361 lines
13 KiB
Python
361 lines
13 KiB
Python
import io
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import os
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import re
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import unittest
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from sglang.test.ci.ci_register import register_amd_ci
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register_amd_ci(est_time=106, suite="stage-b-test-1-gpu-small-amd-mi35x")
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import os
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import time
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from types import SimpleNamespace
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import requests
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import torch
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from sglang.srt.utils import kill_process_tree
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from sglang.srt.utils.common import is_cuda_alike, is_gfx95_supported
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from sglang.test.few_shot_gsm8k import run_eval
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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is_in_ci,
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popen_launch_server,
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)
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class TestOnlineQuantizationMemoryLoad(CustomTestCase):
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runner_args = []
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environment = {}
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@classmethod
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def setUpClass(cls):
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if torch.cuda.device_count() < cls.tp:
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raise unittest.SkipTest(
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f"test requires {cls.tp} devices, only {torch.cuda.device_count()} are available."
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)
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if not is_gfx95_supported():
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raise unittest.SkipTest(
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"online MXFP4 quantization requires an AMD ROCm device with "
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"FP4 hardware support (gfx95x, e.g. MI355x)"
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)
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.stdout = io.StringIO()
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cls.stderr = io.StringIO()
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--quantization",
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"quark_mxfp4",
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# `context-length` limitation required for Qwen MOE model
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# TODO: Remove once https://github.com/sgl-project/sglang/pull/18255 and https://github.com/sgl-project/sglang/pull/18263 are merged.
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"--context-length",
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"3000",
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"--tensor-parallel-size",
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cls.tp if hasattr(cls, "tp") else "1",
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"--log-level",
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"debug",
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*cls.runner_args,
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],
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return_stdout_stderr=(cls.stdout, cls.stderr),
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)
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cls.original_envs = {}
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for env_name, env_value in cls.environment.items():
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original_env = os.environ.get(env_name, None)
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if original_env is not None:
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cls.original_envs[env_name] = os.environ.get(env_name, None)
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os.environ[env_name] = env_value
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url = cls.base_url + "/health"
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timeout = DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
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start_time = time.perf_counter()
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while True:
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try:
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response = requests.get(url)
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if response.status_code == 200:
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print(f"Server {url} is ready")
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break
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except Exception:
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pass
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if time.perf_counter() - start_time > timeout:
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raise RuntimeError(f"Server {url} failed to start in {timeout}s")
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time.sleep(1)
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# Keep the raw server for memory numbers, which are parsed lazily by
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# _test_peak_memory so subclasses that don't test memory (e.g. the
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# NVFP4->MXFP4 accuracy-only class) don't require these log lines.
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cls.combined_output = cls.stdout.getvalue() + cls.stderr.getvalue()
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@classmethod
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def _extract_peak_memory_before_load(cls, log_output):
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"""Extract peak GPU memory value from log output."""
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# Search for the log message pattern
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pattern = r"Peak GPU memory before loading weights:\s+([\d.]+)\s+GiB"
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match = re.search(pattern, log_output)
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if match:
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return match.group(1)
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return None
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@classmethod
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def _extract_memory_increase_load_weights(cls, log_output):
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"""Extract memory increase during load_weights call."""
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# Signed: the value is (free_before - free_after) around load_weights.
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# When the on-device source representation is larger than the loaded
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# result (e.g. requantizing to a more compact format), loading frees
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# net memory and the reported increase is negative.
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pattern = r"Memory increase during load_weights:\s+(-?[\d.]+)\s+GiB"
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match = re.search(pattern, log_output)
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if match:
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return match.group(1)
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return None
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@classmethod
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def tearDownClass(cls):
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for env_name, env_value in cls.original_envs.items():
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os.environ[env_name] = env_value
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kill_process_tree(cls.process.pid)
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cls.stdout.close()
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cls.stderr.close()
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def _test_peak_memory(
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self, threshold, test_start: bool, add_peak_memory_before_load: bool
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):
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"""Helper method to test peak memory against a threshold."""
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if not is_cuda_alike():
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self.skipTest("not is_cuda_alike")
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peak_memory_before_load = self._extract_peak_memory_before_load(
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self.combined_output
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)
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if not peak_memory_before_load:
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raise ValueError("Should have found peak memory")
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peak_memory_before_load = float(peak_memory_before_load)
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memory_increase_load_weights = self._extract_memory_increase_load_weights(
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self.combined_output
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)
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if not memory_increase_load_weights:
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raise ValueError("Should have found memory increase in load_weights")
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memory_increase_load_weights = float(memory_increase_load_weights)
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# NOTE: We can not simply rely on peak memory after `load_weights` as functions used
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# in-between (e.g. NVFP4->MXFP4 requantization) during weight loading may have a higher peak memory footprint
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# than simply the allocated weights.
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if add_peak_memory_before_load:
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reference_gib = memory_increase_load_weights + peak_memory_before_load
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else:
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reference_gib = memory_increase_load_weights
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assert reference_gib < threshold
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if test_start:
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# Weights initialized on meta device (not for dense BF16->MXFP4)
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assert peak_memory_before_load < 5
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def _test_gsm8k(self, accuracy_threshold):
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"""Helper method to test GSM8K accuracy against a threshold."""
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args = SimpleNamespace(
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num_shots=8,
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data_path=None,
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num_questions=500,
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max_new_tokens=512,
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parallel=128,
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host="http://127.0.0.1",
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port=int(self.base_url.split(":")[-1]),
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)
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metrics = run_eval(args)
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print(f"{metrics=}")
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self.assertGreater(metrics["accuracy"], accuracy_threshold)
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class TestOnlineQuantizationMemoryLoadDense(TestOnlineQuantizationMemoryLoad):
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model = "Qwen/Qwen3-8B"
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tp = 1
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def test_peak_memory(self):
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# Original Qwen/Qwen3-8B BF16 model: 15.268 GiB
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self._test_peak_memory(
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threshold=6, test_start=False, add_peak_memory_before_load=True
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)
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def test_gsm8k(self):
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# Original Qwen/Qwen3-8B reference accuracy: ~0.92
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self._test_gsm8k(accuracy_threshold=0.85)
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class TestOnlineQuantizationMemoryLoadMOE(TestOnlineQuantizationMemoryLoad):
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# Unfortunately, smaller models as Qwen/Qwen1.5-MoE-A2.7B or ibm-granite/granite-3.0-3b-a800m-base currently crash in AITER:
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# - Qwen/Qwen1.5-MoE-A2.7B => K // 2 = 704 as intermediate size, not multiple of 128.
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# - ibm-granite/granite-3.0-3b-a800m-base: dtype issue with fp16 in AITER MOE MLP activation
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# so using a large model here.
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model = "Qwen/Qwen3-30B-A3B-Instruct-2507"
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tp = 1
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# TODO: test TP>=2 with an other model (Qwen/Qwen3-30B-A3B-Instruct-2507 crashes in this case as 768/2 = 384, and 384/32 = 12 not divisible by BLOCK_SIZE_N=8. in fused_dynamic_mxfp4_quant_moe_sort.
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def test_peak_memory(self):
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# Original Qwen/Qwen3-30B-A3B-Instruct-2507 BF16 model: 56.940 GiB
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self._test_peak_memory(
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threshold=17, test_start=False, add_peak_memory_before_load=True
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)
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def test_gsm8k(self):
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# Original Qwen/Qwen3-30B-A3B-Instruct-2507 reference accuracy: 0.94
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self._test_gsm8k(accuracy_threshold=0.89)
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class TestNVFP4ToMXFP4MOETP1(TestOnlineQuantizationMemoryLoad):
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# ModelOpt NVFP4 export (quant_method="modelopt", quant_algo="NVFP4") =>
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# Nvfp4SourceConfig(). Exercises the NVFP4 -> MXFP4 MoE requantization path:
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# the per-expert dequantize_nvfp4 + dynamic_mxfp4_quant requant, and the w13
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# gate/up weight_scale_2 split in _requantize_nvfp4_to_mxfp4.
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model = "nvidia/Qwen3-30B-A3B-NVFP4" # NVFP4 model
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tp = 1
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def test_gsm8k(self):
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# Requantized NVFP4 -> MXFP4 observed accuracy: ~0.88
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# (BF16 Qwen/Qwen3-30B-A3B reference: ~0.94).
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self._test_gsm8k(accuracy_threshold=0.85)
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@unittest.skipIf(is_in_ci(), "local test only")
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class TestDeepSeekR10528NVFP4ToMXFP4(TestOnlineQuantizationMemoryLoad):
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# NVFP4 to MXFP4 online requantization for DeepSeek-R1-0528-NVFP4 on TP=8.
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# Exercises the MLA attention path (attention_backend=aiter), multi-threaded
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# weight loading, and the per-expert NVFP4 MoE requantization path.
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model = "nvidia/DeepSeek-R1-0528-NVFP4" # NVFP4 model
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tp = 8
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runner_args = [
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"--attention-backend",
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"aiter",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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]
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def test_gsm8k(self):
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# Requantized NVFP4 -> MXFP4 observed accuracy: ~0.95.
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self._test_gsm8k(accuracy_threshold=0.90)
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class TestFP8ToMXFP4DenseTP1(TestOnlineQuantizationMemoryLoad):
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tp = 1
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model = "Qwen/Qwen3-8B-FP8"
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def test_peak_memory(self):
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# Original Qwen/Qwen3-8B-FP8 model: 8.801 GiB (TP=1, peak_memory_before_load)
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self._test_peak_memory(
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threshold=6.5, test_start=False, add_peak_memory_before_load=True
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)
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def test_gsm8k(self):
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# Original Qwen/Qwen3-8B-FP8 reference accuracy: ~0.92
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self._test_gsm8k(accuracy_threshold=0.868)
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class TestFP8ToMXFP4DenseTP2(TestOnlineQuantizationMemoryLoad):
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tp = 2
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model = "Qwen/Qwen3-8B-FP8"
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def test_peak_memory(self):
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# Original Qwen/Qwen3-8B-FP8 model: 4.663 GiB (TP=2, peak_memory_before_load)
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self._test_peak_memory(
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threshold=4.2, test_start=False, add_peak_memory_before_load=True
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)
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def test_gsm8k(self):
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# Original Qwen/Qwen3-8B-FP8 reference accuracy: ~0.92
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self._test_gsm8k(accuracy_threshold=0.868)
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class TestFP8ToMXFP4MOETP1(TestOnlineQuantizationMemoryLoad):
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model = "Qwen/Qwen3-30B-A3B-Instruct-2507-FP8" # FP8 model
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tp = 1
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def test_peak_memory(self):
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# Original Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 model: 29.103 GiB (TP=1, peak_memory_before_load)
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self._test_peak_memory(
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threshold=18.5, test_start=False, add_peak_memory_before_load=True
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)
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def test_gsm8k(self):
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# Original Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 reference accuracy: ~0.948
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self._test_gsm8k(accuracy_threshold=0.92)
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@unittest.skipIf(is_in_ci(), "local test only")
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class TestDeepSeekFP8ToMXFP4(TestOnlineQuantizationMemoryLoad):
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# Loading should take ~51.65 seconds on TP=8 on MI355X.
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# model = "deepseek-ai/DeepSeek-V3.2" # FP8 model
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model = "deepseek-ai/DeepSeek-V3.2"
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tp = 8
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def test_peak_memory(self):
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# Original deepseek-ai/DeepSeek-V3.2 model: 80.366 GiB (TP=8, peak_memory_before_load)
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self._test_peak_memory(
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threshold=70, test_start=True, add_peak_memory_before_load=False
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) # TP=8
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def test_gsm8k(self):
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# Original deepseek-ai/DeepSeek-V3.2 reference accuracy: ~0.948
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self._test_gsm8k(accuracy_threshold=0.94)
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@unittest.skipIf(is_in_ci(), "local test only")
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class TestKimiK2FP8ToMXFP4(TestOnlineQuantizationMemoryLoad):
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model = "moonshotai/Kimi-K2-Instruct-0905" # FP8 model
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tp = 8
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# Same as in test/registered/amd/test_kimi_k2_instruct.py
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runner_args = [
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"--decode-attention-backend",
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"triton",
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"--prefill-attention-backend",
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"aiter",
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"--trust-remote-code",
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]
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# Same as in test/registered/amd/test_kimi_k2_instruct.py, getting an error otherwise.
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environment = {"SGLANG_ROCM_FUSED_DECODE_MLA": "0"}
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def test_peak_memory(self):
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# Original moonshotai/Kimi-K2-Instruct-0905 model: 121.020 GiB (TP=8, peak_memory_before_load)
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self._test_peak_memory(
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threshold=82, test_start=True, add_peak_memory_before_load=False
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) # TP=8
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def test_gsm8k(self):
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# Original moonshotai/Kimi-K2-Instruct-0905 reference accuracy: ~0.962
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self._test_gsm8k(accuracy_threshold=0.96)
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@unittest.skipIf(is_in_ci(), "local test only")
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class TestMiniMaxFP8ToMXFP4(TestOnlineQuantizationMemoryLoad):
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model = "MiniMaxAI/MiniMax-M2.1" # FP8 model
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tp = 2
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# NOTE: this test is failing in FP16 (default dtype of the original MiniMax-M2.1 model).
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# Hence the usage of `--dtype bfloat16`
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# NOTE: this test requires the following fix for TP>1: https://github.com/sgl-project/sglang/pull/18310
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runner_args = ["--trust-remote-code", "--dtype", "bfloat16"]
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def test_peak_memory(self):
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# Original MiniMaxAI/MiniMax-M2.1 model: 107.375 GiB (TP=2, peak_memory_before_load)
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self._test_peak_memory(
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threshold=72, test_start=True, add_peak_memory_before_load=False
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) # TP=2
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def test_gsm8k(self):
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# Original MiniMaxAI/MiniMax-M2.1 reference accuracy: 0.954
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self._test_gsm8k(accuracy_threshold=0.92)
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if __name__ == "__main__":
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unittest.main()
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