Fix cannot offload whole layer in cpu

ktransformers/local_chat.py

@@ -67,6 +67,7 @@ def local_chat(
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
     if mode == 'long_context':
+        assert config.architectures[0] == "LlamaForCausalLM", "only LlamaForCausalLM support long_context mode"
         torch.set_default_dtype(torch.float16)
     else:
         torch.set_default_dtype(config.torch_dtype)
@@ -143,6 +144,7 @@ def local_chat(
         input_tensor = tokenizer.apply_chat_template(
             messages, add_generation_prompt=True, return_tensors="pt"
         )
+        if mode == 'long_context':
             assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
             "please change max_seq_len in  ~/.ktransformers/config.yaml"
         torch.set_default_dtype(

ktransformers/operators/experts.py

@@ -6,7 +6,7 @@ Author       : Azure-Tang, Boxin Zhang, chenht2022
 Date         : 2024-07-25 11:25:24
 Version      : 0.1.0
 LastEditors  : Azure 
-LastEditTime : 2024-08-27 03:50:23
+LastEditTime : 2024-08-29 09:41:10
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 
@@ -202,7 +202,7 @@ class KExpertsCPU(KExpertsBase):
     def forward(self, input_tensor, expert_ids, weights):
         # generate, capture and run cuda graph
         # print(expert_ids)
-        if input_tensor.size(0)==1:
+        if input_tensor.size(0)==1 and torch.cuda.is_current_stream_capturing():
             # TODO: this branch is unreachable, but the shape of input_tensor([1,hidden_size]) and input_tensor_cpu([hidden_size]) is not compatible
             #print("capturing experts")
             KExpertsCPU.input_tensor_cpu.copy_(input_tensor, non_blocking=True)
@@ -636,7 +636,7 @@ class KDeepseekV2MoE(BaseInjectedModule, DeepseekV2MoE):
         topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         
-        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode"):
+        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
             self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
             if self.config.n_shared_experts is not None:
                 y_ = self.shared_experts(identity).squeeze(0)

ktransformers/operators/linear.py

@@ -6,7 +6,7 @@ Author       : Azure-Tang, Boxin Zhang
 Date         : 2024-07-25 11:25:24
 Version      : 0.1.0
 LastEditors  : Azure 
-LastEditTime : 2024-08-14 14:57:04
+LastEditTime : 2024-08-29 09:11:16
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 
@@ -277,7 +277,7 @@ class KLinearCPUInfer(KLinearBase):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         origin_shape = x.shape # [batch_size, q_len, hidden_size]
-        if origin_shape[1] == 1:
+        if origin_shape[1] == 1 and torch.cuda.is_current_stream_capturing():
             out_device = x.device
             self.input_tensor_cpu.copy_(x, non_blocking=True)
             qlen = origin_shape[1]

ktransformers/operators/models.py

@@ -670,8 +670,9 @@ class KDeepseekV2Model(BaseInjectedModule):
             if self.transfer_map is not None and i in self.transfer_map:
                 prev_stream = torch.cuda.current_stream()
                 cur_device = self.transfer_map[i]
-                if cur_device not in self.stream_device_map:
+                if cur_device not in self.stream_device_map and cur_device.lower() != "cpu":
                     self.stream_device_map[cur_device] = torch.cuda.Stream(cur_device)
+                if cur_device.lower() != "cpu":
                     torch.cuda.set_device(cur_device)
                     self.stream_device_map[cur_device].wait_stream(prev_stream)
                     torch.cuda.set_stream(self.stream_device_map[cur_device])