MLBEDSW-3499: Support for PAD operator
Replaces the PAD operator by hardware padding when possible.
Change-Id: I9dce0885e51a4a73715824d7368637222e39b2b3
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 3759d3b..00edf83 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -156,7 +156,7 @@
return total_padding
-def calc_padding_and_skirt(padding_type, kernel_size, stride, input_dims):
+def calc_padding_and_skirt(padding_type, kernel_size, stride, input_dims, explicit_padding):
ypad = needed_total_padding(int(input_dims[1]), int(stride[1]), int(kernel_size[0]))
xpad = needed_total_padding(int(input_dims[2]), int(stride[2]), int(kernel_size[1]))
if padding_type == Padding.SAME:
@@ -169,6 +169,12 @@
right_pad = 0
top_pad = 0
bottom_pad = 0
+ elif padding_type == Padding.EXPLICIT:
+ # Padding is specified in a PAD operator which has been bypassed.
+ # The top and left padding are taken from the PAD; bottom and right are calculated.
+ top_pad, left_pad, _, _ = explicit_padding
+ bottom_pad = ypad - top_pad
+ right_pad = xpad - left_pad
else:
raise UnsupportedFeatureError(f"Unknown padding")
padding = (top_pad, left_pad, bottom_pad, right_pad)
@@ -537,7 +543,11 @@
dilation_h, dilation_w = op.get_dilation_h_w()
dilated_kernel_size = [dilation_h * (kernel_size[0] - 1) + 1, dilation_w * (kernel_size[1] - 1) + 1]
padding, skirt = calc_padding_and_skirt(
- op.attrs["padding"], dilated_kernel_size, op.attrs["strides"], input_shape
+ op.attrs["padding"],
+ dilated_kernel_size,
+ op.attrs["strides"],
+ input_shape,
+ op.attrs.get("explicit_padding"),
)
op.attrs["explicit_padding"] = padding
@@ -1122,6 +1132,30 @@
return op
+def optimise_pad(op, arch, nng):
+ """
+ Converts tens1 -> PAD -> tens2 -> CONV to tens1 -> CONV
+ if both operations can be run on the NPU.
+ """
+ if (
+ (op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op())
+ and op.run_on_npu
+ and op.attrs["padding"] == Padding.VALID
+ ):
+ pad_op = op.ifm.ops[0]
+ if pad_op.type != Op.Pad or not pad_op.run_on_npu:
+ return op
+ # Bypass the PAD operator
+ op.set_input_tensor(pad_op.ifm, 0)
+ # Adjust the padding attributes of the convolution operator
+ op.attrs["padding"] = Padding.EXPLICIT
+ padding = pad_op.inputs[1].values # 4x2 tensor, first dimension is N, H, W, C
+ top, left, bottom, right = (padding[1][0], padding[2][0], padding[1][1], padding[2][1])
+ op.attrs["explicit_padding"] = (top, left, bottom, right)
+ op.set_ifm_ofm_shapes()
+ return op
+
+
def add_attrs_to_resizebilinear(op, arch, nng):
if op.type == Op.ResizeBilinear and op.run_on_npu:
input_tensor = op.inputs[0]
@@ -1213,7 +1247,11 @@
for idx, sg in enumerate(nng.subgraphs):
# remove passthrough tensors and attempt further optimizations
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
- nng, sg, arch, [remove_passthrough_tensor], [fuse_activation_function_with_prev, add_padding_fields],
+ nng,
+ sg,
+ arch,
+ [remove_passthrough_tensor],
+ [fuse_activation_function_with_prev, optimise_pad, add_padding_fields],
)
# Post-optimisation operator debug tracing
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index c80e18b..af36587 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -201,7 +201,7 @@
OneHot = OperatorInfo()
Pack = OperatorInfo(indices=IFM_INDICES)
PackReshaped = OperatorInfo(indices=IFM_INDICES)
- Pad = OperatorInfo()
+ Pad = OperatorInfo(indices=IFM_INDICES)
PadV2 = OperatorInfo()
Placeholder = OperatorInfo() # Only used in CPU subgraphs
Pow = OperatorInfo()
@@ -335,6 +335,7 @@
class Padding(Enum):
SAME = 0
VALID = 1
+ EXPLICIT = 2 # Padding is specified in a PAD operation (only used for NPU operations)
class ActivationFunction:
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index 7fdc4bd..b3938bc 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -18,8 +18,12 @@
# Unit tests for graph_optimiser
import numpy as np
+from ethosu.vela.data_type import DataType
from ethosu.vela.graph_optimiser import convert_batched_fc_shape
+from ethosu.vela.graph_optimiser import optimise_pad
+from ethosu.vela.nn_graph import Graph
from ethosu.vela.operation import Op
+from ethosu.vela.operation import Padding
from ethosu.vela.tensor import create_const_tensor
from ethosu.vela.tensor import Shape4D
from ethosu.vela.tensor import Tensor
@@ -73,3 +77,44 @@
assert conv_op.type == Op.FullyConnected
assert len(conv_op.ifm.shape) == 2
assert conv_op.ifm.shape == conv_op.ofm.shape
+
+
+def test_optimise_pad():
+ """
+ Tests that the PAD operator is bypassed when followed by a convolution operator,
+ and that the padding of the convolution operation is correctly updated
+ """
+ # Create Pad operation followed by Conv2D
+ quant = testutil.default_quant_params()
+ in_tens = Tensor([1, 76, 75, 64], DataType.uint8, "input")
+ in_tens.quantization = quant
+ pad_input = create_const_tensor("pad_input", [4, 2], DataType.int32, [[0, 0], [2, 1], [1, 1], [0, 0]])
+ temp_tens = Tensor([1, 79, 77, 64], DataType.uint8, "pad_out")
+ temp_tens.quantization = quant.clone()
+ out_tens = Tensor([1, 76, 75, 64], DataType.uint8, "output")
+ out_tens.quantization = quant.clone()
+ weight_tens = Tensor([5, 3, 64, 64], DataType.uint8, "weights")
+ weight_tens.values = np.zeros(weight_tens.shape)
+ weight_tens.quant_values = np.zeros(weight_tens.shape, np.uint8)
+ weight_tens.quantization = quant.clone()
+
+ bias_tens = Tensor([64], DataType.int32, "biases")
+ pad_op = testutil.create_op(Op.Pad, [in_tens, pad_input], temp_tens)
+ attrs = {"padding": Padding.VALID, "stride_w": 2, "stride_h": 2, "dilation_w_factor": 1, "dilation_h_factor": 1}
+ attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1)
+ pad_op.run_on_npu = True
+ conv2d_op = testutil.create_op(Op.Conv2D, [temp_tens, weight_tens, bias_tens], out_tens, attrs)
+ conv2d_op.run_on_npu = True
+ nng = Graph()
+ sg = testutil.create_subgraph([pad_op, conv2d_op])
+ nng.subgraphs.append(sg)
+ arch = testutil.create_arch()
+
+ optimise_pad(conv2d_op, nng, arch)
+
+ op = sg.output_tensors[0].ops[0]
+ assert op.type == Op.Conv2D
+ assert op.attrs["padding"] == Padding.EXPLICIT
+ assert op.attrs["explicit_padding"] == (2, 1, 1, 1)
+ assert op.ifm.shape == [1, 76, 75, 64]
+ assert pad_op not in op.ifm.ops
diff --git a/ethosu/vela/test/testutil.py b/ethosu/vela/test/testutil.py
index c345950..96aeb7e 100644
--- a/ethosu/vela/test/testutil.py
+++ b/ethosu/vela/test/testutil.py
@@ -115,8 +115,9 @@
def create_op(op_type, inputs, output, attrs=None):
op = Operation(op_type, output.name + "_op")
- op.inputs = inputs
- op.outputs = [output]
+ for input in inputs:
+ op.add_input_tensor(input)
+ op.set_output_tensor(output)
if attrs is not None:
op.attrs = attrs
op.set_ifm_ofm_shapes()