563 lines
18 KiB
Python
563 lines
18 KiB
Python
import sys
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import time
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from pathlib import Path
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import pytest
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import requests
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import torch
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import torch.distributed as dist
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from sglang.srt.debug_utils.dumper import (
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_collect_megatron_parallel_info,
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_collect_sglang_parallel_info,
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_Dumper,
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_materialize_value,
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_obj_to_dict,
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_torch_save,
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get_tensor_info,
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get_truncated_value,
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)
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from sglang.srt.environ import temp_set_env
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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from sglang.test.test_utils import run_distributed_test
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register_cuda_ci(est_time=30, suite="nightly-2-gpu", nightly=True)
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register_amd_ci(est_time=60, suite="nightly-amd", nightly=True)
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class TestDumperPureFunctions:
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def test_get_truncated_value(self):
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assert get_truncated_value(None) is None
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assert get_truncated_value(42) == 42
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assert len(get_truncated_value((torch.randn(10), torch.randn(20)))) == 2
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assert get_truncated_value(torch.randn(10, 10)).shape == (10, 10)
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assert get_truncated_value(torch.randn(100, 100)).shape == (5, 5)
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def test_obj_to_dict(self):
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assert _obj_to_dict({"a": 1}) == {"a": 1}
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class Obj:
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x, y = 10, 20
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def method(self):
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pass
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result = _obj_to_dict(Obj())
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assert result["x"] == 10
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assert "method" not in result
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def test_get_tensor_info(self):
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info = get_tensor_info(torch.randn(10, 10))
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for key in ["shape=", "dtype=", "min=", "max=", "mean="]:
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assert key in info
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assert "value=42" in get_tensor_info(42)
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assert "min=None" in get_tensor_info(torch.tensor([]))
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class TestTorchSave:
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def test_normal(self, tmp_path):
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path = str(tmp_path / "a.pt")
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tensor = torch.randn(3, 3)
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_torch_save(tensor, path)
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assert torch.equal(torch.load(path, weights_only=True), tensor)
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def test_parameter_fallback(self, tmp_path):
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class BadParam(torch.nn.Parameter):
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def __reduce_ex__(self, protocol):
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raise RuntimeError("not pickleable")
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path = str(tmp_path / "b.pt")
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param = BadParam(torch.randn(4))
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_torch_save(param, path)
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assert torch.equal(torch.load(path, weights_only=True), param.data)
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def test_silent_skip(self, tmp_path, capsys):
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path = str(tmp_path / "c.pt")
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_torch_save({"fn": lambda: None}, path)
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captured = capsys.readouterr()
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assert "[Dumper] Observe error=" in captured.out
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assert "skip the tensor" in captured.out
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class TestDumperDistributed:
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def test_basic(self, tmp_path):
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with temp_set_env(
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allow_sglang=True,
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SGLANG_DUMPER_ENABLE="1",
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SGLANG_DUMPER_DIR=str(tmp_path),
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):
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run_distributed_test(self._test_basic_func, tmpdir=str(tmp_path))
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@staticmethod
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def _test_basic_func(rank, tmpdir):
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from sglang.srt.debug_utils.dumper import dumper
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tensor = torch.randn(10, 10, device=f"cuda:{rank}")
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dumper.on_forward_pass_start()
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dumper.dump("tensor_a", tensor, arg=100)
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dumper.on_forward_pass_start()
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dumper.set_ctx(ctx_arg=200)
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dumper.dump("tensor_b", tensor)
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dumper.set_ctx(ctx_arg=None)
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dumper.on_forward_pass_start()
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dumper.override_enable(False)
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dumper.dump("tensor_skip", tensor)
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dumper.override_enable(True)
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dumper.on_forward_pass_start()
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dumper.dump_dict("obj", {"a": torch.randn(3, device=f"cuda:{rank}"), "b": 42})
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dist.barrier()
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filenames = _get_filenames(tmpdir)
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_assert_files(
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filenames,
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exist=["tensor_a", "tensor_b", "arg=100", "ctx_arg=200", "obj_a", "obj_b"],
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not_exist=["tensor_skip"],
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)
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def test_http_enable(self):
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with temp_set_env(allow_sglang=True, SGLANG_DUMPER_ENABLE="0"):
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run_distributed_test(self._test_http_func)
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@staticmethod
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def _test_http_func(rank):
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from sglang.srt.debug_utils.dumper import dumper
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assert not dumper._enable
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dumper.on_forward_pass_start()
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for enable in [True, False]:
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dist.barrier()
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if rank == 0:
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time.sleep(0.1)
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requests.post(
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"http://localhost:40000/dumper", json={"enable": enable}
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).raise_for_status()
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dist.barrier()
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assert dumper._enable == enable
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def test_file_content_correctness(self, tmp_path):
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with temp_set_env(
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allow_sglang=True,
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SGLANG_DUMPER_ENABLE="1",
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SGLANG_DUMPER_DIR=str(tmp_path),
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):
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run_distributed_test(self._test_file_content_func, tmpdir=str(tmp_path))
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@staticmethod
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def _test_file_content_func(rank, tmpdir):
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from sglang.srt.debug_utils.dumper import dumper
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tensor = torch.arange(12, device=f"cuda:{rank}").reshape(3, 4).float()
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dumper.on_forward_pass_start()
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dumper.dump("content_check", tensor)
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dist.barrier()
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path = _find_dump_file(tmpdir, rank=rank, name="content_check")
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raw = _load_dump(path)
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assert isinstance(raw, dict), f"Expected dict, got {type(raw)}"
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assert "value" in raw and "meta" in raw
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assert torch.equal(raw["value"], tensor.cpu())
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assert raw["meta"]["name"] == "content_check"
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assert raw["meta"]["rank"] == rank
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class TestDumperFileWriteControl:
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def test_filter(self, tmp_path):
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with temp_set_env(
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allow_sglang=True,
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SGLANG_DUMPER_ENABLE="1",
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SGLANG_DUMPER_DIR=str(tmp_path),
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SGLANG_DUMPER_FILTER="^keep",
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):
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run_distributed_test(self._test_filter_func, tmpdir=str(tmp_path))
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@staticmethod
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def _test_filter_func(rank, tmpdir):
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from sglang.srt.debug_utils.dumper import dumper
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dumper.on_forward_pass_start()
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dumper.dump("keep_this", torch.randn(5, device=f"cuda:{rank}"))
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dumper.dump("skip_this", torch.randn(5, device=f"cuda:{rank}"))
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dumper.dump("not_keep_this", torch.randn(5, device=f"cuda:{rank}"))
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dist.barrier()
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filenames = _get_filenames(tmpdir)
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_assert_files(
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filenames,
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exist=["keep_this"],
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not_exist=["skip_this", "not_keep_this"],
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)
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def test_write_disabled(self, tmp_path):
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with temp_set_env(
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allow_sglang=True,
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SGLANG_DUMPER_ENABLE="1",
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SGLANG_DUMPER_DIR=str(tmp_path),
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SGLANG_DUMPER_WRITE_FILE="0",
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):
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run_distributed_test(self._test_write_disabled_func, tmpdir=str(tmp_path))
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@staticmethod
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def _test_write_disabled_func(rank, tmpdir):
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from sglang.srt.debug_utils.dumper import dumper
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dumper.on_forward_pass_start()
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dumper.dump("no_write", torch.randn(5, device=f"cuda:{rank}"))
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dist.barrier()
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assert len(_get_filenames(tmpdir)) == 0
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def test_save_false(self, tmp_path):
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with temp_set_env(
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allow_sglang=True,
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SGLANG_DUMPER_ENABLE="1",
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SGLANG_DUMPER_DIR=str(tmp_path),
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):
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run_distributed_test(self._test_save_false_func, tmpdir=str(tmp_path))
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@staticmethod
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def _test_save_false_func(rank, tmpdir):
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from sglang.srt.debug_utils.dumper import dumper
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dumper.on_forward_pass_start()
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dumper.dump("no_save_tensor", torch.randn(5, device=f"cuda:{rank}"), save=False)
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dist.barrier()
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assert len(_get_filenames(tmpdir)) == 0
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class TestDumpDictFormat:
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"""Verify that dump files use the dict output format: {"value": ..., "meta": {...}}."""
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def test_dict_format_structure(self, tmp_path):
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dumper = _make_test_dumper(tmp_path)
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tensor = torch.randn(4, 4)
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dumper.dump("fmt_test", tensor, custom_key="hello")
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path = _find_dump_file(str(tmp_path), rank=0, name="fmt_test")
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raw = _load_dump(path)
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assert isinstance(raw, dict)
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assert set(raw.keys()) == {"value", "meta"}
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assert torch.equal(raw["value"], tensor)
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meta = raw["meta"]
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assert meta["name"] == "fmt_test"
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assert meta["custom_key"] == "hello"
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assert "forward_pass_id" in meta
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assert "rank" in meta
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assert "dump_index" in meta
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def test_dict_format_with_context(self, tmp_path):
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dumper = _make_test_dumper(tmp_path)
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dumper.set_ctx(ctx_val=42)
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tensor = torch.randn(2, 2)
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dumper.dump("ctx_fmt", tensor)
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path = _find_dump_file(str(tmp_path), rank=0, name="ctx_fmt")
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raw = _load_dump(path)
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assert raw["meta"]["ctx_val"] == 42
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assert torch.equal(raw["value"], tensor)
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def _make_test_dumper(tmp_path: Path, **overrides) -> _Dumper:
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"""Create a _Dumper for CPU testing without HTTP server or distributed."""
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defaults: dict = dict(
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enable=True,
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base_dir=tmp_path,
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partial_name="test",
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enable_http_server=False,
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)
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d = _Dumper(**{**defaults, **overrides})
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d.on_forward_pass_start()
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return d
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def _get_filenames(tmpdir):
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return {f.name for f in Path(tmpdir).glob("sglang_dump_*/*.pt")}
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def _assert_files(filenames, *, exist=(), not_exist=()):
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for p in exist:
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assert any(p in f for f in filenames), f"{p} not found in {filenames}"
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for p in not_exist:
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assert not any(
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p in f for f in filenames
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), f"{p} should not exist in {filenames}"
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def _load_dump(path: Path) -> dict:
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"""Load a dump file and return the raw dict (with 'value' and 'meta' keys)."""
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return torch.load(path, map_location="cpu", weights_only=False)
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def _find_dump_file(tmpdir, *, rank: int = 0, name: str) -> Path:
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matches = [
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f
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for f in Path(tmpdir).glob("sglang_dump_*/*.pt")
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if f"rank={rank}" in f.name and name in f.name
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]
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assert (
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len(matches) == 1
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), f"Expected 1 file matching rank={rank} name={name}, got {matches}"
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return matches[0]
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class TestMaterializeValue:
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def test_materialize_value_callable(self):
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tensor = torch.randn(3, 3)
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result = _materialize_value(lambda: tensor)
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assert torch.equal(result, tensor)
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def test_materialize_value_passthrough(self):
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tensor = torch.randn(3, 3)
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result = _materialize_value(tensor)
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assert result is tensor
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def test_dump_with_callable_value(self, tmp_path):
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d = _make_test_dumper(tmp_path)
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tensor = torch.randn(4, 4)
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d.dump("lazy_tensor", lambda: tensor)
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_assert_files(_get_filenames(tmp_path), exist=["name=lazy_tensor"])
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path = _find_dump_file(tmp_path, rank=0, name="lazy_tensor")
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assert torch.equal(_load_dump(path)["value"], tensor)
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class TestSaveValue:
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def test_dump_output_format(self, tmp_path):
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dumper = _make_test_dumper(tmp_path)
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tensor = torch.randn(4, 4)
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dumper.dump("dict_test", tensor)
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path = _find_dump_file(tmp_path, rank=0, name="dict_test")
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loaded = _load_dump(path)
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assert torch.equal(loaded["value"], tensor)
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assert loaded["meta"]["name"] == "dict_test"
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assert loaded["meta"]["rank"] == 0
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class TestStaticMetadata:
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def test_static_meta_contains_world_info(self):
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dumper = _make_test_dumper(Path("/tmp"))
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meta = dumper._static_meta
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assert "world_rank" in meta
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assert "world_size" in meta
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assert meta["world_rank"] == 0
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assert meta["world_size"] == 1
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def test_static_meta_caching(self):
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dumper = _make_test_dumper(Path("/tmp"))
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meta1 = dumper._static_meta
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meta2 = dumper._static_meta
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assert meta1 is meta2
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def test_parallel_info_graceful_fallback(self):
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sglang_info = _collect_sglang_parallel_info()
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assert isinstance(sglang_info, dict)
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megatron_info = _collect_megatron_parallel_info()
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assert isinstance(megatron_info, dict)
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def test_dump_includes_static_meta(self, tmp_path):
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dumper = _make_test_dumper(tmp_path)
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tensor = torch.randn(2, 2)
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dumper.dump("meta_test", tensor)
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path = _find_dump_file(tmp_path, rank=0, name="meta_test")
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loaded = _load_dump(path)
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meta = loaded["meta"]
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assert "world_rank" in meta
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assert "world_size" in meta
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class TestDumpGrad:
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def test_dump_grad_basic(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=True)
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x = torch.randn(3, 3, requires_grad=True)
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y = (x * 2).sum()
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d.dump("test_tensor", x)
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y.backward()
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filenames = _get_filenames(tmp_path)
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assert any("name=test_tensor" in f and "grad__" not in f for f in filenames)
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_assert_files(filenames, exist=["grad__test_tensor"])
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def test_dump_grad_non_tensor_skipped(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=True)
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d.dump("not_tensor", 42)
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_assert_files(_get_filenames(tmp_path), not_exist=["grad__"])
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def test_dump_grad_no_requires_grad_skipped(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=True)
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x = torch.randn(3, 3, requires_grad=False)
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d.dump("no_grad_tensor", x)
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_assert_files(
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_get_filenames(tmp_path),
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exist=["name=no_grad_tensor"],
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not_exist=["grad__"],
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)
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def test_dump_grad_captures_forward_pass_id(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=True)
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d._forward_pass_id = 42
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x = torch.randn(3, 3, requires_grad=True)
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y = (x * 2).sum()
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d.dump("id_test", x)
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d._forward_pass_id = 999
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y.backward()
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grad_file = _find_dump_file(tmp_path, name="grad__id_test")
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assert "forward_pass_id=42" in grad_file.name
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def test_dump_grad_file_content(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=True)
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x = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
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y = (x * 3).sum()
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d.dump("content_check", x)
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y.backward()
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grad_path = _find_dump_file(tmp_path, name="grad__content_check")
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expected_grad = torch.full((2, 2), 3.0)
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assert torch.equal(_load_dump(grad_path)["value"], expected_grad)
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def test_disable_value(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_value=False, enable_grad=True)
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x = torch.randn(3, 3, requires_grad=True)
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y = (x * 2).sum()
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d.dump("fwd_disabled", x)
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y.backward()
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filenames = _get_filenames(tmp_path)
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assert not any(
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"name=fwd_disabled" in f and "grad__" not in f for f in filenames
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)
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_assert_files(filenames, exist=["grad__fwd_disabled"])
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def test_disable_grad(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_grad=False)
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x = torch.randn(3, 3, requires_grad=True)
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y = (x * 2).sum()
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d.dump("grad_disabled", x)
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y.backward()
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_assert_files(
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_get_filenames(tmp_path),
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exist=["name=grad_disabled"],
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not_exist=["grad__"],
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)
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class TestDumpModel:
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def test_grad_basic(self, tmp_path):
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d = _make_test_dumper(tmp_path, enable_model_value=False)
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model = torch.nn.Linear(4, 2)
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x = torch.randn(3, 4)
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y = model(x).sum()
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y.backward()
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d.dump_model(model, name_prefix="model")
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_assert_files(
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_get_filenames(tmp_path),
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|
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)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(pytest.main([__file__]))
|