File size: 6,397 Bytes
90d3e4b | 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 | """Opt-in checkpoint regression: JIFFY_GPU_TEST=1 python -m unittest discover -s tests."""
import base64
import io
import json
import os
import time
import unittest
from types import SimpleNamespace
import torch
from PIL import Image
from jiffy import DiffusionDecisions, Question
@unittest.skipUnless(os.environ.get("JIFFY_GPU_TEST") == "1", "opt-in H100 checkpoint test")
class SharedDocumentGpuTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
torch.set_num_threads(4)
cls.backend = DiffusionDecisions.from_backbone()
def compare(self, state, questions):
b = self.backend
contracts = [b.compile({"answer": q}) for q in questions]
prefix, ending = b.tokenize(state, SimpleNamespace(prompt=""), split_document=True)
for contract in contracts:
joined = prefix["input_ids"][0].tolist() + b.processor.tokenizer.encode(contract.prompt + ending, add_special_tokens=False)
self.assertEqual(joined, b.tokenize(state, contract)["input_ids"][0].tolist())
shared = b.shared_document(state, contracts)
started = time.perf_counter()
reference = [b.system_one(state, c) for c in contracts]
sequential_ms = (time.perf_counter() - started) * 1000
max_delta = 0
for actual, expected in zip(shared["results"], reference):
a = actual["answers"]["answer"]["probabilities"]
e = expected["answers"]["answer"]["probabilities"]
delta = max(abs(a[k] - e[k]) for k in a)
max_delta = max(max_delta, delta)
self.assertLess(delta, .03, "cache split/batch exceeded BF16 regression tolerance")
self.assertEqual(max(a, key=a.get), max(e, key=e.get))
alone = b.shared_document(state, contracts[:1], batch_size=1)
p = alone["results"][0]["answers"]["answer"]["probabilities"]
mixed = shared["results"][0]["answers"]["answer"]["probabilities"]
self.assertLess(max(abs(p[k] - mixed[k]) for k in p), .03)
self.assertEqual(shared["diagnostics"]["document_prefills"], 1)
print(json.dumps({"case": self._testMethodName, "shared": shared["diagnostics"],
"sequential_ms": sequential_ms, "max_probability_delta": max_delta}), flush=True)
return shared
def test_short_text_and_batch(self):
q = Question.noul("Is a refund requested?")
result = self.compare("Please refund my duplicate charge immediately.",
[q, Question.noul("Is a repair requested?"), q, q])
self.assertLess(result["diagnostics"]["branch_batches"], 4)
self.assertGreater(result["results"][0]["answers"]["answer"]["noul"], .9)
def test_image(self):
output = io.BytesIO()
Image.new("RGB", (224, 224), "red").save(output, format="PNG")
state = {"text": "Identify the image color.", "images": ["data:image/png;base64," + base64.b64encode(output.getvalue()).decode()]}
question = Question.choice("What color fills the image?", {"red": "Red", "blue": "Blue"})
result = self.compare(state, [question, Question.noul("Is the image red?")])
self.assertEqual(result["results"][0]["answers"]["answer"]["choice"], "red")
def test_32k_text(self):
state = "neutral " * 32000 + "\nPlease refund my duplicate charge immediately."
q = Question.noul("Is a refund requested?")
result = self.compare(state, [q, q, q, q])
self.assertGreater(result["diagnostics"]["document_tokens"], 31000)
self.assertGreater(result["results"][0]["answers"]["answer"]["noul"], .9)
def test_32k_image(self):
output = io.BytesIO()
Image.new("RGB", (224, 224), "red").save(output, format="PNG")
state = {"text": "neutral " * 31500 + "\nIdentify the image color.",
"images": ["data:image/png;base64," + base64.b64encode(output.getvalue()).decode()]}
q = Question.choice("What color fills the image?", {"red": "Red", "blue": "Blue"})
result = self.compare(state, [q, q])
self.assertGreater(result["diagnostics"]["document_tokens"], 31000)
self.assertEqual(result["results"][0]["answers"]["answer"]["choice"], "red")
def test_mixed_protocol_characterization(self):
from jiffy import JevProtocol
payload = {"model": "jev-latest", "state": {"message": "Please refund my duplicate charge immediately."},
"questions": {
"refund": {"type": "noul", "instructions": "Is a refund requested?"},
"team": {"type": "choice", "instructions": "Which team?",
"criteria": {"billing": None, "technical": "Software bugs"}},
"urgency": {"type": "score", "instructions": "How urgent is this?",
"criteria": [{"description": "No urgency expressed"}, ["Explicit urgency"]]}}}
actual = JevProtocol(self.backend).evaluate(payload)
reference = JevProtocol(self.backend, execution="sequential").evaluate(payload)
self.assertLess(actual["usage"]["input_tokens"], reference["usage"]["input_tokens"])
for name, a in actual["answers"].items():
e = reference["answers"][name]
if a["type"] == "noul":
self.assertAlmostEqual(a["noul"], e["noul"], delta=.03)
else:
delta = max(abs(a["probabilities"][k] - e["probabilities"][k]) for k in a["probabilities"])
# Observed Score drift is 0.0428 despite identical tokens. This is
# a characterization bound, not the stricter 0.03 release gate.
self.assertLess(delta, .05 if a["type"] == "score" else .03)
if a["type"] == "score":
print(json.dumps({"case": self._testMethodName, "score_probability_delta": delta,
"strict_0_03_gate_passed": delta < .03}), flush=True)
self.assertEqual(actual["answers"]["team"]["choice"], "billing")
self.assertEqual(actual["answers"]["urgency"]["legend"], reference["answers"]["urgency"]["legend"])
payload["questions"] = {"renamed": payload["questions"]["urgency"]}
alone = JevProtocol(self.backend).evaluate(payload)
self.assertEqual(alone["answers"]["renamed"], actual["answers"]["urgency"])
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