52086394ae
### What this PR does / why we need it?
Convert `vllm-ascend/compilation` to ruff format.
### Does this PR introduce _any_ user-facing change?
During this migration, we encountered some **errors** in our CI and
testing environments, such as:
```
vllm_ascend/utils.py:653: in <module>
def register_ascend_customop(vllm_config: VllmConfig | None = None):
^^^^^^^^^^^^^^^^^
E TypeError: unsupported operand type(s) for |: 'NoneType' and 'NoneType'
```
**1. Root Cause Analysis:**
The project uses a common pattern to break circular dependencies:
```python
if TYPE_CHECKING:
from vllm.config import VllmConfig
else:
VllmConfig = None # Placeholder assigned at runtime
```
When Python parses the function definition `def
register_ascend_customop(vllm_config: VllmConfig | None)`, it attempts
to evaluate the expression `VllmConfig | None`.
Since `VllmConfig` is assigned `None` at runtime, the expression
effectively becomes `None | None`. In Python, `None` is an instance of
`NoneType`. While the `|` operator is implemented for Type objects
(classes), it is not supported for `NoneType` instances, leading to the
`TypeError` shown above.
**2. Solution:**
To maintain the modern `|` syntax required by our new linting standards
while preserving our dependency management strategy, I have introduced:
```python
from __future__ import annotations
```
at the top of the affected files. This enables **Postponed Evaluation of
Annotations (PEP 563)**.
**3. Impact and Benefits:**
- By enabling `annotations`, Python no longer executes the `VllmConfig |
None` operation during module load. Instead, it stores the annotation as
a string literal, completely avoiding the `None | None` calculation.
- We can keep the `VllmConfig = None` placeholders. This ensures that
other modules can still import these symbols without triggering an
`ImportError`, maintaining a stable dependency graph.
- IDEs and static type checkers (MyPy/Pyright) continue to resolve the
types correctly. This allows us to use modern syntax without sacrificing
type safety or runtime stability.
- The only side effect is that `__annotations__` will now return strings
instead of type objects. Since this module does not use runtime type
enforcement or reflection, this change has zero negative impact on
existing functionality.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
from dataclasses import dataclass
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import torch
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from vllm.model_executor.layers.linear import LinearBase
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@dataclass
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class FlashCommon3Context:
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gate: LinearBase | None = None
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topk_weights: torch.Tensor | None = None
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topk_ids: torch.Tensor | None = None
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row_idx: torch.Tensor | None = None
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shared_experts: torch.nn.Module | None = None
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shared_out: torch.Tensor | None = None
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_flash_common3_context: FlashCommon3Context | None = None
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def get_flash_common3_context() -> FlashCommon3Context | None:
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return _flash_common3_context
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def set_flash_common3_context(
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topk_weights: torch.Tensor | None = None,
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topk_ids: torch.Tensor | None = None,
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shared_experts: torch.nn.Module | None = None,
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shared_out: torch.Tensor | None = None,
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):
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global _flash_common3_context
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if _flash_common3_context is None:
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_flash_common3_context = FlashCommon3Context()
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if topk_weights is not None:
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_flash_common3_context.topk_weights = topk_weights
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if topk_ids is not None:
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_flash_common3_context.topk_ids = topk_ids
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if shared_experts is not None:
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_flash_common3_context.shared_experts = shared_experts
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if shared_out is not None:
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_flash_common3_context.shared_out = shared_out
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