[CI]Fixed the spell check function in typos.toml (#6753)
### What this PR does / why we need it?
The incorrect regular expression syntax `.*[UE4M3|ue4m3].*` actually
ignores all words containing any of the following characters: `u, e, 4,
m, 3, |`
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*[UE4M3|ue4m3].*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
===fix===>
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*(UE4M3|ue4m3]).*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
@@ -11,7 +11,7 @@ from vllm_ascend.utils import enable_custom_op
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enable_custom_op()
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class TestDisptachFFNCombine:
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class TestDispatchFFNCombine:
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def __init__(self, rank, world_size, port):
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self.rank = rank
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@@ -208,7 +208,7 @@ class TestDisptachFFNCombine:
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def worker(rank: int, world_size: int, port: int, q: mp.SimpleQueue):
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op = TestDisptachFFNCombine(rank, world_size, port)
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op = TestDispatchFFNCombine(rank, world_size, port)
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op.generate_hcom()
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out1 = op.run_tensor_list()
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q.put(out1)
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@@ -11,7 +11,7 @@ from vllm_ascend.utils import enable_custom_op
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enable_custom_op()
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class TestDisptachFFNCombine:
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class TestDispatchFFNCombine:
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def __init__(self, rank, world_size, port):
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self.rank = rank
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@@ -208,7 +208,7 @@ class TestDisptachFFNCombine:
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def worker(rank: int, world_size: int, port: int, q: mp.SimpleQueue):
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op = TestDisptachFFNCombine(rank, world_size, port)
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op = TestDispatchFFNCombine(rank, world_size, port)
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op.generate_hcom()
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out1 = op.run_tensor_list()
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q.put(out1)
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@@ -124,10 +124,10 @@ def create_test_data(
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logits = torch.randn(num_reqs, vocab_size, device=device, dtype=dtype)
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repetiton_penalty = torch.ones(num_reqs, device=device, dtype=torch.float32)
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repetition_penalty = torch.ones(num_reqs, device=device, dtype=torch.float32)
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for i in range(num_reqs):
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if torch.rand(1) > 0.3:
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repetiton_penalty[i] = torch.rand(1, device=device).item() * 0.8 + 0.6
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repetition_penalty[i] = torch.rand(1, device=device).item() * 0.8 + 0.6
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frequency_penalty = torch.zeros(num_reqs, device=device, dtype=torch.float32)
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for i in range(num_reqs):
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@@ -168,7 +168,7 @@ def create_test_data(
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output_bin_counts[state_idx, token] = count
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sampling_metadata = SamplingMetadata(
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repetition_penalty=repetiton_penalty,
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repetition_penalty=repetition_penalty,
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frequency_penalty=frequency_penalty,
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presence_penalty=presence_penalty,
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temperature=temperature,
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@@ -217,4 +217,3 @@ def test_apply_penalties_and_temperature(
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atol = 1e-02
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rtol = 1e-02
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assert torch.allclose(logits_triton, logits_pytorch_result, atol=atol, rtol=rtol)
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