[Misc] Main2Main v0.33.0

.github/vllm-release-tag.commit

@@ -1 +1 @@
-v0.32.0
+v0.33.0

Dockerfile

@@ -32,7 +32,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

Dockerfile.310p

@@ -30,7 +30,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

Dockerfile.310p.openEuler

@@ -29,7 +29,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

Dockerfile.a3

@@ -35,7 +35,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

Dockerfile.a3.openEuler

@@ -32,7 +32,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

Dockerfile.a5

@@ -35,7 +35,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 ARG CARGO_BUILD_JOBS=16

Dockerfile.a5.openEuler

@@ -32,7 +32,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 ARG CARGO_BUILD_JOBS=16

Dockerfile.openEuler

@@ -32,7 +32,7 @@ RUN python3 -m pip install 'modelscope<1.38' 'ray>=2.47.1,<=2.48.0' 'protobuf>3.
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.32.0
+ARG VLLM_TAG=v0.33.0
 ARG VLLM_COMMIT=""
 ARG PYTORCH_INDEX_URL="https://download.pytorch.org/whl/cpu/"
 RUN if [ -n "$VLLM_COMMIT" ]; then \

README.md

@@ -98,7 +98,7 @@ Below are the maintained branches:
 
 | Branch           | Status       | Note                                 |
 |------------------|--------------|--------------------------------------|
-| main             | Maintained   | CI commitment for vLLM main branch and vLLM v0.32.0 tag |
+| main             | Maintained   | CI commitment for vLLM main branch and vLLM v0.33.0 tag |
 | releases/v0.13.0 | Maintained   | Only bug fixes are allowed, and no new release tags anymore. |
 | releases/v0.18.0 | Maintained   | CI commitment for vLLM 0.18.0 version |
 | releases/v0.23.0 | Maintained   | CI commitment for vLLM 0.23.0 version |

README.zh.md

@@ -100,7 +100,7 @@ vllm-ascend有主干分支和开发分支。
 
 | 分支              | 状态         | 备注                  |
 |------------------|--------------|----------------------|
-| main             | Maintained   | 基于vLLM main分支和vLLM最新版本（v0.32.0）CI看护   |
+| main             | Maintained   | 基于vLLM main分支和vLLM最新版本（v0.33.0）CI看护   |
 | releases/v0.13.0 | Maintained   | 只允许Bug修复，不会再发布新版本 |
 | releases/v0.18.0 | Maintained   | 基于vLLM v0.18.0版本CI看护 |
 | releases/v0.23.0 | Maintained   | 基于vLLM v0.23.0版本CI看护 |

release_notes/fragments/20627.yaml

@@ -0,0 +1,4 @@
+pr: 20627
+entries:
+  - category: dependency
+    text: Updated vLLM release compatibility and Dockerfile defaults to v0.33.0 while retaining support for the verified vLLM main baseline.

tests/e2e/common/rlhf/weight_transfer/weight_source.py

@@ -18,7 +18,6 @@
 from vllm.distributed.weight_transfer.base import ParamMeta, WeightSource
 
 from tests.e2e.common.rlhf.weight_transfer.case import WeightUpdateModelCase
-from vllm_ascend.utils import vllm_version_is
 
 
 def expand_fused_expert_params(name: str, shape: tuple[int, ...]) -> list[tuple[str, tuple[int, ...]]]:
@@ -82,11 +81,6 @@ def _apply_hf_overrides(config, overrides: dict[str, Any]) -> None:
 FIXED_WEIGHT_SEED = 20260915
 
 
-def _source_metadata(func):
-    """vLLM main (#63256) requires a cached property; v0.32.0 still calls it."""
-    return func if vllm_version_is("0.32.0") else cached_property(func)
-
-
 class FixedRandomWeightSource(WeightSource):
     """Generate every reduced-model parameter deterministically on demand.
 
@@ -190,7 +184,7 @@ def _make_tensor(self, name: str, shape: tuple[int, ...], dtype: torch.dtype = t
             return tensor.uniform_(0.9, 1.1)
         return tensor.uniform_(-0.02, 0.02)
 
-    @_source_metadata
+    @cached_property
     def metadata(self) -> list[ParamMeta]:
         return [ParamMeta(name, dtype, shape) for name, shape, dtype in self._parameters]
 
@@ -199,11 +193,9 @@ def __iter__(self):
             for name, shape, dtype in self._parameters:
                 yield name, self._make_tensor(name, shape, dtype)
 
-    if not vllm_version_is("0.32.0"):
-
-        def iter_full(self) -> Iterator[tuple[str, torch.Tensor]]:
-            """vLLM main drives updates through `iter_full()` instead of `__iter__`."""
-            yield from self.__iter__()
+    def iter_full(self) -> Iterator[tuple[str, torch.Tensor]]:
+        """vLLM drives updates through `iter_full()`."""
+        yield from self.__iter__()
 
     @property
     def case_id(self) -> str:
@@ -247,8 +239,7 @@ def packed_buffer_size_for(source: FixedRandomWeightSource) -> int:
     raises ``ValueError: Tensor ... exceeds buffer_size_bytes``. Keep the 1 GiB
     floor and add 128 MiB of headroom above the largest tensor.
     """
-    # `metadata` is a method on v0.32.0 and a cached property on main.
-    metadata = source.metadata() if vllm_version_is("0.32.0") else source.metadata
+    metadata = source.metadata
     max_tensor_bytes = max(math.prod(meta.shape) * meta.dtype.itemsize for meta in metadata)
     return max(max_tensor_bytes + 128 * 2**20, 2**30)
 

tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_num_nans.py

@@ -48,7 +48,7 @@
 # AttributeError. Setting VLLM_VERSION makes ``vllm_version_is`` use it instead
 # of ``vllm.__version__``. This must happen before the first ``vllm_ascend``
 # import below, and it must not override an explicitly exported value.
-os.environ.setdefault("VLLM_VERSION", "0.32.0")
+os.environ.setdefault("VLLM_VERSION", "0.33.0")
 
 from vllm_ascend.device.device_config import get_ascend_device_type
 from vllm_ascend.device.hardware import AscendDeviceType

tests/e2e/pull_request/one_card/test_gumbel_sampling.py

@@ -604,7 +604,7 @@ def test_dspark_uses_ascend_gumbel(self):
         speculator.temperature = torch.tensor([0.5, 1.5], device=DEVICE)
         speculator.seeds = torch.tensor([3, 7], dtype=torch.int64, device=DEVICE)
         speculator._step_cols = torch.arange(2, dtype=torch.int32, device=DEVICE)
-        if vllm_version_is("0.32.0"):
+        if vllm_version_is("0.33.0"):
             # The release tree keeps the raw per-step draft-logits tensor.
             speculator.draft_logits = torch.zeros(2, 2, 1031, device=DEVICE)
             draft_logits = speculator.draft_logits

tests/ut/attention/test_batch_invariance_support.py

@@ -5,7 +5,7 @@
 vLLM #60271 ("Check batch-invariance support for every attention backend") added
 a hard check in ``vllm/v1/worker/gpu/attn_utils.init_attn_backend`` on main:
 ``VLLM_BATCH_INVARIANT`` mode now rejects any attention backend whose
-``supports_batch_invariance()`` returns False. Both v0.32.0 and verified main
+``supports_batch_invariance()`` returns False. Both v0.33.0 and verified main
 enforce this check, so Ascend advertises support in both lanes.
 """
 

tests/ut/attention/test_flash_attn.py

@@ -501,7 +501,7 @@ def test_only_undersized_draft_swa_pages_drop_inherited_padding(
         expected_padding = None if recover else original.page_size_padded
         assert spec.page_size_bytes == (expected_padding or natural_page)
         assert spec.page_size_padded == expected_padding
-    # v0.32.0 has no draft marker; check propagation when upstream supports it.
+    # Both supported versions propagate the draft marker.
     if hasattr(original, "is_draft"):
         assert spec.is_draft is is_draft
     assert config.cache_config.skip_page_size_padded == padding

tests/ut/attention/test_gqa_dcp_split.py

@@ -17,7 +17,6 @@
 )
 from vllm_ascend.attention.context_parallel.common_cp import CPKVScope, use_history_current_split_decode
 from vllm_ascend.compilation.updatable_graph import ContextSource, GraphUpdateTask, SharedSource, UpdatableGraph
-from vllm_ascend.utils import vllm_version_is
 from vllm_ascend.worker.v2.spec_decode.target_dependent_ar.speculator import AscendTargetDependentARSpeculator
 
 
@@ -311,11 +310,8 @@ def merge(*args, **kwargs):
 def test_v2_draft_steps_update_history_and_current_independently(interleave):
     speculator = object.__new__(AscendTargetDependentARSpeculator)
     speculator.attn_architecture = "GQA"
-    if vllm_version_is("0.32.0"):
-        speculator.max_model_len = 32
-    else:
-        # vLLM main exposes max_model_len as a read-only view of vllm_config.
-        speculator.vllm_config = SimpleNamespace(model_config=SimpleNamespace(max_model_len=32))
+    # vLLM main exposes max_model_len as a read-only view of vllm_config.
+    speculator.vllm_config = SimpleNamespace(model_config=SimpleNamespace(max_model_len=32))
     speculator.use_dcp = True
     prepare_local = MagicMock(
         side_effect=lambda lengths: get_dcp_local_seq_lens(
@@ -358,11 +354,8 @@ def test_v2_graph_shared_source_preserves_steps_layers_and_padding(is_prefill, c
     )
     speculator.input_batch = SimpleNamespace(num_reqs=2, seq_lens_np=[13, 31])
     speculator.num_speculative_steps = 3
-    if vllm_version_is("0.32.0"):
-        speculator.max_model_len = 32
-    else:
-        # vLLM main exposes max_model_len as a read-only view of vllm_config.
-        speculator.vllm_config = SimpleNamespace(model_config=SimpleNamespace(max_model_len=32))
+    # vLLM main exposes max_model_len as a read-only view of vllm_config.
+    speculator.vllm_config = SimpleNamespace(model_config=SimpleNamespace(max_model_len=32))
     query = (4, 2, 1) if is_prefill else (1, 1, 1)
     steps = [
         {

tests/ut/attention/test_mla_v1.py

@@ -1947,9 +1947,8 @@ def test_qkv_a_fused_output_gate_disables_mlapo(self, mock_get_current_vllm_conf
         """Kimi-K3 fuses its gate as a third QKV-A shard; MLAPO cannot carry it."""
         mock_get_current_vllm_config.return_value = self.impl.vllm_config
         self.impl.fused_qkv_a_proj.output_sizes = [64, 32, 128]
-        # Only vLLM main builds the three-shard QKV-A-gate projection; release
-        # v0.32.0 keeps a separate gate and a two-shard projection.
-        gate_fused = not vllm_version_is("0.32.0")
+        # Both supported Kimi AMD implementations use the three-shard layout.
+        gate_fused = True
 
         impl = self._rebuild_impl()
 
@@ -2807,13 +2806,8 @@ def test_process_weights_after_loading_refreshes_prolog_v3_weights(self, mock_fo
     def test_process_weights_after_loading_rebinds_incompatible_value(self, mock_format_cast):
         """An incompatible reload must rebind instead of raising from ``copy_``.
 
-        ``replace_parameter(..., prefer_copy=True)`` reuses the stored storage
-        only while shape, dtype and device still match. The hand-rolled
-        ``copy_`` it replaced was unconditional, so a re-derived value that
-        stopped being compatible aborted the whole weight-update transaction
-        with a ``RuntimeError``. That failure mode is the only behaviour this
-        change alters, so it is the part of the contract the unit tests have to
-        pin down.
+        v0.33.0 rebinds an incompatible value; main can copy a smaller value
+        into the parameter's existing storage. Both must accept the new shape.
         """
         layer = MagicMock(spec=LinearBase)
         layer.input_size_per_partition = 10
@@ -2843,7 +2837,7 @@ def test_process_weights_after_loading_rebinds_incompatible_value(self, mock_for
 
         self.impl.process_weights_after_loading(torch.bfloat16)
 
-        if vllm_version_is("0.32.0"):
+        if vllm_version_is("0.33.0"):
             # A re-derived value that no longer fits rebinds the parameter.
             self.assertNotEqual(self.impl.W_UV.data_ptr(), w_uv_ptr)
         else:
@@ -3695,10 +3689,6 @@ def test_split_fused_qkv_lora_plain_layout_has_no_gate():
     torch.testing.assert_close(kv_no_split, qkv_lora[:, 4:])
 
 
-@pytest.mark.skipif(
-    vllm_version_is("0.32.0"),
-    reason="gate-fused QKV-A layout is vLLM main only",
-)
 def test_split_fused_qkv_lora_peels_kimi_output_gate():
     """A gate-fused layout returns the trailing shard separately from q/kv."""
     qkv_lora = torch.arange(15, dtype=torch.float32).reshape(1, 15)

tests/ut/attention/test_sfa_v1.py

@@ -1293,13 +1293,8 @@ def test_process_weights_after_loading_rebinds_incompatible_value(
     ):
         """An incompatible reload must rebind instead of raising from ``copy_``.
 
-        ``replace_parameter(..., prefer_copy=True)`` reuses the stored storage
-        only while shape, dtype and device still match. The hand-rolled
-        ``copy_`` it replaced was unconditional, so a re-derived value that
-        stopped being compatible aborted the whole weight-update transaction
-        with a ``RuntimeError``. That failure mode is the only behaviour this
-        change alters, so it is the part of the contract the unit tests have to
-        pin down.
+        v0.33.0 rebinds an incompatible value; main can copy a smaller value
+        into the parameter's existing storage. Both must accept the new shape.
         """
         layer = self._setup_kv_b_proj()
         mock_format_cast.return_value = layer.weight
@@ -1320,7 +1315,7 @@ def test_process_weights_after_loading_rebinds_incompatible_value(
 
         self.impl.process_weights_after_loading(torch.bfloat16)
 
-        if vllm_version_is("0.32.0"):
+        if vllm_version_is("0.33.0"):
             # A re-derived value that no longer fits rebinds the parameter.
             self.assertNotEqual(self.impl.W_UV.data_ptr(), w_uv_ptr)
         else:

tests/ut/core/test_kv_cache_interface.py

@@ -7,6 +7,10 @@
 import pytest
 import torch
 from vllm.v1.core.block_pool import BlockPool
+
+# vLLM #61183 moved `is_kv_cache_spec_uniform` to `kv_cache_planning` on main;
+# the v0.33.0 lane still ships it in `kv_cache_utils`.
+from vllm.v1.core.kv_cache_planning import is_kv_cache_spec_uniform
 from vllm.v1.core.single_type_kv_cache_manager import CircularBufferManager, register_all_kvcache_specs
 from vllm.v1.kv_cache_interface import (
     CircularBufferSpec,
@@ -28,14 +32,6 @@
     supports_bounded_replay,
     supports_component_major_mla_pd,
 )
-from vllm_ascend.utils import vllm_version_is
-
-# vLLM #61183 moved `is_kv_cache_spec_uniform` to `kv_cache_planning` on main;
-# the v0.32.0 lane still ships it in `kv_cache_utils`.
-if vllm_version_is("0.32.0"):
-    from vllm.v1.core.kv_cache_utils import is_kv_cache_spec_uniform
-else:
-    from vllm.v1.core.kv_cache_planning import is_kv_cache_spec_uniform
 
 
 def _mla_spec():

tests/ut/distributed/ascend_store/test_request_lifecycle.py

@@ -38,7 +38,6 @@
     KVCacheStoreRecvingThread,
     KVCacheStoreSendingThread,
 )
-from vllm_ascend.utils import vllm_version_is
 
 
 def aligned_buffer(size):
@@ -350,11 +349,10 @@ def run_request(req_id, tokens, expected_hit, expected_local=0):
             scheduler.update_from_output(output, result)
             if drafts:
                 expected_computed = computed_before_update - len(drafts) + accepted
-                if not vllm_version_is("0.32.0"):
-                    # vLLM main trims ``num_computed_tokens`` to cover only the
-                    # tokens actually kept when a request stops mid token-list.
-                    appended = request.num_output_tokens - output_tokens_before_update
-                    expected_computed -= (accepted + 1) - appended
+                # vLLM main trims ``num_computed_tokens`` to cover only the
+                # tokens actually kept when a request stops mid token-list.
+                appended = request.num_output_tokens - output_tokens_before_update
+                expected_computed -= (accepted + 1) - appended
                 assert request.num_computed_tokens == expected_computed
             if speculative_method:
                 scheduler.update_draft_token_ids(
@@ -434,7 +432,7 @@ def run_request(req_id, tokens, expected_hit, expected_local=0):
         # pure full-attention dspark layout sees the doubled local prefix hit.
         only_full_attention = isinstance(plan.kv_cache_groups[0].kv_cache_spec, FullAttentionSpec)
         mixed_local = 128
-        if not vllm_version_is("0.32.0") and speculative_method == "dspark" and only_full_attention:
+        if speculative_method == "dspark" and only_full_attention:
             mixed_local = 256
         run_request("mixed-local-remote", list(range(513)), 512, expected_local=mixed_local)
         assert scheduler.reset_prefix_cache()

tests/ut/distributed/kv_transfer/kv_p2p/test_mooncake_connector.py

@@ -18,6 +18,13 @@
 import zmq
 from vllm.config import KVTransferConfig
 from vllm.utils.network_utils import make_zmq_path
+
+# vLLM #61183 moved these KV-cache planning helpers to `kv_cache_planning` on
+# main; the v0.33.0 lane still ships them in `kv_cache_utils`.
+from vllm.v1.core.kv_cache_planning import (
+    get_kv_cache_config_from_groups,
+    is_kv_cache_spec_uniform,
+)
 from vllm.v1.kv_cache_interface import (
     FullAttentionSpec,
     KVCacheGroupSpec,
@@ -52,20 +59,7 @@
     transfer_groups_need_independent_block_ids,
     zmq_ctx,
 )
-from vllm_ascend.utils import get_kv_cache_tensor_layers, vllm_version_is
-
-# vLLM #61183 moved these KV-cache planning helpers to `kv_cache_planning` on
-# main; the v0.32.0 lane still ships them in `kv_cache_utils`.
-if vllm_version_is("0.32.0"):
-    from vllm.v1.core.kv_cache_utils import (
-        get_kv_cache_config_from_groups,
-        is_kv_cache_spec_uniform,
-    )
-else:
-    from vllm.v1.core.kv_cache_planning import (
-        get_kv_cache_config_from_groups,
-        is_kv_cache_spec_uniform,
-    )
+from vllm_ascend.utils import get_kv_cache_tensor_layers
 
 GET_META_MSG = b"get_meta_msg"
 DONE_RECVING_MSG = b"done_recving_msg"