diff options
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 44 |
1 files changed, 41 insertions, 3 deletions
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 fp_math 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 check_quantized_tens_scaling_equal 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 @@ def fuse_activation_function_with_prev(op, arch, nng): 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 @@ def fixup_bias_tensors(op, arch, nng): 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 |