chore: vendor sglang v0.5.10 snapshot

third_party/sglang/test/srt/ascend/test_embed_interpolate_unittest.py

@@ -0,0 +1,104 @@
+import unittest
+
+import torch
+
+from sglang.srt.configs.qwen3_vl import Qwen3VLConfig
+from sglang.srt.distributed.parallel_state import (
+    init_distributed_environment,
+    initialize_model_parallel,
+)
+from sglang.srt.layers.dp_attention import initialize_dp_attention
+from sglang.srt.layers.quantization.unquant import (
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.srt.models.qwen3_vl import Qwen3VLMoeVisionModel
+from sglang.srt.server_args import ServerArgs, set_global_server_args_for_scheduler
+
+
+def unpack(tensor, dim_len, pack_len):
+    dim_part = dim_len // pack_len
+    ret_val = tensor.reshape(dim_part, dim_part, pack_len, pack_len, -1)
+    ret_val = ret_val.permute(4, 0, 2, 1, 3).reshape(1, -1, dim_len, dim_len)
+    return ret_val
+
+
+class TestEmbedInterpolate(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.pDevice = torch.get_default_device()
+        torch.set_default_device("npu")
+
+    @classmethod
+    def tearDownClass(cls):
+        torch.set_default_device(cls.pDevice)
+
+    def test_embed_interpolate(self):
+        self.assertTrue(issubclass(UnquantizedLinearMethod, LinearMethodBase))
+        t_dim = [16, 32]
+        s_dim = [192, 574]
+        sarg = ServerArgs(model_path="dummy", device="npu")
+        mconf = Qwen3VLConfig(
+            hidden_size=64,
+            num_heads=1,
+            num_position_embeddings=2304,
+            patch_size=16,
+            spatial_merge_size=2,
+            temporal_patch_size=2,
+            deepstack_visual_indexes=[5, 11, 17],
+            in_channels=3,
+            depth=24,
+            intermediate_size=256,
+            hidden_act="gelu_pytorch_tanh",
+            out_hidden_size=2560,
+        )
+        set_global_server_args_for_scheduler(sarg)
+        init_distributed_environment(
+            backend="gloo",
+            world_size=1,
+            rank=0,
+            local_rank=0,
+            distributed_init_method="tcp://127.0.0.1:2646",
+        )
+        initialize_model_parallel()
+        initialize_dp_attention(
+            server_args=sarg,
+            model_config=mconf,
+        )
+        model = Qwen3VLMoeVisionModel(
+            mconf,
+            quant_config=None,
+            norm_eps=1e-6,
+            prefix="visual",
+        )
+        grid_thw = torch.tensor(
+            [(t, s, s) for t, s in zip(t_dim, s_dim)], dtype=torch.int32
+        )
+        embeddings = model.fast_pos_embed_interpolate(grid_thw)
+
+        embeddings_s0 = embeddings[: s_dim[0] * s_dim[0], :]
+        embeddings_s1 = embeddings[s_dim[0] * s_dim[0] : 2 * s_dim[0] * s_dim[0], :]
+        self.assertTrue(torch.allclose(embeddings_s0, embeddings_s1, atol=5e-5))
+
+        embeddings_l = embeddings[
+            t_dim[0] * s_dim[0] * s_dim[0] : t_dim[0] * s_dim[0] * s_dim[0]
+            + s_dim[1] * s_dim[1],
+            :,
+        ]
+        embeddings_s0 = torch.nn.functional.interpolate(
+            unpack(embeddings_s0, s_dim[0], 2),
+            size=(48, 48),
+            mode="area",
+        )
+        embeddings_r = torch.nn.functional.interpolate(
+            unpack(embeddings_l, s_dim[1], 2),
+            size=(48, 48),
+            mode="area",
+        )
+        self.assertTrue(
+            torch.allclose(embeddings_s0, embeddings_r, atol=5e-1, rtol=5e-1)
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()