tmp

ktransformers/local_chat.py

@@ -58,7 +58,7 @@ def local_chat(
     gguf_path: str | None = None,
     max_new_tokens: int = 300,
     cpu_infer: int = Config().cpu_infer,
-    use_cuda_graph: bool = True,
+    use_cuda_graph: bool = False,
     prompt_file : str | None = None,
     mode: str = "normal",
     force_think: bool = False,
@@ -160,6 +160,9 @@ def local_chat(
         input_tensor = tokenizer.apply_chat_template(
             messages, add_generation_prompt=True, return_tensors="pt"
         )
+
+        # input_tensor = torch.tensor([[0, 6657, 84646]], device=input_tensor.device)
+
         if force_think:
             token_thinks = torch.tensor([tokenizer.encode("<think>\\n",add_special_tokens=False)],device=input_tensor.device)
             input_tensor = torch.cat(
@@ -181,4 +184,6 @@ def local_chat(
 
 
 if __name__ == "__main__":
-    fire.Fire(local_chat)
+    # fire.Fire(local_chat)
\ No newline at end of file
+    # local_chat(model_path="/mnt/data/model/DeepSeek-R1", gguf_path="/mnt/data/model/DeepseekV3-q4km-gguf", cpu_infer=33, force_think=False)
+    local_chat(model_path="/mnt/data/model/Moonlight-16B-A3B-Instruct", gguf_path="/mnt/data/model/Moonlight-16B-A3B-Instruct-GGUF", cpu_infer=33, force_think=False)
\ No newline at end of file

ktransformers/operators/attention.py

@@ -441,10 +441,10 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # mla_wrapper run output: [tokens, self.num_heads, self.kv_lora_rank]
             # attn_output [bsz, q_len, self.num_heads, self.kv_lora_rank]
             # out_absorb [self.num_heads, self.v_head_dim, self.kv_lora_rank]
-            attn_output = attn_output.transpose(1, 2)
-            attn_output = torch.matmul(attn_output, out_absorb.mT)
+            attn_output = attn_output.transpose(1, 2) # [bsz, self.num_heads, q_len, self.kv_lora_rank]
+            attn_output = torch.matmul(attn_output, out_absorb.mT) # [bsz, self.num_heads, q_len, self.v_head_dim]
             
-            attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
+            attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) # [bsz, q_len, self.num_heads * self.v_head_dim]
             attn_output = self.o_proj(attn_output)
 
             return attn_output, None, past_key_value

ktransformers/operators/experts.py

@@ -450,9 +450,9 @@ class KExpertsTorch(KExpertsBase):
                     self.up[i] = w["up"][i, ...].to(device=device, dtype=self.dtype)
                     self.down[i] = w["down"][i, ...].to(device=device, dtype=self.dtype)
         
-        self.up = torch.cat(self.gate, dim=0)
+        self.up = torch.cat(self.up, dim=0)
         self.gate = torch.cat(self.gate, dim=0)
-        self.down = torch.cat(self.gate, dim=0)
+        self.down = torch.cat(self.down, dim=0)
         return 
 
     def unload(self):

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml

 - match:
     class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
   replace:
-    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    class: ktransformers.operators.RoPE.RotaryEmbeddingV3
     kwargs:
       generate_device: "cuda"
       prefill_device: "cuda"