Support replication axis in dump comparator (#19282)

2026-02-25 09:48:43 +08:00
parent 2e2b18e870
commit b7af58b9af
10 changed files with 778 additions and 62 deletions
@@ -148,7 +148,7 @@ class TestCpZigzagTpE2E:
            if isinstance(plan, ReorderPlan):
                current = execute_reorder_plan(plan, current)
            else:
-                current = execute_unshard_plan(plan, current)
+                current, _ = execute_unshard_plan(plan, current)

        assert len(current) == 1
        assert torch.allclose(current[0], full_tensor)
@@ -5,12 +5,16 @@ import torch

 from sglang.srt.debug_utils.comparator.aligner.unshard.executor import (
    _apply_unshard,
+    _verify_replicated_group,
    execute_unshard_plan,
 )
 from sglang.srt.debug_utils.comparator.aligner.unshard.planner import (
    compute_unshard_plan,
 )
-from sglang.srt.debug_utils.comparator.aligner.unshard.types import AxisInfo
+from sglang.srt.debug_utils.comparator.aligner.unshard.types import (
+    AxisInfo,
+    PickParams,
+)
 from sglang.srt.debug_utils.comparator.dims import ParallelAxis, parse_dims
 from sglang.test.ci.ci_register import register_cpu_ci

@@ -29,9 +33,10 @@ class TestExecuteUnshardPlan:
        plans = compute_unshard_plan(dim_specs, parallel_infos)
        assert len(plans) == 1

-        result = execute_unshard_plan(plans[0], shards)
+        result, warnings = execute_unshard_plan(plans[0], shards)
        assert len(result) == 1
        assert torch.allclose(result[0], full_tensor)
+        assert warnings == []

    def test_scrambled_world_ranks_correct_result(self) -> None:
        full_tensor = torch.randn(4, 8)
@@ -54,9 +59,10 @@ class TestExecuteUnshardPlan:
            shards[1],  # world_rank=3, axis_rank=1
        ]

-        result = execute_unshard_plan(plans[0], tensors_ordered_by_world_rank)
+        result, warnings = execute_unshard_plan(plans[0], tensors_ordered_by_world_rank)
        assert len(result) == 1
        assert torch.allclose(result[0], full_tensor)
+        assert warnings == []

    def test_single_step_reduces_tensor_count(self) -> None:
        """8 tensors with 2 groups of 4 produce 2 output tensors."""
@@ -85,10 +91,10 @@ class TestExecuteUnshardPlan:
            for tp_rank in range(4):
                tensors.append(source[tp_rank])

-        intermediate = execute_unshard_plan(plans[0], tensors)
+        intermediate, _ = execute_unshard_plan(plans[0], tensors)
        assert len(intermediate) == 4

-        final = execute_unshard_plan(plans[1], intermediate)
+        final, _ = execute_unshard_plan(plans[1], intermediate)
        assert len(final) == 1

    def test_cp_tp_concat(self) -> None:
@@ -116,7 +122,7 @@ class TestExecuteUnshardPlan:

        current = tensors
        for plan in plans:
-            current = execute_unshard_plan(plan, current)
+            current, _ = execute_unshard_plan(plan, current)

        assert len(current) == 1
        assert torch.allclose(current[0], full_tensor)
@@ -157,7 +163,7 @@ class TestExecuteUnshardPlan:

        current = tensors
        for plan in plans:
-            current = execute_unshard_plan(plan, current)
+            current, _ = execute_unshard_plan(plan, current)

        assert len(current) == 1
        assert torch.allclose(current[0], full_tensor)
@@ -169,7 +175,12 @@ class TestExecuteUnshardPlan:
            pass

        with pytest.raises(ValueError, match="Unsupported unshard"):
-            _apply_unshard(_FakeParams(), [torch.randn(2, 2)])
+            _apply_unshard(
+                _FakeParams(),
+                [torch.randn(2, 2)],
+                axis=ParallelAxis.TP,
+                group_index=0,
+            )

    def test_cp_tp_ep_three_axis_concat(self) -> None:
        """CP=2 + TP=2 + EP=2: three-step unshard reconstructs original tensor."""
@@ -205,7 +216,7 @@ class TestExecuteUnshardPlan:

        current = tensors
        for plan in plans:
-            current = execute_unshard_plan(plan, current)
+            current, _ = execute_unshard_plan(plan, current)

        assert len(current) == 1
        assert torch.allclose(current[0], full_tensor)
@@ -253,11 +264,211 @@ class TestExecuteUnshardPlan:

        current = tensors
        for plan in plans:
-            current = execute_unshard_plan(plan, current)
+            current, _ = execute_unshard_plan(plan, current)

        assert len(current) == 1
        assert torch.allclose(current[0], full_tensor)


+class TestPickOperation:
+    def test_pick_single_group(self) -> None:
+        """PickParams picks the first tensor from a single group."""
+        tensor = torch.randn(4, 8)
+        dim_specs = parse_dims("h d")
+        parallel_infos = [
+            {ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2)},
+            {ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2)},
+        ]
+
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+        assert len(plans) == 1
+        assert isinstance(plans[0].params, PickParams)
+
+        result, warnings = execute_unshard_plan(plans[0], [tensor, tensor.clone()])
+        assert len(result) == 1
+        assert torch.allclose(result[0], tensor)
+        assert warnings == []
+
+    def test_pick_multiple_groups(self) -> None:
+        """PickParams with multiple groups picks one from each."""
+        dim_specs = parse_dims("h(tp)")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+        ]
+
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+        pick_plans = [p for p in plans if isinstance(p.params, PickParams)]
+        assert len(pick_plans) == 1
+        assert pick_plans[0].axis == ParallelAxis.CP
+
+        tensor = torch.randn(4)
+        tensors = [tensor.clone() for _ in range(4)]
+
+        result, warnings = execute_unshard_plan(pick_plans[0], tensors)
+        assert len(result) == 2
+        assert warnings == []
+
+    def test_replicated_tp_sharded_cp_e2e(self) -> None:
+        """CP2 TP2, dims='b s(cp) d': replicated TP pick + sharded CP concat round-trip."""
+        torch.manual_seed(42)
+        full_tensor = torch.randn(4, 8, 16)
+        cp_chunks = list(full_tensor.chunk(2, dim=1))
+
+        tensors: list[torch.Tensor] = []
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = []
+        for cp_rank in range(2):
+            for tp_rank in range(2):
+                tensors.append(cp_chunks[cp_rank].clone())
+                parallel_infos.append(
+                    {
+                        ParallelAxis.CP: AxisInfo(axis_rank=cp_rank, axis_size=2),
+                        ParallelAxis.TP: AxisInfo(axis_rank=tp_rank, axis_size=2),
+                    }
+                )
+
+        dim_specs = parse_dims("b s(cp) d")
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+        assert len(plans) == 2
+
+        current = tensors
+        for plan in plans:
+            current, _ = execute_unshard_plan(plan, current)
+
+        assert len(current) == 1
+        assert torch.allclose(current[0], full_tensor)
+
+    def test_fully_replicated_e2e(self) -> None:
+        """CP2 TP2, dims='b h d': fully replicated → 2 pick steps → 1 tensor."""
+        torch.manual_seed(42)
+        full_tensor = torch.randn(4, 8, 16)
+
+        tensors: list[torch.Tensor] = []
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = []
+        for cp_rank in range(2):
+            for tp_rank in range(2):
+                tensors.append(full_tensor.clone())
+                parallel_infos.append(
+                    {
+                        ParallelAxis.CP: AxisInfo(axis_rank=cp_rank, axis_size=2),
+                        ParallelAxis.TP: AxisInfo(axis_rank=tp_rank, axis_size=2),
+                    }
+                )
+
+        dim_specs = parse_dims("b h d")
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+        assert len(plans) == 2
+        assert all(isinstance(p.params, PickParams) for p in plans)
+
+        current = tensors
+        for plan in plans:
+            current, _ = execute_unshard_plan(plan, current)
+
+        assert len(current) == 1
+        assert torch.allclose(current[0], full_tensor)
+
+
+class TestVerifyReplicatedGroup:
+    def test_warns_on_mismatch(self) -> None:
+        """_verify_replicated_group produces warning when replicas differ."""
+        tensor_a = torch.ones(4)
+        tensor_b = torch.ones(4) + 0.1
+
+        warnings = _verify_replicated_group(
+            [tensor_a, tensor_b],
+            axis=ParallelAxis.TP,
+            group_index=0,
+        )
+        assert len(warnings) == 1
+        assert warnings[0].axis == "tp"
+        assert warnings[0].group_index == 0
+        assert warnings[0].differing_index == 1
+        assert warnings[0].baseline_index == 0
+        assert warnings[0].max_abs_diff == pytest.approx(0.1, abs=1e-5)
+
+    def test_no_warn_when_identical(self) -> None:
+        """_verify_replicated_group produces no warning for identical replicas."""
+        tensor = torch.randn(4, 8)
+
+        warnings = _verify_replicated_group(
+            [tensor, tensor.clone()],
+            axis=ParallelAxis.TP,
+            group_index=0,
+        )
+        assert warnings == []
+
+    def test_multiple_mismatches(self) -> None:
+        """_verify_replicated_group reports each differing replica."""
+        baseline = torch.zeros(4)
+        other_a = torch.ones(4)
+        other_b = torch.ones(4) * 2
+
+        warnings = _verify_replicated_group(
+            [baseline, other_a, other_b],
+            axis=ParallelAxis.CP,
+            group_index=1,
+        )
+        assert len(warnings) == 2
+        assert warnings[0].differing_index == 1
+        assert warnings[1].differing_index == 2
+        assert warnings[1].max_abs_diff == pytest.approx(2.0, abs=1e-5)
+
+    def test_execute_returns_warnings(self) -> None:
+        """execute_unshard_plan returns warnings for replicated mismatch."""
+        dim_specs = parse_dims("h d")
+        parallel_infos = [
+            {ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2)},
+            {ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2)},
+        ]
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+
+        tensor_a = torch.zeros(4)
+        tensor_b = torch.ones(4)
+
+        result, warnings = execute_unshard_plan(plans[0], [tensor_a, tensor_b])
+        assert len(result) == 1
+        assert len(warnings) == 1
+        assert torch.allclose(result[0], tensor_a)
+
+    def test_atol_boundary_within(self) -> None:
+        """Difference exactly at atol (1e-6) → torch.allclose passes → no warning."""
+        baseline = torch.zeros(4)
+        other = torch.full((4,), 1e-6)
+
+        warnings = _verify_replicated_group(
+            [baseline, other],
+            axis=ParallelAxis.TP,
+            group_index=0,
+        )
+        assert warnings == []
+
+    def test_atol_boundary_exceeded(self) -> None:
+        """Difference just above atol (1e-6 + 1e-9) → torch.allclose fails → warning."""
+        baseline = torch.zeros(4)
+        other = torch.full((4,), 1e-6 + 1e-9)
+
+        warnings = _verify_replicated_group(
+            [baseline, other],
+            axis=ParallelAxis.TP,
+            group_index=0,
+        )
+        assert len(warnings) == 1
+        assert warnings[0].differing_index == 1
+
+
 if __name__ == "__main__":
    sys.exit(pytest.main([__file__]))
@@ -5,7 +5,11 @@ import pytest
 from sglang.srt.debug_utils.comparator.aligner.unshard.planner import (
    compute_unshard_plan,
 )
-from sglang.srt.debug_utils.comparator.aligner.unshard.types import AxisInfo
+from sglang.srt.debug_utils.comparator.aligner.unshard.types import (
+    AxisInfo,
+    ConcatParams,
+    PickParams,
+)
 from sglang.srt.debug_utils.comparator.dims import ParallelAxis, parse_dims
 from sglang.test.ci.ci_register import register_cpu_ci

@@ -228,7 +232,7 @@ class TestComputeUnshardPlan:
        assert len(plans[2].groups) == 1
        assert len(plans[2].groups[0]) == 2

-    def test_replicated_axis_raises(self) -> None:
+    def test_sharded_axis_missing_from_rank_raises(self) -> None:
        """A world_rank missing a sharded axis raises ValueError."""
        dim_specs = parse_dims("s(cp) h(tp)")
        parallel_infos = [
@@ -238,7 +242,159 @@ class TestComputeUnshardPlan:
            },
            {
                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
-                # missing TP — replicated
+                # missing TP — sharded axis absent from rank
+            },
+        ]
+        with pytest.raises(ValueError, match="missing parallel_info"):
+            compute_unshard_plan(dim_specs, parallel_infos)
+
+
+class TestReplicatedAxes:
+    def test_replicated_tp_with_sharded_cp(self) -> None:
+        """CP2 TP2, dims='b s(cp) d' → PickPlan(TP) + ConcatPlan(CP)."""
+        dim_specs = parse_dims("b s(cp) d")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+        ]
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+
+        assert len(plans) == 2
+        assert plans[0].axis == ParallelAxis.TP
+        assert isinstance(plans[0].params, PickParams)
+        assert len(plans[0].groups) == 2
+        for group in plans[0].groups:
+            assert len(group) == 2
+
+        assert plans[1].axis == ParallelAxis.CP
+        assert isinstance(plans[1].params, ConcatParams)
+        assert plans[1].params.dim == 1
+
+    def test_fully_replicated(self) -> None:
+        """CP2 TP2, dims='b h d' → PickPlan(CP) + PickPlan(TP)."""
+        dim_specs = parse_dims("b h d")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+        ]
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+
+        assert len(plans) == 2
+        assert all(isinstance(p.params, PickParams) for p in plans)
+        axes = {p.axis for p in plans}
+        assert axes == {ParallelAxis.CP, ParallelAxis.TP}
+
+    def test_multiple_replicated_one_sharded(self) -> None:
+        """CP2 TP2 EP2, dims='h(tp)' → PickPlan(CP) + PickPlan(EP) + ConcatPlan(TP)."""
+        dim_specs = parse_dims("h(tp)")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = []
+        for cp_rank in range(2):
+            for ep_rank in range(2):
+                for tp_rank in range(2):
+                    parallel_infos.append(
+                        {
+                            ParallelAxis.CP: AxisInfo(axis_rank=cp_rank, axis_size=2),
+                            ParallelAxis.EP: AxisInfo(axis_rank=ep_rank, axis_size=2),
+                            ParallelAxis.TP: AxisInfo(axis_rank=tp_rank, axis_size=2),
+                        }
+                    )
+
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+
+        assert len(plans) == 3
+        pick_plans = [p for p in plans if isinstance(p.params, PickParams)]
+        concat_plans = [p for p in plans if isinstance(p.params, ConcatParams)]
+        assert len(pick_plans) == 2
+        assert len(concat_plans) == 1
+        assert concat_plans[0].axis == ParallelAxis.TP
+
+        replicated_axes = {p.axis for p in pick_plans}
+        assert replicated_axes == {ParallelAxis.CP, ParallelAxis.EP}
+
+    def test_replicated_scrambled_ranks(self) -> None:
+        """Scrambled world_rank order with replicated axis."""
+        dim_specs = parse_dims("h(tp)")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=1, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+        ]
+        plans = compute_unshard_plan(dim_specs, parallel_infos)
+
+        assert len(plans) == 2
+        assert plans[0].axis == ParallelAxis.CP
+        assert isinstance(plans[0].params, PickParams)
+        assert plans[1].axis == ParallelAxis.TP
+        assert isinstance(plans[1].params, ConcatParams)
+
+    def test_replicated_axis_inconsistent_size_raises(self) -> None:
+        """Replicated axis with inconsistent sizes raises ValueError."""
+        dim_specs = parse_dims("h(tp)")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=4),
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
+            },
+        ]
+        with pytest.raises(ValueError, match="Inconsistent axis_size"):
+            compute_unshard_plan(dim_specs, parallel_infos)
+
+    def test_replicated_axis_missing_from_rank_raises(self) -> None:
+        """A rank missing a replicated axis that other ranks have raises ValueError."""
+        dim_specs = parse_dims("h(tp)")
+        parallel_infos: list[dict[ParallelAxis, AxisInfo]] = [
+            {
+                ParallelAxis.CP: AxisInfo(axis_rank=0, axis_size=2),
+                ParallelAxis.TP: AxisInfo(axis_rank=0, axis_size=2),
+            },
+            {
+                # missing CP — replicated axis absent from this rank
+                ParallelAxis.TP: AxisInfo(axis_rank=1, axis_size=2),
            },
        ]
        with pytest.raises(ValueError, match="missing parallel_info"):