Refactor scheduler and engine.py (#4163)

* refactor sequence states

* fix pd, better property

* skip decoding warmup

* rename

* add more profile logs

* add config builder

* add engine_loop and input_maker

* fix close

* fix ut

* update end session

* fix profiler

* typo fix

Author: grimoire

lmdeploy/pytorch/engine/config_builder.py

@@ -0,0 +1,111 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os
+
+from lmdeploy.messages import PytorchEngineConfig, SpeculativeConfig
+from lmdeploy.pytorch.config import (BackendConfig, CacheConfig, DistConfig, MiscConfig, SchedulerConfig,
+                                     SpecDecodeConfig)
+from lmdeploy.utils import get_logger, get_max_batch_size, get_model
+
+
+class ConfigBuilder:
+
+    @staticmethod
+    def update_engine_config(engine_config: PytorchEngineConfig):
+        """Update pytorch engine config."""
+        logger = get_logger('lmdeploy')
+
+        # make sure engine exits
+        if engine_config is None:
+            engine_config = PytorchEngineConfig()
+        else:
+            engine_config = copy.deepcopy(engine_config)
+
+        if engine_config.max_batch_size is None:
+            engine_config.max_batch_size = get_max_batch_size(engine_config.device_type)
+
+        if engine_config.dllm_block_length is not None:
+            max_prefill_token_num = engine_config.max_prefill_token_num
+            max_batch_size = engine_config.max_batch_size
+            if max_batch_size * engine_config.dllm_block_length > max_prefill_token_num:
+                engine_config.max_batch_size = max_prefill_token_num // engine_config.dllm_block_length
+                logger.warning(f'Update max_batch_size to {engine_config.max_batch_size} '
+                               f'since dllm_block_length({engine_config.dllm_block_length}) * max_batch_size '
+                               f'({max_batch_size}) > max_prefill_token_num ({max_prefill_token_num}).')
+
+        if engine_config.dp != 1:
+            if engine_config.tp == 1 and engine_config.ep == 1:
+                logger.warning('Data parallelism is enabled but tensor parallelism and '
+                               'expert parallelism are not enabled. Setting dp=1.')
+                engine_config.dp = 1
+                engine_config.dp_rank = 0
+
+        return engine_config
+
+    @staticmethod
+    def build_scheduler_config(engine_config: PytorchEngineConfig):
+        """Build scheduler config."""
+        scheduler_config = SchedulerConfig(max_batches=engine_config.max_batch_size,
+                                           max_session_len=engine_config.session_len,
+                                           prefill_interval=engine_config.prefill_interval)
+        return scheduler_config
+
+    @staticmethod
+    def build_cache_config(engine_config: PytorchEngineConfig):
+        """Build cache config."""
+        cache_config = CacheConfig(
+            max_batches=engine_config.max_batch_size,
+            block_size=engine_config.block_size,
+            num_cpu_blocks=engine_config.num_cpu_blocks,
+            num_gpu_blocks=engine_config.num_gpu_blocks,
+            cache_max_entry_count=engine_config.cache_max_entry_count,
+            max_prefill_token_num=engine_config.max_prefill_token_num,
+            enable_prefix_caching=engine_config.enable_prefix_caching,
+            quant_policy=engine_config.quant_policy,
+            device_type=engine_config.device_type,
+            migration_backend=engine_config.migration_backend,
+            role=engine_config.role,
+            # reserve 1 blocks for dummy input and padding
+            num_reserved_gpu_blocks=1)
+        return cache_config
+
+    @staticmethod
+    def build_backend_config(engine_config: PytorchEngineConfig):
+        """Build backend config."""
+        backend_config = BackendConfig(
+            eager_mode=engine_config.eager_mode,
+            device_type=engine_config.device_type,
+        )
+        return backend_config
+
+    @staticmethod
+    def build_dist_config(engine_config: PytorchEngineConfig):
+        """Build dist config."""
+        dist_config = DistConfig.from_engine_config(engine_config=engine_config)
+        return dist_config
+
+    @staticmethod
+    def build_misc_config(engine_config: PytorchEngineConfig):
+        """Build misc config."""
+        misc_config = MiscConfig.from_engine_config(engine_config)
+        return misc_config
+
+    @staticmethod
+    def build_specdecode_config(target_model, speculative_config: SpeculativeConfig, engine_config: PytorchEngineConfig,
+                                cache_config: CacheConfig):
+        """Build spec decode config."""
+        specdecode_config = None
+        if speculative_config is not None:
+            draft_model = speculative_config.model
+            if draft_model and not os.path.exists(speculative_config.model):
+                draft_model = get_model(draft_model, engine_config.download_dir, engine_config.revision)
+
+            specdecode_config = SpecDecodeConfig.from_config(
+                method=speculative_config.method,
+                num_speculative_tokens=speculative_config.num_speculative_tokens,
+                model=draft_model,
+                target_model=target_model,
+                target_cache_cfg=cache_config,
+                dtype=engine_config.dtype,
+            )
+        return specdecode_config