[FDConfig]Remove max_num_batched_tokens/max_num_seqs in parallel config (#4116)

* remove max_num_batched_tokens in parallel config

* remove max_num_seqs

* update test case

* fix test

* fix

---------

Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>

fastdeploy/config.py

@@ -275,7 +275,6 @@ class ParallelConfig:
         From old wersion worker args
         TODO(gongshaotian): Reclassify
         """
-        self.max_num_seqs: int = 34
         # Set default block num for profile run
         self.total_block_num: int = 2000
         # block size
@@ -297,7 +296,6 @@ class ParallelConfig:
         # Do profile or not
         self.do_profile: bool = False
 
-        self.max_num_batched_tokens: int = 2048
         # splitwise role
         self.splitwise_role: str = "mixed"
         # guided decoding backend
@@ -1109,8 +1107,6 @@ class FDConfig:
         speculative_config: SpeculativeConfig = None,
         tokenizer: str = None,
         max_model_len: int = 8192,
-        max_num_seqs: int = 8,
-        max_num_batched_tokens: Optional[int] = None,
         ips: str = None,
         use_warmup: bool = False,
         engine_worker_queue_port: str = "8002",
@@ -1143,19 +1139,18 @@ class FDConfig:
         self.moba_attention_config: Optional[MobaAttentionConfig] = moba_attention_config
         # Initialize cuda graph capture list
         if self.graph_opt_config.cudagraph_capture_sizes is None:
-            self.graph_opt_config._set_cudagraph_sizes(max_num_seqs=self.parallel_config.max_num_seqs)
+            self.graph_opt_config._set_cudagraph_sizes(max_num_seqs=self.scheduler_config.max_num_seqs)
 
         if self.graph_opt_config.cudagraph_only_prefill:
             self.graph_opt_config.init_with_cudagrpah_size(max_capture_size=512)
         else:
-            self.graph_opt_config.init_with_cudagrpah_size(max_capture_size=self.parallel_config.max_num_seqs)
+            self.graph_opt_config.init_with_cudagrpah_size(max_capture_size=self.scheduler_config.max_num_seqs)
 
         # TODO(wangmingkai02): change graph_opt_level=2 when using static mode with cinn
         if self.graph_opt_config.graph_opt_level == 2:
             self.graph_opt_config.graph_opt_level = 1
 
         self.tokenizer = tokenizer
-        self.max_num_batched_tokens = max_num_batched_tokens
         self.ips = ips
         self.tool_parser = tool_parser
 
@@ -1177,7 +1172,6 @@ class FDConfig:
                     self.node_rank = idx
 
         self.max_model_len = max_model_len
-        self.max_num_seqs = max_num_seqs
         self.limit_mm_per_prompt = limit_mm_per_prompt
         self.mm_processor_kwargs = mm_processor_kwargs
         self.use_warmup = use_warmup
@@ -1243,22 +1237,22 @@ class FDConfig:
 
         self.paddle_commit_id = paddle.version.commit
 
-        if self.max_num_batched_tokens is None:
+        if self.scheduler_config.max_num_batched_tokens is None:
             if int(envs.ENABLE_V1_KVCACHE_SCHEDULER):
                 if paddle.is_compiled_with_xpu():
-                    self.max_num_batched_tokens = self.max_model_len
+                    self.scheduler_config.max_num_batched_tokens = self.max_model_len
                 else:
-                    self.max_num_batched_tokens = 8192  # if set to max_model_len, it's easy to be OOM
+                    self.scheduler_config.max_num_batched_tokens = 8192  # if set to max_model_len, it's easy to be OOM
             else:
                 if self.cache_config.enable_chunked_prefill:
-                    self.max_num_batched_tokens = 2048
+                    self.scheduler_config.max_num_batched_tokens = 2048
                 else:
-                    self.max_num_batched_tokens = self.max_model_len
+                    self.scheduler_config.max_num_batched_tokens = self.max_model_len
 
         if self.long_prefill_token_threshold == 0:
             self.long_prefill_token_threshold = int(self.max_model_len * 0.04)
 
-        self.cache_config.postprocess(self.max_num_batched_tokens, self.max_num_seqs)
+        self.cache_config.postprocess(self.scheduler_config.max_num_batched_tokens, self.scheduler_config.max_num_seqs)
         self.cache_config.max_block_num_per_seq = int(self.max_model_len // self.cache_config.block_size)
 
         if self.guided_decoding_backend == "auto":
@@ -1272,19 +1266,24 @@ class FDConfig:
         """
         check the legality of config
         """
-        assert self.max_num_seqs <= 256, (
-            "The parameter `max_num_seqs` is not allowed to exceed 256, " f"but now it's {self.max_num_seqs}."
+        assert self.scheduler_config.max_num_seqs <= 256, (
+            "The parameter `max_num_seqs` is not allowed to exceed 256, "
+            f"but now it's {self.scheduler_config.max_num_seqs}."
         )
         assert self.nnode >= 1, f"nnode: {self.nnode} should no less than 1"
         assert self.max_model_len >= 16, f"max_model_len: {self.max_model_len} should be larger than 16"
-        assert self.max_num_seqs >= 1, f"max_num_seqs: {self.max_num_seqs} should be larger than 1"
-        assert self.max_num_batched_tokens >= self.max_num_seqs, (
-            f"max_num_batched_tokens: {self.max_num_batched_tokens} "
-            f"should be larger than or equal to max_num_seqs: {self.max_num_seqs}"
+        assert (
+            self.scheduler_config.max_num_seqs >= 1
+        ), f"max_num_seqs: {self.scheduler_config.max_num_seqs} should be larger than 1"
+        assert self.scheduler_config.max_num_batched_tokens >= self.scheduler_config.max_num_seqs, (
+            f"max_num_batched_tokens: {self.scheduler_config.max_num_batched_tokens} "
+            f"should be larger than or equal to max_num_seqs: {self.scheduler_config.max_num_seqs}"
         )
-        assert self.max_num_batched_tokens <= self.max_model_len * self.max_num_seqs, (
-            f"max_num_batched_tokens: {self.max_num_batched_tokens} should be larger"
-            f"than or equal to max_num_seqs: {self.max_num_seqs} * max_model_len: {self.max_model_len}"
+        assert (
+            self.scheduler_config.max_num_batched_tokens <= self.max_model_len * self.scheduler_config.max_num_seqs
+        ), (
+            f"max_num_batched_tokens: {self.scheduler_config.max_num_batched_tokens} should be larger"
+            f"than or equal to max_num_seqs: {self.scheduler_config.max_num_seqs} * max_model_len: {self.max_model_len}"
         )
         assert (
             self.max_num_partial_prefills >= 1
@@ -1305,13 +1304,13 @@ class FDConfig:
 
         if not self.cache_config.enable_chunked_prefill:
             if not envs.ENABLE_V1_KVCACHE_SCHEDULER:
-                assert self.max_num_batched_tokens >= self.max_model_len, (
-                    f"max_num_batched_tokens: {self.max_num_batched_tokens} "
+                assert self.scheduler_config.max_num_batched_tokens >= self.max_model_len, (
+                    f"max_num_batched_tokens: {self.scheduler_config.max_num_batched_tokens} "
                     f"should be larger than or equal to max_model_len: {self.max_model_len}"
                 )
         else:
-            assert self.max_num_batched_tokens >= self.cache_config.block_size, (
-                f"max_num_batched_tokens: {self.max_num_batched_tokens} "
+            assert self.scheduler_config.max_num_batched_tokens >= self.cache_config.block_size, (
+                f"max_num_batched_tokens: {self.scheduler_config.max_num_batched_tokens} "
                 f"should be larger than or equal to block_size: {self.cache_config.block_size}"
             )