[Fix] Repair CI fixtures and ROCm speculative tree device checks (#40325)

This commit is contained in:
Xiaoyu Zhang
2026-09-19 18:16:05 +08:00
committed by GitHub
parent 567d5925fe
commit 0b0d2c257a
4 changed files with 16 additions and 11 deletions
@@ -48,7 +48,7 @@ inline void build_tree_kernel_efficient(
TensorMatcher({batch_size, parent_width})
.with_strides({parent_list.size(1) == 0 ? -1 : parent_list.size(1), 1})
.with_dtype<int64_t>()
.with_device<kDLCUDA>(device)
.with_device<kDLGPU>(device)
.verify(parent_list);
CHECK_HOST(depth == 1 || parent_width.unwrap() == topk * (depth - 1) + 1);
TensorMatcher({batch_size, draft_token_num - 1})
@@ -138,7 +138,7 @@ inline void verify_tree_greedy(
SymbolicDevice device;
TensorMatcher({batch_size, draft_tokens})
.with_dtype<int64_t>()
.with_device<kDLCUDA>(device)
.with_device<kDLGPU>(device)
.verify(candidates)
.verify(retrive_index)
.verify(retrive_next_token)
@@ -183,7 +183,7 @@ inline void reconstruct_indices_from_tree_mask(
// Bytes, not element type -- same reasoning as build_tree_kernel_efficient:
// the kernel casts straight to bool* and callers are free to spell a 1-byte
// mask as bool or uint8.
TensorMatcher({-1}).with_device<kDLCUDA>(device).verify(tree_mask);
TensorMatcher({-1}).with_device<kDLGPU>(device).verify(tree_mask);
CHECK_HOST(tree_mask.dtype().bits == 8);
CHECK_HOST(tree_mask.numel() >= batch_size * draft_token_num * draft_token_num);
TensorMatcher({batch_size}).with_dtype<int64_t>().with_device(device).verify(verified_seq_len);
@@ -78,7 +78,7 @@ class _TokenToKVPool:
extra_key_buffer if extra_key_buffer is not None else swa_key_buffer
)
self.full_to_swa_index_mapping = full_to_swa_index_mapping
self.swa_page_size = page_size
self.swa_kv_pool = _Pool(page_size)
def get_swa_key_buffer_radix(self, layer_id: int) -> torch.Tensor:
_ = layer_id
@@ -86,7 +86,7 @@ class _TokenToKVPool:
def get_extra_key_page_size(self, layer_id: int) -> int:
_ = layer_id
return self.swa_page_size
return self.swa_kv_pool.page_size
def get_extra_key_buffer(self, layer_id: int) -> torch.Tensor:
_ = layer_id
@@ -20,6 +20,7 @@ import unittest
from pathlib import Path
from unittest.mock import MagicMock, patch
from sglang.srt.runtime_context import get_parallel
from sglang.test.ci.ci_register import register_mlx_ci
register_mlx_ci(est_time=5, suite="stage-a-unit-test-mlx")
@@ -267,6 +268,7 @@ class TestSchedulerProfilerManagerMPS(unittest.TestCase):
torch.mps.profiler, "metal_capture", return_value=capture_ctx
),
mock_patch("torch.distributed.barrier"),
get_parallel().override(tp_rank=0, pp_size=1, moe_ep_size=1),
):
result = mgr._start_profile()
self.assertTrue(result.success, result.message)
@@ -16,6 +16,7 @@ from sglang.srt.managers.schedule_batch import ScheduleBatch
from sglang.srt.managers.scheduler import Scheduler
from sglang.srt.managers.utils import GenerationBatchResult
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.runtime_context import get_parallel
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
@@ -120,12 +121,11 @@ class TestSchedulerIdleStepCounters(CustomTestCase):
scheduler.disagg_decode_transfer_queue.queue = [
object()
]
parallel = SimpleNamespace(
pp_async_batch_depth=depth,
enable_dsa_prefill_context_parallel=False,
)
with (
patch(f"{PP_MODULE}.get_parallel", return_value=parallel),
get_parallel().override(
pp_size=2,
pp_async_batch_depth=depth,
),
patch(
f"{PP_MODULE}.get_disagg",
return_value=SimpleNamespace(
@@ -278,7 +278,10 @@ class TestSchedulerIdleStepCounters(CustomTestCase):
scheduler.run_batch = run_batch
scheduler.process_batch_result = process_batch_result
with self.assertRaises(StopIteration):
with (
get_parallel().override(pp_rank=0, attn_tp_rank=0, attn_cp_rank=0),
self.assertRaises(StopIteration),
):
event_loop(scheduler)
self.assertEqual(observed_idle_flags, [False, False, after_idle, False])