Serialize NamedDataStoreOutput into PTD.

exir/_serialize/_serialize.py

@@ -6,13 +6,14 @@
 
 # pyre-strict
 
-from typing import Dict, Optional, Tuple
+from typing import Dict, Optional, Set, Tuple
 
 from executorch.exir._serialize import _serialize_pte_binary
 
 from executorch.exir._serialize._cord import Cord
 from executorch.exir._serialize._named_data_store import NamedDataStoreOutput
 from executorch.exir._serialize.data_serializer import (
+    DataEntry,
     DataPayload,
     DataSerializer,
     TensorEntry,
@@ -74,39 +75,59 @@ def serialize_for_executorch(
                         tensor.extra_tensor_info.fully_qualified_name
                     ] = TensorLayout(tensor.scalar_type, tensor.sizes, tensor.dim_order)
 
+    if len(fqn_to_tensor_layout) == 0 and (
+        named_data is None or len(named_data.external_data) == 0
+    ):
+        return pte, ptd_files
+
+    # Consolidate tensors and opaque data with the same external tag so they
+    # can be saved to the same PTD.
+    all_external_tags: Set[str] = set()
+    if named_data is not None and len(named_data.external_data) > 0:
+        assert (
+            len(named_data.buffers) > 0
+        ), "External data exists, but there are no buffers provided."
+        all_external_tags = set(named_data.external_data.keys())
+
     if len(fqn_to_tensor_layout) > 0:
         # emitter_output.external_constant_map contains the mapping from
         # {file: {fqn: index into external_constant_buffer}}
         # Contains the locations of the tensor buffers, and must be non-empty
         # if there are external tensors to serialize.
-        assert emitter_output.external_constant_map is not None
-        for (
-            filename,
-            fqn_to_index,
-        ) in (
-            # pyre-ignore Undefined attribute [16]: Optional type has no attribute `items`.
-            emitter_output.external_constant_map.items()
-        ):
-            # Create a TensorEntry for each external tensor.
-            fqn_to_tensor_entry: Dict[str, TensorEntry] = {}
-            for fqn, index in fqn_to_index.items():
-                assert fqn in fqn_to_tensor_layout
-                fqn_to_tensor_entry[fqn] = TensorEntry(
-                    buffer_index=index,
-                    layout=fqn_to_tensor_layout[fqn],
-                )
-
-            ptd_files[filename] = data_serializer.serialize(
-                DataPayload(
-                    buffers=emitter_output.external_constant_buffer,
-                    fqn_to_tensor=fqn_to_tensor_entry,
-                )
+        assert (
+            emitter_output.external_constant_map is not None
+        ), "External exists, but there are no buffers provided."
+        all_external_tags = all_external_tags | set(
+            emitter_output.external_constant_map.keys()
+        )
+
+    for tag in all_external_tags:
+        fqn_to_tensor_entry: Dict[str, TensorEntry] = {}
+        # pyre-ignore[16]: Undefined attribute: `Optional` has no attribute `get`.
+        fqn_to_index = emitter_output.external_constant_map.get(tag, {})
+        # Create a TensorEntry for each external tensor.
+        for fqn, index in fqn_to_index.items():
+            assert fqn in fqn_to_tensor_layout
+            fqn_to_tensor_entry[fqn] = TensorEntry(
+                buffer_index=index,
+                layout=fqn_to_tensor_layout[fqn],
             )
 
-    if named_data is None or len(named_data.external_data) == 0:
-        return pte, ptd_files
+        # Extract external data.
+        key_to_data: Dict[str, DataEntry] = {}
+        # pyre-ignore[16]: Undefined attribute: `Optional` has no attribute `get`.
+        key_to_buffer_index = named_data.external_data.get(tag, {})
+        for key, index in key_to_buffer_index.items():
+            # pyre-ignore[16]: Undefined attribute: `Optional` has no attribute `buffers`.
+            key_to_data[key] = DataEntry(index, named_data.buffers[index].alignment)
 
-    if len(named_data.buffers) == 0:
-        raise RuntimeError("External data exists, but there are no buffers provided.")
+        # Serialize into PTD file.
+        ptd_files[tag] = data_serializer.serialize(
+            DataPayload(
+                buffers=emitter_output.external_constant_buffer,
+                fqn_to_tensor=fqn_to_tensor_entry,
+                key_to_data=key_to_data,
+            )
+        )
 
     return pte, ptd_files

exir/_serialize/data_serializer.py

@@ -38,6 +38,21 @@ class TensorEntry:
     layout: TensorLayout
 
 
+@dataclass
+class DataEntry:
+    """Represents a single blob in `DataPayload`, specifying its location
+    and metadata.
+
+    Attributes:
+       buffer_index: The index inside `DataPayload.buffers` that this
+            DataEntry refers to.
+       alignment: The alignment of the data.
+    """
+
+    buffer_index: int
+    alignment: int
+
+
 @dataclass
 class DataPayload:
     """Contains the data and metadata required for serialization.
@@ -49,10 +64,12 @@ class DataPayload:
     Attributes:
         buffers: a sequence of tensor buffers.
         fqn_to_tensor: a map from fully qualified names to serializable tensors.
+        key_to_data: a map from unique keys to serializable opaque data.
     """
 
     buffers: Sequence[bytes]
     fqn_to_tensor: Dict[str, TensorEntry]
+    key_to_data: Dict[str, DataEntry]
 
 
 class DataSerializer(ABC):

extension/flat_tensor/serialize/serialize.py

@@ -7,6 +7,7 @@
 # pyre-strict
 
 import json
+import math
 import os
 import tempfile
 from dataclasses import dataclass
@@ -19,6 +20,7 @@
 from executorch.exir._serialize._flatbuffer import _flatc_compile, _flatc_decompile
 from executorch.exir._serialize._program import _insert_flatbuffer_header
 from executorch.exir._serialize.data_serializer import (
+    DataEntry,
     DataPayload,
     DataSerializer,
     TensorEntry,
@@ -29,6 +31,7 @@
 from executorch.extension.flat_tensor.serialize.flat_tensor_schema import (
     DataSegment,
     FlatTensor,
+    NamedData,
     TensorMetadata,
 )
 
@@ -202,6 +205,24 @@ def to_bytes(self) -> bytes:
         return data
 
 
+@dataclass
+class AlignedData:
+    """
+    Holds data that should be aligned, for serialization.
+
+    Attributes:
+        data: The data to serialize, as a cord.
+        alignment: The alignment required for the data.
+    """
+
+    data: Cord
+    alignment: int
+
+    def __init__(self, data: Cord, alignment: Optional[int] = None) -> None:
+        self.data = data
+        self.alignment = alignment or 1
+
+
 def _get_extended_header(flat_tensor_data: bytes) -> Optional[FlatTensorHeader]:
     """Returns the extended header of the flat_tensor data, if present and valid."""
     try:
@@ -216,7 +237,7 @@ def _get_extended_header(flat_tensor_data: bytes) -> Optional[FlatTensorHeader]:
 def _extract_tensors(
     fqn_to_tensor: Dict[str, TensorEntry],
     buffers: Sequence[bytes],
-    segments: List[Cord],
+    segments: List[AlignedData],
     tensor_alignment: int,
 ) -> List[TensorMetadata]:
     """Places tensors into a single segment, aligned to tensor_alignment within
@@ -265,10 +286,43 @@ def _extract_tensors(
                 offset=offset,
             )
         )
-    segments.append(tensor_data)
+    segments.append(AlignedData(tensor_data))
     return tensors
 
 
+def _extract_named_data(
+    key_to_data: Dict[str, DataEntry],
+    buffers: Sequence[bytes],
+    segments: List[AlignedData],
+) -> List[NamedData]:
+    """Places named data into segments and record the alignment for each.
+
+    Args:
+        key_to_data: A map from keys to opaque data entries.
+        buffers: A sequence of buffers holding opaque blob data.
+        segments: A list of segments to append data to. Modified in-place.
+
+    Returns:
+        A list of NamedData describing the offsets to the opaque blob data.
+    """
+
+    # Map from buffer_idx to segment_idx.
+    segment_index_map: Dict[int, int] = {}
+
+    named_data: List[NamedData] = []
+    for key, data_entry in key_to_data.items():
+        buffer_idx = data_entry.buffer_index
+        segment_index = segment_index_map.get(buffer_idx, None)
+        if segment_index is None:
+            segment_index = len(segments)
+            segment_index_map[buffer_idx] = segment_index
+            segments.append(
+                AlignedData(Cord(buffers[buffer_idx]), data_entry.alignment)
+            )
+        named_data.append(NamedData(key=key, segment_index=segment_index))
+    return named_data
+
+
 class FlatTensorSerializer(DataSerializer):
     """A concrete implementation of the DataSerializer interface that
     serializes and deserializes data to/from the FlatTensor format.
@@ -289,35 +343,37 @@ def serialize(
     ) -> Cord:
         """Serializes a list of tensors and named data into a blob."""
 
-        segments: List[Cord] = []
+        segments: List[AlignedData] = []
         tensors = _extract_tensors(
             data.fqn_to_tensor,
             data.buffers,
             segments,
             self.config.tensor_alignment,
         )
+        named_data = _extract_named_data(data.key_to_data, data.buffers, segments)
 
         data_segments: List[DataSegment] = []
-        segment_data = Cord()
+        aggregated_segment_data = Cord()
         for segment in segments:
             prev_end = (
                 (data_segments[-1].offset + data_segments[-1].size)
                 if data_segments
                 else 0
             )
+            alignment = math.lcm(self.config.segment_alignment, segment.alignment)
             data_segments.append(
                 DataSegment(
-                    offset=aligned_size(prev_end, self.config.segment_alignment),
-                    size=len(segment),
+                    offset=aligned_size(prev_end, alignment),
+                    size=len(segment.data),
                 )
             )
-            # Pad segment_data to segment alignment.
+            # Pad aggregated_segment_data to segment alignment.
             segment_pad_length = padding_required(
-                len(segment_data), self.config.segment_alignment
+                len(aggregated_segment_data), alignment
             )
             if segment_pad_length > 0:
-                segment_data.append(b"\x00" * segment_pad_length)
-            segment_data.append(segment)
+                aggregated_segment_data.append(b"\x00" * segment_pad_length)
+            aggregated_segment_data.append(segment.data)
 
         # Create FlatTensor, which describes of the contents of the file and
         # points to all the data segments. It will be serialized to flatbuffer.
@@ -326,7 +382,7 @@ def serialize(
             tensor_alignment=self.config.tensor_alignment,
             tensors=tensors,
             segments=data_segments,
-            named_data=[],
+            named_data=named_data,
         )
 
         flatbuffer_payload = _serialize_to_flatbuffer(flat_tensor)
@@ -351,7 +407,7 @@ def serialize(
             flatbuffer_offset=padded_header_length,
             flatbuffer_size=len(flatbuffer_payload),
             segment_base_offset=segment_base_offset,
-            segment_data_size=len(segment_data),
+            segment_data_size=len(aggregated_segment_data),
         ).to_bytes()
 
         # Pad header and payload to segment alignment.
@@ -371,15 +427,15 @@ def serialize(
         assert eh.flatbuffer_size == original_flatbuffer_payload_size
         assert eh.segment_base_offset == segment_base_offset
         assert eh.flatbuffer_offset == padded_header_length
-        assert eh.segment_data_size == len(segment_data)
+        assert eh.segment_data_size == len(aggregated_segment_data)
 
         del header_data
         del flatbuffer_payload
 
         # Place everything into one segment.
         payload = Cord()
         payload.append(injected_flatbuffer_data)
-        payload.append(segment_data)
+        payload.append(aggregated_segment_data)
 
         return payload