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70 lines
No EOL
3.4 KiB
Python
70 lines
No EOL
3.4 KiB
Python
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from ktransformers.operators.base_operator import BaseInjectedModule
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from ktransformers.util.custom_loader import GGUFLoader
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from transformers import PretrainedConfig
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import torch.nn as nn
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from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MLP
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from ktransformers.models.modeling_qwen2_moe import Qwen2MoeMLP
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from ktransformers.models.modeling_smallthinker import SmallthinkerDenseMlpBlock
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from ktransformers.models.modeling_glm4_moe import Glm4MoeMLP
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class kDeepseekV3MLP(DeepseekV3MLP, BaseInjectedModule):
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def __init__(self,
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key: str,
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gguf_loader : GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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prefill_device: str = "cuda",
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generate_device: str = "cuda",
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**kwargs):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
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self.orig_module.__init__(orig_module.config,
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orig_module.hidden_size, orig_module.intermediate_size)
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def forward(self, x, bsz_tensor):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
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return down_proj
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class KQwen2MoeMLP(Qwen2MoeMLP, BaseInjectedModule):
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def __init__(self,
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key: str,
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gguf_loader : GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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prefill_device: str = "cuda",
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generate_device: str = "cuda",
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**kwargs):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
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self.orig_module.__init__(orig_module.config,
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orig_module.intermediate_size)
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def forward(self, x, bsz_tensor):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
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return down_proj
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class KSmallthinkerDenseMlpBlock(SmallthinkerDenseMlpBlock, BaseInjectedModule):
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def __init__(self,
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key: str,
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gguf_loader : GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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prefill_device: str = "cuda",
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generate_device: str = "cuda",
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**kwargs):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
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self.orig_module.__init__(orig_module.config)
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def forward(self, x, bsz_tensor):
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down_proj = self.down(nn.functional.relu(self.gate(x, bsz_tensor)) * self.up(x, bsz_tensor), bsz_tensor)
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return down_proj
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class KGlm4MoeMLP(Glm4MoeMLP, BaseInjectedModule):
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def __init__(self,
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key: str,
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gguf_loader : GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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prefill_device: str = "cuda",
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generate_device: str = "cuda",
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**kwargs):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
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self.orig_module.__init__(orig_module.config, orig_module.hidden_size, orig_module.intermediate_size)
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def forward(self, x, bsz_tensor):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
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return down_proj |