Tool to dump and compare internal activation tensors (#7976)

python/sglang/srt/debug_utils.py

@@ -1,74 +0,0 @@
-import os
-import time
-from pathlib import Path
-
-import torch
-
-from sglang.srt.utils import get_bool_env_var
-
-
-class _Dumper:
-    """Utility to dump tensors, which can be useful when comparison checking models.
-
-    Example usage:
-    debug_utils.dumper.dump("layer_start_hidden_states", hidden_states, layer_id=self.layer_id)
-    """
-
-    def __init__(self):
-        self._enable = get_bool_env_var("SGLANG_DUMPER_ENABLE", "true")
-        self._base_dir = Path(os.environ.get("SGLANG_DUMPER_DIR", "/tmp"))
-        self._enable_write_file = get_bool_env_var("SGLANG_DUMPER_WRITE_FILE", "1")
-        self._partial_name = str(time.time())
-        self.forward_pass_id = None
-
-    def dump(self, name, value, **kwargs):
-        if not self._enable:
-            return
-
-        from sglang.srt.distributed import get_tensor_model_parallel_rank
-
-        rank = get_tensor_model_parallel_rank()
-        full_kwargs = dict(
-            forward_pass_id=self.forward_pass_id,
-            name=name,
-            **kwargs,
-        )
-        full_filename = "___".join(f"{k}={v}" for k, v in full_kwargs.items()) + ".pt"
-        path = (
-            self._base_dir / f"sglang_dump_{self._partial_name}_{rank}" / full_filename
-        )
-
-        sample_value = self._get_sample_value(name, value)
-
-        print(
-            f"[{rank}, {time.time()}] {path} "
-            f"type={type(value)} "
-            f"shape={value.shape if isinstance(value, torch.Tensor) else None} "
-            f"dtype={value.dtype if isinstance(value, torch.Tensor) else None} "
-            f"sample_value={sample_value}"
-        )
-
-        if self._enable_write_file:
-            path.parent.mkdir(parents=True, exist_ok=True)
-            torch.save(value, str(path))
-
-    def _get_sample_value(self, name, value):
-        if value is None:
-            return None
-
-        if isinstance(value, tuple):
-            return [self._get_sample_value(name, x) for x in value]
-
-        if not isinstance(value, torch.Tensor):
-            return None
-
-        if value.numel() < 200:
-            return value
-
-        slices = [
-            slice(0, 5) if dim_size > 200 else slice(None) for dim_size in value.shape
-        ]
-        return value[tuple(slices)]
-
-
-dumper = _Dumper()

python/sglang/srt/debug_utils/dump_comparator.py

@@ -0,0 +1,131 @@
+import argparse
+import functools
+import re
+from pathlib import Path
+
+import polars as pl
+import torch
+
+from sglang.srt.debug_utils.dumper import get_truncated_value
+
+
+def main(args):
+    df_target = read_meta(args.target_path)
+    df_target = df_target.sort("rank", "dump_index")
+    df_target = df_target.filter(
+        (pl.col("forward_pass_id") >= args.start_id)
+        & (pl.col("forward_pass_id") <= args.end_id)
+    )
+    assert all(
+        c in df_target.columns
+        for c in ["rank", "forward_pass_id", "dump_index", "name"]
+    )
+
+    df_baseline = read_meta(args.baseline_path)
+    print("df_target", df_target)
+    print("df_baseline", df_baseline)
+
+    for row in df_target.iter_rows(named=True):
+        rows_baseline = df_baseline.filter(
+            (
+                pl.col("forward_pass_id")
+                == row["forward_pass_id"] - args.start_id + args.baseline_start_id
+            )
+            & functools.reduce(
+                lambda a, b: a & b,
+                [
+                    pl.col(col) == row[col]
+                    for col in row.keys()
+                    if col not in ["forward_pass_id", "dump_index", "filename"]
+                ],
+            )
+        )
+        assert len(rows_baseline) == 1, f"{rows_baseline=}"
+        row_baseline = rows_baseline.to_dicts()[0]
+
+        path_baseline = Path(args.baseline_path) / row_baseline["filename"]
+        path_target = Path(args.target_path) / row["filename"]
+        print(f"Check: target={str(path_target)} baseline={str(path_baseline)}")
+        check_tensor_pair(path_baseline=path_baseline, path_target=path_target)
+        print()
+
+
+def read_meta(directory):
+    directory = Path(directory)
+    assert directory.is_dir(), f"{directory=} should be a directory"
+
+    rows = []
+    for p in directory.glob("*.pt"):
+        full_kwargs = {}
+        for kv in p.stem.split("___"):
+            k, v = kv.split("=")
+            full_kwargs[k] = v
+        rows.append(
+            {
+                "filename": str(p.name),
+                **full_kwargs,
+            }
+        )
+
+    df = pl.DataFrame(rows)
+    df = df.with_columns(
+        pl.col("forward_pass_id").cast(int),
+        pl.col("rank").cast(int),
+    )
+    return df
+
+
+def check_tensor_pair(path_baseline, path_target):
+    x_baseline = torch.load(path_baseline, weights_only=True)
+    x_target = torch.load(path_target, weights_only=True)
+
+    print(
+        f"[shape] {x_baseline.shape} vs {x_target.shape}\t"
+        f"[dtype] {x_baseline.dtype} vs {x_target.dtype}"
+    )
+
+    if x_baseline.shape != x_target.shape:
+        print(f"❌ Shape mismatch")
+        return
+
+    raw_abs_diff = (x_target - x_baseline).abs()
+
+    max_abs_diff = raw_abs_diff.max().item()
+    mean_abs_diff = raw_abs_diff.mean().item()
+    rel_diff = _calc_rel_diff(x_target, x_baseline)
+
+    needs_print = max_abs_diff > 1e-3
+
+    print(
+        "\t".join(
+            f"{'❌' if value > 1e-3 else '✅'} {name}={value}"
+            for name, value in [
+                ("rel_diff", rel_diff),
+                ("max_abs_diff", max_abs_diff),
+                ("mean_abs_diff", mean_abs_diff),
+            ]
+        )
+    )
+
+    if needs_print:
+        print(f"x_baseline(sample)={get_truncated_value(x_baseline)}")
+        print(f"x_target(sample)={get_truncated_value(x_target)}")
+
+
+# Copied from DeepGEMM
+def _calc_rel_diff(x: torch.Tensor, y: torch.Tensor):
+    x, y = x.double(), y.double()
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return 1 - sim
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--baseline-path", type=str)
+    parser.add_argument("--target-path", type=str)
+    parser.add_argument("--start-id", type=int, default=0)
+    parser.add_argument("--end-id", type=int, default=1000000)
+    parser.add_argument("--baseline-start-id", type=int, default=0)
+    args = parser.parse_args()
+    main(args)

python/sglang/srt/debug_utils/dumper.py

@@ -0,0 +1,108 @@
+import os
+import time
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+
+
+class _Dumper:
+    """Utility to dump tensors, which can be useful when comparison checking models.
+
+    Example usage:
+    dumper.on_forward_pass_start()
+    dumper.dump("layer_start__hidden_states", hidden_states, layer_id=self.layer_id)
+
+    Import from non-SGLang system:
+    ```
+    import sys
+    sys.path.append("/YOUR_PATH/sglang/python/sglang/srt/debug_utils")
+    from dumper import dumper
+    ```
+
+    Related: `sglang.srt.debug_utils.dump_comparator` for dump comparison
+    """
+
+    def __init__(self):
+        # Do not import `sglang` to make this file standalone
+        self._enable = bool(int(os.environ.get("SGLANG_DUMPER_ENABLE", "1")))
+        self._base_dir = Path(os.environ.get("SGLANG_DUMPER_DIR", "/tmp"))
+        self._enable_write_file = bool(
+            int(os.environ.get("SGLANG_DUMPER_WRITE_FILE", "1"))
+        )
+        self._partial_name: Optional[str] = None
+        self._dump_index = 0
+        self._forward_pass_id = 0
+
+    def on_forward_pass_start(self):
+        self._forward_pass_id += 1
+        print(
+            f"[Dumper] [{time.time()}] on_forward_pass_start id={self._forward_pass_id}"
+        )
+
+    def dump(self, name, value, **kwargs):
+        if not self._enable:
+            return
+
+        assert (
+            self._forward_pass_id >= 1
+        ), "Do you forget to call `dumper.on_forward_pass_start()`?"
+        self._dump_index += 1
+
+        if self._partial_name is None:
+            self._partial_name = _get_partial_name()
+
+        rank = dist.get_rank()
+        full_kwargs = dict(
+            forward_pass_id=self._forward_pass_id,
+            rank=rank,
+            name=name,
+            dump_index=self._dump_index,
+            **kwargs,
+        )
+        full_filename = "___".join(f"{k}={v}" for k, v in full_kwargs.items()) + ".pt"
+        path = self._base_dir / f"sglang_dump_{self._partial_name}" / full_filename
+
+        sample_value = get_truncated_value(value)
+
+        print(
+            f"[Dumper] [{rank}, {time.time()}] {path} "
+            f"type={type(value)} "
+            f"shape={value.shape if isinstance(value, torch.Tensor) else None} "
+            f"dtype={value.dtype if isinstance(value, torch.Tensor) else None} "
+            f"sample_value={sample_value}"
+        )
+
+        if self._enable_write_file:
+            path.parent.mkdir(parents=True, exist_ok=True)
+            torch.save(value, str(path))
+
+
+def _get_partial_name():
+    rank = dist.get_rank()
+    object_list = [str(time.time()) if rank == 0 else None]
+    dist.broadcast_object_list(object_list, device="cuda")
+    return object_list[0]
+
+
+def get_truncated_value(value):
+    if value is None:
+        return None
+
+    if isinstance(value, tuple):
+        return [get_truncated_value(x) for x in value]
+
+    if not isinstance(value, torch.Tensor):
+        return None
+
+    if value.numel() < 200:
+        return value
+
+    slices = [
+        slice(0, 5) if dim_size > 200 else slice(None) for dim_size in value.shape
+    ]
+    return value[tuple(slices)]
+
+
+dumper = _Dumper()