EngineX-Ascend/enginex-ascend-910-vllm
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init v0.11.0rc0

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luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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74
vllm_ascend/models/qwen2_5_vl.py
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@@ -42,6 +42,8 @@ from vllm.model_executor.models.qwen2_5_vl import (
from vllm.model_executor.models.utils import maybe_prefix
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm_ascend.utils import vllm_version_is
MIN_PAD_SIZE = 64 # min_size to pad weight
MAX_PAD_SIZE = 128 # max_size to pad weight
@@ -291,6 +293,40 @@ class AscendQwen2_5_VisionTransformer(Qwen2_5_VisionTransformer):
self.hidden_size, -1)
return out_weight
def pad_qkv_weight_scale_offset(self, data):
reshaped_data = data.reshape(
-1, 3, self.origin_hidden_size_per_attention_head, 1)
data1 = reshaped_data[:, :, :self.
half_origin_hidden_size_per_attention_head, :]
data2 = reshaped_data[:, :, self.
half_origin_hidden_size_per_attention_head:, :]
data1_paded = torch.nn.functional.pad(
data1, (0, 0, 0, self.half_pad_hidden_size_per_attention_head, 0,
0, 0, 0))
data2_paded = torch.nn.functional.pad(
data2, (0, 0, 0, self.half_pad_hidden_size_per_attention_head, 0,
0, 0, 0))
res = torch.cat([data1_paded, data2_paded], dim=2)
res = res.reshape(-1, 1)
return res
def pad_qkv_deq_scale_quant_bias(self, data):
reshaped_data = data.reshape(
-1, 3, self.origin_hidden_size_per_attention_head)
data1 = reshaped_data[:, :, :self.
half_origin_hidden_size_per_attention_head]
data2 = reshaped_data[:, :,
self.half_origin_hidden_size_per_attention_head:]
data1_paded = torch.nn.functional.pad(
data1, (0, self.half_pad_hidden_size_per_attention_head))
data2_paded = torch.nn.functional.pad(
data2, (0, self.half_pad_hidden_size_per_attention_head))
res = torch.cat([data1_paded, data2_paded], dim=2)
res = res.reshape(-1)
return res
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
stacked_params_mapping: list[tuple[str, str, Union[str, int]]] = [
@@ -318,11 +354,23 @@ class AscendQwen2_5_VisionTransformer(Qwen2_5_VisionTransformer):
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
if ("attn.proj.weight" in name) and self.enable_pad:
if ("attn.proj.weight_scale" in name or
"attn.proj.weight_offset" in name) and self.enable_pad:
continue
elif ("attn.proj.deq_scale" in name
or "attn.proj.quant_bias" in name) and self.enable_pad:
continue
elif ("attn.qkv.weight_scale" in name
or "attn.qkv.weight_offset" in name) and self.enable_pad:
param.data = self.pad_qkv_weight_scale_offset(param.data)
elif ("attn.qkv.deq_scale" in name
or "attn.qkv.quant_bias" in name) and self.enable_pad:
param.data = self.pad_qkv_deq_scale_quant_bias(param.data)
elif ("attn.proj.weight" in name) and self.enable_pad:
param.data = self.pad_proj_weight(param.data)
if ("attn.qkv.weight" in name) and self.enable_pad:
elif ("attn.qkv.weight" in name) and self.enable_pad:
param.data = self.pad_qkv_weight(param.data)
if ("attn.qkv.bias" in name) and self.enable_pad:
elif ("attn.qkv.bias" in name) and self.enable_pad:
param.data = self.pad_qkv_bias(param.data)
loaded_params.add(name)
return loaded_params
@@ -450,12 +498,20 @@ class AscendQwen2_5_VLForConditionalGeneration(
super().__init__(vllm_config=vllm_config, prefix=prefix)
config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.visual = AscendQwen2_5_VisionTransformer(
vision_config=config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
quant_config=self._maybe_ignore_quant_config(quant_config),
prefix=maybe_prefix(prefix, "visual"),
)
if vllm_version_is("0.10.2"):
self.visual = AscendQwen2_5_VisionTransformer(
vision_config=config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
quant_config=self._maybe_ignore_quant_config(quant_config),
prefix=maybe_prefix(prefix, "visual"),
)
else:
self.visual = AscendQwen2_5_VisionTransformer(
vision_config=config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
quant_config=quant_config,
prefix=maybe_prefix(prefix, "visual"),
)
def _process_image_input(self, image_input) -> tuple[torch.Tensor, ...]: