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[CI] change spell checker from codespell to typos (#18711)

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Signed-off-by: Andy Xie <andy.xning@gmail.com>
This commit is contained in:
Ning Xie
2025-06-12 10:57:10 +08:00
committed by GitHub
parent 42f52cc95b
commit 2f1c19b245
57 changed files with 335 additions and 163 deletions
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4
vllm/model_executor/layers/mamba/mamba_mixer2.py
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@@ -319,7 +319,7 @@ class MambaMixer2(CustomOp):
n_groups == 1, # if there was only one group
)
intermediate_settings = (intermediate_size, 0, False)
head_setings = (self.num_heads, 0, False)
head_settings = (self.num_heads, 0, False)
# - the weight already has a "weight_loader" attribute
# which set_weight_attrs will raise if we do not
@@ -372,7 +372,7 @@ class MambaMixer2(CustomOp):
intermediate_settings,
group_shard_settings,
group_shard_settings,
head_setings, # for dt
head_settings, # for dt
],
self.tp_size,
tp_rank,

2
vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py
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@@ -516,7 +516,7 @@ def _chunk_state_varlen_kernel(
offs_n[None, :] * stride_chunk_states_dstate)
else:
# - this seems repetitve, buts its to help the compiler
# - this seems repetitive, buts its to help the compiler
if start_idx < pid_c * chunk_size:
past_states_ptrs = chunk_states_ptr + (
offs_m[:, None] * stride_chunk_states_hdim +

2
vllm/model_executor/layers/quantization/utils/int8_utils.py
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@@ -219,7 +219,7 @@ def per_token_group_quant_int8(
quantized tensor along with the scaling factor used for quantization.
Args:
x: The input tenosr with ndim >= 2.
x: The input tensor with ndim >= 2.
group_size: The group size used for quantization.
eps: The minimum to avoid dividing zero.
dtype: The dype of output tensor. Note that only `torch.int8`

2
vllm/model_executor/model_loader/bitsandbytes_loader.py
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@@ -401,7 +401,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
self.target_modules.append(
name.replace(rep_name, sub_name))
# Add original module name even if the module has stacked map,
# in case model has a mixture of disk-merged and disk-splitted
# in case model has a mixture of disk-merged and disk-split
# weights with same last name.
self.target_modules.append(name)

6
vllm/model_executor/models/baichuan.py
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@@ -131,7 +131,7 @@ class BaiChuanAttention(nn.Module):
self.num_heads = (self.total_num_heads //
tensor_model_parallel_world_size)
self.head_dim = hidden_size // self.total_num_heads
self.postion_embedding = position_embedding
self.position_embedding = position_embedding
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
@@ -151,7 +151,7 @@ class BaiChuanAttention(nn.Module):
quant_config=quant_config,
)
# Create the alibi slopes and slice them.
if self.postion_embedding == "ALIBI":
if self.position_embedding == "ALIBI":
tp_rank = get_tensor_model_parallel_rank()
head_start = tp_rank * self.num_heads
head_end = (tp_rank + 1) * self.num_heads
@@ -187,7 +187,7 @@ class BaiChuanAttention(nn.Module):
) -> torch.Tensor:
qkv, _ = self.W_pack(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
if self.postion_embedding != "ALIBI":
if self.position_embedding != "ALIBI":
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v)
output, _ = self.o_proj(attn_output)

6
vllm/model_executor/models/deepseek_vl2.py
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@@ -344,7 +344,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
self.image_newline = nn.Parameter(
torch.randn(self.projector_config.n_embed) * embed_std)
# This is a typo in original implementation
self.view_seperator = nn.Parameter(
self.view_separator = nn.Parameter(
torch.randn(self.projector_config.n_embed) * embed_std)
else:
raise ValueError(
@@ -549,13 +549,13 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
if self.global_view_pos == "head":
global_local_features = torch.cat([
global_features,
self.view_seperator[None, :],
self.view_separator[None, :],
local_features,
])
else:
global_local_features = torch.cat([
local_features,
self.view_seperator[None, :],
self.view_separator[None, :],
global_features,
])

2
vllm/model_executor/models/eagle.py
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@@ -197,7 +197,7 @@ class EAGLE(nn.Module):
return logits
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
# This implementation is incompitable with https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B
# This implementation is incompatible with https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B
# due to missing lm_head weights and its config being that of a
# Llama model. Here's a compatible version with the same weights:
# https://huggingface.co/abhigoyal/EAGLE-LLaMA3-Instruct-8B-vllm

8
vllm/model_executor/models/gemma3_mm.py
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@@ -634,13 +634,13 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
kwargs["has_images"] = True
# NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
# This is a HACK. Fix this.
start_idices = (positions == 0).cpu().nonzero()
num_seqs = len(start_idices)
start_indices = (positions == 0).cpu().nonzero()
num_seqs = len(start_indices)
seq_lens = []
for i in range(num_seqs):
start_idx = start_idices[i].item()
start_idx = start_indices[i].item()
if i < num_seqs - 1:
end_idx = start_idices[i + 1].item()
end_idx = start_indices[i + 1].item()
else:
end_idx = len(input_ids)
seq_lens.append(end_idx - start_idx)

2
vllm/model_executor/models/llama4.py
View File

@@ -52,7 +52,7 @@ class Llama4MoE(nn.Module):
renormalize: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
router_scores, router_indices = fast_topk(gating_output, topk, dim=-1)
# psuedo-standard is that the router scores are floats
# pseudo-standard is that the router scores are floats
router_scores = torch.sigmoid(router_scores.float())
return (router_scores, router_indices.to(torch.int32))

10
vllm/model_executor/models/mixtral_quant.py
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@@ -114,9 +114,9 @@ class MixtralMoE(nn.Module):
f"Tensor parallel size {self.tp_size} is greater than "
f"the number of experts {self.num_total_experts}.")
# Split experts equally between ranks
self.expert_indicies = np.array_split(range(
self.num_total_experts), self.tp_size)[self.rank].tolist()
if not self.expert_indicies:
self.expert_indices = np.array_split(range(self.num_total_experts),
self.tp_size)[self.rank].tolist()
if not self.expert_indices:
raise ValueError(
f"Rank {self.rank} has no experts assigned to it.")
@@ -125,7 +125,7 @@ class MixtralMoE(nn.Module):
config.hidden_size,
config.intermediate_size,
quant_config=quant_config)
if idx in self.expert_indicies else None
if idx in self.expert_indices else None
for idx in range(self.num_total_experts)
])
self.gate = ReplicatedLinear(config.hidden_size,
@@ -146,7 +146,7 @@ class MixtralMoE(nn.Module):
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
final_hidden_states = None
for expert_idx in self.expert_indicies:
for expert_idx in self.expert_indices:
expert_layer = self.experts[expert_idx]
expert_mask = (selected_experts == expert_idx)
expert_weights = (routing_weights * expert_mask).sum(dim=-1,

2
vllm/model_executor/models/ovis.py
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@@ -283,7 +283,7 @@ class OvisProcessingInfo(BaseProcessingInfo):
def get_image_size_with_most_features(self) -> ImageSize:
height, width = self.get_hf_processor().get_image_size()
hs = self.get_hf_config().visual_tokenizer_config.hidden_stride
# NOTE(Isotr0py): 9 is `max_partion` hardcoded in original code
# NOTE(Isotr0py): 9 is `max_partition` hardcoded in original code
# https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/modeling_ovis.py#L96
return ImageSize(width=width * hs * 9, height=height * hs * 9)

10
vllm/model_executor/models/phi3_small.py
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@@ -145,7 +145,7 @@ class Phi3SmallSelfAttention(nn.Module):
self.num_q_per_kv = self.num_heads // self.num_key_value_heads
if self.tp_size > 1:
assert self.num_key_value_heads % self.tp_size == 0
self.num_kv_heads_per_partion = max(
self.num_kv_heads_per_partition = max(
1, self.num_key_value_heads // self.tp_size)
self.num_heads_per_partition = self.num_heads // self.tp_size
@@ -212,7 +212,7 @@ class Phi3SmallSelfAttention(nn.Module):
bs_params = {
'max_seqlen': self.max_position_embeddings,
'num_heads': self.num_heads_per_partition,
"num_kv_heads": self.num_kv_heads_per_partion,
"num_kv_heads": self.num_kv_heads_per_partition,
"block_size": self.sparse_block_size,
"local_blocks": self.local_blocks,
"vert_stride": self.vert_stride,
@@ -222,7 +222,7 @@ class Phi3SmallSelfAttention(nn.Module):
self.attn = Attention(self.num_heads_per_partition,
self.head_dim,
self.scale,
num_kv_heads=self.num_kv_heads_per_partion,
num_kv_heads=self.num_kv_heads_per_partition,
cache_config=cache_config,
quant_config=quant_config,
blocksparse_params=bs_params,
@@ -243,8 +243,8 @@ class Phi3SmallSelfAttention(nn.Module):
# NOTE: this is required by RotaryEmbed, which indeed does not have to
# TODO: allow 3D QK for rotary forward
q = q.reshape(-1, self.head_dim * self.num_heads_per_partition)
k = k.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
v = v.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
k = k.reshape(-1, self.head_dim * self.num_kv_heads_per_partition)
v = v.reshape(-1, self.head_dim * self.num_kv_heads_per_partition)
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v)

8
vllm/model_executor/models/phi4mm_audio.py
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@@ -41,7 +41,7 @@ class ConformerEncoderLayer(nn.Module):
for the last pointwise conv after swish activation.
depthwise_seperable_out_channel: int
if set different to 0, the number of
depthwise_seperable_out_channel will be used as a
depthwise_seperable_out_channel will be used as a
channel_out of the second conv1d layer.
otherwise, it equal to 0, the second conv1d layer is skipped.
depthwise_multiplier: int
@@ -126,7 +126,7 @@ class ConformerEncoderLayer(nn.Module):
(Multi-Head Attention),
1 = typical Multi-Head Attention,
1 < attn_group_sizes < attention_heads = Grouped-Query Attention
attn_group_sizes = attenion_heads = Multi-Query Attention
attn_group_sizes = attention_heads = Multi-Query Attention
"""
def __init__(
@@ -318,7 +318,7 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
1 = typical Multi-Head Attention,
1 < attention_group_size < attention_heads = Grouped-Query
Attention
attention_group_size = attenion_heads = Multi-Query Attention
attention_group_size = attention_heads = Multi-Query Attention
"""
def __init__(
@@ -744,7 +744,7 @@ class ConformerEncoder(TransformerEncoderBase):
1 = typical Multi-Head Attention,
1 < attention_group_size < attention_heads = Grouped-Query
Attention
attention_group_size = attenion_heads = Multi-Query Attention
attention_group_size = attention_heads = Multi-Query Attention
"""
extra_multi_layer_output_idxs: list[int]

6
vllm/model_executor/models/phimoe.py
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@@ -147,15 +147,15 @@ class mp(torch.autograd.Function):
grad_at_output = grad_at_output * multiplier
grad_at_scores_expaned = masked_gates * grad_at_output.mul(-1)
grad_at_scores_expaned.scatter_add_(
grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
grad_at_scores_expanded.scatter_add_(
dim=-1,
index=selected_experts,
src=grad_at_output,
)
return (
grad_at_scores_expaned,
grad_at_scores_expanded,
None,
None,
None,