MLBEDSW-3772 Reshape removal
-Removed reshapes in the original graph
-Removed the addition of reshapes to the
optimized graph
-Reshapes with different ifm/ofm quantisation will remain
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I94862be53dac0d7434815e2aee5ca678228495f8
diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py
index 8a1770e..656a7e6 100644
--- a/ethosu/vela/softmax.py
+++ b/ethosu/vela/softmax.py
@@ -39,8 +39,8 @@
from .operation_util import create_shl
from .operation_util import create_shr
from .operation_util import create_sub
+from .shape4d import Shape4D
from .tensor import create_const_tensor
-from .tensor import create_reshape_tensor
from .tensor import TensorPurpose
@@ -214,12 +214,13 @@
ofm = self.op.outputs[0]
# Reshape ifm/ofm (if needed)
- full_shape = self.op.ifm_shapes[0].as_list()
- if full_shape[0] > 1:
- full_shape[1] *= full_shape[0]
- full_shape[0] = 1
- ifm = create_reshape_tensor(ifm, full_shape)
- ofm = create_reshape_tensor(ofm, full_shape, False)
+ ifm_shape = self.op.ifm_shapes[0]
+ if ifm_shape.batch > 1:
+ ifm_shape.height = ifm_shape.batch * ifm_shape.height
+ ifm_shape.batch = 1
+ self.op.ifm.avoid_NHCWB16 = True
+ self.op.ofm_shapes[0] = ifm_shape.clone()
+ self.op.ofm.avoid_NHCWB16 = True
if ifm.dtype in (DataType.uint8, DataType.int8) and ofm.dtype == ifm.dtype:
return self.get_graph_8bit(ifm, ofm)
@@ -233,7 +234,6 @@
exp_lut = self.generate_exp_table(self.op.attrs.get("beta", 1.0), ifm.quantization.scale_f32)
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
@@ -245,7 +245,6 @@
one_scale_quant.zero_point = 0
two_scale_quant = one_scale_quant.clone()
two_scale_quant.scale_f32 = 2.0
- ifm.quantization.zero_point = 0
pass_number = 0
def add_op_get_ofm(op):
@@ -255,13 +254,25 @@
return op.ofm
# PASS 0 - Depthwise Maxpool
- ifm_max = add_op_get_ofm(create_depthwise_maxpool(f"{self.op.name}_maxpool{pass_number}", ifm, no_scale_quant))
+ ifm_shape = self.op.ifm_shapes[0]
+ ifm_max = add_op_get_ofm(
+ create_depthwise_maxpool(f"{self.op.name}_maxpool{pass_number}", ifm, ifm_shape, no_scale_quant)
+ )
# PASS 1 - Sub+LUT(exp)
sub_op_quantization = one_scale_quant.clone()
sub_op_quantization.zero_point = 127
- ifm_max = create_reshape_tensor(ifm_max, [1, ifm.shape[1], ifm.shape[2], 1])
- sub_op = create_sub(f"{self.op.name}_sub{pass_number}", ifm, ifm_max, sub_op_quantization, dtype=DataType.int32)
+ ifm_max_shape = Shape4D([1, ifm_shape.height, ifm_shape.width, 1])
+ ifm_max.avoid_NHCWB16 = True
+ sub_op = create_sub(
+ f"{self.op.name}_sub{pass_number}",
+ ifm,
+ ifm_max,
+ sub_op_quantization,
+ dtype=DataType.int32,
+ ifm_shape=ifm_shape,
+ ifm2_shape=ifm_max_shape,
+ )
sub_op.set_activation_lut(
create_const_tensor(
f"{sub_op.name}_exp_lut", [1, 1, 1, 256], DataType.int32, exp_lut, np.int32, TensorPurpose.LUT
@@ -415,7 +426,9 @@
shr30_op.add_input_tensor(scaled_exp)
shr30_op.add_input_tensor(right_shift)
shr30_op.set_output_tensor(ofm)
- shr30_op.set_ifm_ofm_shapes()
+ shr30_op.ifm_shapes.append(Shape4D(scaled_exp.shape))
+ shr30_op.ifm_shapes.append(Shape4D(right_shift.shape))
+ shr30_op.ofm_shapes.append(Shape4D(scaled_exp.shape))
DebugDatabase.add_optimised(self.op, shr30_op)
return shr30_op
@@ -432,12 +445,24 @@
return op.ofm
# PASS 0 - Depthwise Maxpool
- ifm_max = add_op_get_ofm(create_depthwise_maxpool(f"{self.op.name}_maxpool{pass_number}", ifm, no_scale_quant))
+ ifm_shape = self.op.ifm_shapes[0]
+ ifm_max = add_op_get_ofm(
+ create_depthwise_maxpool(f"{self.op.name}_maxpool{pass_number}", ifm, ifm_shape, no_scale_quant)
+ )
# PASS 1 - Sub
- ifm_max = create_reshape_tensor(ifm_max, [1, ifm.shape[1], ifm.shape[2], 1])
+ ifm_max_shape = Shape4D([1, ifm_shape.height, ifm_shape.width, 1])
+ ifm_max.avoid_NHCWB16 = True
sub1_ofm = add_op_get_ofm(
- create_sub(f"{self.op.name}_sub{pass_number}", ifm, ifm_max, ifm.quantization.clone(), dtype=DataType.int32)
+ create_sub(
+ f"{self.op.name}_sub{pass_number}",
+ ifm,
+ ifm_max,
+ ifm.quantization.clone(),
+ dtype=DataType.int32,
+ ifm_shape=ifm_shape,
+ ifm2_shape=ifm_max_shape,
+ )
)
# PASS 2 - Mul
@@ -537,7 +562,9 @@
shr13_op.add_input_tensor(mul_ofm)
shr13_op.add_input_tensor(reciprocal_right_shift)
shr13_op.set_output_tensor(ofm)
- shr13_op.set_ifm_ofm_shapes()
+ shr13_op.ifm_shapes.append(Shape4D(mul_ofm.shape))
+ shr13_op.ifm_shapes.append(Shape4D(reciprocal_right_shift.shape))
+ shr13_op.ofm_shapes.append(Shape4D(mul_ofm.shape))
DebugDatabase.add_optimised(self.op, shr13_op)
return shr13_op