
add_op.set_output_tensor(nr_x)
@@ -469,6 +496,7 @@
mul28_op.add_input_tensor(
create_const_tensor("two_const", [1, 1, 1, 1], DataType.int32, [2], np.int32, quantization=no_scale_quant)
)
+ mul28_op.activation = activation.clone()
scale_factor = Tensor(reduce_sum_shape, DataType.int32, mul28_op.name + "_0")
scale_factor.quantization = one_scale_quant
mul28_op.set_output_tensor(scale_factor)
@@ -478,6 +506,7 @@
mul_op = Operation(Op.Mul, self.op.name + "_mul29")
mul_op.add_input_tensor(ifm_exp)
mul_op.add_input_tensor(scale_factor)
+ mul_op.activation = activation2.clone()
scaled_exp = Tensor(ifm_exp.shape, DataType.int32, mul_op.name + "_0")
scaled_exp.quantization = one_scale_quant.clone()
scaled_exp.quantization.scale_f32 = 2.0
@@ -486,7 +515,7 @@
# PASS 30 - SHR
shr30_op = Operation(Op.SHR, self.op.name + "_shr30")
- shr30_op.attrs["rounding_mode"] = b"NATURAL"
+ shr30_op.attrs["rounding_mode"] = NpuRoundingMode.NATURAL
shr30_op.add_input_tensor(scaled_exp)
shr30_op.add_input_tensor(right_shift)
shr30_op.set_output_tensor(ofm)