Merge pull request #657 from kvcache-ai/feat-absorb-for-long-prefill

Feat absorb for long prefill

ktransformers/local_chat.py

@@ -28,7 +28,7 @@ from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
 from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
 from ktransformers.models.modeling_llama import LlamaForCausalLM
 from ktransformers.models.modeling_mixtral import MixtralForCausalLM
-from ktransformers.util.utils import prefill_and_generate
+from ktransformers.util.utils import prefill_and_generate, get_compute_capability
 from ktransformers.server.config.config import Config
 from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled
 
@@ -64,7 +64,6 @@ def local_chat(
     force_think: bool = False,
 ):
 
-
     torch.set_grad_enabled(False)
 
     Config().cpu_infer = cpu_infer
@@ -169,7 +168,7 @@ def local_chat(
             assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
             "please change max_seq_len in  ~/.ktransformers/config.yaml"
         
-        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or "DeepseekV3ForCausalLM") and flashinfer_enabled:
+        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
             generated = prefill_and_generate(
                 model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think,
                 use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim

ktransformers/operators/attention.py

@@ -16,6 +16,7 @@ from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_ro
 from typing import Optional, Tuple
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_gguf import GGUFLoader
+from ktransformers.util.utils import get_compute_capability
 import logging
 from transformers.configuration_utils import PretrainedConfig
 from transformers.cache_utils import Cache
@@ -48,12 +49,14 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                  prefill_device: str = "cuda",
                  generate_device: str = "cuda",
                  chunck_size: int = 1000,
+                 absorb_for_prefill: bool = False,
                  **kwargs):
         BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs)
         self.orig_module.__init__(orig_module.config,
             orig_module.layer_idx)
         self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
         self.mla_wrapper = None
+        self.absorb_for_prefill = absorb_for_prefill
 
     def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
         if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
@@ -242,7 +245,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             q_nope = q_nope.transpose(1, 2) # q_len is 1, no GPU overhead, same below
             q_nope = torch.matmul(q_nope, q_absorb) # batched MM
             q_nope = q_nope.transpose(1, 2)
-            assert q_nope.is_contiguous()
+            #assert q_nope.is_contiguous()
             
             # q_nope [bsz, q_len, self.num_heads, self.kv_lora_rank]
             # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim]
@@ -282,6 +285,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # 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)
             
             attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
             attn_output = self.o_proj(attn_output)
@@ -380,7 +384,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim] k_pe [bsz, q_len, 1, self.qk_rope_head_dim]
         
         # decode
-        if q_len == 1:
+        if q_len == 1 or self.absorb_for_prefill:
             if past_key_value is not None:
                 cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
                 compressed_kv_with_k_pe, page_table = past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
@@ -395,27 +399,41 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             q_nope = q_nope.transpose(1, 2) # q_len is 1, no GPU overhead, same below
             q_nope = torch.matmul(q_nope, q_absorb) # batched MM
             q_nope = q_nope.transpose(1, 2)
-            assert q_nope.is_contiguous()
+            q_nope = q_nope.contiguous()
+            #assert q_nope.is_contiguous()
             
             # q_nope [bsz, q_len, self.num_heads, self.kv_lora_rank]
             # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim]
-            q_nope.squeeze_(1)
+            q_nope.squeeze_(0)
-            q_pe.squeeze_(1)
+            q_pe.squeeze_(0)
 
             # flash attn doesn't support head_dim bigger than 256, use flashinfer
             if self.mla_wrapper is None:
                 self.mla_wrapper = MLAWrapperSingleton.get_instance(self.device, 1, past_key_value.max_pages, use_cuda_graph = True)
-                if self.mla_wrapper.need_plan:
+            if self.mla_wrapper.need_plan:
-                    self.mla_wrapper.need_plan = False
+                self.mla_wrapper.need_plan = False
+                if q_len == 1:
                     self.mla_wrapper.plan(None,None,None,
-                                      position_ids.squeeze(1)+1,
+                                        position_ids.squeeze(1)+1,
-                                      self.num_heads,
+                                        self.num_heads,
-                                      self.kv_lora_rank,
+                                        self.kv_lora_rank,
-                                      self.qk_rope_head_dim,
+                                        self.qk_rope_head_dim,
-                                      past_key_value.page_size,
+                                        past_key_value.page_size,
-                                      self.softmax_scale,
+                                        self.softmax_scale,
-                                      q_nope.dtype,
+                                        q_nope.dtype,
-                                      compressed_kv.dtype)
+                                        compressed_kv.dtype)
+                else:
+                    qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device=self.device)
+                    kv_len_arr = torch.tensor([position_ids[0, -1].item()+1], dtype=torch.int32, device=self.device)
+                    self.mla_wrapper.plan(qo_indptr,None,None,
+                                        kv_len_arr,
+                                        self.num_heads,
+                                        self.kv_lora_rank,
+                                        self.qk_rope_head_dim,
+                                        past_key_value.page_size,
+                                        self.softmax_scale,
+                                        q_nope.dtype,
+                                        compressed_kv.dtype)
             attn_output = self.mla_wrapper.run(q_nope, q_pe, compressed_kv, k_pe).view(bsz, q_len, self.num_heads, self.kv_lora_rank)
 
             """
@@ -441,10 +459,11 @@ 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 = attn_output.transpose(1, 2) # [bsz, self.num_heads, q_len, self.kv_lora_rank]
-            attn_output = torch.matmul(attn_output, out_absorb.mT)
+            attn_output = torch.matmul(attn_output, out_absorb.mT) # [bsz, self.num_heads, q_len, self.v_head_dim]
+            attn_output = attn_output.transpose(1, 2).contiguous() # [bsz, q_len, self.num_heads, self.kv_lora_rank]
             
-            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
@@ -571,7 +590,8 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if os.name == 'nt':
+        if os.name == 'nt' or get_compute_capability()<8:
+            print("for Windows or GPU before ampere, use forward_windows")
             return self.forward_windows(
                 hidden_states,
                 attention_mask,

ktransformers/operators/experts.py

@@ -159,7 +159,7 @@ class KExpertsCPU(KExpertsBase):
         down_ptr = ctypes.addressof(
             ctypes.cast(self.down.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
         )
-        # print(self.gate_qtype, self.up_qtype, self.down_qtype)
+        #print(self.gate_type, self.up_type, self.down_type)
         n_routed_experts = self.n_routed_experts
         # n_routed_experts = len(self.orig_module)
         moe_config = MOEConfig(
@@ -459,9 +459,9 @@ 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/operators/flashinfer_wrapper.py

@@ -9,7 +9,7 @@ flashinfer_enabled = False
 
 try:
     import flashinfer
-    flashinfer_enabled = False # disabled now, TODO:use new version of flashinfer and enable
+    flashinfer_enabled = True
     print("found flashinfer")
     
 except ImportError:
@@ -132,14 +132,14 @@ class MLAWrapper():
             head_dim_ckv,
             head_dim_kpe,
             page_size,
-            False, # causal is False for decoding
+            True, # causal
             sm_scale,
             q_data_type,
             kv_data_type,
         )
 
     def run(self, q_nope, q_pe, ckv, k_pe, return_lse = False):
-        return self.wrapper.run(q_nope, q_pe, ckv, k_pe, return_lse)
+        return self.wrapper.run(q_nope, q_pe, ckv, k_pe, return_lse = return_lse)
 
 class MLAWrapperSingleton():
     wrappers:dict = {}
@@ -179,6 +179,17 @@ class MLAWrapperSingleton():
                 sm_scale,
                 q_data_type,
                 kv_data_type,)
+            wrapper.need_plan = False
+            
+    @classmethod
+    def need_plan_all(cls):
+        for device, wrapper in cls.wrappers.items():
+            wrapper.need_plan = True
+        
+    @classmethod
+    def reset_buffer(cls):
+        for device, wrapper in cls.wrappers.items():
+            wrapper.qo_indptr_buf[1] = 1
             
 
 if __name__ == "__main__":
@@ -187,8 +198,9 @@ if __name__ == "__main__":
     page_size = 64
     num_heads = 128
 
-    q_nope = torch.randn((1, num_heads, 512), dtype=torch.bfloat16, device="cuda")
+    q_len = 10
-    q_pe = torch.randn((1, num_heads, 64), dtype=torch.bfloat16, device="cuda")
+    q_nope = torch.randn((q_len, num_heads, 512), dtype=torch.bfloat16, device="cuda")
+    q_pe = torch.randn((q_len, num_heads, 64), dtype=torch.bfloat16, device="cuda")
     ckv = torch.randn((max_pages, page_size, 512), dtype=torch.bfloat16, device="cuda")
     k_pe = torch.randn((max_pages, page_size, 64), dtype=torch.bfloat16, device="cuda")
     
@@ -199,10 +211,10 @@ if __name__ == "__main__":
         max_pages,
     )
     
-    kv_len_arr = torch.tensor([10], dtype=torch.int32, device="cuda")
+    kv_len_arr = torch.tensor([q_len], dtype=torch.int32, device="cuda")
-
+    qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device="cuda")
     wrapper.plan(
-        None,
+        qo_indptr,
         None,
         None,
         kv_len_arr,
@@ -216,6 +228,7 @@ if __name__ == "__main__":
     )
 
     attn_output = wrapper.run(q_nope, q_pe, ckv, k_pe)
+    print(attn_output.shape)
 
     k = (
         torch.cat([ckv, k_pe], dim=-1)
@@ -235,6 +248,7 @@ if __name__ == "__main__":
         False,
         192 ** (-0.5)
     )
+    print(attn_ref.shape)
     
     torch.testing.assert_close(attn_output, attn_ref, rtol=1e-3, atol=1e-3)
     print("test past")

ktransformers/operators/models.py

@@ -56,7 +56,7 @@ from ktransformers.models.modeling_deepseek import (
 from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
 from ktransformers.models.configuration_llama import LlamaConfig
 from ktransformers.operators.base_operator import BaseInjectedModule
-from ktransformers.util.utils import InferenceState
+from ktransformers.util.utils import InferenceState, get_compute_capability
 from ktransformers.util.custom_gguf import GGUFLoader
 from transformers.configuration_utils import PretrainedConfig
 from ktransformers.models.modeling_llama import (
@@ -649,9 +649,14 @@ class KDeepseekV2Model(BaseInjectedModule):
         if per_layer_prefill_flag:
             causal_mask = None
         else:
-            causal_mask = self._update_causal_mask(
+            if os.name == 'nt' or get_compute_capability()<8:
-                attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+                print("for Windows or GPU before ampere, use forward_windows")
-            )
+                # only use mask in forward windows or can't flash attn
+                causal_mask = self._update_causal_mask(
+                    attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+                )
+            else:
+                causal_mask = None
 
         # embed positions
         hidden_states = inputs_embeds

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

@@ -60,6 +60,7 @@
     kwargs:
       generate_device: "cuda"
       prefill_device: "cuda"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
 - match:
     name: "^model$"
   replace:

ktransformers/optimize/optimize_rules/Moonlight-16B-A3B.yaml

@@ -0,0 +1,86 @@
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.RotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^lm_head$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+# if want to use more VRAM, use experts Marlin and disable CUDA Graph(disable CUDA Graph may cause low performance)
+#- match:
+#    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+#  replace:
+#    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+#    kwargs:
+#      prefill_device: "cuda"
+#      prefill_op: "KExpertsTorch"
+#      generate_device: "cuda"
+#      generate_op: "KExpertsMarlin"
+#  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"

ktransformers/server/backend/interfaces/ktransformers.py

@@ -14,6 +14,7 @@ from ktransformers.models.custom_cache import StaticCache
 from ktransformers.util.cuda_graph_runner import CUDAGraphRunner
 from ktransformers.local_chat import custom_models, default_optimize_rules
 from ktransformers.util.utils import get_device
+from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled, MLAWrapperSingleton
 
 
 warm_uped = False
@@ -186,6 +187,8 @@ class KTransformersInterface(TransformersInterface):
             input_ids = input_ids.to("cpu")
         inputs_embeds = self.model.model.embed_tokens(input_ids).to(device)
         torch.cuda.set_device(device)
+        if flashinfer_enabled:
+            MLAWrapperSingleton.need_plan_all()
         if self.use_static_cache:
             logits = self.model(
                 inputs_embeds=inputs_embeds,
@@ -198,6 +201,8 @@ class KTransformersInterface(TransformersInterface):
         else:
             logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
 
+        if flashinfer_enabled:
+            MLAWrapperSingleton.reset_buffer()
         self.prepare_logits_wrapper(input_ids, device)
         next_token = self.logits_to_token(logits[0, -1, :])
         yield self.append_new_tokens(next_token)

ktransformers/server/backend/interfaces/transformers.py

@@ -333,7 +333,7 @@ class TransformersInterface(BackendInterfaceBase):
         for i in range(1, self.args.max_new_tokens):
             
             with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True):
-                if i > 1 and flashinfer_enabled:
+                if flashinfer_enabled:
                     MLAWrapperSingleton.plan_all(None,None,None,self.active_cache_position.to(torch.int32)+1,
                                              num_heads=self.model.config.num_attention_heads, head_dim_ckv=self.model.config.kv_lora_rank, 
                                              head_dim_kpe=self.model.config.qk_rope_head_dim, page_size=self.cache.page_size,

ktransformers/util/custom_gguf.py

@@ -330,6 +330,8 @@ class GGUFLoader:
             values = GGML_DEQUANTIZE[ggml_name](data)
             values = torch.from_numpy(values.copy())
 
+        if ggml_name == "BF16":
+            values = values.view(torch.bfloat16)
         values = values.view(shape[-2::-1])
 
         return values

ktransformers/util/utils.py

@@ -21,6 +21,18 @@ from ktransformers.operators.flashinfer_wrapper import MLAWrapperSingleton
 
 warm_uped = False
 
+def get_compute_capability(device:torch.device = None):
+    if torch.cuda.is_available():
+        if device is None:
+            num_gpus = torch.cuda.device_count()
+            min_compute_capability_major = 100
+            for gpu_id in range(num_gpus):
+                gpu_props = torch.cuda.get_device_properties(gpu_id)
+                min_compute_capability_major = min(min_compute_capability_major, gpu_props.major)
+            return min_compute_capability_major
+        else:
+            return torch.cuda.get_device_properties(device)
+
 def set_module(model, submodule_key, module):
     tokens = submodule_key.split('.')
     sub_tokens = tokens[:-1]
@@ -164,6 +176,9 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
             inputs_embeds = model.model.embed_tokens(inputs.to("cpu"))
         else:
             inputs_embeds = model.model.embed_tokens(inputs.to("cpu")).to(torch_device)
+        if use_flashinfer_mla:
+            MLAWrapperSingleton.need_plan_all()
+            
         logits = model(
             inputs_embeds = inputs_embeds, cache_position=cache_position, past_key_values=past_key_values, return_dict=False, use_cache=True
         )[0][:,-1,:].unsqueeze(0).clone().to(torch_device)
@@ -186,6 +201,9 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
         else:
             next_token = torch.argmax(next_token_scores, dim=-1)
         first_token_time = time.time() - start_time
+        
+        if use_flashinfer_mla:
+            MLAWrapperSingleton.reset_buffer()
 
         prefill_count = seq_length
         prefill_time = first_token_time
@@ -203,22 +221,22 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
             
         start_time = time.time()
         for i in range(1, max_new_tokens):
+            if use_flashinfer_mla:
+                MLAWrapperSingleton.plan_all(None,None,None,position_ids.squeeze(1)+1,
+                                             num_heads, head_dim_ckv, head_dim_kpe, past_key_values.page_size,
+                                             q_head_dim ** (-0.5), torch.bfloat16, torch.bfloat16)
             global warm_uped
             if use_cuda_graph and ( (warm_uped == True and int(i) == 1) or (warm_uped == False and int(i) == 2) ):
                 warm_uped = True
                 cuda_graph_runner = CUDAGraphRunner()
                 cuda_graph_runner.capture(model, next_token.unsqueeze(0), position_ids, cache_position, past_key_values, torch_device, return_dict=False, use_cache=True)
-            if i > 1 and use_flashinfer_mla:
-                MLAWrapperSingleton.plan_all(None,None,None,position_ids.squeeze(1)+1,
-                                             num_heads, head_dim_ckv, head_dim_kpe, past_key_values.page_size,
-                                             q_head_dim ** (-0.5), torch.bfloat16, torch.bfloat16)
             next_token = decode_one_tokens(cuda_graph_runner, next_token.unsqueeze(0), position_ids, cache_position, past_key_values, use_cuda_graph).to(torch_device)
             inputs = torch.cat((inputs, next_token.unsqueeze(0)), dim=-1)
             generated_ids[:, cache_position] = next_token.int()
             tokens.append(int(next_token))
             seq_length += 1
             
-            if next_token[0].item() == tokenizer.eos_token_id or tokenizer.decode(next_token) == '<|im_end|>':
+            if next_token[0].item() == tokenizer.eos_token_id or tokenizer.decode(next_token.tolist()) == '<|im_end|>':
                 print(stream.end(), end="", flush=True)
                 break
             else: