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efff1ff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | """Backend comparison: torch fp32 vs ONNX fp32 vs ONNX int8.
Answers the two deployment questions directly, with measurements:
Q1 Does ONNX Runtime beat PyTorch on CPU for this model?
Q2 Does int8 quantization change the answers?
Accuracy is compared against ground truth, not just "do the two agree", because
two backends can agree and both be wrong.
"""
from __future__ import annotations
import json
import os
import statistics
import sys
import time
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT / "src"))
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3")
import numpy as np
import torch
from docxextract import parsing
from docxextract.config import Settings
KEYS = ["INVOICE NO", "Invoice Date", "Total Amount", "PO Number",
"Payment Term", "GSTIN", "Vendor Id", "Subtotal"]
REPEATS = 3
def normalise(text: str) -> str:
import re
import string
text = text.lower()
text = "".join(ch for ch in text if ch not in set(string.punctuation))
text = re.sub(r"\b(a|an|the)\b", " ", text)
return " ".join(text.split())
class TorchBackend:
name = "torch_fp32"
def __init__(self, model_id: str, threads: int):
torch.set_num_threads(threads)
from transformers import pipeline
self.pipe = pipeline("document-question-answering", model=model_id,
device="cpu")
self.tokenizer = None
def answer(self, words, boxes, key):
result = self.pipe(image=None, question=key,
word_boxes=[[w, list(b)] for w, b in zip(words, boxes)],
top_k=1)
while isinstance(result, list):
result = result[0]
return str(result["answer"]).strip(), float(result["score"])
class OnnxBackend:
def __init__(self, model_id: str, path: Path, threads: int,
name: str = "onnx"):
import onnxruntime as ort
from transformers import AutoTokenizer
self.name = name
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
options = ort.SessionOptions()
options.intra_op_num_threads = threads
options.inter_op_num_threads = 1
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
self.session = ort.InferenceSession(
str(path), options, providers=["CPUExecutionProvider"])
def answer(self, words, boxes, key):
# text_pair must be a flat list of word strings, matching the
# pipeline's LayoutLM tokenisation path.
word_boxes = list(zip(words, boxes))
encoding = self.tokenizer(
text=key.split(), text_pair=list(words), is_split_into_words=True,
return_token_type_ids=True, truncation="only_second",
max_length=self.tokenizer.model_max_length)
sequence_ids = encoding.sequence_ids(0)
# This must mirror DocumentQuestionAnsweringPipeline.preprocess exactly:
# context token -> the word's real box
# SEP token -> [1000, 1000, 1000, 1000]
# everything else (question, CLS) -> [0, 0, 0, 0]
# Using zeros for SEP shifts the learned position embeddings and
# silently degrades answers, which is exactly what happens if this is
# copied from memory instead of the pipeline source.
sep_id = self.tokenizer.sep_token_id
bbox = []
for token_id, sid, wid in zip(encoding["input_ids"], sequence_ids,
encoding.word_ids(0)):
if sid == 1:
bbox.append(word_boxes[wid][1])
elif token_id == sep_id:
bbox.append([1000, 1000, 1000, 1000])
else:
bbox.append([0, 0, 0, 0])
feed = {
"input_ids": np.array([encoding["input_ids"]], dtype=np.int64),
"attention_mask": np.array([encoding["attention_mask"]], dtype=np.int64),
"token_type_ids": np.array([encoding["token_type_ids"]], dtype=np.int64),
"bbox": np.array([bbox], dtype=np.int64).reshape(1, -1),
}
start, end = self.session.run(None, feed)
start, end = start[0], end[0]
# Mirror the pipeline's p_mask: only context tokens may start/end a span.
for i, sid in enumerate(sequence_ids):
if sid != 1:
start[i] = end[i] = -1e4
s = int(np.argmax(start))
e = int(np.argmax(end))
if e < s:
e = s
# The pipeline returns the ORIGINAL word strings joined by spaces,
# not a decode of the subword ids. These differ for dates, currency
# symbols and hyphenated identifiers, so they must not be conflated.
word_ids = encoding.word_ids(0)
w_start, w_end = word_ids[s], word_ids[e]
if w_start is None or w_end is None:
return "", 0.0
answer = " ".join(words[w_start:w_end + 1])
return answer, float(np.exp(start[s]) * np.exp(end[e]))
def main() -> None:
settings = Settings()
threads = 8
model_id = settings.model_id
truths = json.loads((ROOT / "corpus" / "ground_truth.json").read_text())
backends: list[object] = [TorchBackend(model_id, threads)]
onnx_dir = ROOT / "artifacts" / "onnx" / model_id.replace("/", "__")
if (onnx_dir / "model.onnx").exists():
backends.append(OnnxBackend(model_id, onnx_dir / "model.onnx", threads,
"onnx_fp32"))
if (onnx_dir / "model_int8.onnx").exists():
backends.append(OnnxBackend(model_id, onnx_dir / "model_int8.onnx", threads,
"onnx_int8"))
print(f"model={model_id} threads={threads} "
f"documents={len(truths)} keys={len(KEYS)}\n")
results: dict[str, dict] = {b.name: {"correct": 0, "total": 0,
"times": [], "answers": {}}
for b in backends}
truth_map = {"INVOICE NO": "invoice_number", "Invoice Date": "invoice_date",
"Total Amount": "total", "PO Number": "po_number",
"Payment Term": "payment_terms", "GSTIN": "gstin",
"Vendor Id": "vendor_tax_id", "Subtotal": "subtotal"}
for record in truths:
data = Path(record["path"]).read_bytes()
parsed = parsing.parse(data, settings)
page = parsed.pages[0]
for key in KEYS:
gold = str(record.get(truth_map[key], ""))
for backend in backends:
backend.answer(page.words, page.boxes, key) # warm
started = time.perf_counter()
answer, _score = backend.answer(page.words, page.boxes, key)
elapsed = time.perf_counter() - started
slot = results[backend.name]
slot["times"].append(elapsed)
slot["answers"][(record["document_id"], key)] = answer
if normalise(answer) == normalise(gold):
slot["correct"] += 1
slot["total"] += 1
print("=" * 74)
print(f"{'backend':<14} {'ms/key':>9} {'speedup':>9} {'accuracy':>10} "
f"{'agreement w/ torch':>21}")
print("=" * 74)
baseline_ms = statistics.median(results["torch_fp32"]["times"]) * 1000
torch_answers = results["torch_fp32"]["answers"]
for backend in backends:
slot = results[backend.name]
ms = statistics.median(slot["times"]) * 1000
acc = slot["correct"] / slot["total"]
agree = sum(1 for k, v in slot["answers"].items()
if normalise(v) == normalise(torch_answers.get(k, "")))
total_keys = len(slot["answers"])
agree_str = f"{agree}/{total_keys}"
print(f"{backend.name:<14} {ms:>9.0f} {baseline_ms/ms:>8.2f}x "
f"{acc:>9.1%} {agree_str:>21}")
print("\nPER-KEY ACCURACY")
keys_hdr = f"{'backend':<14}" + "".join(f"{k[:13]:>15}" for k in KEYS)
print(keys_hdr)
for backend in backends:
slot = results[backend.name]
row = f"{backend.name:<14}"
for key in KEYS:
hits = total = 0
for record in truths:
gold = str(record.get(truth_map[key], ""))
pred = slot["answers"].get((record["document_id"], key), "")
total += 1
hits += normalise(pred) == normalise(gold)
row += f"{hits/total:>14.0%} "
print(row)
json.dump({name: {"median_ms": statistics.median(slot["times"]) * 1000,
"accuracy": slot["correct"] / slot["total"],
"n": slot["total"]}
for name, slot in results.items()},
(ROOT / "results" / "backend_comparison.json").open("w"),
indent=2)
if __name__ == "__main__":
main()
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