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003fe57 e845a2b 003fe57 e845a2b 003fe57 e845a2b 003fe57 e845a2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """GLiNER2.5-Decide on LiteRT with every trained head: gliner2's own schema handling and decoders over LiteRT graphs.
gliner2's span architecture runs five trained modules: the encoder, `span_rep`, `count_pred`, `count_embed` and the
classifier. This runtime is gliner2's SpanExtractor with those five replaced by LiteRT graphs; everything else (schema
building, the collator, span scoring, decoding, overlap rules, chunking for long text) is the library's own code.
from litert_span_runtime import LiteRTSpanExtractor
from gliner2 import Schema
model = LiteRTSpanExtractor("path/to/bundle")
model.classify_text("Please cancel my order.", {"intent": ["cancel", "refund", "track"]})
model.extract_entities("Alice founded Acme in Toronto.", ["person", "organization", "city"])
model.extract_relations("Alice founded Acme in Toronto.", ["founded", "located_in"])
Every public call of gliner2's extractor works (extract, batch_extract, extract_entities, classify_text, extract_json,
extract_relations and their batch_* and *_long forms). A request over 512 tokens is refused with a ValueError, never
truncated: use the *_long calls with a chunk_size that fits, e.g. extract_entities_long(text, labels, chunk_size=300).
Graphs: the encoder file's `e{N}` signatures (hidden states for N = 128, 160, 256, 512 tokens; requests over 512 tokens are
refused, use the library's *_long calls to chunk), and the heads file's `w{W}` (span representations), `count`,
`c{C}_m{M}` (count embeddings) and `k{K}` (classifier rows). The span file (`span=`: the same `w{W}`, `count` and `k{K}`,
built so LiteRT's GPU delegate compiles them) and the count file (`count=`: `c{C}_m{M}`) stand in for the heads file;
with both, the heads file is not loaded:
model = LiteRTSpanExtractor("path/to/bundle", span="gliner25_decide_span_wfp16.tflite",
count="gliner25_decide_count_wfp16.tflite") Needs gliner2==2.0.0 (no PyTorch weights are loaded), torch
(the library's tensor types), numpy and ai-edge-litert. The bundle folder holds the graphs, the float16 token table,
the tokenizer files and source/config.json (the checkpoint's configuration).
"""
from __future__ import annotations
import json
import re
from pathlib import Path
from types import SimpleNamespace
import numpy as np
import torch
from gliner2.inference.engine import SpanExtractor
from gliner2.models.base import load_extractor_tokenizer
from gliner2.processor import SchemaTransformer
TABLE = "gliner25_decide_embeddings_f16.bin"
class Model:
"""A LiteRT CompiledModel with every signature callable by name: model(signature, feed) -> {output: float32 array}."""
def __init__(self, path: Path, threads: int = 4, accelerator: str = "cpu", cache: str = ""):
from ai_edge_litert.compiled_model import CompiledModel, CpuOptions, GpuOptions, HardwareAccelerator, Options
if accelerator == "cpu":
options = Options(HardwareAccelerator.CPU, cpu_options=CpuOptions(num_threads=threads, xnnpack_weight_cache_path=cache))
else: # "gpu" (FP32) or "gpu-fp16"
options = Options(HardwareAccelerator.GPU, gpu_options=GpuOptions(enforce_f32=accelerator == "gpu", constant_tensor_sharing=True))
self.model = CompiledModel.from_file(str(path), options=options)
self.signatures, self.buffers = {}, {}
for index in range(len(self.model.get_signature_list())):
sig = self.model.get_signature_by_index(index)
key = sig["key"]
details = self.model.get_input_tensor_details(key)
self.signatures[key] = {
"index": index, "inputs": sig["inputs"], "outputs": sig["outputs"],
"in_shapes": {k: tuple(int(x) for x in v["shape"]) for k, v in details.items()},
"out_shapes": {k: tuple(int(x) for x in v["shape"]) for k, v in self.model.get_output_tensor_details(key).items()},
}
def keys(self, pattern: str) -> list[tuple[tuple[int, ...], str]]:
"""Signatures whose name matches `pattern`, with the pattern's integer groups, sorted."""
found = []
for key in self.signatures:
m = re.fullmatch(pattern, key)
if m:
found.append((tuple(int(g) for g in m.groups()), key))
return sorted(found)
def __call__(self, key: str, feed: dict) -> dict:
sig = self.signatures[key]
if key not in self.buffers:
self.buffers[key] = (self.model.create_input_buffers(sig["index"]), self.model.create_output_buffers(sig["index"]))
ins, outs = self.buffers[key]
for buffer, name in zip(ins, sig["inputs"]):
buffer.write(np.ascontiguousarray(np.asarray(feed[name], np.float32)))
self.model.run_by_index(sig["index"], ins, outs)
return {name: buffer.read(int(np.prod(sig["out_shapes"][name])), np.float32).reshape(sig["out_shapes"][name])
for buffer, name in zip(outs, sig["outputs"])}
def smallest(options: list[tuple[tuple[int, ...], str]], need: int, what: str) -> tuple[int, str]:
for (size, *_), key in options:
if size >= need:
return size, key
raise ValueError(f"{what} {need} exceeds the largest graph bucket {options[-1][0][0]}")
class Encoder(torch.nn.Module):
"""The encoder: token ids -> hidden states, through the host-mapped float16 table and the `e{N}` signatures."""
def __init__(self, model: Model, table: np.ndarray):
super().__init__()
self.model, self.table = model, table
self.buckets = model.keys(r"e(\d+)")
def forward(self, input_ids, attention_mask, **_):
batch, length = input_ids.shape
out = torch.zeros(batch, length, self.table.shape[1])
for b in range(batch):
n = int(attention_mask[b].sum())
size, key = smallest(self.buckets, n, "Request tokens")
ids = np.zeros(size, np.int64)
ids[:n] = input_ids[b, :n].numpy()
mask = np.zeros((1, size), np.float32)
mask[0, :n] = 1.0
hidden = self.model(key, {"inputs_embeds": self.table[ids].astype(np.float32)[None], "attention_mask": mask})["hidden_states"]
out[b, :n] = torch.from_numpy(hidden[0, :n])
return SimpleNamespace(last_hidden_state=out)
class SpanRep(torch.nn.Module):
"""span_rep: word states [B, L, D] -> span representations [B, L, max_width, D] (the library's span layout)."""
def __init__(self, model: Model):
super().__init__()
self.model = model
self.buckets = model.keys(r"w(\d+)")
def forward(self, h, span_idx=None):
batch, length, d = h.shape
rows = []
for b in range(batch):
size, key = smallest(self.buckets, length, "Text words")
words = np.zeros((1, size, d), np.float32)
words[0, :length] = h[b].numpy()
rows.append(torch.from_numpy(self.model(key, {"words": words})["span_rep"][0, :length]))
return torch.stack(rows, 0)
class CountPred(torch.nn.Module):
def __init__(self, model: Model):
super().__init__()
self.model = model
def forward(self, prompt):
return torch.from_numpy(self.model("count", {"prompt": prompt.numpy()})["count_logits"])
class CountEmbed(torch.nn.Module):
"""count_embed(fields [M, D], count) -> [count, M, D]: the smallest `c{C}_m{M}` that holds both, sliced."""
max_count = 20
def __init__(self, model: Model):
super().__init__()
self.model = model
self.buckets = model.keys(r"c(\d+)_m(\d+)")
def forward(self, fields, gold_count_val: int):
count, m = min(int(gold_count_val), self.max_count), fields.shape[0]
for (steps, width), key in sorted(self.buckets, key=lambda x: (x[0][0] * x[0][1], x[0])):
if steps >= count and width >= m:
padded = np.zeros((width, fields.shape[1]), np.float32)
padded[:m] = fields.numpy()
return torch.from_numpy(self.model(key, {"fields": padded})["struct_proj"][:count, :m])
raise ValueError(f"{m} fields with count {count} exceed the largest count graph")
class Classifier(torch.nn.Module):
"""classifier(states [n, D]) -> [n, 1], in chunks of the largest `k{K}` signature (the classifier is row-wise)."""
def __init__(self, model: Model):
super().__init__()
self.model = model
self.buckets = model.keys(r"k(\d+)")
def forward(self, states):
n, d = states.shape
out, start = [], 0
while start < n:
rows = min(n - start, self.buckets[-1][0][0])
size, key = smallest(self.buckets, rows, "Classifier rows")
padded = np.zeros((size, d), np.float32)
padded[:rows] = states[start:start + rows].numpy()
out.append(torch.from_numpy(self.model(key, {"states": padded})["logits"][:rows]))
start += rows
return torch.cat(out).reshape(n, 1)
class LiteRTSpanExtractor(SpanExtractor):
"""gliner2's SpanExtractor (schemas, collator, scoring and decoders) with every trained module on LiteRT."""
def __init__(self, bundle: str, encoder: str = "gliner25_decide_wfp16.tflite", heads: str = "gliner25_decide_heads_wfp16.tflite",
accelerator: str = "cpu", threads: int = 4, cache: str = "", span: str | None = None, count: str | None = None):
torch.nn.Module.__init__(self)
folder = Path(bundle)
config = json.loads((folder / "source" / "config.json").read_text())
self.config = SimpleNamespace(**config)
self.max_width = config["max_width"]
self.processor = SchemaTransformer(tokenizer=load_extractor_tokenizer(str(folder)), token_pooling=config.get("token_pooling", "first"))
self.strict_extraction = True
self._device_anchor = torch.nn.Parameter(torch.zeros(1), requires_grad=False) # the runtime reads device and dtype from parameters()
encoder_model = Model(folder / encoder, threads, accelerator, cache)
# The span file (span_rep, count prediction, classifier) and the count file (count_embed) stand in for the heads file.
heads_model = None if span and count else Model(folder / heads, threads)
span_model = Model(folder / span, threads) if span else heads_model
count_model = Model(folder / count, threads) if count else heads_model
hidden = encoder_model.signatures[encoder_model.keys(r"e(\d+)")[0][1]]["in_shapes"]["inputs_embeds"][2]
rows = (folder / TABLE).stat().st_size // (2 * hidden)
table = np.memmap(folder / TABLE, dtype="<f2", mode="r", shape=(rows, hidden))
self.encoder = Encoder(encoder_model, table)
self.span_rep = SpanRep(span_model)
self.count_pred = CountPred(span_model)
self.count_embed = CountEmbed(count_model)
self.classifier = Classifier(span_model)
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