906 lines
29 KiB
Python
906 lines
29 KiB
Python
import io
|
|
import sys
|
|
import threading
|
|
import time
|
|
from contextlib import contextmanager
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
import requests
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
from sglang.srt.debug_utils.dumper import (
|
|
_collect_megatron_parallel_info,
|
|
_collect_sglang_parallel_info,
|
|
_collective_with_timeout,
|
|
_Dumper,
|
|
_DumperConfig,
|
|
_format_tags,
|
|
_materialize_value,
|
|
_obj_to_dict,
|
|
_torch_save,
|
|
get_tensor_info,
|
|
get_truncated_value,
|
|
)
|
|
from sglang.srt.environ import temp_set_env
|
|
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
|
|
from sglang.test.test_utils import run_distributed_test
|
|
|
|
register_cuda_ci(est_time=30, suite="nightly-2-gpu", nightly=True)
|
|
register_amd_ci(est_time=60, suite="nightly-amd", nightly=True)
|
|
|
|
|
|
@contextmanager
|
|
def _capture_stdout():
|
|
captured = io.StringIO()
|
|
old_stdout = sys.stdout
|
|
sys.stdout = captured
|
|
try:
|
|
yield captured
|
|
finally:
|
|
sys.stdout = old_stdout
|
|
|
|
|
|
class TestDumperConfig:
|
|
def test_from_env_defaults_match_dataclass_defaults(self):
|
|
assert _DumperConfig.from_env() == _DumperConfig()
|
|
|
|
def test_from_env_bool(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_ENABLE="1"):
|
|
assert _DumperConfig.from_env().enable is True
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_ENABLE="false"):
|
|
assert _DumperConfig.from_env().enable is False
|
|
|
|
def test_from_env_str(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_FILTER="layer_id=0"):
|
|
assert _DumperConfig.from_env().filter == "layer_id=0"
|
|
|
|
def test_from_env_dir(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_DIR="/my/dir"):
|
|
assert _DumperConfig.from_env().dir == "/my/dir"
|
|
|
|
def test_from_env_int(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_COLLECTIVE_TIMEOUT="120"):
|
|
assert _DumperConfig.from_env().collective_timeout == 120
|
|
|
|
def test_configure_overrides(self):
|
|
d = _make_test_dumper("/tmp")
|
|
d.configure(enable=False)
|
|
assert d._config.enable is False
|
|
d.configure(enable=True)
|
|
assert d._config.enable is True
|
|
|
|
def test_type_validation(self):
|
|
with pytest.raises(TypeError, match="enable.*expected bool.*got str"):
|
|
_DumperConfig(enable="yes")
|
|
with pytest.raises(
|
|
TypeError, match="collective_timeout.*expected int.*got str"
|
|
):
|
|
_DumperConfig(collective_timeout="abc")
|
|
with pytest.raises(TypeError, match="filter.*expected str.*got int"):
|
|
_DumperConfig(filter=123)
|
|
|
|
def test_configure_default_skips_when_env_set(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_FILTER="from_env"):
|
|
d = _Dumper(config=_DumperConfig.from_env())
|
|
d.configure_default(filter="from_code")
|
|
assert d._config.filter == "from_env"
|
|
|
|
def test_configure_default_applies_when_no_env(self):
|
|
d = _Dumper(config=_DumperConfig.from_env())
|
|
d.configure_default(filter="from_code")
|
|
assert d._config.filter == "from_code"
|
|
|
|
|
|
class TestDumperPureFunctions:
|
|
def test_get_truncated_value(self):
|
|
assert get_truncated_value(None) is None
|
|
assert get_truncated_value(42) == 42
|
|
assert len(get_truncated_value((torch.randn(10), torch.randn(20)))) == 2
|
|
assert get_truncated_value(torch.randn(10, 10)).shape == (10, 10)
|
|
assert get_truncated_value(torch.randn(100, 100)).shape == (5, 5)
|
|
|
|
def test_obj_to_dict(self):
|
|
assert _obj_to_dict({"a": 1}) == {"a": 1}
|
|
|
|
class Obj:
|
|
x, y = 10, 20
|
|
|
|
def method(self):
|
|
pass
|
|
|
|
result = _obj_to_dict(Obj())
|
|
assert result["x"] == 10
|
|
assert "method" not in result
|
|
|
|
def test_get_tensor_info(self):
|
|
info = get_tensor_info(torch.randn(10, 10))
|
|
for key in ["shape=", "dtype=", "min=", "max=", "mean="]:
|
|
assert key in info
|
|
|
|
assert "value=42" in get_tensor_info(42)
|
|
assert "min=None" in get_tensor_info(torch.tensor([]))
|
|
|
|
|
|
class TestTorchSave:
|
|
def test_normal(self, tmp_path):
|
|
path = str(tmp_path / "a.pt")
|
|
tensor = torch.randn(3, 3)
|
|
|
|
_torch_save(tensor, path)
|
|
|
|
assert torch.equal(torch.load(path, weights_only=True), tensor)
|
|
|
|
def test_parameter_fallback(self, tmp_path):
|
|
class BadParam(torch.nn.Parameter):
|
|
def __reduce_ex__(self, protocol):
|
|
raise RuntimeError("not pickleable")
|
|
|
|
path = str(tmp_path / "b.pt")
|
|
param = BadParam(torch.randn(4))
|
|
|
|
_torch_save(param, path)
|
|
|
|
assert torch.equal(torch.load(path, weights_only=True), param.data)
|
|
|
|
def test_silent_skip(self, tmp_path, capsys):
|
|
path = str(tmp_path / "c.pt")
|
|
|
|
_torch_save({"fn": lambda: None}, path)
|
|
|
|
captured = capsys.readouterr()
|
|
assert "[Dumper] Observe error=" in captured.out
|
|
assert "skip the tensor" in captured.out
|
|
|
|
|
|
class TestCollectiveTimeout:
|
|
def test_watchdog_fires_on_timeout(self):
|
|
block_event = threading.Event()
|
|
output = ""
|
|
|
|
def run_with_timeout():
|
|
nonlocal output
|
|
with _capture_stdout() as captured:
|
|
_collective_with_timeout(
|
|
lambda: block_event.wait(),
|
|
operation_name="test_blocked_op",
|
|
timeout_seconds=2,
|
|
)
|
|
output = captured.getvalue()
|
|
|
|
worker = threading.Thread(target=run_with_timeout)
|
|
worker.start()
|
|
|
|
time.sleep(4)
|
|
block_event.set()
|
|
worker.join(timeout=5)
|
|
|
|
print(f"Captured output: {output!r}")
|
|
assert "WARNING" in output
|
|
assert "test_blocked_op" in output
|
|
assert "2s" in output
|
|
|
|
|
|
class TestDumperDistributed:
|
|
def test_basic(self, tmp_path):
|
|
with temp_set_env(
|
|
allow_sglang=True,
|
|
SGLANG_DUMPER_ENABLE="1",
|
|
SGLANG_DUMPER_DIR=str(tmp_path),
|
|
):
|
|
run_distributed_test(self._test_basic_func, tmpdir=str(tmp_path))
|
|
|
|
@staticmethod
|
|
def _test_basic_func(rank, tmpdir):
|
|
from sglang.srt.debug_utils.dumper import dumper
|
|
|
|
tensor = torch.randn(10, 10, device=f"cuda:{rank}")
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.dump("tensor_a", tensor, arg=100)
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.set_ctx(ctx_arg=200)
|
|
dumper.dump("tensor_b", tensor)
|
|
dumper.set_ctx(ctx_arg=None)
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.configure(filter=r"^$")
|
|
dumper.dump("tensor_skip", tensor)
|
|
dumper.configure(filter=None)
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.dump_dict("obj", {"a": torch.randn(3, device=f"cuda:{rank}"), "b": 42})
|
|
|
|
dist.barrier()
|
|
filenames = _get_filenames(tmpdir)
|
|
_assert_files(
|
|
filenames,
|
|
exist=["tensor_a", "tensor_b", "arg=100", "ctx_arg=200", "obj_a", "obj_b"],
|
|
not_exist=["tensor_skip"],
|
|
)
|
|
|
|
def test_collective_timeout(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_ENABLE="1"):
|
|
run_distributed_test(self._test_collective_timeout_func)
|
|
|
|
@staticmethod
|
|
def _test_collective_timeout_func(rank):
|
|
dumper = _Dumper(
|
|
config=_DumperConfig(
|
|
enable=True,
|
|
collective_timeout=3,
|
|
enable_http_server=False,
|
|
),
|
|
)
|
|
|
|
with _capture_stdout() as captured:
|
|
if rank != 0:
|
|
time.sleep(6)
|
|
dumper.on_forward_pass_start()
|
|
|
|
output = captured.getvalue()
|
|
print(f"Rank {rank} captured output: {output!r}")
|
|
|
|
if rank == 0:
|
|
assert "WARNING" in output, f"Expected WARNING in rank 0 output: {output}"
|
|
assert "has not completed after 3s" in output
|
|
|
|
def test_http_configure(self):
|
|
with temp_set_env(allow_sglang=True, SGLANG_DUMPER_ENABLE="0"):
|
|
run_distributed_test(self._test_http_configure_func)
|
|
|
|
@staticmethod
|
|
def _test_http_configure_func(rank):
|
|
from sglang.srt.debug_utils.dumper import dumper
|
|
|
|
assert not dumper._config.enable
|
|
dumper.on_forward_pass_start()
|
|
|
|
base_url = "http://localhost:40000"
|
|
|
|
# (1) enable toggle
|
|
for enable in [True, False]:
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
requests.post(
|
|
f"{base_url}/dumper/configure", json={"enable": enable}
|
|
).raise_for_status()
|
|
dist.barrier()
|
|
assert dumper._config.enable == enable
|
|
|
|
# (2) multi-field configure
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
requests.post(
|
|
f"{base_url}/dumper/configure",
|
|
json={"enable": True, "filter": "layer_id=0", "dir": "/tmp/test_http"},
|
|
).raise_for_status()
|
|
dist.barrier()
|
|
assert dumper._config.enable is True
|
|
assert dumper._config.filter == "layer_id=0"
|
|
assert dumper._config.dir == "/tmp/test_http"
|
|
|
|
# (3) clear optional field
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
requests.post(
|
|
f"{base_url}/dumper/configure",
|
|
json={"filter": None},
|
|
).raise_for_status()
|
|
dist.barrier()
|
|
assert dumper._config.filter is None
|
|
|
|
# (4) reset
|
|
dumper._dump_index = 42
|
|
dumper._forward_pass_id = 99
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
requests.post(f"{base_url}/dumper/reset").raise_for_status()
|
|
dist.barrier()
|
|
assert dumper._dump_index == 0
|
|
assert dumper._forward_pass_id == 0
|
|
|
|
# (5) error: unknown field -> 400
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
resp = requests.post(
|
|
f"{base_url}/dumper/configure",
|
|
json={"nonexistent_field": 123},
|
|
)
|
|
assert resp.status_code == 400
|
|
|
|
# (6) error: wrong type -> 400
|
|
dist.barrier()
|
|
if rank == 0:
|
|
time.sleep(0.1)
|
|
resp = requests.post(
|
|
f"{base_url}/dumper/configure",
|
|
json={"enable": "not_a_bool"},
|
|
)
|
|
assert resp.status_code == 400
|
|
|
|
def test_file_content_correctness(self, tmp_path):
|
|
with temp_set_env(
|
|
allow_sglang=True,
|
|
SGLANG_DUMPER_ENABLE="1",
|
|
SGLANG_DUMPER_DIR=str(tmp_path),
|
|
):
|
|
run_distributed_test(self._test_file_content_func, tmpdir=str(tmp_path))
|
|
|
|
@staticmethod
|
|
def _test_file_content_func(rank, tmpdir):
|
|
from sglang.srt.debug_utils.dumper import dumper
|
|
|
|
tensor = torch.arange(12, device=f"cuda:{rank}").reshape(3, 4).float()
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.dump("content_check", tensor)
|
|
|
|
dist.barrier()
|
|
path = _find_dump_file(tmpdir, rank=rank, name="content_check")
|
|
raw = _load_dump(path)
|
|
assert isinstance(raw, dict), f"Expected dict, got {type(raw)}"
|
|
assert "value" in raw and "meta" in raw
|
|
assert torch.equal(raw["value"], tensor.cpu())
|
|
assert raw["meta"]["name"] == "content_check"
|
|
assert raw["meta"]["rank"] == rank
|
|
|
|
|
|
class TestDumperFileWriteControl:
|
|
def test_filter(self, tmp_path):
|
|
with temp_set_env(
|
|
allow_sglang=True,
|
|
SGLANG_DUMPER_ENABLE="1",
|
|
SGLANG_DUMPER_DIR=str(tmp_path),
|
|
SGLANG_DUMPER_FILTER="name=keep",
|
|
):
|
|
run_distributed_test(self._test_filter_func, tmpdir=str(tmp_path))
|
|
|
|
@staticmethod
|
|
def _test_filter_func(rank, tmpdir):
|
|
from sglang.srt.debug_utils.dumper import dumper
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.dump("keep_this", torch.randn(5, device=f"cuda:{rank}"))
|
|
dumper.dump("skip_this", torch.randn(5, device=f"cuda:{rank}"))
|
|
dumper.dump("not_keep_this", torch.randn(5, device=f"cuda:{rank}"))
|
|
|
|
dist.barrier()
|
|
filenames = _get_filenames(tmpdir)
|
|
_assert_files(
|
|
filenames,
|
|
exist=["keep_this"],
|
|
not_exist=["skip_this", "not_keep_this"],
|
|
)
|
|
|
|
def test_save_false(self, tmp_path):
|
|
with temp_set_env(
|
|
allow_sglang=True,
|
|
SGLANG_DUMPER_ENABLE="1",
|
|
SGLANG_DUMPER_DIR=str(tmp_path),
|
|
):
|
|
run_distributed_test(self._test_save_false_func, tmpdir=str(tmp_path))
|
|
|
|
@staticmethod
|
|
def _test_save_false_func(rank, tmpdir):
|
|
from sglang.srt.debug_utils.dumper import dumper
|
|
|
|
dumper.on_forward_pass_start()
|
|
dumper.dump("no_save_tensor", torch.randn(5, device=f"cuda:{rank}"), save=False)
|
|
|
|
dist.barrier()
|
|
assert len(_get_filenames(tmpdir)) == 0
|
|
|
|
|
|
class TestOutputControl:
|
|
def test_file_enabled_by_default(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
d.dump("file_on", torch.randn(3, 3))
|
|
|
|
_assert_files(_get_filenames(tmp_path), exist=["file_on"])
|
|
|
|
def test_file_disabled(self, tmp_path, capsys):
|
|
d = _make_test_dumper(tmp_path, enable_output_file=False)
|
|
d.dump("file_off", torch.randn(3, 3))
|
|
|
|
assert len(_get_filenames(tmp_path)) == 0
|
|
assert "file_off" in capsys.readouterr().out
|
|
|
|
def test_console_enabled_by_default(self, tmp_path, capsys):
|
|
d = _make_test_dumper(tmp_path)
|
|
d.dump("console_on", torch.randn(3, 3))
|
|
|
|
captured = capsys.readouterr()
|
|
assert "[Dumper.Value]" in captured.out
|
|
assert "console_on" in captured.out
|
|
|
|
def test_console_disabled(self, tmp_path, capsys):
|
|
d = _make_test_dumper(tmp_path, enable_output_console=False)
|
|
d.dump("console_off", torch.randn(3, 3))
|
|
|
|
assert "console_off" not in capsys.readouterr().out
|
|
_assert_files(_get_filenames(tmp_path), exist=["console_off"])
|
|
|
|
def test_capture_output_basic(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
tensor = torch.randn(4, 4)
|
|
|
|
with d.capture_output() as captured:
|
|
d.dump("cap_basic", tensor)
|
|
|
|
assert "cap_basic" in captured
|
|
assert set(captured["cap_basic"].keys()) == {"value", "meta"}
|
|
assert torch.equal(captured["cap_basic"]["value"], tensor)
|
|
assert captured["cap_basic"]["meta"]["name"] == "cap_basic"
|
|
|
|
def test_capture_output_no_file(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
|
|
with d.capture_output() as captured:
|
|
d.dump("cap_no_file", torch.randn(3, 3))
|
|
|
|
assert "cap_no_file" in captured
|
|
assert len(_get_filenames(tmp_path)) == 0
|
|
|
|
def test_capture_output_multiple(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
|
|
with d.capture_output() as captured:
|
|
d.dump("first", torch.randn(2, 2))
|
|
d.dump("second", torch.randn(3, 3))
|
|
|
|
assert set(captured.keys()) == {"first", "second"}
|
|
assert captured["first"]["value"].shape == (2, 2)
|
|
assert captured["second"]["value"].shape == (3, 3)
|
|
|
|
def test_capture_output_value_cloned(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
tensor = torch.zeros(3, 3)
|
|
|
|
with d.capture_output() as captured:
|
|
d.dump("clone_check", tensor)
|
|
|
|
tensor.fill_(999.0)
|
|
assert torch.equal(captured["clone_check"]["value"], torch.zeros(3, 3))
|
|
|
|
def test_capture_output_respects_filter(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter="name=keep")
|
|
|
|
with d.capture_output() as captured:
|
|
d.dump("keep_this", torch.randn(3, 3))
|
|
d.dump("skip_this", torch.randn(3, 3))
|
|
|
|
assert "keep_this" in captured
|
|
assert "skip_this" not in captured
|
|
|
|
|
|
class TestDumpDictFormat:
|
|
"""Verify that dump files use the dict output format: {"value": ..., "meta": {...}}."""
|
|
|
|
def test_dict_format_structure(self, tmp_path):
|
|
dumper = _make_test_dumper(tmp_path)
|
|
tensor = torch.randn(4, 4)
|
|
dumper.dump("fmt_test", tensor, custom_key="hello")
|
|
|
|
path = _find_dump_file(str(tmp_path), rank=0, name="fmt_test")
|
|
raw = _load_dump(path)
|
|
|
|
assert isinstance(raw, dict)
|
|
assert set(raw.keys()) == {"value", "meta"}
|
|
assert torch.equal(raw["value"], tensor)
|
|
|
|
meta = raw["meta"]
|
|
assert meta["name"] == "fmt_test"
|
|
assert meta["custom_key"] == "hello"
|
|
assert "forward_pass_id" in meta
|
|
assert "rank" in meta
|
|
assert "dump_index" in meta
|
|
|
|
def test_dict_format_with_context(self, tmp_path):
|
|
dumper = _make_test_dumper(tmp_path)
|
|
dumper.set_ctx(ctx_val=42)
|
|
tensor = torch.randn(2, 2)
|
|
dumper.dump("ctx_fmt", tensor)
|
|
|
|
path = _find_dump_file(str(tmp_path), rank=0, name="ctx_fmt")
|
|
raw = _load_dump(path)
|
|
|
|
assert raw["meta"]["ctx_val"] == 42
|
|
assert torch.equal(raw["value"], tensor)
|
|
|
|
|
|
def _make_test_dumper(tmp_path, **overrides) -> _Dumper:
|
|
"""Create a _Dumper for CPU testing without HTTP server or distributed."""
|
|
config = _DumperConfig(
|
|
enable=True,
|
|
dir=str(tmp_path),
|
|
partial_name="test",
|
|
enable_http_server=False,
|
|
**overrides,
|
|
)
|
|
d = _Dumper(config=config)
|
|
d.on_forward_pass_start()
|
|
return d
|
|
|
|
|
|
def _get_filenames(tmpdir):
|
|
return {f.name for f in Path(tmpdir).glob("sglang_dump_*/*.pt")}
|
|
|
|
|
|
def _assert_files(filenames, *, exist=(), not_exist=()):
|
|
for p in exist:
|
|
assert any(p in f for f in filenames), f"{p} not found in {filenames}"
|
|
for p in not_exist:
|
|
assert not any(
|
|
p in f for f in filenames
|
|
), f"{p} should not exist in {filenames}"
|
|
|
|
|
|
def _load_dump(path: Path) -> dict:
|
|
"""Load a dump file and return the raw dict (with 'value' and 'meta' keys)."""
|
|
return torch.load(path, map_location="cpu", weights_only=False)
|
|
|
|
|
|
def _find_dump_file(tmpdir, *, rank: int = 0, name: str) -> Path:
|
|
matches = [
|
|
f
|
|
for f in Path(tmpdir).glob("sglang_dump_*/*.pt")
|
|
if f"rank={rank}" in f.name and name in f.name
|
|
]
|
|
assert (
|
|
len(matches) == 1
|
|
), f"Expected 1 file matching rank={rank} name={name}, got {matches}"
|
|
return matches[0]
|
|
|
|
|
|
class TestMaterializeValue:
|
|
def test_materialize_value_callable(self):
|
|
tensor = torch.randn(3, 3)
|
|
result = _materialize_value(lambda: tensor)
|
|
assert torch.equal(result, tensor)
|
|
|
|
def test_materialize_value_passthrough(self):
|
|
tensor = torch.randn(3, 3)
|
|
result = _materialize_value(tensor)
|
|
assert result is tensor
|
|
|
|
def test_dump_with_callable_value(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
tensor = torch.randn(4, 4)
|
|
d.dump("lazy_tensor", lambda: tensor)
|
|
|
|
_assert_files(_get_filenames(tmp_path), exist=["name=lazy_tensor"])
|
|
|
|
path = _find_dump_file(tmp_path, rank=0, name="lazy_tensor")
|
|
assert torch.equal(_load_dump(path)["value"], tensor)
|
|
|
|
|
|
class TestSaveValue:
|
|
def test_dump_output_format(self, tmp_path):
|
|
dumper = _make_test_dumper(tmp_path)
|
|
tensor = torch.randn(4, 4)
|
|
|
|
dumper.dump("dict_test", tensor)
|
|
|
|
path = _find_dump_file(tmp_path, rank=0, name="dict_test")
|
|
loaded = _load_dump(path)
|
|
assert torch.equal(loaded["value"], tensor)
|
|
assert loaded["meta"]["name"] == "dict_test"
|
|
assert loaded["meta"]["rank"] == 0
|
|
|
|
|
|
class TestStaticMetadata:
|
|
def test_static_meta_contains_world_info(self):
|
|
dumper = _make_test_dumper("/tmp")
|
|
meta = dumper._static_meta
|
|
assert "world_rank" in meta
|
|
assert "world_size" in meta
|
|
assert meta["world_rank"] == 0
|
|
assert meta["world_size"] == 1
|
|
|
|
def test_static_meta_caching(self):
|
|
dumper = _make_test_dumper("/tmp")
|
|
meta1 = dumper._static_meta
|
|
meta2 = dumper._static_meta
|
|
assert meta1 is meta2
|
|
|
|
def test_parallel_info_graceful_fallback(self):
|
|
sglang_info = _collect_sglang_parallel_info()
|
|
assert isinstance(sglang_info, dict)
|
|
|
|
megatron_info = _collect_megatron_parallel_info()
|
|
assert isinstance(megatron_info, dict)
|
|
|
|
def test_dump_includes_static_meta(self, tmp_path):
|
|
dumper = _make_test_dumper(tmp_path)
|
|
tensor = torch.randn(2, 2)
|
|
|
|
dumper.dump("meta_test", tensor)
|
|
|
|
path = _find_dump_file(tmp_path, rank=0, name="meta_test")
|
|
loaded = _load_dump(path)
|
|
meta = loaded["meta"]
|
|
assert "world_rank" in meta
|
|
assert "world_size" in meta
|
|
|
|
|
|
class TestDumpGrad:
|
|
def test_dump_grad_basic(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=True)
|
|
x = torch.randn(3, 3, requires_grad=True)
|
|
y = (x * 2).sum()
|
|
|
|
d.dump("test_tensor", x)
|
|
y.backward()
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
assert any("name=test_tensor" in f and "grad__" not in f for f in filenames)
|
|
_assert_files(filenames, exist=["grad__test_tensor"])
|
|
|
|
def test_dump_grad_non_tensor_skipped(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=True)
|
|
d.dump("not_tensor", 42)
|
|
|
|
_assert_files(_get_filenames(tmp_path), not_exist=["grad__"])
|
|
|
|
def test_dump_grad_no_requires_grad_skipped(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=True)
|
|
x = torch.randn(3, 3, requires_grad=False)
|
|
d.dump("no_grad_tensor", x)
|
|
|
|
_assert_files(
|
|
_get_filenames(tmp_path),
|
|
exist=["name=no_grad_tensor"],
|
|
not_exist=["grad__"],
|
|
)
|
|
|
|
def test_dump_grad_captures_forward_pass_id(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=True)
|
|
d._forward_pass_id = 42
|
|
x = torch.randn(3, 3, requires_grad=True)
|
|
y = (x * 2).sum()
|
|
|
|
d.dump("id_test", x)
|
|
d._forward_pass_id = 999
|
|
y.backward()
|
|
|
|
grad_file = _find_dump_file(tmp_path, name="grad__id_test")
|
|
assert "forward_pass_id=42" in grad_file.name
|
|
|
|
def test_dump_grad_file_content(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=True)
|
|
x = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
|
|
y = (x * 3).sum()
|
|
|
|
d.dump("content_check", x)
|
|
y.backward()
|
|
|
|
grad_path = _find_dump_file(tmp_path, name="grad__content_check")
|
|
expected_grad = torch.full((2, 2), 3.0)
|
|
assert torch.equal(_load_dump(grad_path)["value"], expected_grad)
|
|
|
|
def test_disable_value(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_value=False, enable_grad=True)
|
|
x = torch.randn(3, 3, requires_grad=True)
|
|
y = (x * 2).sum()
|
|
|
|
d.dump("fwd_disabled", x)
|
|
y.backward()
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
assert not any(
|
|
"name=fwd_disabled" in f and "grad__" not in f for f in filenames
|
|
)
|
|
_assert_files(filenames, exist=["grad__fwd_disabled"])
|
|
|
|
def test_disable_grad(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_grad=False)
|
|
x = torch.randn(3, 3, requires_grad=True)
|
|
y = (x * 2).sum()
|
|
|
|
d.dump("grad_disabled", x)
|
|
y.backward()
|
|
|
|
_assert_files(
|
|
_get_filenames(tmp_path),
|
|
exist=["name=grad_disabled"],
|
|
not_exist=["grad__"],
|
|
)
|
|
|
|
|
|
class TestKvFilter:
|
|
def test_format_tags(self):
|
|
assert _format_tags({"a": 1, "b": "hello"}) == "a=1___b=hello"
|
|
assert _format_tags({}) == ""
|
|
|
|
def test_filter_matches_extra_kwargs(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter="layer_id=0")
|
|
d.dump("tensor_a", torch.randn(3), layer_id=0)
|
|
d.dump("tensor_b", torch.randn(3), layer_id=1)
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["tensor_a"], not_exist=["tensor_b"])
|
|
|
|
def test_filter_matches_global_ctx(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter="ctx_arg=200")
|
|
d.set_ctx(ctx_arg=200)
|
|
d.dump("tensor_a", torch.randn(3))
|
|
d.set_ctx(ctx_arg=None)
|
|
d.dump("tensor_b", torch.randn(3))
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["tensor_a"], not_exist=["tensor_b"])
|
|
|
|
def test_filter_matches_name(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter="name=keep")
|
|
d.dump("keep_this", torch.randn(3))
|
|
d.dump("skip_this", torch.randn(3))
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["keep_this"], not_exist=["skip_this"])
|
|
|
|
def test_filter_regex(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter=r"layer_id=[0-2]")
|
|
d.dump("t0", torch.randn(3), layer_id=0)
|
|
d.dump("t1", torch.randn(3), layer_id=1)
|
|
d.dump("t5", torch.randn(3), layer_id=5)
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["name=t0", "name=t1"], not_exist=["name=t5"])
|
|
|
|
def test_no_filter_dumps_all(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
d.dump("a", torch.randn(3))
|
|
d.dump("b", torch.randn(3))
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["name=a", "name=b"])
|
|
|
|
|
|
class TestDumpModel:
|
|
def test_grad_basic(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_value=False)
|
|
model = torch.nn.Linear(4, 2)
|
|
x = torch.randn(3, 4)
|
|
y = model(x).sum()
|
|
y.backward()
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
_assert_files(
|
|
_get_filenames(tmp_path),
|
|
exist=["grad__model__weight", "grad__model__bias"],
|
|
)
|
|
|
|
def test_value_basic(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_grad=False)
|
|
model = torch.nn.Linear(4, 2, bias=False)
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
_assert_files(
|
|
_get_filenames(tmp_path),
|
|
exist=["model__weight"],
|
|
)
|
|
|
|
def test_no_grad_skipped(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_value=False)
|
|
model = torch.nn.Linear(4, 2)
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
assert len(filenames) == 0
|
|
|
|
def test_filter(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, filter="weight")
|
|
model = torch.nn.Linear(4, 2)
|
|
x = torch.randn(3, 4)
|
|
y = model(x).sum()
|
|
y.backward()
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
_assert_files(
|
|
_get_filenames(tmp_path),
|
|
exist=["model__weight", "grad__model__weight"],
|
|
not_exist=["model__bias", "grad__model__bias"],
|
|
)
|
|
|
|
def test_grad_file_content(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_value=False)
|
|
model = torch.nn.Linear(4, 2, bias=False)
|
|
x = torch.ones(1, 4)
|
|
y = model(x).sum()
|
|
y.backward()
|
|
|
|
d.dump_model(model, name_prefix="p")
|
|
|
|
path = _find_dump_file(tmp_path, name="grad__p__weight")
|
|
assert torch.equal(_load_dump(path)["value"], model.weight.grad)
|
|
|
|
def test_disable_model_grad(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_grad=False)
|
|
model = torch.nn.Linear(4, 2)
|
|
x = torch.randn(3, 4)
|
|
y = model(x).sum()
|
|
y.backward()
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
assert all("grad" not in f for f in filenames)
|
|
|
|
def test_disable_model_value(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path, enable_model_value=False)
|
|
model = torch.nn.Linear(4, 2, bias=False)
|
|
x = torch.ones(1, 4)
|
|
y = model(x).sum()
|
|
y.backward()
|
|
|
|
d.dump_model(model, name_prefix="model")
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
assert all("grad" in f for f in filenames)
|
|
|
|
|
|
class TestCleanup:
|
|
def test_cleanup_removes_old_dumps(self, tmp_path):
|
|
old_dir = tmp_path / "sglang_dump_old"
|
|
old_dir.mkdir()
|
|
(old_dir / "dummy.pt").touch()
|
|
|
|
dumper = _make_test_dumper(tmp_path, cleanup_previous=True)
|
|
dumper.dump("new_tensor", torch.randn(3, 3))
|
|
|
|
assert not old_dir.exists()
|
|
_assert_files(_get_filenames(tmp_path), exist=["new_tensor"])
|
|
|
|
def test_no_cleanup_by_default(self, tmp_path):
|
|
old_dir = tmp_path / "sglang_dump_old"
|
|
old_dir.mkdir()
|
|
(old_dir / "dummy.pt").touch()
|
|
|
|
dumper = _make_test_dumper(tmp_path)
|
|
dumper.dump("new_tensor", torch.randn(3, 3))
|
|
|
|
assert old_dir.exists()
|
|
_assert_files(_get_filenames(tmp_path), exist=["new_tensor"])
|
|
|
|
|
|
class TestReset:
|
|
def test_reset_clears_state(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
d.set_ctx(layer_id=1)
|
|
d.dump("before_reset", torch.randn(3, 3))
|
|
|
|
d.reset()
|
|
|
|
assert d._dump_index == 0
|
|
assert d._forward_pass_id == 0
|
|
assert d._global_ctx == {}
|
|
|
|
def test_dump_works_after_reset(self, tmp_path):
|
|
d = _make_test_dumper(tmp_path)
|
|
d.dump("pre", torch.randn(3, 3))
|
|
|
|
d.reset()
|
|
d.on_forward_pass_start()
|
|
d.dump("post", torch.randn(3, 3))
|
|
|
|
filenames = _get_filenames(tmp_path)
|
|
_assert_files(filenames, exist=["pre", "post"])
|
|
post_file = _find_dump_file(tmp_path, name="post")
|
|
assert "dump_index=1" in post_file.name
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(pytest.main([__file__]))
|