vrr/kvcache-ai-ktransformers
2
0
Fork 0
mirror of https://github.com/kvcache-ai/ktransformers.git synced 2025-09-11 07:44:35 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

support glm4moe

Browse source
This commit is contained in:
djw 2025-07-25 17:22:20 +00:00
parent 1677e90092
commit d03d92ba53
31 changed files with 2265 additions and 74 deletions
Download patch file Download diff file Expand all files Collapse all files

33
ktransformers/operators/mlp.py
View file

@ -5,6 +5,8 @@ from transformers import PretrainedConfig
import torch.nn as nn
from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MLP
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeMLP
from ktransformers.models.modeling_smallthinker import SmallthinkerDenseMlpBlock
from ktransformers.models.modeling_glm4_moe import Glm4MoeMLP
class kDeepseekV3MLP(DeepseekV3MLP, BaseInjectedModule):
def __init__(self,
key: str,
@ -32,6 +34,37 @@ class KQwen2MoeMLP(Qwen2MoeMLP, BaseInjectedModule):
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
self.orig_module.__init__(orig_module.config,
orig_module.intermediate_size)
def forward(self, x, bsz_tensor):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
return down_proj
class KSmallthinkerDenseMlpBlock(SmallthinkerDenseMlpBlock, BaseInjectedModule):
def __init__(self,
key: str,
gguf_loader : GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module,
prefill_device: str = "cuda",
generate_device: str = "cuda",
**kwargs):
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
self.orig_module.__init__(orig_module.config)
def forward(self, x, bsz_tensor):
down_proj = self.down(nn.functional.relu(self.gate(x, bsz_tensor)) * self.up(x, bsz_tensor), bsz_tensor)
return down_proj
class KGlm4MoeMLP(Glm4MoeMLP, BaseInjectedModule):
def __init__(self,
key: str,
gguf_loader : GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module,
prefill_device: str = "cuda",
generate_device: str = "cuda",
**kwargs):
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
self.orig_module.__init__(orig_module.config, orig_module.hidden_size, orig_module.intermediate_size)
def forward(self, x, bsz_tensor):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
return down_proj