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6c67d478e3
Add spec types, YAML parser support, and plugin stubs for Huffman and Pruning compression methods. The plugins raise CompressionError when invoked, to be replaced with working implementations later. BUG=part of #3256
130 lines
3.3 KiB
Python
130 lines
3.3 KiB
Python
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Compression specifications.
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This module provides tools for specifying how a model should be compressed,
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notably, a method for reading such a specification in YAML, e.g., from a file.
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Such 'specfiles' are written during model development to specify which tensors
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should be compressed, by what method, and according to what parameters.
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specfiles are read by the compression tool. They are not used by the TFLM
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interpreter.
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"""
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from dataclasses import dataclass
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import yaml
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EXAMPLE_YAML_SPEC = """
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tensors:
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- subgraph: 0
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tensor: 42
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compression:
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- lut:
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index_bitwidth: 4
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- subgraph: 0
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tensor: 55
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compression:
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- lut:
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index_bitwidth: 2
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""" # This example is checked in this module's unit test.
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class CompressionMethod:
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pass
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@dataclass
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class Tensor:
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"A compression specification for the indicated tensor."
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subgraph: int
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tensor: int
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compression: list[CompressionMethod]
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@dataclass
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class LookUpTableCompression(CompressionMethod):
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"""LUT compression using lookup tables.
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Attributes:
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index_bitwidth: Number of bits per index (1-7).
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"""
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index_bitwidth: int
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@dataclass
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class HuffmanCompression(CompressionMethod):
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"""Huffman compression using Xtensa-format decode tables.
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Supported tensor types: INT8, INT16 only.
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"""
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pass
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@dataclass
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class PruningCompression(CompressionMethod):
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"""Pruning (sparsity) compression.
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Supported tensor types: All TFLM tensor types.
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"""
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pass
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class ParseError(Exception):
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"Raised when the spec string cannot be parsed."
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def __init__(self, message="error parsing spec", wrapped_exception=None):
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super().__init__(f"{message}: {str(wrapped_exception)}")
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self.original_exception = wrapped_exception
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def _parse_compression_method(comp: dict) -> CompressionMethod:
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"""Parse a single compression method from YAML dict."""
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if "lut" in comp:
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return LookUpTableCompression(index_bitwidth=comp["lut"]["index_bitwidth"])
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elif "huffman" in comp:
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return HuffmanCompression()
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elif "pruning" in comp:
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return PruningCompression()
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else:
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raise ParseError(f"Unknown compression method: {list(comp.keys())}")
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def parse_yaml(y: str) -> list[Tensor]:
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"Parses a compression spec in a YAML string into its Python representation."
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try:
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config = yaml.safe_load(y)
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tensors = []
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for item in config["tensors"]:
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methods = []
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for comp in item["compression"]:
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methods.append(_parse_compression_method(comp))
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tensor = Tensor(
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subgraph=item["subgraph"],
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tensor=item["tensor"],
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compression=methods,
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)
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tensors.append(tensor)
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except ParseError:
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raise
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except Exception as e:
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raise ParseError() from e
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return tensors
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