[Fix] Repair CI fixtures and ROCm speculative tree device checks (#40325)
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@@ -48,7 +48,7 @@ inline void build_tree_kernel_efficient(
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TensorMatcher({batch_size, parent_width})
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.with_strides({parent_list.size(1) == 0 ? -1 : parent_list.size(1), 1})
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.with_dtype<int64_t>()
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.with_device<kDLCUDA>(device)
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.with_device<kDLGPU>(device)
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.verify(parent_list);
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CHECK_HOST(depth == 1 || parent_width.unwrap() == topk * (depth - 1) + 1);
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TensorMatcher({batch_size, draft_token_num - 1})
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@@ -138,7 +138,7 @@ inline void verify_tree_greedy(
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SymbolicDevice device;
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TensorMatcher({batch_size, draft_tokens})
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.with_dtype<int64_t>()
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.with_device<kDLCUDA>(device)
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.with_device<kDLGPU>(device)
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.verify(candidates)
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.verify(retrive_index)
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.verify(retrive_next_token)
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@@ -183,7 +183,7 @@ inline void reconstruct_indices_from_tree_mask(
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// Bytes, not element type -- same reasoning as build_tree_kernel_efficient:
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// the kernel casts straight to bool* and callers are free to spell a 1-byte
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// mask as bool or uint8.
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TensorMatcher({-1}).with_device<kDLCUDA>(device).verify(tree_mask);
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TensorMatcher({-1}).with_device<kDLGPU>(device).verify(tree_mask);
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CHECK_HOST(tree_mask.dtype().bits == 8);
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CHECK_HOST(tree_mask.numel() >= batch_size * draft_token_num * draft_token_num);
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TensorMatcher({batch_size}).with_dtype<int64_t>().with_device(device).verify(verified_seq_len);
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@@ -78,7 +78,7 @@ class _TokenToKVPool:
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extra_key_buffer if extra_key_buffer is not None else swa_key_buffer
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)
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self.full_to_swa_index_mapping = full_to_swa_index_mapping
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self.swa_page_size = page_size
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self.swa_kv_pool = _Pool(page_size)
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def get_swa_key_buffer_radix(self, layer_id: int) -> torch.Tensor:
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_ = layer_id
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@@ -86,7 +86,7 @@ class _TokenToKVPool:
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def get_extra_key_page_size(self, layer_id: int) -> int:
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_ = layer_id
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return self.swa_page_size
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return self.swa_kv_pool.page_size
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def get_extra_key_buffer(self, layer_id: int) -> torch.Tensor:
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_ = layer_id
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@@ -20,6 +20,7 @@ import unittest
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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from sglang.srt.runtime_context import get_parallel
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from sglang.test.ci.ci_register import register_mlx_ci
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register_mlx_ci(est_time=5, suite="stage-a-unit-test-mlx")
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@@ -267,6 +268,7 @@ class TestSchedulerProfilerManagerMPS(unittest.TestCase):
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torch.mps.profiler, "metal_capture", return_value=capture_ctx
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),
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mock_patch("torch.distributed.barrier"),
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get_parallel().override(tp_rank=0, pp_size=1, moe_ep_size=1),
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):
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result = mgr._start_profile()
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self.assertTrue(result.success, result.message)
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@@ -16,6 +16,7 @@ from sglang.srt.managers.schedule_batch import ScheduleBatch
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from sglang.srt.managers.scheduler import Scheduler
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from sglang.srt.managers.utils import GenerationBatchResult
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.runtime_context import get_parallel
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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from sglang.test.ci.ci_register import register_cpu_ci
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from sglang.test.test_utils import CustomTestCase
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@@ -120,12 +121,11 @@ class TestSchedulerIdleStepCounters(CustomTestCase):
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scheduler.disagg_decode_transfer_queue.queue = [
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object()
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]
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parallel = SimpleNamespace(
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pp_async_batch_depth=depth,
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enable_dsa_prefill_context_parallel=False,
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)
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with (
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patch(f"{PP_MODULE}.get_parallel", return_value=parallel),
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get_parallel().override(
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pp_size=2,
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pp_async_batch_depth=depth,
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),
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patch(
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f"{PP_MODULE}.get_disagg",
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return_value=SimpleNamespace(
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@@ -278,7 +278,10 @@ class TestSchedulerIdleStepCounters(CustomTestCase):
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scheduler.run_batch = run_batch
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scheduler.process_batch_result = process_batch_result
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with self.assertRaises(StopIteration):
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with (
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get_parallel().override(pp_rank=0, attn_tp_rank=0, attn_cp_rank=0),
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self.assertRaises(StopIteration),
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):
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event_loop(scheduler)
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self.assertEqual(observed_idle_flags, [False, False, after_idle, False])
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