[CI] Move CPU-only unit tests to the CPU suite and trim dead 5090 registrations (#33654)

test/registered/models/test_vit_pos_embed_interpolate.py

@@ -1,123 +0,0 @@
-"""Bit-exact unit test for the vectorized ViT position-embedding interpolation.
-
-The vectorized path (``fast_pos_embed_interpolate_vectorized``) removes the
-per-image Python loop / CPU<->GPU sync of the legacy implementations. It is meant
-to be a pure speedup, so it must be numerically *identical* (bit-exact, rtol=0
-atol=0) to the loop version it replaces -- for single images, many images, video
-(t>1), and mixed-size batches, in both bf16 and fp32.
-
-The interpolation is a sequence of embedding lookups + arithmetic, so it runs and
-is bit-exact on CPU; the test exercises CUDA too when available. It calls the real
-model methods on a lightweight stub holding a real ``nn.Embedding`` (no model
-weights / distributed init needed).
-
-    python -m pytest test/registered/models/test_vit_pos_embed_interpolate.py -v
-"""
-
-import unittest
-from types import SimpleNamespace
-
-import torch
-import torch.nn as nn
-
-from sglang.test.ci.ci_register import (
-    register_amd_ci,
-    register_cpu_ci,
-    register_cuda_ci,
-)
-from sglang.test.test_utils import CustomTestCase
-
-register_cpu_ci(est_time=20, suite="base-a-test-cpu")
-register_cuda_ci(est_time=20, stage="base-a", runner_config="1-gpu-small")
-register_amd_ci(est_time=20, stage="stage-a", runner_config="1-gpu-small-amd")
-
-NUM_POS = 2304  # Qwen3-VL num_position_embeddings -> 48x48 grid
-HIDDEN = 64  # small hidden dim keeps the unit test fast
-MERGE = 2
-
-# t, h, w grids (h, w are multiples of MERGE). Covers single / large-upsample /
-# multi-mixed / video / video+image / many-duplicate.
-GRID_CASES = {
-    "single": [[1, 16, 16]],
-    "single_large": [[1, 64, 98]],  # h, w may exceed grid side (upsample)
-    "multi_mixed": [[1, 16, 24], [1, 32, 12], [1, 8, 40]],
-    "video": [[4, 16, 20]],
-    "video_plus_image": [[3, 12, 16], [1, 20, 28], [2, 8, 8]],
-    "many": [[1, 24, 24]] * 8,
-}
-
-
-def _devices():
-    devs = [torch.device("cpu")]
-    if torch.cuda.is_available():
-        devs.append(torch.device("cuda"))
-    return devs
-
-
-class TestViTPosEmbedInterpolate(CustomTestCase):
-    def _check(self, stub, legacy_fn, vectorized_fn, grid, label):
-        ref = legacy_fn(stub, grid)
-        out = vectorized_fn(stub, grid)
-        self.assertEqual(ref.shape, out.shape, f"{label}: shape mismatch")
-        self.assertTrue(
-            torch.equal(ref, out),
-            f"{label}: not bit-exact, max|diff|="
-            f"{(ref.float() - out.float()).abs().max().item():.3e}",
-        )
-
-    def test_qwen3_vl_vectorized_matches_loop(self):
-        try:
-            from sglang.srt.models.qwen3_vl import Qwen3VLMoeVisionModel as M
-        except Exception as e:  # heavy optional deps (flashinfer, ...) unavailable
-            self.skipTest(f"cannot import Qwen3VLMoeVisionModel: {e}")
-
-        for device in _devices():
-            for dtype in (torch.bfloat16, torch.float32):
-                stub = SimpleNamespace(
-                    num_grid_per_side=int(NUM_POS**0.5),
-                    spatial_merge_size=MERGE,
-                    num_position_embeddings=NUM_POS,
-                    pos_embed=nn.Embedding(NUM_POS, HIDDEN).to(
-                        device=device, dtype=dtype
-                    ),
-                    dtype=dtype,
-                    device=device,
-                )
-                for name, grid in GRID_CASES.items():
-                    self._check(
-                        stub,
-                        M.fast_pos_embed_interpolate_from_list,
-                        M.fast_pos_embed_interpolate_vectorized,
-                        grid,
-                        f"qwen3_vl/{name}/{dtype}/{device.type}",
-                    )
-
-    def test_moss_vl_vectorized_matches_loop(self):
-        try:
-            from sglang.srt.models.moss_vl import MossVLVisionModel as M
-        except Exception as e:
-            self.skipTest(f"cannot import MossVLVisionModel: {e}")
-
-        for device in _devices():
-            for dtype in (torch.bfloat16, torch.float32):
-                stub = SimpleNamespace(
-                    spatial_merge_size=MERGE,
-                    num_position_embeddings=NUM_POS,
-                    pos_embed=nn.Embedding(NUM_POS, HIDDEN).to(
-                        device=device, dtype=dtype
-                    ),
-                )
-                for name, grid in GRID_CASES.items():
-                    # the legacy moss method consumes a [num_images, 3] tensor
-                    grid_t = torch.tensor(grid, device=device)
-                    self._check(
-                        stub,
-                        M.fast_pos_embed_interpolate,
-                        M.fast_pos_embed_interpolate_vectorized,
-                        grid_t,
-                        f"moss_vl/{name}/{dtype}/{device.type}",
-                    )
-
-
-if __name__ == "__main__":
-    unittest.main()