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https://github.com/vee1e/tflite-micro.git
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e0b2c281f2
Add decode module with DecodeType constants and DecodeCommonMetadata, per the TFLM DECODE Operator Design document. BUG=part of #3256
238 lines
7.3 KiB
Python
238 lines
7.3 KiB
Python
# Copyright 2026 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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"""DECODE compression module."""
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# Implements the DECODE operator compression scheme described in the
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# "TFLM DECODE Operator Design" document, revised May 20, 2025.
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#
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# The DECODE operator transforms an encoded tensor, alongside a paired
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# ancillary data tensor, into a tensor ready for use as input to any
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# operator. For example, an encoded tensor might contain compressed
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# data, while the paired ancillary data tensor holds the information
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# necessary for decompression. The DECODE operator's output is a fully
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# decompressed tensor.
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#
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# DECODE operators are inserted into the TfLite model subgraph
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# immediately before each operation that uses a decodable tensor as
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# input.
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#
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# Ancillary Data Tensor
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#
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# The ancillary data tensor contains the information necessary for
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# decoding. It begins with a 16-byte DECODE Common Metadata (DCM)
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# header, followed by decode-type-specific ancillary data.
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#
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# DECODE Common Metadata (DCM)
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#
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# Byte 0: Decode type
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# 0-127: TFLM-supported decode operations (see below)
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# 128-255: Custom operations requiring application-registered
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# handlers
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#
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# Supported decode types:
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#
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# 0: LUT decompression
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# All TFLM tensor types supported in reference and optimized
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# code.
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#
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# 1: Huffman decompression using Xtensa format decode tables
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# INT8 and INT16 tensor types only, in reference and optimized
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# code.
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#
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# 2: Pruning decompression
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# All TFLM tensor types supported in reference and optimized
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# code.
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#
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# 3-127: Reserved
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#
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# 128-255: Custom decode types
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# Requires user-supplied encoding module and decoding ancillary
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# data.
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#
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# Byte 1: DCM version (currently 1)
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#
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# Bytes 2-3: Reserved
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#
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# Bytes 4-15: User-defined
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# Used by TFLM decode types to avoid requiring additional alignment
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# of metadata or ancillary data.
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#
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# The 16-byte DCM size ensures that subsequent metadata and ancillary
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# data are 128-bit aligned, which is required for some optimized
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# decoding operations such as Xtensa LUT decompression.
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#
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# For TFLM decode types, ancillary data starts immediately after the
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# DCM. For custom decode types, the location is determined by
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# user-defined metadata.
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from dataclasses import dataclass
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from typing import Protocol
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class DecodeType:
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"""Decode operation type (0-255).
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Use predefined constants for built-in types or DecodeType.custom()
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for custom types:
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DecodeType.LUT # 0
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DecodeType.HUFFMAN # 1
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DecodeType.PRUNING # 2
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DecodeType.custom(200) # Custom type 128-255
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"""
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# Built-in decode types (class variables set after class definition)
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LUT: 'DecodeType'
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HUFFMAN: 'DecodeType'
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PRUNING: 'DecodeType'
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def __init__(self, code: int, name: str = None):
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"""Initialize DecodeType.
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Args:
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code: Integer code 0-255
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name: Optional name for the type. If not provided:
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- Codes 0-127: Named "TYPE_{code}"
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- Codes 128-255: Named "CUSTOM_{code}"
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"""
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if not 0 <= code <= 255:
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raise ValueError(f"Decode type must be 0-255, got {code}")
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self.code = code
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# Auto-generate name if not provided
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if name is None:
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self.name = f"CUSTOM_{code}" if code >= 128 else f"TYPE_{code}"
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else:
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self.name = name
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self._is_custom = code >= 128
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@property
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def is_custom(self) -> bool:
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"""True if this is a custom decode type (128-255)."""
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return self._is_custom
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@classmethod
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def custom(cls, code: int) -> 'DecodeType':
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"""Create custom decode type (128-255).
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Args:
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code: Integer code 128-255
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Returns:
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DecodeType with name CUSTOM_{code}
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"""
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if not 128 <= code <= 255:
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raise ValueError(f"Custom decode type must be 128-255, got {code}")
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return cls(code)
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def __int__(self):
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"""Convert to integer for serialization."""
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return self.code
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def __eq__(self, other):
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if isinstance(other, DecodeType):
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return self.code == other.code
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return self.code == other
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def __repr__(self):
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return f"DecodeType.{self.name}({self.code})"
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# Define built-in decode type constants
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DecodeType.LUT = DecodeType(0, "LUT")
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DecodeType.HUFFMAN = DecodeType(1, "HUFFMAN")
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DecodeType.PRUNING = DecodeType(2, "PRUNING")
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@dataclass
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class DecodeCommonMetadata:
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"""16-byte DECODE Common Metadata (DCM) header.
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Attributes:
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decode_type: Decode operation type. Use DecodeType constants or
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DecodeType.custom(code) for custom types.
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version: DCM version (currently 1).
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user_data: 12 bytes of user-defined data (bytes 4-15 of DCM). Used by TFLM
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decode types to avoid requiring additional alignment of metadata
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or ancillary data.
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"""
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decode_type: DecodeType
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version: int = 1
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user_data: bytes = b'\x00' * 12
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def to_bytes(self) -> bytes:
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"""Serialize DCM to 16-byte sequence."""
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decode_code = int(self.decode_type)
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if len(self.user_data) < 12:
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# Pad with zeros if user_data is too short
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user_data = self.user_data + b'\x00' * (12 - len(self.user_data))
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else:
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user_data = self.user_data[:12]
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result = bytearray(16)
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result[0] = decode_code
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result[1] = self.version
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# bytes 2-3 remain zero (reserved)
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result[4:16] = user_data
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return bytes(result)
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class AncillaryDataSerializer(Protocol):
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"""Protocol for objects that can serialize ancillary data."""
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def to_bytes(self) -> bytes:
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...
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@dataclass
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class AncillaryDataTensor:
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"""Complete Ancillary Data Tensor (ADT): DCM + decode-type-specific data.
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The ADT is stored as a buffer in the TFLite model. It begins with a 16-byte
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DCM header, followed by decode-type-specific ancillary data.
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Attributes:
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dcm: The DECODE Common Metadata header.
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ancillary_data: The decode-type-specific ancillary data, either as raw bytes
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or as an object implementing the AncillaryDataSerializer
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protocol. May be None if only the DCM is needed.
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"""
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dcm: DecodeCommonMetadata
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ancillary_data: AncillaryDataSerializer | bytes | None = None
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def with_ancillary_data(
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self, data: AncillaryDataSerializer | bytes) -> 'AncillaryDataTensor':
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"""Create new ADT with ancillary data added.
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Args:
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data: Ancillary data to add, either as raw bytes or as an object
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implementing AncillaryDataSerializer.
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Returns:
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New AncillaryDataTensor with the specified ancillary data.
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"""
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return AncillaryDataTensor(self.dcm, data)
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def to_bytes(self) -> bytes:
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"""Serialize entire ADT to bytes.
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Returns:
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Byte sequence containing DCM followed by ancillary data (if present).
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"""
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dcm_bytes = self.dcm.to_bytes()
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if self.ancillary_data is None:
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return dcm_bytes
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if isinstance(self.ancillary_data, bytes):
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return dcm_bytes + self.ancillary_data
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return dcm_bytes + self.ancillary_data.to_bytes()
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