[Lint]Style: Convert vllm-ascend/compilation to ruff format (#5912)
### 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>
This commit is contained in:
SILONG ZENG
@@ -17,8 +17,7 @@
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#
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import torch
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import torch._inductor.pattern_matcher as pm
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from torch._inductor.pattern_matcher import (PatternMatcherPass,
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PatternPrettyPrinter)
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from torch._inductor.pattern_matcher import PatternMatcherPass, PatternPrettyPrinter
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from vllm.attention.layer import Attention
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from vllm.compilation.vllm_inductor_pass import VllmInductorPass
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from vllm.config import VllmConfig, get_layers_from_vllm_config
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@@ -27,13 +26,7 @@ from vllm.logger import logger
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class QKNormRopeFusionPattern:
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def __init__(self,
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vllm_config,
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head_dim,
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num_heads,
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num_kv_heads,
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eps=1e-6):
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def __init__(self, vllm_config, head_dim, num_heads, num_kv_heads, eps=1e-6):
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self.vllm_config = vllm_config
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self.head_dim = head_dim
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self.num_heads = num_heads
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@@ -45,65 +38,38 @@ class QKNormRopeFusionPattern:
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def get_inputs(self):
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T = 5
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qkv = torch.empty(T,
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self.q_size + 2 * self.kv_size,
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dtype=torch.bfloat16,
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device="npu")
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q_weight = torch.empty(self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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k_weight = torch.empty(self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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cos = torch.empty(1,
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T,
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1,
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self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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sin = torch.empty(1,
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T,
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1,
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self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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qkv = torch.empty(T, self.q_size + 2 * self.kv_size, dtype=torch.bfloat16, device="npu")
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q_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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k_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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cos = torch.empty(1, T, 1, self.head_dim, dtype=torch.bfloat16, device="npu")
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sin = torch.empty(1, T, 1, self.head_dim, dtype=torch.bfloat16, device="npu")
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return [qkv, q_weight, k_weight, cos, sin]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(
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qkv: torch.Tensor, q_weight: torch.Tensor, k_weight: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
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):
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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def pattern(qkv: torch.Tensor, q_weight: torch.Tensor,
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k_weight: torch.Tensor, cos: torch.Tensor,
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sin: torch.Tensor):
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
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q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight, self.eps)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
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dim=-1)
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
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self.head_dim)
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q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight,
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self.eps)
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
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self.head_dim)
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k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight,
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self.eps)
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
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k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight, self.eps)
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q_flat = q_norm_out.view(q.shape)
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q_reshape = q_flat.contiguous().view(1, q_flat.shape[0], -1,
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self.head_dim)
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q_reshape = q_flat.contiguous().view(1, q_flat.shape[0], -1, self.head_dim)
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k_flat = k_norm_out.view(k.shape)
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k_reshape = k_flat.contiguous().view(1, k_flat.shape[0], -1,
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self.head_dim)
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k_reshape = k_flat.contiguous().view(1, k_flat.shape[0], -1, self.head_dim)
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q_rope, k_rope = torch.ops.npu.npu_apply_rotary_pos_emb(
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q_reshape, k_reshape, cos, sin)
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q_rope, k_rope = torch.ops.npu.npu_apply_rotary_pos_emb(q_reshape, k_reshape, cos, sin)
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return q_rope, k_rope, v
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def replacement(qkv: torch.Tensor, q_weight: torch.Tensor,
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k_weight: torch.Tensor, cos: torch.Tensor,
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sin: torch.Tensor):
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def replacement(
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qkv: torch.Tensor, q_weight: torch.Tensor, k_weight: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
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):
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results = torch.ops.vllm.qkv_rmsnorm_rope(
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input=qkv,
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q_weight=q_weight,
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@@ -115,22 +81,16 @@ class QKNormRopeFusionPattern:
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q_bias=None,
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k_bias=None,
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sin=sin,
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cos=cos)
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cos=cos,
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)
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return results
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
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class QKNormRopeFusionPatternWithBias:
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def __init__(self,
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vllm_config,
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head_dim,
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num_heads,
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num_kv_heads,
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eps=1e-6):
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def __init__(self, vllm_config, head_dim, num_heads, num_kv_heads, eps=1e-6):
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self.head_dim = head_dim
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads
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@@ -142,71 +102,55 @@ class QKNormRopeFusionPatternWithBias:
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def get_inputs(self):
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T = 5
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qkv = torch.empty(T,
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self.q_size + 2 * self.kv_size,
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dtype=torch.bfloat16,
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device="npu")
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q_weight = torch.empty(self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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k_weight = torch.empty(self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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qkv = torch.empty(T, self.q_size + 2 * self.kv_size, dtype=torch.bfloat16, device="npu")
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q_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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k_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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q_bias = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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k_bias = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
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cos = torch.empty(1,
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T,
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1,
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self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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sin = torch.empty(1,
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T,
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1,
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self.head_dim,
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dtype=torch.bfloat16,
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device="npu")
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cos = torch.empty(1, T, 1, self.head_dim, dtype=torch.bfloat16, device="npu")
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sin = torch.empty(1, T, 1, self.head_dim, dtype=torch.bfloat16, device="npu")
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return [qkv, q_weight, k_weight, q_bias, k_bias, cos, sin]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(
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qkv: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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q_bias: torch.Tensor,
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k_bias: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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):
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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def pattern(qkv: torch.Tensor, q_weight: torch.Tensor,
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k_weight: torch.Tensor, q_bias: torch.Tensor,
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k_bias: torch.Tensor, cos: torch.Tensor,
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sin: torch.Tensor):
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
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dim=-1)
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
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self.head_dim)
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q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight,
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self.eps)
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
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q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight, self.eps)
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q_normed = q_norm_out + q_bias
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
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self.head_dim)
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k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight,
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self.eps)
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
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k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight, self.eps)
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k_normed = k_norm_out + k_bias
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q_flat = q_normed.view(q.shape)
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q_reshape = q_flat.contiguous().view(1, q_flat.shape[0], -1,
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self.head_dim)
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q_reshape = q_flat.contiguous().view(1, q_flat.shape[0], -1, self.head_dim)
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k_flat = k_normed.view(k.shape)
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k_reshape = k_flat.contiguous().view(1, k_flat.shape[0], -1,
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self.head_dim)
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k_reshape = k_flat.contiguous().view(1, k_flat.shape[0], -1, self.head_dim)
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q_rope, k_rope = torch.ops.npu.npu_apply_rotary_pos_emb(
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q_reshape, k_reshape, cos, sin)
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q_rope, k_rope = torch.ops.npu.npu_apply_rotary_pos_emb(q_reshape, k_reshape, cos, sin)
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return q_rope, k_rope, v
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def replacement(qkv: torch.Tensor, q_weight: torch.Tensor,
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k_weight: torch.Tensor, q_bias: torch.Tensor,
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k_bias: torch.Tensor, cos: torch.Tensor,
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sin: torch.Tensor):
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def replacement(
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qkv: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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q_bias: torch.Tensor,
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k_bias: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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):
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results = torch.ops.vllm.qkv_rmsnorm_rope(
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input=qkv,
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q_weight=q_weight,
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@@ -218,11 +162,11 @@ class QKNormRopeFusionPatternWithBias:
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q_bias=q_bias,
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k_bias=k_bias,
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cos=cos,
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sin=sin)
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sin=sin,
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)
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return results
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
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class QKNormRopeFusionPass(VllmInductorPass):
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@@ -232,44 +176,38 @@ class QKNormRopeFusionPass(VllmInductorPass):
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def __init__(self, vllm_config: VllmConfig):
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super().__init__(vllm_config)
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self.pattern_match_passes: PatternMatcherPass = PatternMatcherPass(
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pass_name="qknorm_rope_fusion_pass")
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self.pattern_match_passes: PatternMatcherPass = PatternMatcherPass(pass_name="qknorm_rope_fusion_pass")
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dtype = vllm_config.model_config.dtype
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if dtype not in (torch.bfloat16, torch.float16):
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logger.debug(
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"QKNorm and Rope fusion not enabled: unsupported dtype %s",
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dtype)
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logger.debug("QKNorm and Rope fusion not enabled: unsupported dtype %s", dtype)
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return
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# use one attn layer to get meta (such as head_dim) for QKNormRopeFusionPattern
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attn_layers: dict[str, Attention] = get_layers_from_vllm_config(
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vllm_config, Attention)
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attn_layers: dict[str, Attention] = get_layers_from_vllm_config(vllm_config, Attention)
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if len(attn_layers) == 0:
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logger.debug(
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"QKNorm and Rope fusion enabled, but no Attention layers were discovered."
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)
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logger.debug("QKNorm and Rope fusion enabled, but no Attention layers were discovered.")
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return
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layer = next(iter(attn_layers.values()))
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for epsilon in [1e-6, 1e-5]:
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if layer.head_size != 128:
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logger.debug(
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"QKNorm and Rope fusion not enabled: head_dim %d is not equal of 128",
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layer.head_size)
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logger.debug("QKNorm and Rope fusion not enabled: head_dim %d is not equal of 128", layer.head_size)
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continue
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QKNormRopeFusionPattern(vllm_config=vllm_config,
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head_dim=layer.head_size,
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num_heads=layer.num_heads,
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num_kv_heads=layer.num_kv_heads,
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eps=epsilon).register(
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self.pattern_match_passes)
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QKNormRopeFusionPattern(
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vllm_config=vllm_config,
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head_dim=layer.head_size,
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num_heads=layer.num_heads,
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num_kv_heads=layer.num_kv_heads,
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eps=epsilon,
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).register(self.pattern_match_passes)
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QKNormRopeFusionPatternWithBias(vllm_config=vllm_config,
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head_dim=layer.head_size,
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num_heads=layer.num_heads,
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num_kv_heads=layer.num_kv_heads,
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eps=epsilon).register(
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self.pattern_match_passes)
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QKNormRopeFusionPatternWithBias(
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vllm_config=vllm_config,
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head_dim=layer.head_size,
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num_heads=layer.num_heads,
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num_kv_heads=layer.num_kv_heads,
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eps=epsilon,
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).register(self.pattern_match_passes)
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def __call__(self, graph: torch.fx.Graph):
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self.begin()
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