Merge pull request #382 from ceerRep/server-prefix-cache

fix server and add prefix cache for server

ktransformers/models/custom_cache.py

@@ -172,6 +172,18 @@ class StaticCache(transformers.StaticCache):
             self.key_cache[layer_idx].zero_()
             if self.value_cache[layer_idx] is not None:
                 self.value_cache[layer_idx].zero_()
+            self.past_tokens[layer_idx] = 0
+
+    def remove_suffix(self, start_pos):
+        for layer_idx in range(len(self.key_cache)):
+            # In-place ops prevent breaking the static address
+            if self.is_MLA:
+                k_cache = self.key_cache[layer_idx]
+                k_cache.view(-1, k_cache.shape[-1])[start_pos:].zero_()
+            else:
+                self.key_cache[layer_idx][..., start_pos:, :].zero_()
+                self.value_cache[layer_idx][..., start_pos:, :].zero_()
+            self.past_tokens[layer_idx] = start_pos
     
     def get_max_cache_shape(self) -> Tuple[int, int, int, int]:
         """Returns the maximum shape of the cache."""

ktransformers/operators/attention.py

@@ -129,8 +129,8 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # compressed_kv [pages, page_size, 1, self.kv_lora_rank]
             
         q_absorb, out_absorb = self.get_absorbed()
-        if hasattr(self.orig_module, 'kv_b_proj'):
-            del self.orig_module.kv_b_proj
+        # if hasattr(self.orig_module, 'kv_b_proj'):
+        #     del self.orig_module.kv_b_proj
 
         # q_nope [bsz, self.num_heads, q_len, self.qk_nope_head_dim]
         # q_pe [bsz, self.num_heads, q_len, self.qk_rope_head_dim]
@@ -223,6 +223,16 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim)
         compressed_kv = compressed_kv.view(bsz, q_len, 1, self.kv_lora_rank)
 
+        kv_seq_len = q_len
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        
         cos, sin = self.rotary_emb(q_pe, position_ids)
         q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, unsqueeze_dim=2)
         # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim] k_pe [bsz, q_len, 1, self.qk_rope_head_dim]
@@ -293,26 +303,28 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                 cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
                 k_pe.squeeze(0)
                 compressed_kv.squeeze(0)
-                past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
-                k_pe.unsqueeze(0)
-                compressed_kv.unsqueeze(0)
-        
-            k_pe = k_pe[:, :q_len]
-            compressed_kv = compressed_kv[:, :q_len]
+                compressed_kv_with_k_pe, _ = past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
+                compressed_kv, k_pe = torch.split(
+                    compressed_kv_with_k_pe, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+                )
+            k_pe = k_pe.view(bsz, -1, self.qk_rope_head_dim)
+            k_pe = k_pe[:, :kv_seq_len]
+            compressed_kv = compressed_kv.view(bsz, -1, self.kv_lora_rank)
+            compressed_kv = compressed_kv[:, :kv_seq_len]
             kv = (
                 self.kv_b_proj(compressed_kv)
-                .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+                .view(bsz, kv_seq_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
             )
             k_nope, value_states = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
             query_states = k_pe.new_empty(bsz, q_len, self.num_heads, self.q_head_dim)
             query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
             query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
 
-            key_states = k_pe.new_empty(bsz, q_len, self.num_heads, self.q_head_dim)
+            key_states = k_pe.new_empty(bsz, kv_seq_len, self.num_heads, self.q_head_dim)
             key_states[:, :, :, :self.qk_nope_head_dim] = k_nope
-            key_states[:, :, :, self.qk_nope_head_dim:] = k_pe
+            key_states[:, :, :, self.qk_nope_head_dim:] = k_pe.view(bsz, kv_seq_len, 1, -1)
             
-            value_states = value_states.view(bsz, q_len, self.num_heads, self.v_head_dim)
+            value_states = value_states.view(bsz, kv_seq_len, self.num_heads, self.v_head_dim)
             value_states_padded = torch.nn.functional.pad(value_states, [0, query_states.shape[-1] - value_states.shape[-1]], value=0)
 
             attn_output = flash_attn_func(
@@ -363,6 +375,16 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim)
         compressed_kv = compressed_kv.view(bsz, q_len, 1, self.kv_lora_rank)
 
+        kv_seq_len = q_len
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        
         cos, sin = self.rotary_emb(q_pe, position_ids)
         q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, unsqueeze_dim=2)
         # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim] k_pe [bsz, q_len, 1, self.qk_rope_head_dim]
@@ -441,26 +463,28 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                 cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
                 k_pe.squeeze(0)
                 compressed_kv.squeeze(0)
-                past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
-                k_pe.unsqueeze(0)
-                compressed_kv.unsqueeze(0)
-        
-            k_pe = k_pe[:, :q_len]
-            compressed_kv = compressed_kv[:, :q_len]
+                compressed_kv_with_k_pe, _ = past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
+                compressed_kv, k_pe = torch.split(
+                    compressed_kv_with_k_pe, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+                )
+            k_pe = k_pe.view(bsz, -1, self.qk_rope_head_dim)
+            k_pe = k_pe[:, :kv_seq_len]
+            compressed_kv = compressed_kv.view(bsz, -1, self.kv_lora_rank)
+            compressed_kv = compressed_kv[:, :kv_seq_len]
             kv = (
                 self.kv_b_proj(compressed_kv)
-                .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+                .view(bsz, kv_seq_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
             )
             k_nope, value_states = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
             query_states = k_pe.new_empty(bsz, q_len, self.num_heads, self.q_head_dim)
             query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
             query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
 
-            key_states = k_pe.new_empty(bsz, q_len, self.num_heads, self.q_head_dim)
+            key_states = k_pe.new_empty(bsz, kv_seq_len, self.num_heads, self.q_head_dim)
             key_states[:, :, :, :self.qk_nope_head_dim] = k_nope
-            key_states[:, :, :, self.qk_nope_head_dim:] = k_pe
+            key_states[:, :, :, self.qk_nope_head_dim:] = k_pe.view(bsz, kv_seq_len, 1, -1)
             
-            value_states = value_states.view(bsz, q_len, self.num_heads, self.v_head_dim)
+            value_states = value_states.view(bsz, kv_seq_len, self.num_heads, self.v_head_dim)
             value_states_padded = torch.nn.functional.pad(value_states, [0, query_states.shape[-1] - value_states.shape[-1]], value=0)
 
             attn_output = flash_attn_func(

ktransformers/server/api/openai/endpoints/chat.py

@@ -5,18 +5,15 @@ from fastapi import APIRouter
 from fastapi.requests import Request
 from ktransformers.server.utils.create_interface import get_interface
 from ktransformers.server.schemas.assistants.streaming import chat_stream_response
-from ktransformers.server.schemas.endpoints.chat import ChatCompletionCreate,ChatCompletionChunk,ChatCompletionObject
+from ktransformers.server.schemas.endpoints.chat import ChatCompletionCreate,ChatCompletionChunk,ChatCompletionObject, Usage
 from ktransformers.server.backend.base import BackendInterfaceBase
+from ktransformers.server.config.config import Config
 
 router = APIRouter()
 
-models = [
-    {"id": "0", "name": "ktranformers-model"},
-]
-
 @router.get('/models', tags=['openai'])
 async def list_models():
-    return models
+    return [{"id": Config().model_name, "name": Config().model_name}]
 
 
 @router.post('/chat/completions', tags=['openai'])
@@ -36,7 +33,8 @@ async def chat_completion(request:Request,create:ChatCompletionCreate):
                 yield chunk
         return chat_stream_response(request,inner())
     else:
-        comp = ChatCompletionObject(id=id,object='chat.completion.chunk',created=int(time()))
+        comp = ChatCompletionObject(id=id,object='chat.completion',created=int(time()))
+        comp.usage = Usage(completion_tokens=1, prompt_tokens=1, total_tokens=2)
         async for token in interface.inference(input_message,id):
             comp.append_token(token)
         return comp

ktransformers/server/args.py

@@ -90,7 +90,8 @@ class ArgumentParser:
         # user config
         parser.add_argument("--user_secret_key", type=str, default=self.cfg.user_secret_key)
         parser.add_argument("--user_algorithm", type=str, default=self.cfg.user_algorithm)
-        parser.add_argument("--force_think", type=bool, default=self.cfg.user_force_think)
+        parser.add_argument("--force_think", action=argparse.BooleanOptionalAction, type=bool, default=self.cfg.user_force_think)
+        parser.add_argument("--use_cuda_graph", action=argparse.BooleanOptionalAction, type=bool, default=self.cfg.use_cuda_graph)
 
         # web config
         parser.add_argument("--web_cross_domain", type=bool, default=self.cfg.web_cross_domain)

ktransformers/server/backend/interfaces/ktransformers.py

@@ -15,7 +15,9 @@ 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
 
+
 warm_uped = False
+
 class KTransformersThreadContext(TransformersThreadContext):
     pass
 
@@ -74,13 +76,13 @@ class KTransformersInterface(TransformersInterface):
         self._infer_lock = asyncio.Lock()
 
     def decode_one_tokens(self):
+        global warm_uped
+
         device_map = self.model.gguf_loader.tensor_device_map
         torch_device = get_device("blk.0.self_attn", device_map)
         torch_device = "cuda:0" if torch_device == "cuda" else torch_device
-        global warm_uped
         torch.cuda.set_device(torch_device)
-        if self.args.use_cuda_graph and warm_uped == True:
-            
+        if warm_uped and self.args.use_cuda_graph:
             if not hasattr(self, "cuda_graph_runner"):
                 self.cuda_graph_runner = CUDAGraphRunner()
                 self.cuda_graph_runner.capture(
@@ -127,24 +129,43 @@ class KTransformersInterface(TransformersInterface):
     @torch.no_grad
     def prefill(self, input_ids: torch.Tensor, is_new: bool):
         input_ids_length = input_ids.shape[-1]
-        self.profiler.set_counter("prefill", input_ids_length)
         logger.debug(f"input_ids: {input_ids.shape}")
 
         device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
         device = "cuda:0" if device == "cuda" else device
 
         if is_new:
-            self.cache.reset()
             self.ever_generated_ids.clear()
-            former_seq_length = 0
-            self.seq_length = input_ids_length
+            same_prefix = 0
+            flat_input_ids = input_ids.flatten()
+
+            if getattr(self, 'generated_ids', None) is None:
                 self.generated_ids = torch.zeros(
                     self.args.batch_size,
-                self.seq_length + self.args.max_new_tokens + 1,
+                    input_ids.shape[-1] + self.args.max_new_tokens + 1,
                     dtype=torch.int,
                     device=self.args.device,
                 )
+                self.seq_length = 1            
+            
+            flat_prev_ids = self.generated_ids.flatten()
+            for i in range(min(self.seq_length, flat_input_ids.shape[0]) - 1):
+                if flat_input_ids[i] == flat_prev_ids[i]:
+                    same_prefix += 1
                 else:
+                    break
+            
+            logger.debug(f"same prefix len: {same_prefix}")
+            self.cache.remove_suffix(same_prefix)
+            self.seq_length = same_prefix
+            self.generated_ids = self.generated_ids[..., :same_prefix]
+            input_ids = input_ids[..., same_prefix:]
+            input_ids_length = input_ids.shape[-1]
+
+        self.ever_generated_ids.clear()
+        self.profiler.set_counter("prefill", input_ids_length)
+        logger.debug(f"input_ids: {input_ids.shape}")
+
         logger.debug(f"generate_ids: {self.generated_ids.shape}")
         former_seq_length = self.seq_length
         self.seq_length += input_ids_length
@@ -155,6 +176,7 @@ class KTransformersInterface(TransformersInterface):
                 self.args.batch_size, delta_length, dtype=torch.int, device=self.args.device
             )
             self.generated_ids = torch.cat([self.generated_ids, new_generate_ids], dim=-1)
+        
         logger.debug(f"cache position: {former_seq_length} to {self.seq_length}")
         cache_position = torch.arange(former_seq_length, self.seq_length, device=device)
         self.generated_ids[:, cache_position] = input_ids.to(self.args.device).to(torch.int)
@@ -176,6 +198,7 @@ class KTransformersInterface(TransformersInterface):
         else:
             logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
 
+        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

@@ -170,7 +170,7 @@ class TransformersInterface(BackendInterfaceBase):
         for m in messages[1:]:
             if m["role"] == "user" and new_messages[-1]["role"] == "user":
                 logger.warning("merge two adjacent user messages")
-                new_messages[-1]["content"] += m["content"]
+                new_messages[-1]["content"] += '\n' + m["content"]
             else:
                 new_messages.append(m)
         # if (self.last_request_id is not None) and self.last_request_id == thread_id:
@@ -179,7 +179,11 @@ class TransformersInterface(BackendInterfaceBase):
         #     input_ids = self.tokenizer.apply_chat_template(
         #         new_messages, return_tensors="pt", add_generation_prompt=True
         #     ).to(self.args.device)
-        input_ids = self.tokenizer.apply_chat_template(new_messages,return_tensors='pt',add_generation_prompt=True).to(self.args.device)
+        input_str: str = self.tokenizer.apply_chat_template(new_messages,tokenize=False,add_generation_prompt=True)
+        # drop <think> token in chat template
+        if input_str.endswith('<think>\n'):
+            input_str = input_str[:-len('<think>\n')]
+        input_ids = self.tokenizer.encode(input_str, return_tensors="pt").to(self.args.device)
         if (self.last_request_id is not None) and self.last_request_id == thread_id:
             x = self.generated_ids[:,:self.seq_length]
             y = input_ids[:,:self.seq_length]
@@ -198,14 +202,28 @@ class TransformersInterface(BackendInterfaceBase):
         self.seq_length += 1
         return self.streamer.put(new_tokens)
 
-    def logits_to_token(self, logits: torch.Tensor):
-        logits = logits / self.args.temperature if self.args.temperature!=0 else logits
+    def prepare_logits_wrapper(self, inputs, device):
+        generation_config, model_kwargs = self.model._prepare_generation_config(
+            None, max_length=self.args.max_new_tokens,
+            do_sample=True, 
+            top_k=self.args.top_k, 
+            top_p=self.args.top_p, 
+            temperature=self.args.temperature,
+            repetition_penalty=self.args.repetition_penalty # change this to modify generate config
+        )
+        self.inputs = inputs
+        self.generation_config = generation_config
+        try: # transformers==4.43
+            self.logits_warper = (
+                self.model._get_logits_warper(generation_config,device=device)
+            )
+        except: 
+            self.logits_warper = (
+                self.model._get_logits_warper(generation_config)
+            )
 
-        for token_idx in self.ever_generated_ids:
-            if logits[token_idx] < 0:
-                logits[token_idx] *= self.args.repetition_penalty
-            else:
-                logits[token_idx] /= self.args.repetition_penalty
+    def logits_to_token(self, logits: torch.Tensor):
+        logits = self.logits_warper(self.inputs.view(1, -1), logits.view(1, -1))
 
         probs = torch.nn.functional.softmax(logits, dim=-1)
 
@@ -239,21 +257,40 @@ class TransformersInterface(BackendInterfaceBase):
     @torch.no_grad
     def prefill(self, input_ids: torch.Tensor, is_new: bool):
         input_ids_length = input_ids.shape[-1]
-        self.profiler.set_counter("prefill", input_ids_length)
         logger.debug(f"input_ids: {input_ids.shape}")
 
         if is_new:
-            self.cache.reset()
             self.ever_generated_ids.clear()
-            former_seq_length = 0
-            self.seq_length = input_ids_length
+            same_prefix = 0
+            flat_input_ids = input_ids.flatten()
+
+            if getattr(self, 'generated_ids', None) is None:
                 self.generated_ids = torch.zeros(
                     self.args.batch_size,
-                self.seq_length + self.args.max_new_tokens + 1,
+                    input_ids.shape[-1] + self.args.max_new_tokens + 1,
                     dtype=torch.int,
                     device=self.args.device,
                 )
+                self.seq_length = 1            
+            
+            flat_prev_ids = self.generated_ids.flatten()
+            for i in range(min(self.seq_length, flat_input_ids.shape[0]) - 1):
+                if flat_input_ids[i] == flat_prev_ids[i]:
+                    same_prefix += 1
                 else:
+                    break
+            
+            logger.debug(f"same prefix len: {same_prefix}")
+            self.cache.remove_suffix(same_prefix)
+            self.seq_length = same_prefix
+            self.generated_ids = self.generated_ids[..., :same_prefix]
+            input_ids = input_ids[..., same_prefix:]
+            input_ids_length = input_ids.shape[-1]
+        
+        self.ever_generated_ids.clear()
+        self.profiler.set_counter("prefill", input_ids_length)
+        logger.debug(f"input_ids: {input_ids.shape}")
+
         logger.debug(f"generate_ids: {self.generated_ids.shape}")
         former_seq_length = self.seq_length
         self.seq_length += input_ids_length
@@ -264,6 +301,7 @@ class TransformersInterface(BackendInterfaceBase):
                 self.args.batch_size, delta_length, dtype=torch.int, device=self.args.device
             )
             self.generated_ids = torch.cat([self.generated_ids, new_generate_ids], dim=-1)
+            
         logger.debug(f"cache position: {former_seq_length} to {self.seq_length}")
         cache_position = torch.arange(former_seq_length, self.seq_length, device=self.args.device)
         self.generated_ids[:, cache_position] = input_ids.to(self.args.device).to(torch.int)
@@ -285,6 +323,7 @@ class TransformersInterface(BackendInterfaceBase):
         else:
             logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
 
+        self.prepare_logits_wrapper(input_ids, device)
         next_token = self.logits_to_token(logits[0, -1, :])
         yield self.append_new_tokens(next_token)
 
@@ -321,6 +360,7 @@ class TransformersInterface(BackendInterfaceBase):
                 return True
 
     async def inference(self, local_messages, thread_id: str):
+        self.streamer.reset()
         self.profiler.create_and_start_timer("tokenize")
         if isinstance(local_messages, List):
             input_ids = self.format_and_tokenize_input_ids(thread_id, local_messages)
@@ -330,8 +370,9 @@ class TransformersInterface(BackendInterfaceBase):
             #input_ids = torch.tensor([[6366]], device=input_ids.device)
         else:
             raise ValueError("local_messages should be List or str")
+        
         if Config().user_force_think:
-            token_thinks = torch.tensor([self.tokenizer.encode("<think>\\n",add_special_tokens=False)],device=input_ids.device)
+            token_thinks = torch.tensor([self.tokenizer.encode("<think>\n",add_special_tokens=False)],device=input_ids.device)
             input_ids = torch.cat(
                 [input_ids, token_thinks], dim=1
             )
@@ -339,11 +380,14 @@ class TransformersInterface(BackendInterfaceBase):
         self.profiler.pause_timer("tokenize")
 
         self.profiler.create_and_start_timer("prefill")
-        if Config().user_force_think:
-            t = "<think>\n"
-            print(t,end="",flush=True)
-            yield t
+
+               
         for t in self.prefill(input_ids, self.check_is_new(thread_id)):
+            # output think token after prefill done
+            if Config().user_force_think:
+                think = '<think>\n'
+                print(think, end="",flush=True)
+                yield think
             if t is not None:
                 print(t, end="",flush=True)
                 yield t

ktransformers/server/main.py

@@ -105,6 +105,7 @@ def custom_openapi(app):
 
 def main():
     cfg = Config()
+
     arg_parser = ArgumentParser(cfg)
 
     # 初始化消息