MLBEDSW-2836 Change sets to tuples
Replace conditional checks against sets with tuples.
If not requiring uniqueness, or complex set operations, it is quicker to
use tuples instead.
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: Ie8732c8d46067244963936c53f0ec81adda50372
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 4a85750..d0d3d7c 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -43,9 +43,9 @@
from .tensor import QuantizationParameters
from .tensor import Tensor
-passthrough_nodes = set((Op.Identity,))
+passthrough_nodes = (Op.Identity,)
-memory_only_ops = set((Op.Reshape,))
+memory_only_ops = (Op.Reshape,)
def remove_passthrough_tensor(tens, arch, nng):
@@ -663,7 +663,7 @@
# Reorder activation op if it's after the memory only operations
def fixup_act_reorder(op, arch, nng):
- if op.type.is_relu_op() or op.type in set((Op.Sigmoid, Op.Tanh)):
+ if op.type.is_relu_op() or op.type in (Op.Sigmoid, Op.Tanh):
prep_op = get_prepend_op(op)
if prep_op is not None:
act_op = op.clone("_reordered")
diff --git a/ethosu/vela/high_level_command_stream.py b/ethosu/vela/high_level_command_stream.py
index 30e22ff..d057d17 100644
--- a/ethosu/vela/high_level_command_stream.py
+++ b/ethosu/vela/high_level_command_stream.py
@@ -55,8 +55,8 @@
new_start_coord[idx] += split_offset[idx]
new_end_coord[idx] += split_offset[idx]
- if split_offset is None and npu_block_type in set(
- (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum)
+ if (split_offset is None) and (
+ npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum)
):
# these types of operations do a "dot product" or sum over the entire IFM
new_start_coord[-1] = 0
diff --git a/ethosu/vela/nn_graph.py b/ethosu/vela/nn_graph.py
index 12edf5e..b287785 100644
--- a/ethosu/vela/nn_graph.py
+++ b/ethosu/vela/nn_graph.py
@@ -178,7 +178,7 @@
visit_tensor(inp)
inp.consumer_list.append(op)
- if op.type in set((Op.Placeholder, Op.SubgraphInput)):
+ if op.type in (Op.Placeholder, Op.SubgraphInput):
assert len(op.outputs) == 1
self.input_tensors.append(op.outputs[0])
@@ -321,7 +321,7 @@
all_ops = self.get_all_ops()
unique_ops = []
for op in all_ops:
- if op.type in set((Op.Const, Op.Identity, Op.Placeholder)):
+ if op.type in (Op.Const, Op.Identity, Op.Placeholder):
continue
attrs = op.attrs.copy()
diff --git a/ethosu/vela/npu_performance.py b/ethosu/vela/npu_performance.py
index d28df97..2d7a1b0 100644
--- a/ethosu/vela/npu_performance.py
+++ b/ethosu/vela/npu_performance.py
@@ -45,7 +45,7 @@
ofm_block = Block(block_config_ps2[-3], block_config_ps2[-4], block_config_ps2[-1])
kernel = ps2.primary_op.kernel
- if ps2.npu_block_type in set((NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct)):
+ if ps2.npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct):
op = ps2.primary_op
ifm_block_depth = arch.calc_ifm_block_depth(op.ifm.shape[-1], op.ifm.dtype.size_in_bits())
else:
@@ -499,7 +499,7 @@
ifm_read_multiple = 1
weight_read_multiple = 0
- if ps.placement in set((PassPlacement.MemoryOnly, PassPlacement.StartupInit)):
+ if ps.placement in (PassPlacement.MemoryOnly, PassPlacement.StartupInit):
return bws, macs, cycles, blocks, ifm_read_multiple, weight_read_multiple # nothing real happening in this pass
min_block_size = arch.min_block_sizes[ps.npu_block_type]
@@ -537,13 +537,11 @@
ifm_block_depth, ofm_block, primary_op.kernel, ifm_resampling_mode=ifm_tensor.resampling_mode
)
- if npu_block_type in set(
- (
- NpuBlockType.ConvolutionMxN,
- NpuBlockType.ConvolutionDepthWise,
- NpuBlockType.Pooling,
- NpuBlockType.ReduceSum,
- )
+ if npu_block_type in (
+ NpuBlockType.ConvolutionMxN,
+ NpuBlockType.ConvolutionDepthWise,
+ NpuBlockType.Pooling,
+ NpuBlockType.ReduceSum,
):
# extent the ifm to full dimension
ifm_tensor_brick_size = tuple(numeric_util.full_shape(4, list(ifm_tensor.brick_size), 1))
@@ -640,8 +638,8 @@
n_kernel_xy = kernel_dims[0] * kernel_dims[1]
n_input_channels_at_a_time = block_config[2]
- if npu_block_type == NpuBlockType.Pooling or block_traversal in set(
- (TensorBlockTraversal.PartKernelFirst, TensorBlockTraversal.DepthWise)
+ if (npu_block_type == NpuBlockType.Pooling) or (
+ block_traversal in (TensorBlockTraversal.PartKernelFirst, TensorBlockTraversal.DepthWise)
):
n_input_channels_at_a_time = numeric_util.round_up_divide(n_input_channels_at_a_time, 4)
n_kernel_xy = max(
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py
index ea2eaa4..9bc04f2 100644
--- a/ethosu/vela/pass_packing.py
+++ b/ethosu/vela/pass_packing.py
@@ -72,7 +72,7 @@
binary_elem_wise_main_ops = Op.op_set(Op.is_binary_elementwise_op)
-unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op) # Unary element-wise operations
+unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op)
elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
diff --git a/ethosu/vela/register_command_stream_util.py b/ethosu/vela/register_command_stream_util.py
index ca7e6bc..ce49fc2 100644
--- a/ethosu/vela/register_command_stream_util.py
+++ b/ethosu/vela/register_command_stream_util.py
@@ -46,7 +46,7 @@
BASE_PTR_INDEX_MEM2MEM = int((1 << 8) | (3 << 0))
-UNARY_ELEMWISE_OPS = set((NpuElementWiseOp.ABS, NpuElementWiseOp.LRELU, NpuElementWiseOp.CLZ,))
+UNARY_ELEMWISE_OPS = (NpuElementWiseOp.ABS, NpuElementWiseOp.LRELU, NpuElementWiseOp.CLZ)
def to_npu_kernel(kernel: Kernel) -> NpuKernel:
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py
index b07b4dc..f6e628c 100644
--- a/ethosu/vela/tensor.py
+++ b/ethosu/vela/tensor.py
@@ -557,8 +557,10 @@
return self.consumer_list
def get_address_ranges_for_coordinates(self, start_coord, end_coord):
- if self.sub_purpose in set(
- (TensorSubPurpose.RollingBufferX, TensorSubPurpose.RollingBufferY, TensorSubPurpose.RollingBufferXY)
+ if self.sub_purpose in (
+ TensorSubPurpose.RollingBufferX,
+ TensorSubPurpose.RollingBufferY,
+ TensorSubPurpose.RollingBufferXY,
):
# build dummy coordinates that cover the entire buffer
start_coord = [0] * len(start_coord)
@@ -637,7 +639,7 @@
augmented_shape[1] = 1
else:
- assert self.format in set((TensorFormat.Unknown, TensorFormat.WeightsCompressed))
+ assert self.format in (TensorFormat.Unknown, TensorFormat.WeightsCompressed)
return None, None
strides = [0] * len(augmented_shape)
@@ -774,9 +776,7 @@
return address_offset
def is_allocated_in_tensor_arena(self, scratch_tensor_mem_area):
- if self.mem_area == scratch_tensor_mem_area and (self.mem_type in set((MemType.Scratch, MemType.Scratch_fast))):
- return True
- return False
+ return (self.mem_area == scratch_tensor_mem_area) and (self.mem_type in (MemType.Scratch, MemType.Scratch_fast))
def equivalent(self, tens):
return self.equivalence_id == tens.equivalence_id
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index 93b97f6..df52478 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -121,7 +121,7 @@
if dtype == DataType.uint8:
tens.quantization.quant_min = 0
tens.quantization.quant_max = (1 << dtype.bits) - 1
- elif dtype in set((DataType.int8, DataType.int16, DataType.int32, DataType.int64)):
+ elif dtype in (DataType.int8, DataType.int16, DataType.int32, DataType.int64):
tens.quantization.quant_min = -(1 << (dtype.bits - 1))
tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1
diff --git a/ethosu/vela/tflite_writer.py b/ethosu/vela/tflite_writer.py
index e82fb5e..f747d47 100644
--- a/ethosu/vela/tflite_writer.py
+++ b/ethosu/vela/tflite_writer.py
@@ -76,7 +76,7 @@
self.scratch_fast_buf_id = 1 # Always assign scratch_fast to buffer 1
self.buffers_to_write = [] # have an empty array there
- self.ops_to_ignore = set((Op.Const, Op.Placeholder, Op.SubgraphInput))
+ self.ops_to_ignore = (Op.Const, Op.Placeholder, Op.SubgraphInput)
self.tensors_to_reshape = {}
@@ -405,7 +405,7 @@
# Ensure that the order of the offsets match the order of the tensors
for tens, idx in self.tensor_map.items():
# Set offsets for tensor allocated in Tensor Arena or in the scratch_fast area
- if tens.mem_type in set((MemType.Scratch, MemType.Scratch_fast)):
+ if tens.mem_type in (MemType.Scratch, MemType.Scratch_fast):
offsets[idx] = np.int32(tens.address) if tens.address is not None else np.int32(0)
self.nng.metadata.append(("OfflineMemoryAllocation", np.array([version, subgraph_idx, nbr_tensors] + offsets)))
diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index 40ebcd0..fce17d1 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -535,8 +535,7 @@
if ps.scale_tensor is not None:
rescale_for_faf = False
- activation_ops = set((Op.Sigmoid, Op.Tanh))
- if (ps.ops[-1].type in activation_ops) and (ps.npu_block_type != NpuBlockType.ElementWise):
+ if (ps.ops[-1].type in (Op.Sigmoid, Op.Tanh)) 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)
if ps.scale_tensor.ops[0].type == Op.DMA: