[Inference]Test for new Async engine

colossalai/inference/async_engine.py

@@ -0,0 +1,133 @@
+import asyncio
+
+from colossalai.inference.dynamic_batching.ray_dist_init import Driver
+
+from .dynamic_batching.io_struct import RequestOutput
+from .dynamic_batching.sampling_params import SamplingParams
+
+
+class RequestTracker:
+    """
+    A class for trace down all the requests, abstraction for async
+    """
+
+    def __init__(self) -> None:
+        self._requests: asyncio.Queue[str] = asyncio.Queue()
+        self._finished_requests: asyncio.Queue[RequestOutput] = asyncio.Queue()
+        self.new_requests_event = None
+
+    def __contains__(self, item):
+        return item in self._requests
+
+    def init_event(self):
+        self.new_requests_event = asyncio.Event()
+
+    def add_request(self, request_id: str):
+        """Add a request to be sent to the engine on the next background
+        loop iteration."""
+        self._requests.put_nowait(request_id)
+        self.new_requests_event.set()  # NOTE: we may find a better way to clear this event
+
+    def add_stop(self):
+        """
+        Add a StopIteration flag to stop async generator.
+        """
+        self._finished_requests.put_nowait(StopIteration)
+        self.new_requests_event.clear()
+
+    def process_request_output(self, request_output: RequestOutput) -> None:
+        """Process a request output from the engine."""
+        self._finished_requests.put_nowait(request_output)
+
+    async def wait_for_new_requests(self):
+        await self.new_requests_event.wait()
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self) -> RequestOutput:
+        result = await self._finished_requests.get()
+        # print("result of ", result)
+        if result is StopIteration:
+            raise StopAsyncIteration
+        return result
+
+
+class Async_Engine:
+
+    """
+    Use an engine to launch RAY Driver --> RAY Worker --> Async_Manager
+    Background loop: inference reqs in waiting list (Listen)
+    Request Tracker: manage incoming requests and restore finished ones
+    Generate: exposed func for add new input and return finished ones
+    """
+
+    def __init__(
+        self,
+        router_config,
+        engine_config,
+        start_engine_loop: bool = True,
+    ) -> None:
+        self.driver = Driver(router_config=router_config, engine_config=engine_config)
+        self.background_loop = None
+        self.start_engine_loop = start_engine_loop
+        self._request_tracker = RequestTracker()
+
+    def _step(self):
+        """
+        Logic for handling requests
+        """
+        request_outputs = self.driver.step()
+        if request_outputs is not None:
+            for request_output in request_outputs:
+                self._request_tracker.process_request_output(request_output)
+            self._request_tracker.add_stop()
+
+    def abort(self, request_id: str):
+        self.driver.abort(request_id)
+
+    def _has_requests_in_progress(self):
+        return self.driver.is_running()
+
+    async def run_loop_fwd(self):
+        has_requests_in_progress = self._has_requests_in_progress()
+        while True:
+            if not has_requests_in_progress:
+                await self._request_tracker.wait_for_new_requests()
+            self._step()
+            await asyncio.sleep(0)
+
+    @property
+    def is_running(self):
+        return self.background_loop is not None and not self.background_loop.done()
+
+    def start_background_loop(self):
+        if self.is_running:
+            raise RuntimeError("Background loop is already running.")
+
+        self._request_tracker.init_event()
+
+        self.background_loop_unshielded = asyncio.get_event_loop().create_task(self.run_loop_fwd())
+        self.background_loop = asyncio.shield(self.background_loop_unshielded)
+
+    async def add_request(self, request_id: str, prompt: str, sampling_params: SamplingParams):
+        self.driver.add_input(request_id, prompt, sampling_params)
+        self._request_tracker.add_request(request_id)
+
+    async def generate(self, request_id: str, prompt: str, sampling_params: SamplingParams):
+        """
+        The only exposed func, adding new request and return a async generator that yields the existing results.
+        """
+        try:
+            if not self.is_running:
+                self.start_background_loop()
+
+            await self.add_request(request_id, prompt, sampling_params)
+
+            async for request_output in self._request_tracker:
+                yield request_output
+
+        except (Exception, asyncio.CancelledError) as e:
+            # If there is an exception or coroutine is cancelled, abort the request.
+            self.abort_request(request_id)
+            raise e

colossalai/inference/async_manager.py

@@ -0,0 +1,150 @@
+from typing import List
+
+from .dynamic_batching.io_struct import Batch, Req, RequestOutput
+from .manager import DynamicBatchManager
+from .tensor_parallel import TPInferEngine
+
+
+class Async_DynamicBatchManager(DynamicBatchManager):
+    def __init__(
+        self,
+        tp_engine: TPInferEngine,
+        max_total_token_num,
+        batch_max_tokens,
+        eos_id,
+        model,
+        log_stats=True,
+        log_stats_interval=10,
+        running_batch: Batch = None,
+        waiting_req_list: List = [],
+    ):
+        """
+        Args:   tp_engine : The tp engine that dynamic batch manager hold, defined before dynamic batch manager
+                max_total_token_num : max_total_token_num for memory manager, default to: max batch size * (max input len + max output len)
+                batch_max_tokens : max tokens of one batch, default to (max input + output len) * num_requests
+                running_max_req_size : max request size of running batch, equals to MAX_BATCH_SIZE of tp engine
+                eos_id : The end token of a seq
+                model: the model weight dir path, the app will load config, weights and tokenizer from this dir
+                log_stats : whether to log stats
+                log_stats_interval : log stats interval
+                running_batch : running batch
+                waiting_req_list : list of waiting requests, initialized before dynamic batch manager
+        """
+        super().__init__(
+            tp_engine,
+            max_total_token_num,
+            batch_max_tokens,
+            eos_id,
+            model,
+            log_stats,
+            log_stats_interval,
+            running_batch,
+            waiting_req_list,
+        )
+
+    def _step(self):
+        """
+        Logic for handling requests
+        """
+        has_new_finished = False
+        if self.running_batch is None:
+            new_batch = self.req_queue.generate_new_batch(self.running_batch)
+            if new_batch is not None:
+                self.stats_tool.count_prompt_tokens(new_batch)
+                self.running_batch = new_batch
+                has_new_finished, outputs = self._prefill_batch(self.running_batch)
+                self._filter_runing_batch()
+                self.has_wait_tokens = 0
+
+        else:
+            if self.has_wait_tokens < self.max_wait_tokens:
+                self.stats_tool.count_output_tokens(self.running_batch)
+                has_new_finished, outputs = self._decode_batch(self.running_batch)
+                self._filter_runing_batch()
+                self.has_wait_tokens += 1
+
+            else:
+                new_mini_batch = self.req_queue.generate_new_batch(self.running_batch)
+                if new_mini_batch is not None:
+                    self.stats_tool.count_prompt_tokens(new_mini_batch)
+                    has_new_finished, outputs = self._prefill_batch(new_mini_batch)
+                    if not new_mini_batch.is_clear():
+                        self._merge_batch(self.running_batch, new_mini_batch)
+                        self.running_batch.merge(new_mini_batch)
+                    self.has_wait_tokens = 0
+
+                else:
+                    self.stats_tool.count_output_tokens(self.running_batch)
+                    has_new_finished, outputs = self._decode_batch(self.running_batch)
+                    self._filter_runing_batch()
+                    self.has_wait_tokens += 1
+
+        if has_new_finished:
+            return outputs
+        return None
+
+    def _prefill_batch(self, batch):
+        """
+        For all batches, no matter it is a new batch or a mini batch, we need to do prefill first.
+        """
+        self._init_batch(batch)
+
+        # TODO: figure out if cache and batch id is needed
+        ans = self.engine._prefill_batch(batch.batch_id)
+        req_to_out_token_id = ans
+        self._add_token_id_to_req(batch, req_to_out_token_id)
+        has_new_finished_req = batch.mark_finished_req(self.eos_id)
+        outputs = self._handle_finish_req(batch, has_new_finished_req)
+        return has_new_finished_req, outputs
+        # delete finished reqs
+
+    def _decode_batch(self, batch: Batch):
+        """
+        Decoding process
+        """
+        ans = self.engine._decode_batch(batch.batch_id)
+        req_to_out_token_id = ans
+        self._add_token_id_to_req(batch, req_to_out_token_id)
+        has_new_finished_req = batch.mark_finished_req(self.eos_id)
+        outputs = self._handle_finish_req(batch, has_new_finished_req)
+        return has_new_finished_req, outputs
+
+    def _handle_finish_req(self, batch: Batch, has_new_finished_req):
+        if has_new_finished_req:
+            finished_reqs = batch.filter_finished()
+            if batch.is_clear():
+                self._remove_batch(batch)
+            else:
+                self._filter_batch(batch)
+            return self._output_process(finished_reqs)
+        return None
+
+    def _output_process(self, finished_reqs: List[Req]):
+        """
+        Process the output of a batch.
+        """
+        outputs = []
+        for req in finished_reqs:
+            output = self.tokenizer.decode(req.output_ids)
+            outputs.append(RequestOutput(req.request_id, req.prompts, req.prompt_ids, output))
+        return outputs
+
+
+def start_dynamic_batching(args, tp_engine, waiting_req_list):
+    try:
+        batch_manager = Async_DynamicBatchManager(
+            tp_engine=tp_engine,
+            max_total_token_num=args.max_total_token_num,
+            batch_max_tokens=args.batch_max_tokens,
+            eos_id=args.eos_id,
+            model=args.model,
+            log_stats=not args.disable_log_stats,
+            log_stats_interval=args.log_stats_interval,
+            waiting_req_list=waiting_req_list,
+        )
+
+    except Exception:
+        batch_manager.clean_up()
+        raise
+
+    return batch_manager

colossalai/inference/dynamic_batching/get_tokenizer.py

@@ -1,6 +1,12 @@
+"""
+Motivated by VllM (https://github.com/vllm-project/vllm), This module is trying to resolve the tokenizer issue.
+
+license: MIT, see LICENSE for more details.
+"""
+
 from transformers import AutoTokenizer
 
-_FAST_LLAMA_TOKENIZER = "/home/lccd/share/llama-tokenizer"
+_FAST_LLAMA_TOKENIZER = "hf-internal-testing/llama-tokenizer"
 
 
 def get_tokenizer(

colossalai/inference/dynamic_batching/infer_batch.py

@@ -1,15 +1,16 @@
+# Adapted from https://github.com/ModelTC/lightllm
+
 import collections
 from dataclasses import dataclass
-from typing import Dict, List , Tuple
+from typing import Dict, List, Tuple
 
 import numpy as np
 import torch
 
 from colossalai.inference.tensor_parallel import MemoryManager
 
-# make batch infer state an attr of InferBatch
-
 
+# make batch infer state an attr of InferBatch
 class InferSamplingParams:
     def __init__(
         self,
@@ -65,7 +66,7 @@ def init_batch(
         cache_manager: MemoryManager,
         vocab_size: int,
         max_total_len: int,
-    ) -> 'InferBatch':
+    ) -> "InferBatch":
         input_lengths = []
         all_input_ids = []
         requests_idx_mapping = {}
@@ -76,7 +77,7 @@ def init_batch(
         nopad_total_token_num = 0
         nopad_max_len_in_batch = 0
         nopad_b_loc = torch.empty((len(requests), max_total_len + 12), dtype=torch.long, device="cuda")
-        # to avoid memory leak , we pre-allocate 12 more space for each batch. 
+        # to avoid memory leak , we pre-allocate 12 more space for each batch.
         nopad_b_start_loc = torch.zeros(len(requests), dtype=torch.int32, device="cuda")
         for i, r in enumerate(requests):
             # request id -> idx in list mapping
@@ -142,10 +143,9 @@ def free_self(self) -> None:
             )
         remove_index = torch.cat(remove_index, dim=-1)
         self.cache_manager.free(remove_index)
-
 
     @torch.no_grad()
-    def filter(self, request_ids: List[int]) -> 'InferBatch':
+    def filter(self, request_ids: List[int]) -> "InferBatch":
         """
         Filter finished batch and return a new InferBatch with left ones.
         """
@@ -226,7 +226,7 @@ def filter(self, request_ids: List[int]) -> 'InferBatch':
 
     @classmethod
     @torch.no_grad()
-    def merge(cls, batch1, batch2) -> 'InferBatch':
+    def merge(cls, batch1, batch2) -> "InferBatch":
         """
         Return megerd new InferBatch
         """