MLBEDSW-3148: Refactor Operation
- op.type is now an enum instead of a string
- Removed unused operator codes
- Refactored some attributes like npu_block_type, fused_activation_function
- Refactored operator index calculation
- Refactored a number of operator sets
Change-Id: I641f65ee375794b7aec42abc0664251ae37d78e8
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index 8426705..9453521 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -28,6 +28,7 @@
from .numeric_util import round_up
from .numeric_util import round_up_divide
from .operation import NpuBlockType
+from .operation import Op
from .scaling import quantise_scale
from .scaling import reduced_quantise_scale
from .tensor import create_equivalence_id
@@ -336,7 +337,7 @@
is_depthwise = tens.block_traversal == TensorBlockTraversal.DepthWise
is_partkernel = tens.block_traversal == TensorBlockTraversal.PartKernelFirst
- if tens.consumer_list[0].type == "Conv2DBackpropInputSwitchedBias":
+ if tens.consumer_list[0].type == Op.Conv2DBackpropInputSwitchedBias:
# Transpose Convoluion, reverse weights in H and W axes
weights = np.flip(weights, axis=(0, 1))
@@ -406,9 +407,9 @@
assert tens.purpose == TensorPurpose.FeatureMap
assert tens.format == TensorFormat.NHWC
# the connected operator should expect a bias input unless it is a FullyConnected
- assert "Bias" in tens.consumer_list[0].type or tens.consumer_list[0].type.startswith("FullyConnected")
+ assert tens.consumer_list[0].type.needs_bias()
# the input bias tensor is the same as that connected to the operator
- _, _, bias_tens, _ = tens.consumer_list[0].get_ifm_weights_biases_ofm()
+ bias_tens = tens.consumer_list[0].bias
assert tens is bias_tens
# the operator should only have a single output
@@ -508,14 +509,13 @@
op = tens.find_npu_op()
if op is None:
continue
- npu_usage_of_tensor = op.attrs["npu_block_type"]
needs_dma = tens.needs_dma()
if ps.cascade.strategy == SchedulingStrategy.WeightStream and needs_dma:
ofm_depth_step = ps.block_config[-1]
else:
ofm_depth_step = tens.shape[-1]
compress_weights(
- arch, nng, tens, npu_usage_of_tensor, ps.block_config[-1], ofm_depth_step, op.get_dilation_h_w()
+ arch, nng, tens, op.type.npu_block_type, ps.block_config[-1], ofm_depth_step, op.get_dilation_h_w()
)
# Update source tensor
if needs_dma:
@@ -527,7 +527,7 @@
if ps.scale_tensor is not None:
rescale_for_faf = False
- activation_ops = set(("Sigmoid", "Tanh"))
+ activation_ops = set((Op.Sigmoid, Op.Tanh))
if (ps.ops[-1].type in activation_ops) and (ps.npu_block_type != NpuBlockType.ElementWise):
rescale_for_faf = True
calc_scales_and_pack_biases(ps.scale_tensor, arch, ofm_depth_step, rescale_for_faf)