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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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122
vllm/model_executor/models/minicpm3.py
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@@ -24,6 +24,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only MiniCPM3 model compatible with HuggingFace weights."""
from typing import Any, Optional
import torch
@@ -34,20 +35,23 @@ from vllm.attention import Attention
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
ColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.models.minicpm import (MiniCPMDecoderLayer,
MiniCPMForCausalLM,
MiniCPMModel)
from vllm.model_executor.models.minicpm import (
MiniCPMDecoderLayer,
MiniCPMForCausalLM,
MiniCPMModel,
)
from .utils import make_layers
class MiniCPM3Attention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
@@ -83,33 +87,37 @@ class MiniCPM3Attention(nn.Module):
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.q_a_proj = ReplicatedLinear(self.hidden_size,
self.q_lora_rank,
bias=False,
quant_config=quant_config)
self.q_a_proj = ReplicatedLinear(
self.hidden_size, self.q_lora_rank, bias=False, quant_config=quant_config
)
self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
self.q_b_proj = ColumnParallelLinear(q_lora_rank,
self.num_heads * self.qk_head_dim,
bias=False,
quant_config=quant_config)
self.q_b_proj = ColumnParallelLinear(
q_lora_rank,
self.num_heads * self.qk_head_dim,
bias=False,
quant_config=quant_config,
)
self.kv_a_proj_with_mqa = ReplicatedLinear(self.hidden_size,
self.kv_lora_rank +
self.qk_rope_head_dim,
bias=False,
quant_config=quant_config)
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
eps=config.rms_norm_eps)
self.kv_a_proj_with_mqa = ReplicatedLinear(
self.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=False,
quant_config=quant_config,
)
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
self.kv_b_proj = ColumnParallelLinear(
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
quant_config=quant_config)
quant_config=quant_config,
)
# O projection.
self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config)
self.o_proj = RowParallelLinear(
self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
)
self.rotary_emb = get_rope(
self.qk_rope_head_dim,
@@ -118,13 +126,15 @@ class MiniCPM3Attention(nn.Module):
base=rope_theta,
rope_scaling=rope_scaling,
)
self.attn = Attention(self.num_local_heads,
self.qk_head_dim,
self.scaling,
num_kv_heads=self.num_local_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_local_heads,
self.qk_head_dim,
self.scaling,
num_kv_heads=self.num_local_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -135,55 +145,52 @@ class MiniCPM3Attention(nn.Module):
q = self.q_a_layernorm(q)
q, _ = self.q_b_proj(q)
q = q.view(-1, self.num_local_heads, self.qk_head_dim)
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
dim=-1)
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
latent_cache, _ = self.kv_a_proj_with_mqa(hidden_states)
kv_a, _ = latent_cache.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
latent_cache = latent_cache.unsqueeze(1)
kv_a = self.kv_a_layernorm(kv_a.contiguous())
kv, _ = self.kv_b_proj(kv_a)
kv = kv.view(-1, self.num_local_heads,
self.qk_nope_head_dim + self.v_head_dim)
kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k_pe = latent_cache[:, :, self.kv_lora_rank:]
k_pe = latent_cache[:, :, self.kv_lora_rank :]
q_pe, k_pe = self.rotary_emb(
positions,
q_pe.reshape(-1, self.num_local_heads * self.qk_rope_head_dim),
k_pe.reshape(-1, self.qk_rope_head_dim))
k_pe.reshape(-1, self.qk_rope_head_dim),
)
q_pe = q_pe.view(-1, self.num_local_heads, self.qk_rope_head_dim)
k_pe = k_pe.view(-1, 1, self.qk_rope_head_dim)
q[..., self.qk_nope_head_dim:] = q_pe
q[..., self.qk_nope_head_dim :] = q_pe
k = torch.empty_like(q)
k[..., :self.qk_nope_head_dim] = k_nope
k[..., self.qk_nope_head_dim:] = k_pe
k[..., : self.qk_nope_head_dim] = k_nope
k[..., self.qk_nope_head_dim :] = k_pe
q = q.reshape(-1, self.num_local_heads * self.qk_head_dim)
k = k.view(-1, self.num_local_heads * self.qk_head_dim)
v = torch.nn.functional.pad(
v, [0, self.qk_head_dim - self.v_head_dim],
value=0).view(-1, self.num_local_heads * self.qk_head_dim)
v, [0, self.qk_head_dim - self.v_head_dim], value=0
).view(-1, self.num_local_heads * self.qk_head_dim)
attn_output = self.attn(q, k, v)
attn_output = attn_output.view(
-1, self.num_local_heads,
self.qk_head_dim)[..., :self.v_head_dim].reshape(
-1, self.num_local_heads * self.v_head_dim)
attn_output = attn_output.view(-1, self.num_local_heads, self.qk_head_dim)[
..., : self.v_head_dim
].reshape(-1, self.num_local_heads * self.v_head_dim)
output, _ = self.o_proj(attn_output)
return output
class MiniCPM3DecoderLayer(MiniCPMDecoderLayer):
def _init_attn_block(self):
self.input_layernorm = RMSNorm(self.config.hidden_size,
eps=self.config.rms_norm_eps)
self.input_layernorm = RMSNorm(
self.config.hidden_size, eps=self.config.rms_norm_eps
)
self.self_attn = MiniCPM3Attention(
config=self.config,
hidden_size=self.hidden_size,
@@ -203,7 +210,6 @@ class MiniCPM3DecoderLayer(MiniCPMDecoderLayer):
class MiniCPM3Model(MiniCPMModel):
def _init_layers(
self,
prefix: str,
@@ -214,8 +220,10 @@ class MiniCPM3Model(MiniCPMModel):
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: MiniCPM3DecoderLayer(
config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.layers")
config, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.layers",
)
class MiniCPM3ForCausalLM(MiniCPMForCausalLM):