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>

1 files changed, 45 insertions, 0 deletions

diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index 7fdc4bd8..b3938bcc 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 @@ def test_convert_batched_fc():
     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