MLBEDSW-4022: support PAD followed by pool operator
PAD followed by max/average pool is run on NPU if NPU
padding can be used. Average pool is converted to depthwise.
Change-Id: Icc3652e6d9ecff5ac3dc7d92080313d90c245404
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index f5006c6..e1ceb9f 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -26,6 +26,7 @@
from . import lut
from . import rewrite_graph
from . import scaling
+from .api import NpuRoundingMode
from .data_type import DataType
from .debug_database import DebugDatabase
from .errors import UnsupportedFeatureError
@@ -46,6 +47,7 @@
from .tensor import create_const_tensor
from .tensor import QuantizationParameters
from .tensor import Tensor
+from .tensor import TensorPurpose
from .tflite_mapping import optype_to_builtintype
passthrough_nodes = (Op.Identity,)
@@ -1174,19 +1176,55 @@
return op
-def optimise_pad(op, arch, nng):
+def optimise_pad(op: Operation, 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())
+ (op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op() or op.type.is_pool_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
+ if op.type.is_avgpool_op():
+ # Average pool is converted to depthwise, because NPU average pool + same padding
+ # has a special implementation that is different from PAD followed by average pool with
+ # valid padding.
+ k_w, k_h = op.kernel.width, op.kernel.height
+ ifm = op.ifm
+ # Remember other inputs
+ other_inputs = op.inputs[1:]
+ # Create a weight tensor, all weights are set to 1/(kernel width * kernel height)
+ quantization = QuantizationParameters(0.0, 255.0)
+ quantization.scale_f32 = 1.0 / (k_w * k_h)
+ quantization.zero_point = 0
+ shape = [k_h, k_w, 1, op.ofm.shape[-1]]
+ weights = np.full(shape, 1)
+
+ weight_tens = create_const_tensor(
+ op.name + "_weights",
+ shape,
+ op.ifm.dtype,
+ weights,
+ np.uint8,
+ purpose=TensorPurpose.Weights,
+ quantization=quantization,
+ )
+ weight_tens.quant_values = weights
+ op.type = Op.DepthwiseConv2DBias
+ op.inputs = []
+ op.add_input_tensor(ifm)
+ op.add_input_tensor(weight_tens)
+ # Add bias tensor, all biases set to 0
+ op.inputs.append(None)
+ fixup_bias_tensors(op, arch, nng)
+ # Add other inputs
+ op.inputs.extend(other_inputs)
+ op.rounding_mode = NpuRoundingMode.NATURAL
+
# Bypass the PAD operator
op.set_input_tensor(pad_op.ifm, 0)
# Adjust the padding attributes of the convolution operator
@@ -1231,7 +1269,7 @@
bias_values = [0] * nr_biases
bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], DataType.int32, bias_values)
bias_tensor.quant_values = bias_tensor.values
- op.set_input_tensor(bias_tensor, -1)
+ op.set_input_tensor(bias_tensor, op.type.info.indices.biases[0])
return op