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4a28d4d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 | """Test quant_policy=QuantPolicy.TURBO_QUANT (K=4bit, V=2bit mixed precision)
for PytorchEngine.
This module tests both functional correctness and accuracy of quant_policy=QuantPolicy.TURBO_QUANT against a non-
quantized (quant_policy=QuantPolicy.NONE) baseline.
"""
import gc
import pytest
import torch
from lmdeploy import GenerationConfig, PytorchEngineConfig, pipeline
from lmdeploy.messages import QuantPolicy, Response
# Use smaller model to avoid OOM when running both quant_policy=QuantPolicy.NONE
# and quant_policy=QuantPolicy.TURBO_QUANT
MODEL_ID = 'Qwen/Qwen3-8B'
# =============================================================================
# Shared Fixtures
# =============================================================================
@pytest.fixture(scope='session')
def model_id():
"""Model ID for testing."""
return MODEL_ID
@pytest.fixture(scope='session')
def pipe_no_quant(model_id):
"""Create pipeline without quantization (baseline).
This fixture has session scope to avoid reloading the model for each test. Caller is responsible for cleanup.
"""
engine_config = PytorchEngineConfig(
tp=1,
cache_max_entry_count=0.05,
quant_policy=QuantPolicy.NONE, # No quantization
)
pipe = pipeline(model_id, backend_config=engine_config, log_level='INFO')
yield pipe
# Cleanup
pipe.close()
del pipe
gc.collect()
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
torch.cuda.empty_cache()
@pytest.fixture(scope='session')
def pipe_quant_42(model_id):
"""Create pipeline with quant_policy=QuantPolicy.TURBO_QUANT.
This fixture has session scope to avoid reloading the model for each test. Caller is responsible for cleanup.
"""
engine_config = PytorchEngineConfig(
tp=1,
cache_max_entry_count=0.05,
quant_policy=QuantPolicy.TURBO_QUANT, # K=4bit, V=2bit mixed precision
)
pipe = pipeline(model_id, backend_config=engine_config, log_level='INFO')
yield pipe
# Cleanup
pipe.close()
del pipe
gc.collect()
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
torch.cuda.empty_cache()
# =============================================================================
# Basic Functional Tests (quant_policy=QuantPolicy.TURBO_QUANT only)
# =============================================================================
class TestQuantPolicy42Basic:
"""Basic functional tests for quant_policy=QuantPolicy.TURBO_QUANT.
These tests verify that the quantized model can perform basic inference without errors. They test single prompt,
batch prompts, and generation config.
"""
@pytest.fixture(scope='class')
def pipe(self):
"""Create pipeline with quant_policy=QuantPolicy.TURBO_QUANT."""
engine_config = PytorchEngineConfig(
tp=1,
cache_max_entry_count=0.1,
quant_policy=QuantPolicy.TURBO_QUANT,
)
pipe = pipeline(MODEL_ID, backend_config=engine_config, log_level='INFO')
yield pipe
pipe.close()
del pipe
gc.collect()
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
torch.cuda.empty_cache()
def test_infer_single_prompt(self, pipe):
"""Test single prompt inference with
quant_policy=QuantPolicy.TURBO_QUANT."""
prompt = 'Hello, how are you?'
response = pipe.infer(prompt, max_new_tokens=30)
assert isinstance(response, Response)
assert hasattr(response, 'text')
assert len(response.text) > 0
assert len(response.text.strip()) > 0
def test_infer_batch_prompts(self, pipe):
"""Test batch inference with quant_policy=QuantPolicy.TURBO_QUANT."""
prompts = ['What is AI?', 'Hello!']
responses = pipe.infer(prompts, max_new_tokens=20)
assert isinstance(responses, list)
assert len(responses) == len(prompts)
for resp in responses:
assert isinstance(resp, Response)
assert len(resp.text) > 0
def test_infer_with_generation_config(self, pipe):
"""Test inference with GenerationConfig."""
gen_config = GenerationConfig(max_new_tokens=20, temperature=0.7)
prompt = 'Tell me a short joke'
response = pipe.infer(prompt, gen_config=gen_config)
assert isinstance(response, Response)
assert len(response.text) > 0
# =============================================================================
# Accuracy Tests (quant_policy=QuantPolicy.NONE vs quant_policy=QuantPolicy.TURBO_QUANT)
# =============================================================================
class TestQuantPolicy42Accuracy:
"""Accuracy tests comparing quant_policy=QuantPolicy.TURBO_QUANT against
non-quantized baseline.
These tests verify the numerical accuracy/precision of quant_policy=QuantPolicy.TURBO_QUANT
(K=4bit, V=2bit mixed precision) by comparing against quant_policy=QuantPolicy.NONE.
Error thresholds are relaxed due to aggressive quantization:
- MAE < 0.1 on logits
- Max AE < 0.5 on logits
"""
def test_logits_accuracy(self, pipe_no_quant, pipe_quant_42):
"""Test logits accuracy by comparing output logits.
Compares logits between quantized and non-quantized models.
Uses deterministic generation settings for reproducibility.
Thresholds:
- Mean absolute error (MAE) < 0.1
- Max absolute error < 0.5
"""
gen_config = GenerationConfig(
max_new_tokens=0, # Required for logits output
temperature=0.0,
top_p=1.0,
top_k=1,
output_logits='all',
)
prompt = 'The capital of France is'
response_no_quant = pipe_no_quant.infer(prompt, gen_config=gen_config)
response_quant = pipe_quant_42.infer(prompt, gen_config=gen_config)
assert isinstance(response_no_quant, Response)
assert isinstance(response_quant, Response)
if response_no_quant.logits is not None and response_quant.logits is not None:
logits_no_quant = response_no_quant.logits
logits_quant = response_quant.logits
assert logits_no_quant.shape == logits_quant.shape, \
f'Logits shape mismatch: {logits_no_quant.shape} vs {logits_quant.shape}'
abs_error = (logits_no_quant - logits_quant).abs()
mean_abs_error = abs_error.mean().item()
max_abs_error = abs_error.max().item()
print('\nLogits accuracy metrics:')
print(f' Mean absolute error: {mean_abs_error:.6f}')
print(f' Max absolute error: {max_abs_error:.6f}')
assert mean_abs_error < 0.1, \
f'Mean absolute error {mean_abs_error:.6f} exceeds threshold 0.1'
assert max_abs_error < 0.5, \
f'Max absolute error {max_abs_error:.6f} exceeds threshold 0.5'
else:
pytest.skip('Logits not available for comparison')
def test_token_accuracy(self, pipe_no_quant, pipe_quant_42):
"""Test token-level accuracy by comparing output token IDs.
Checks that both models generate output and compares token match rate.
Note: With aggressive quantization (K=4bit, V=2bit), token match rate
can be low - this is expected behavior.
"""
gen_config = GenerationConfig(
max_new_tokens=20,
temperature=0.0,
top_p=1.0,
top_k=1,
)
prompt = 'Hello, how are you?'
response_no_quant = pipe_no_quant.infer(prompt, gen_config=gen_config)
response_quant = pipe_quant_42.infer(prompt, gen_config=gen_config)
assert isinstance(response_no_quant, Response)
assert isinstance(response_quant, Response)
tokens_no_quant = response_no_quant.token_ids
tokens_quant = response_quant.token_ids
min_len = min(len(tokens_no_quant), len(tokens_quant))
if min_len > 0:
matching_tokens = sum(1 for i in range(min_len)
if tokens_no_quant[i] == tokens_quant[i])
match_rate = matching_tokens / min_len
print('\nToken accuracy metrics:')
print(f' Baseline tokens: {len(tokens_no_quant)}')
print(f' Quantized tokens: {len(tokens_quant)}')
print(f' Matching tokens: {matching_tokens}/{min_len}')
print(f' Match rate: {match_rate:.2%}')
# Basic sanity check - both models should produce output
assert len(tokens_no_quant) > 0, 'Baseline produced no tokens'
assert len(tokens_quant) > 0, 'Quantized model produced no tokens'
else:
pytest.skip('No tokens generated for comparison')
def test_text_quality(self, pipe_no_quant, pipe_quant_42):
"""Test that quantized output is still meaningful text.
Verifies the quantized model produces coherent text output, even if not exactly matching the non-quantized
baseline.
"""
gen_config = GenerationConfig(
max_new_tokens=30,
temperature=0.7,
top_p=0.9,
)
prompt = 'Write a short story about a robot.'
response_no_quant = pipe_no_quant.infer(prompt, gen_config=gen_config)
response_quant = pipe_quant_42.infer(prompt, gen_config=gen_config)
assert isinstance(response_no_quant, Response)
assert isinstance(response_quant, Response)
assert len(response_no_quant.text.strip()) > 0, 'Baseline output is empty'
assert len(response_quant.text.strip()) > 0, 'Quantized output is empty'
print('\nText quality metrics:')
print(f' Baseline text length: {len(response_no_quant.text)}')
print(f' Quantized text length: {len(response_quant.text)}')
def test_logprobs_sanity(self, pipe_no_quant, pipe_quant_42):
"""Test that logprobs are reasonable when available."""
gen_config = GenerationConfig(
max_new_tokens=10,
temperature=0.0,
top_p=1.0,
top_k=1,
logprobs=1,
)
prompt = 'What is 2+2?'
response_no_quant = pipe_no_quant.infer(prompt, gen_config=gen_config)
response_quant = pipe_quant_42.infer(prompt, gen_config=gen_config)
assert isinstance(response_no_quant, Response)
assert isinstance(response_quant, Response)
if response_no_quant.logprobs is not None and response_quant.logprobs is not None:
print('\nLogprobs available for both models')
assert isinstance(response_no_quant.logprobs, list)
assert isinstance(response_quant.logprobs, list)
else:
print('\nLogprobs not available (this is expected for some configurations)')
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