[Bug fix] Fix block num in scheduler v1 for release2.0.4 (#3314)

* fix bug for scheduler v0

* fix block num setting in scheduler v1

* fix block num setting in scheduler v1

* fix block num setting in scheduler v1

* fix block num setting in scheduler v1

* fix block num setting in scheduler v1

fastdeploy/cache_manager/prefix_cache_manager.py

@@ -64,6 +64,9 @@ class PrefixCacheManager:
         self.speculative_config = config.speculative_config
         self.local_data_parallel_id = local_data_parallel_id
 
+        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
+            self.num_gpu_blocks = self.cache_config.total_block_num
+        else:
             self.num_gpu_blocks = self.cache_config.prefill_kvcache_block_num
         self.num_cpu_blocks = self.cache_config.num_cpu_blocks
         self.gpu_free_block_list = list(range(self.num_gpu_blocks - 1, -1, -1))

fastdeploy/engine/args_utils.py

@@ -18,6 +18,7 @@ import json
 from dataclasses import asdict, dataclass
 from dataclasses import fields as dataclass_fields
 from typing import Any, Dict, List, Optional
+import os
 
 from fastdeploy.engine.config import (
     CacheConfig,
@@ -854,7 +855,10 @@ class EngineArgs:
             if self.enable_chunked_prefill:
                 self.max_num_batched_tokens = 2048
             else:
+                if not int(os.getenv('ENABLE_V1_KVCACHE_SCHEDULER', '0')):
                     self.max_num_batched_tokens = self.max_model_len
+                else:
+                    self.max_num_batched_tokens = 8192
         scheduler_cfg = self.create_scheduler_config()
         speculative_cfg = self.create_speculative_config()
         graph_opt_cfg = self.create_graph_optimization_config()

fastdeploy/engine/config.py

@@ -211,6 +211,8 @@ class CacheConfig:
         self.gpu_memory_utilization = gpu_memory_utilization
         self.num_gpu_blocks_override = num_gpu_blocks_override
         self.kv_cache_ratio = kv_cache_ratio
+        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
+            self.kv_cache_ratio = 1.0
         self.enc_dec_block_num = enc_dec_block_num
         self.prealloc_dec_block_slot_num_threshold = prealloc_dec_block_slot_num_threshold
         self.cache_dtype = cache_dtype
@@ -291,6 +293,9 @@ class CacheConfig:
         self.dec_token_num = self.enc_dec_block_num * self.block_size
         if self.num_gpu_blocks_override is not None:
             self.total_block_num = self.num_gpu_blocks_override
+            if envs.ENABLE_V1_KVCACHE_SCHEDULER:
+                self.prefill_kvcache_block_num = self.total_block_num
+            else:
                 self.prefill_kvcache_block_num = int(self.total_block_num * self.kv_cache_ratio)
         else:
             length = num_total_tokens // number_of_tasks
@@ -304,6 +309,9 @@ class CacheConfig:
         reset gpu block number
         """
         self.total_block_num = num_gpu_blocks
+        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
+            self.prefill_kvcache_block_num = self.total_block_num
+        else:
             self.prefill_kvcache_block_num = int(self.total_block_num * self.kv_cache_ratio)
         llm_logger.info(
             f"Reset block num, the total_block_num:{self.total_block_num},"
@@ -796,7 +804,10 @@ class Config:
             if self.cache_config.enable_chunked_prefill:
                 self.max_num_batched_tokens = 2048
             else:
+                if not int(os.getenv('ENABLE_V1_KVCACHE_SCHEDULER', '0')):
                     self.max_num_batched_tokens = self.max_model_len
+                else:
+                    self.max_num_batched_tokens = 8192
 
         if self.long_prefill_token_threshold == 0:
             self.long_prefill_token_threshold = int(self.max_model_len * 0.04)
@@ -844,6 +855,7 @@ class Config:
         )
 
         if not self.cache_config.enable_chunked_prefill:
+            if not int(os.getenv('ENABLE_V1_KVCACHE_SCHEDULER', '0')):
                 assert self.max_num_batched_tokens >= self.max_model_len, (
                     f"max_num_batched_tokens: {self.max_num_batched_tokens} "
                     f"should be larger than or equal to max_model_len: {self.max_model_len}"

fastdeploy/engine/sched/resource_manager_v1.py

@@ -234,8 +234,7 @@ class ResourceManagerV1(ResourceManager):
                     llm_logger.debug(
                         f"scheduler prefill task: {request} request.need_prefill_tokens {request.need_prefill_tokens} request.num_computed_tokens {request.num_computed_tokens}"
                     )
-                    num_new_tokens = request.prompt_token_ids_len - request.num_computed_tokens
+                    num_new_tokens = self._get_num_new_tokens(request, token_budget)
-                    num_new_tokens = min(num_new_tokens, token_budget)
                     num_new_block = self.get_new_block_nums(request, num_new_tokens)
                     # Allocate blocks to prefill
                     if self.cache_manager.can_allocate_gpu_blocks(num_new_block):

fastdeploy/worker/gpu_model_runner.py

@@ -208,11 +208,15 @@ class GPUModelRunner(ModelRunnerBase):
             request = req_dicts[i]
             idx = request.idx
             if request.task_type.value == RequestType.PREFILL.value:  # prefill task
-                logger.debug(f"Handle prefill request {request} at idx {idx}")
                 prefill_start_index = request.prefill_start_index
                 prefill_end_index = request.prefill_end_index
                 length = prefill_end_index - prefill_start_index
                 input_ids = request.prompt_token_ids + request.output_token_ids
+                logger.debug(
+                    f"Handle prefill request {request} at idx {idx}, "
+                    f"{prefill_start_index=}, {prefill_end_index=}, "
+                    f"need_prefilled_token_num={len(input_ids)}"
+                )
                 self.share_inputs["input_ids"][idx : idx + 1, :length] = np.array(
                     input_ids[prefill_start_index:prefill_end_index]
                 )