MLBEDSW-3019: Add profiling debug database
- Added mechanism to track input to output graph transforms for
debugging the resultant command stream.
- Provides base implementation for MLBEDSW-2661
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I2dfe8a409fbde7ad0282bfab5acb11ba1c8b82d8
diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py
index 12c2016..efd91a3 100644
--- a/ethosu/vela/softmax.py
+++ b/ethosu/vela/softmax.py
@@ -25,6 +25,7 @@
from . import fp_math
from . import scaling
from .data_type import DataType
+from .debug_database import DebugDatabase
from .operation import Op
from .operation import Operation
from .tensor import create_const_tensor
@@ -220,6 +221,9 @@
def get_graph_8bit(self, ifm, ofm):
exp_lut = self.generate_exp_table(self.op.attrs.get("beta", 1.0), ifm.quantization.scale_f32)
+ ifm = create_reshape_tensor(ifm, ifm.get_full_shape())
+ DebugDatabase.add_optimised(self.op, ifm.ops[0])
+ ofm = create_reshape_tensor(ofm, ofm.get_full_shape(), False)
no_scale_quant = ifm.quantization.clone()
no_scale_quant.scale_f32 = None
no_scale_quant.zero_point = 0
@@ -245,6 +249,7 @@
ifm_max = Tensor([1, maxpool_h, 1, 1], ifm.dtype, maxpool_op.name + "_0")
ifm_max.quantization = no_scale_quant
maxpool_op.set_output_tensor(ifm_max)
+ DebugDatabase.add_optimised(self.op, maxpool_op)
# PASS 1 - Sub+LUT(exp)
sub_op = Operation(Op.Sub, self.op.name + "_sub1")
@@ -261,6 +266,7 @@
ifm_exp.quantization.quant_min = -128
ifm_exp.quantization.quant_max = 127
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")
@@ -274,6 +280,7 @@
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)
+ DebugDatabase.add_optimised(self.op, shr2_op)
# PASS 3 - Reduce sum
reduce_sum_op = Operation(Op.ReduceSum, self.op.name + "_reduce_sum3")
@@ -290,6 +297,7 @@
sum_of_exp = Tensor(reduce_sum_shape, DataType.int32, reduce_sum_op.name + "_0")
sum_of_exp.quantization = no_scale_quant
reduce_sum_op.set_output_tensor(sum_of_exp)
+ DebugDatabase.add_optimised(self.op, reduce_sum_op)
# PASS 4 - CLZ
clz_op = Operation(Op.CLZ, self.op.name + "_clz4")
@@ -297,6 +305,7 @@
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)
+ DebugDatabase.add_optimised(self.op, clz_op)
# PASS 5 - Sub
sub5_op = Operation(Op.Sub, self.op.name + "_sub5")
@@ -314,6 +323,7 @@
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)
+ DebugDatabase.add_optimised(self.op, sub5_op)
# PASS 6 - Sub
one = create_const_tensor("one_const", [1, 1, 1, 1], DataType.int32, [1], np.int32, quantization=no_scale_quant)
@@ -323,6 +333,7 @@
headroom = Tensor(reduce_sum_shape, DataType.int32, sub6_op.name + "_0")
headroom.quantization = no_scale_quant
sub6_op.set_output_tensor(headroom)
+ DebugDatabase.add_optimised(self.op, sub6_op)
# PASS 7 - SHL
shl7_op = Operation(Op.SHL, self.op.name + "_shl7")
@@ -331,6 +342,7 @@
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)
# PASS 8 - Sub
sub8_op = Operation(Op.Sub, self.op.name + "_sub8")
@@ -343,6 +355,7 @@
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)
+ DebugDatabase.add_optimised(self.op, sub8_op)
# PASS 9 - SHL
shl9_op = Operation(Op.SHL, self.op.name + "_shl9")
@@ -351,6 +364,7 @@
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)
+ DebugDatabase.add_optimised(self.op, shl9_op)
# PASS 10 - Add
add10_op = Operation(Op.Add, self.op.name + "_add10")
@@ -364,6 +378,7 @@
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)
+ DebugDatabase.add_optimised(self.op, add10_op)
# PASS 11 - Multiply
mul11_op = Operation(Op.Mul, self.op.name + "_mul11")
@@ -382,6 +397,7 @@
rescaled.quantization = one_scale_quant.clone()
rescaled.quantization.scale_f32 = 2.0
mul11_op.set_output_tensor(rescaled)
+ DebugDatabase.add_optimised(self.op, mul11_op)
# PASS 12 - Add
add12_op = Operation(Op.Add, self.op.name + "_add12")
@@ -394,6 +410,7 @@
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)
+ DebugDatabase.add_optimised(self.op, add12_op)
nr_x = rescale_w_offset
F2_one = create_const_tensor(
@@ -411,6 +428,7 @@
half_denominator_times_x.quantization = one_scale_quant.clone()
half_denominator_times_x.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(half_denominator_times_x)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 14, 19, 24 - SUB
sub_op = Operation(Op.Sub, self.op.name + "_sub%d" % (14 + i * 5))
sub_op.add_input_tensor(F2_one)
@@ -418,6 +436,7 @@
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)
+ DebugDatabase.add_optimised(self.op, sub_op)
# PASS 15, 20, 25 - MUL
mul_op = Operation(Op.Mul, self.op.name + "_mul%d" % (15 + i * 5))
mul_op.add_input_tensor(nr_x)
@@ -426,6 +445,7 @@
to_rescale.quantization = one_scale_quant.clone()
to_rescale.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(to_rescale)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 16, 21, 26 - MUL
shl_op = Operation(Op.Mul, self.op.name + "_mul%d" % (16 + i * 5))
shl_op.add_input_tensor(to_rescale)
@@ -433,6 +453,7 @@
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)
+ DebugDatabase.add_optimised(self.op, shl_op)
# PASS 17, 22, 27 - ADD
add_op = Operation(Op.Add, self.op.name + "_add%d" % (17 + i * 5))
add_op.add_input_tensor(nr_x)
@@ -440,6 +461,7 @@
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)
+ DebugDatabase.add_optimised(self.op, add_op)
# PASS 28 - Multiply
mul28_op = Operation(Op.Mul, self.op.name + "_mul28")
@@ -450,6 +472,7 @@
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)
+ DebugDatabase.add_optimised(self.op, mul28_op)
# PASS 29 - Multiply
mul_op = Operation(Op.Mul, self.op.name + "_mul29")
@@ -459,6 +482,7 @@
scaled_exp.quantization = one_scale_quant.clone()
scaled_exp.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(scaled_exp)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 30 - SHR
shr30_op = Operation(Op.SHR, self.op.name + "_shr30")
@@ -466,6 +490,7 @@
shr30_op.add_input_tensor(scaled_exp)
shr30_op.add_input_tensor(right_shift)
shr30_op.set_output_tensor(ofm)
+ DebugDatabase.add_optimised(self.op, shr30_op)
return shr30_op
@@ -476,6 +501,7 @@
# PASS 0 - Depthwise Maxpool
maxpool_op = self.op.clone("_maxpool0")
maxpool_op.type = Op.MaxPool
+ DebugDatabase.add_optimised(self.op, maxpool_op)
maxpool_h = ifm.shape[1] * ifm.shape[2]
maxpool_w = ifm.shape[3]
maxpool_ifm_shape = [1, maxpool_h, maxpool_w, 1]
@@ -490,6 +516,7 @@
maxpool_ofm = Tensor([1, maxpool_h, 1, 1], ifm.dtype, maxpool_op.name + "_0")
maxpool_ofm.quantization = no_scale_quant
maxpool_op.set_output_tensor(maxpool_ofm)
+ DebugDatabase.add_optimised(self.op, maxpool_op)
# PASS 1 - Sub
sub1_op = Operation(Op.Sub, self.op.name + "_sub1")
@@ -498,6 +525,7 @@
sub1_ofm = Tensor(ifm.shape, DataType.int32, sub1_op.name + "_0")
sub1_ofm.quantization = ifm.quantization.clone()
sub1_op.set_output_tensor(sub1_ofm)
+ DebugDatabase.add_optimised(self.op, sub1_op)
# PASS 2 - Mul
beta = self.op.attrs.get("beta", 1.0)
@@ -516,6 +544,7 @@
mul2_ofm.quantization = ofm.quantization.clone()
mul2_ofm.quantization.scale_f32 = mul2_out_range
mul2_op.set_output_tensor(mul2_ofm)
+ DebugDatabase.add_optimised(self.op, mul2_op)
# PASS 3 - Add+LUT(exp)
add_op = Operation(Op.Add, self.op.name + "_add3")
@@ -533,6 +562,7 @@
exp_ofm = Tensor(mul2_ofm.shape, DataType.int16, add_op.name + "_0")
exp_ofm.quantization = mul2_ofm.quantization.clone()
add_op.set_output_tensor(exp_ofm)
+ DebugDatabase.add_optimised(self.op, add_op)
# PASS 4 - Reduce sum
reduce_sum_op = Operation(Op.ReduceSum, self.op.name + "_reduce_sum4")
@@ -549,6 +579,7 @@
sum_of_exp = Tensor(reduce_sum_shape, DataType.int32, reduce_sum_op.name + "_0")
sum_of_exp.quantization = no_scale_quant
reduce_sum_op.set_output_tensor(sum_of_exp)
+ DebugDatabase.add_optimised(self.op, reduce_sum_op)
# PASS 5 - CLZ
clz_op = Operation(Op.CLZ, self.op.name + "_clz5")
@@ -556,6 +587,7 @@
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)
+ DebugDatabase.add_optimised(self.op, clz_op)
# PASS 6 - Sub
sub6_op = Operation(Op.Sub, self.op.name + "_sub6")
@@ -568,6 +600,7 @@
reciprocal_right_shift = Tensor(reduce_sum_shape, DataType.int32, sub6_op.name + "_0")
reciprocal_right_shift.quantization = no_scale_quant
sub6_op.set_output_tensor(reciprocal_right_shift)
+ DebugDatabase.add_optimised(self.op, sub6_op)
# PASS 7 - SHL
shl7_op = Operation(Op.SHL, self.op.name + "_shl7")
@@ -580,6 +613,7 @@
constant_one = Tensor(reduce_sum_shape, DataType.int32, shl7_op.name + "_0")
constant_one.quantization = no_scale_quant
shl7_op.set_output_tensor(constant_one)
+ DebugDatabase.add_optimised(self.op, shl7_op)
# PASS 8 - Sub
sub8_op = Operation(Op.Sub, self.op.name + "_sub8")
@@ -588,6 +622,7 @@
sum_of_exps_minus_one = Tensor(reduce_sum_shape, DataType.int32, sub8_op.name + "_0")
sum_of_exps_minus_one.quantization = no_scale_quant
sub8_op.set_output_tensor(sum_of_exps_minus_one)
+ DebugDatabase.add_optimised(self.op, sub8_op)
# PASS 9 - SHL
shl9_op = Operation(Op.SHL, self.op.name + "_shl9")
@@ -596,6 +631,7 @@
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)
+ DebugDatabase.add_optimised(self.op, shl9_op)
# PASS 10 - SHR
shr10_op = Operation(Op.SHR, self.op.name + "_shr10")
@@ -608,6 +644,7 @@
shifted_sum_minus_one_16 = Tensor(reduce_sum_shape, DataType.int32, shr10_op.name + "_0")
shifted_sum_minus_one_16.quantization = shifted_sum_minus_one.quantization.clone()
shr10_op.set_output_tensor(shifted_sum_minus_one_16)
+ DebugDatabase.add_optimised(self.op, shr10_op)
# PASS 11 - Sub+LUT(one over one plus x)
sub11_op = Operation(Op.Sub, self.op.name + "_sub11")
@@ -630,6 +667,7 @@
reciprocal_scale = Tensor(reduce_sum_shape, DataType.int16, sub11_op.name + "_0")
reciprocal_scale.quantization = no_scale_quant
sub11_op.set_output_tensor(reciprocal_scale)
+ DebugDatabase.add_optimised(self.op, sub11_op)
# PASS 12 - Multiply
mul_op = Operation(Op.Mul, self.op.name + "_mul12")
@@ -638,11 +676,13 @@
mul_ofm = Tensor(exp_ofm.shape, DataType.int32, mul_op.name + "_0")
mul_ofm.quantization = no_scale_quant
mul_op.set_output_tensor(mul_ofm)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 13 - SHR
shr13_op = Operation(Op.SHR, self.op.name + "_shr13")
shr13_op.add_input_tensor(mul_ofm)
shr13_op.add_input_tensor(reciprocal_right_shift)
shr13_op.set_output_tensor(ofm)
+ DebugDatabase.add_optimised(self.op, shr13_op)
return shr13_op