MLBEDSW-2066 Improve Exception messages
Minor refactoring to use fstrings.
Improve Error classes to correctly inherit the base class.
Use existing exception classes instead of plain exceptions where it
makes sense.
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I0941c04e91010da1db77299517a8e2d896371e77
diff --git a/ethosu/vela/architecture_features.py b/ethosu/vela/architecture_features.py
index 354ab12..576f793 100644
--- a/ethosu/vela/architecture_features.py
+++ b/ethosu/vela/architecture_features.py
@@ -531,13 +531,11 @@
self._set_default_sys_config()
elif vela_config_files is None:
- raise CliOptionError("--config", vela_config_files, "CLI Option not specified")
+ raise CliOptionError("--config", vela_config_files, "Vela config file not specified")
else:
raise CliOptionError(
- "--system-config",
- self.system_config,
- "Section {} not found in Vela config file".format(sys_cfg_section),
+ "--system-config", self.system_config, f"Section {sys_cfg_section} not found in Vela config file",
)
# read the memory mode
@@ -559,11 +557,11 @@
self._set_default_mem_mode()
elif vela_config_files is None:
- raise CliOptionError("--config", vela_config_files, "CLI Option not specified")
+ raise CliOptionError("--config", vela_config_files, "Vela config file not specified")
else:
raise CliOptionError(
- "--memory-mode", self.memory_mode, "Section {} not found in Vela config file".format(mem_mode_section),
+ "--memory-mode", self.memory_mode, f"Section {mem_mode_section} not found in Vela config file",
)
# override sram to onchipflash
@@ -645,9 +643,7 @@
particular option then the key from the parent section is used, regardless of the parsing order
"""
if not self.vela_config.has_section(section):
- raise ConfigOptionError(
- "section", "{}. The section was not found in the Vela config file(s)".format(section)
- )
+ raise ConfigOptionError("section", f"{section}. The section was not found in the Vela config file(s)")
result = str(current_value)
if self.vela_config.has_option(section, "inherit"):
@@ -655,8 +651,7 @@
# check for recursion loop
if inheritance_section == section:
raise ConfigOptionError(
- "inherit",
- "{}. This references its own section and recursion is not allowed".format(inheritance_section),
+ "inherit", f"{inheritance_section}. This references its own section and recursion is not allowed",
)
result = self._read_config(inheritance_section, key, result)
diff --git a/ethosu/vela/compiler_driver.py b/ethosu/vela/compiler_driver.py
index 6c7fdc1..78d7f12 100644
--- a/ethosu/vela/compiler_driver.py
+++ b/ethosu/vela/compiler_driver.py
@@ -267,9 +267,9 @@
alloc_results.append(alloc_success)
if not alloc_results[-1]:
raise VelaError(
- "Sram limit {} bytes, has been exceeded by the scratch fast tensor. "
+ f"Sram limit {arch.sram_size} bytes, has been exceeded by the scratch fast tensor. "
"Increasing the value of --weight-estimation-scaling may help to resolve the issue. "
- "See OPTIONS.md for more information.".format(arch.sram_size)
+ "See OPTIONS.md for more information"
)
else:
tensor_allocation.allocate_tensors(
diff --git a/ethosu/vela/errors.py b/ethosu/vela/errors.py
index 2a635d0..b241db8 100644
--- a/ethosu/vela/errors.py
+++ b/ethosu/vela/errors.py
@@ -23,7 +23,7 @@
"""Base class for vela exceptions"""
def __init__(self, data):
- self.data = "Error: " + data
+ self.data = f"Error! {data}"
def __str__(self):
return repr(self.data)
@@ -33,14 +33,14 @@
"""Raised when reading an input file results in errors"""
def __init__(self, file_name, msg):
- self.data = "Reading input file {}: {}".format(file_name, msg)
+ super().__init__(f"Reading input file '{file_name}': {msg}")
class UnsupportedFeatureError(VelaError):
"""Raised when the input network uses non-supported features that cannot be handled"""
def __init__(self, data):
- self.data = "Input network uses a feature that is currently not supported: {}".format(data)
+ super().__init__(f"Input network uses a feature that is currently not supported: {data}")
class CliOptionError(VelaError):
@@ -52,7 +52,7 @@
"""
def __init__(self, option, option_value, msg):
- self.data = "Incorrect argument to CLI option: {} = {}: {}".format(option, option_value, msg)
+ super().__init__(f"Incorrect argument to CLI option {option}={option_value}: {msg}")
class ConfigOptionError(VelaError):
@@ -64,18 +64,17 @@
"""
def __init__(self, option, option_value, option_valid_values=None):
- self.data = "Invalid configuration of {} = {}".format(option, option_value)
+ data = f"Invalid configuration of {option}={option_value}"
if option_valid_values is not None:
- self.data += " (must be {}).".format(option_valid_values)
- else:
- self.data += "."
+ data += f" (must be {option_valid_values})"
+ super().__init__(data)
class AllocationError(VelaError):
"""Raised when allocation fails"""
def __init__(self, msg):
- self.data = msg
+ super().__init__(f"Allocation failed: {msg}")
def OperatorError(op, msg):
@@ -86,36 +85,30 @@
:param msg: str object that contains a description of the specific error encountered
"""
+ def _print_tensors(tensors):
+ lines = []
+ for idx, tens in enumerate(tensors):
+ if isinstance(tens, Tensor):
+ tens_name = tens.name
+ else:
+ tens_name = "Not a Tensor"
+ lines.append(f" {idx} = {tens_name}")
+ return lines
+
assert isinstance(op, Operation)
if op.op_index is None:
- data = "Invalid {} (name = {}) operator in the internal representation.".format(op.type, op.name)
+ lines = [f"Invalid {op.type} (name = {op.name}) operator in the internal representation. {msg}"]
else:
- data = "Invalid {} (op_index = {}) operator in the input network.".format(op.type, op.op_index)
+ lines = [f"Invalid {op.type} (op_index = {op.op_index}) operator in the input network. {msg}"]
- data += " {}\n".format(msg)
+ lines += [" Input tensors:"]
+ lines += _print_tensors(op.inputs)
- data += " Input tensors:\n"
- for idx, tens in enumerate(op.inputs):
- if isinstance(tens, Tensor):
- tens_name = tens.name
- else:
- tens_name = "Not a Tensor"
+ lines += [" Output tensors:"]
+ lines += _print_tensors(op.outputs)
- data += " {} = {}\n".format(idx, tens_name)
-
- data += " Output tensors:\n"
- for idx, tens in enumerate(op.outputs):
- if isinstance(tens, Tensor):
- tens_name = tens.name
- else:
- tens_name = "Not a Tensor"
-
- data += " {} = {}\n".format(idx, tens_name)
-
- data = data[:-1] # remove last newline
-
- raise VelaError(data)
+ raise VelaError("\n".join(lines))
def TensorError(tens, msg):
@@ -126,32 +119,26 @@
:param msg: str object that contains a description of the specific error encountered
"""
+ def _print_operators(ops):
+ lines = []
+ for idx, op in enumerate(ops):
+ if isinstance(op, Operation):
+ op_type = op.type
+ op_id = f"({op.op_index})"
+ else:
+ op_type = "Not an Operation"
+ op_id = ""
+ lines.append(f" {idx} = {op_type} {op_id}")
+ return lines
+
assert isinstance(tens, Tensor)
- data = "Invalid {} tensor. {}\n".format(tens.name, msg)
+ lines = [f"Invalid {tens.name} tensor. {msg}"]
- data += " Driving operators:\n"
- for idx, op in enumerate(tens.ops):
- if isinstance(op, Operation):
- op_type = op.type
- op_id = op.op_index
- else:
- op_type = "Not an Operation"
- op_id = ""
+ lines += [" Driving operators:"]
+ lines += _print_operators(tens.ops)
- data += " {} = {} ({})\n".format(idx, op_type, op_id)
+ lines += [" Consuming operators:"]
+ lines += _print_operators(tens.consumer_list)
- data += " Consuming operators:\n"
- for idx, op in enumerate(tens.consumer_list):
- if isinstance(op, Operation):
- op_type = op.type
- op_id = op.op_index
- else:
- op_type = "Not an Operation"
- op_id = ""
-
- data += " {} = {} ({})\n".format(idx, op_type, op_id)
-
- data = data[:-1] # remove last newline
-
- raise VelaError(data)
+ raise VelaError("\n".join(lines))
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 13f08f2..15d1352 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -42,6 +42,7 @@
from .tensor import create_reshape_tensor
from .tensor import QuantizationParameters
from .tensor import Tensor
+from .tflite_mapping import optype_to_builtintype
passthrough_nodes = (Op.Identity,)
@@ -157,7 +158,7 @@
top_pad = 0
bottom_pad = 0
else:
- raise UnsupportedFeatureError("Unknown padding {}".format(str(padding_type)))
+ raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad)
return padding, skirt
@@ -168,19 +169,17 @@
if padding_type == b"SAME":
ypad = needed_total_padding(int(input_dims[1]) * upscaling_factor, int(stride[1]), int(kernel_height))
xpad = needed_total_padding(int(input_dims[2]) * upscaling_factor, int(stride[2]), int(kernel_width))
-
right_pad = max(((xpad + 1) // upscaling_factor) - 1, 0)
bottom_pad = max(((ypad + 1) // upscaling_factor) - 1, 0)
left_pad = max(kernel_width - 1 - right_pad, 0)
top_pad = max(kernel_height - 1 - bottom_pad, 0)
-
elif padding_type == b"VALID":
right_pad = max(kernel_width - 2, 0)
bottom_pad = max(kernel_height - 2, 0)
left_pad = kernel_width - 1
top_pad = kernel_height - 1
else:
- assert 0, "Unknown padding"
+ raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = padding
@@ -504,7 +503,7 @@
kernel_size = op.attrs["ksize"][1:3]
input_shape = op.inputs[0].shape
else:
- raise UnsupportedFeatureError("Unknown operation that uses padding: {}".format(op.type))
+ raise UnsupportedFeatureError(f"Unknown operation that uses padding: {optype_to_builtintype(op.type)}")
if op.type == Op.Conv2DBackpropInputSwitchedBias:
upscaling_factor = op.outputs[0].shape[1] // input_shape[1]
@@ -560,9 +559,8 @@
weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape))
else:
raise UnsupportedFeatureError(
- "Unsupported DepthwiseConv2d with depth_multiplier = {}, ifm channels = {}, ofm channels = {}".format(
- op.attrs["depth_multiplier"], ifm_tensor.shape[3], ofm_tensor.shape[3]
- )
+ f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},",
+ f" ifm channels = {ifm_tensor.shape[3]}, ofm channels = {ofm_tensor.shape[3]}",
)
DebugDatabase.add_optimised(op, op)
return op
diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py
index 9e0ed01..096a65c 100644
--- a/ethosu/vela/high_level_command_to_npu_op.py
+++ b/ethosu/vela/high_level_command_to_npu_op.py
@@ -46,6 +46,7 @@
from .architecture_features import Block
from .data_type import DataType
from .debug_database import DebugDatabase
+from .errors import UnsupportedFeatureError
from .high_level_command_stream import Box
from .high_level_command_stream import Command
from .high_level_command_stream import DMA
@@ -307,7 +308,7 @@
elif faf == Op.LUT:
act_op = NpuActivationOp.TABLE_LOOKUP
elif not faf.is_relu_op():
- raise Exception("Unsupported fused_activation_function = " + faf.name)
+ raise UnsupportedFeatureError(f"Unsupported fused_activation_function: {faf.name}")
act = NpuActivation(act_op)
act.min = op.activation.min
diff --git a/ethosu/vela/shared_buffer_allocation.py b/ethosu/vela/shared_buffer_allocation.py
index 21b048b..600b317 100644
--- a/ethosu/vela/shared_buffer_allocation.py
+++ b/ethosu/vela/shared_buffer_allocation.py
@@ -26,7 +26,7 @@
from .architecture_features import Block
from .architecture_features import SharedBufferArea
from .architecture_features import SHRAMElements
-from .errors import VelaError
+from .errors import AllocationError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .operation import Kernel
from .operation import NpuBlockType
@@ -259,7 +259,7 @@
if arch.override_block_config:
config = alloc.try_block(arch.override_block_config)
if config is None:
- raise VelaError("Block config override '{0}' cannot be allocated".format(arch.override_block_config))
+ raise AllocationError(f"Block config override '{arch.override_block_config}' cannot be allocated")
return [config]
# Constrain the search space if the OFM is smaller than the max block size
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py
index d75b787..de97710 100644
--- a/ethosu/vela/tensor.py
+++ b/ethosu/vela/tensor.py
@@ -29,6 +29,7 @@
import numpy as np
+from . import errors # Import this way due to cyclic imports
from . import numeric_util
from .data_type import BaseType
from .data_type import DataType
@@ -610,7 +611,7 @@
if end_coord[2] > crossing_x:
addresses[1] = self.address_for_coordinate([start_coord[0], start_coord[1], crossing_x, start_coord[3]])
- raise Exception("Striping in vertical direction is not supported")
+ raise errors.UnsupportedFeatureError("Striping in vertical direction is not supported")
if end_coord[1] > crossing_y:
addresses[2] = self.address_for_coordinate([start_coord[0], crossing_y, start_coord[2], start_coord[3]])
if end_coord[1] > crossing_y and end_coord[2] > crossing_x:
@@ -717,7 +718,7 @@
if index < len(self.weight_compressed_offsets) - 1:
# There are no half-way points in the weights
if (depth % brick_depth) != 0:
- raise Exception("Offset into weights must be aligned to a brick")
+ raise errors.UnsupportedFeatureError("Offset into weights must be aligned to a brick")
return index
diff --git a/ethosu/vela/tensor_allocation.py b/ethosu/vela/tensor_allocation.py
index 9736ca2..7f66579 100644
--- a/ethosu/vela/tensor_allocation.py
+++ b/ethosu/vela/tensor_allocation.py
@@ -91,7 +91,7 @@
if not all(op and op.run_on_npu for op in tens.ops + tens.consumer_list):
# This is a CPU tensor, verify alignment
if tens.address % alignment != 0:
- raise AllocationError("Tensor {} not aligned to {} bytes".format(tens.name, alignment))
+ raise AllocationError(f"Tensor '{tens.name}' not aligned to {alignment} bytes")
def verify_allocation(live_ranges: LiveRangeGraph, alignment: int):
@@ -104,14 +104,8 @@
overlap, tens_n, tens_m = n.overlaps_address(m)
if overlap and not (tens_n.equivalent(tens_m) and tens_n.address == tens_m.address):
raise AllocationError(
- "Overlapping buffers: {}: {} -> {} and {}: {} -> {}".format(
- n.name,
- tens_n.address,
- tens_n.address + n.size,
- m.name,
- tens_m.address,
- tens_m.address + m.size,
- )
+ f"Overlapping buffers: {n.name}: {tens_n.address} -> {tens_n.address + n.size}"
+ f" and {m.name}: {tens_m.address} -> {tens_m.address + m.size}"
)
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index df52478..eff702b 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -264,8 +264,9 @@
def parse_operator_code(self, code):
c = code.BuiltinCode()
if c not in builtin_operator_map:
- msg = "The input file contains operator code {} which is currently not supported".format(c)
- raise InputFileError(self.name, msg)
+ raise InputFileError(
+ self.name, f"The input file contains operator code '{c}' which is currently not supported"
+ )
op_type, ser = builtin_operator_map[c]
custom_code = None
if c == BuiltinOperator.CUSTOM:
diff --git a/ethosu/vela/tflite_writer.py b/ethosu/vela/tflite_writer.py
index f747d47..06026ba 100644
--- a/ethosu/vela/tflite_writer.py
+++ b/ethosu/vela/tflite_writer.py
@@ -21,6 +21,7 @@
from flatbuffers import encode
from flatbuffers.builder import UOffsetTFlags
+from .errors import VelaError
from .nn_graph import PassPlacement
from .operation import Op
from .tensor import MemType
@@ -44,7 +45,7 @@
def FinishWithFileIdentifier(self, rootTable, fid):
if fid is None or len(fid) != 4:
- raise Exception("fid must be 4 chars")
+ raise VelaError("FileIdentifier must be 4 chars")
flags = N.Uint8Flags
prepSize = 4
diff --git a/ethosu/vela/vela.py b/ethosu/vela/vela.py
index d27eef0..bfc76ec 100644
--- a/ethosu/vela/vela.py
+++ b/ethosu/vela/vela.py
@@ -55,7 +55,7 @@
nng = model_reader.read_model(input_name, model_reader_options)
if not nng:
- raise InputFileError(input_name, "input file could not be read")
+ raise InputFileError(input_name, "Input file could not be read")
if compiler_options.verbose_operators:
nng.print_operators()
@@ -364,7 +364,7 @@
if args.config is not None:
for filename in args.config:
if not os.access(filename, os.R_OK):
- raise InputFileError(filename, "File not found or is not readable.")
+ raise InputFileError(filename, "File not found or is not readable")
sys.setrecursionlimit(args.recursion_limit)
diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index fce17d1..188b16a 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -452,18 +452,14 @@
for weight_scale in weight_scales
]
else:
- raise UnsupportedFeatureError(
- "Compression of {} is not implemented; tensor: {}".format(ifm_dtype, tens.name)
- )
+ raise UnsupportedFeatureError(f"Compression of {ifm_dtype} is not implemented; Tensor: '{tens.name}'")
else:
if ifm_dtype == DataType.uint8:
scales = [np.double(ifm_scale * weight_scale * 0x3000) for weight_scale in weight_scales]
elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16:
scales = [(np.double(ifm_scale * 0x3000) * np.double(weight_scale)) for weight_scale in weight_scales]
else:
- raise UnsupportedFeatureError(
- "Compression of {} is not implemented; tensor: {}".format(ifm_dtype, tens.name)
- )
+ raise UnsupportedFeatureError(f"Compression of {ifm_dtype} is not implemented; Tensor: '{tens.name}'")
# quantise all of the weight scales into (scale_factor, shift)
if ifm_dtype == DataType.int16: