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/operation_util.py b/ethosu/vela/operation_util.py
index 7015b79..e7e4bbb 100644
--- a/ethosu/vela/operation_util.py
+++ b/ethosu/vela/operation_util.py
@@ -24,7 +24,7 @@
from .operation import Op
from .operation import Operation
from .operation import Padding
-from .tensor import create_reshape_tensor
+from .shape4d import Shape4D
from .tensor import QuantizationParameters
from .tensor import Tensor
@@ -44,12 +44,17 @@
def create_depthwise_maxpool(
- name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None
+ name: str,
+ ifm: Tensor,
+ inp_shape: Shape4D,
+ quantization: QuantizationParameters,
+ activation: Optional[ActivationFunction] = None,
) -> Operation:
op = Operation(Op.MaxPool, name)
- height = ifm.shape[1] * ifm.shape[2]
- width = ifm.shape[3]
- ifm_shape = [1, height, width, 1]
+ height = inp_shape.height * inp_shape.width
+ width = inp_shape.depth
+ ifm_shape = Shape4D([1, height, width, 1])
+
op.attrs["padding"] = Padding.VALID
op.attrs["stride_w"] = 1
op.attrs["stride_h"] = 1
@@ -58,11 +63,14 @@
op.attrs["strides"] = [1, op.attrs["stride_h"], op.attrs["stride_w"], 1]
op.attrs["ksize"] = [1, op.attrs["filter_height"], op.attrs["filter_width"], 1]
op.activation = activation
- op.inputs = [create_reshape_tensor(ifm, ifm_shape)]
+ op.inputs = [ifm]
ofm = Tensor([1, height, 1, 1], ifm.dtype, op.name + "_tens0")
ofm.quantization = quantization
op.set_output_tensor(ofm)
- op.set_ifm_ofm_shapes()
+ op.ifm_shapes.append(ifm_shape)
+ op.ofm_shapes.append(Shape4D(ofm.shape))
+ op.ifm.avoid_NHCWB16 = True
+ op.ofm.avoid_NHCWB16 = True
return op
@@ -95,8 +103,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(Op.Add, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(
+ Op.Add, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
def create_rescale_add(
@@ -108,8 +120,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- op = create_binary_elementwise(Op.RescaleAdd, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ op = create_binary_elementwise(
+ Op.RescaleAdd, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
op.rescale = rescale
return op
@@ -121,8 +137,9 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_unary_elementwise(Op.CLZ, name, ifm, quantization, activation, dtype, attrs)
+ return create_unary_elementwise(Op.CLZ, name, ifm, quantization, activation, dtype, attrs, ifm_shape)
def create_mul(
@@ -133,8 +150,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(Op.Mul, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(
+ Op.Mul, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
def create_shl(
@@ -145,8 +166,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(Op.SHL, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(
+ Op.SHL, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
def create_shr(
@@ -157,8 +182,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(Op.SHR, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(
+ Op.SHR, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
def create_sub(
@@ -169,8 +198,12 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(Op.Sub, name, ifm, ifm2, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(
+ Op.Sub, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape
+ )
def create_unary_elementwise(
@@ -181,8 +214,9 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
) -> Operation:
- return create_binary_elementwise(op_type, name, ifm, None, quantization, activation, dtype, attrs)
+ return create_binary_elementwise(op_type, name, ifm, None, quantization, activation, dtype, attrs, ifm_shape, None)
def create_binary_elementwise(
@@ -194,19 +228,34 @@
activation: Optional[ActivationFunction] = None,
dtype: Optional[DataType] = None,
attrs: Optional[dict] = None,
+ ifm_shape: Optional[Shape4D] = None,
+ ifm2_shape: Optional[Shape4D] = None,
) -> Operation:
+ if ifm_shape is None:
+ ifm_shape = Shape4D(ifm.shape)
op = Operation(op_type, name)
op.add_input_tensor(ifm)
+ op.ifm_shapes.append(ifm_shape)
if ifm2:
op.add_input_tensor(ifm2)
+ if ifm2_shape is None:
+ ifm2_shape = Shape4D(ifm2.shape)
+ op.ifm_shapes.append(ifm2_shape)
op.activation = activation
if not dtype:
dtype = ifm.dtype
if attrs:
op.attrs.update(attrs)
- ofm_shape = ifm.shape if ifm2 is None or ifm_ifm2_correct_order(ifm.shape, ifm2.shape) else ifm2.shape
- ofm = Tensor(ofm_shape, dtype, f"{op.name}_tens0")
+
+ if ifm2 is None:
+ ofm_shape = ifm_shape
+ else:
+ in_shape = [] if ifm.shape == [] else ifm_shape.as_list()
+ in2_shape = [] if ifm2.shape == [] else ifm2_shape.as_list()
+ ofm_shape = ifm_shape if ifm_ifm2_correct_order(in_shape, in2_shape) else ifm2_shape
+
+ ofm = Tensor(ofm_shape.as_list(), dtype, f"{op.name}_tens0")
ofm.quantization = quantization
op.set_output_tensor(ofm)
- op.set_ifm_ofm_shapes()
+ op.ofm_shapes.append(ofm_shape)
return op