[fix] Device and format and implementation optimization

.gitignore

@@ -22,9 +22,12 @@ libs
 .classpath
 bin/
 
-#node
+# node
 node_modules/
 
-#vscode
+# vscode
 .vscode
+
+# dir
 tests/integration/models/
+engines/python/setup/djl_python/tests/resources*

engines/python/setup/djl_python/rolling_batch/__init__.py

@@ -11,4 +11,4 @@
 # BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for
 # the specific language governing permissions and limitations under the License.
 
-from .scheduler_rolling_batch import SchedulerRollingBatch
+from .scheduler_rolling_batch import SchedulerRollingBatch

engines/python/setup/djl_python/rolling_batch/rolling_batch.py

@@ -193,9 +193,9 @@ def postprocess_results(self, batch_size):
             res = {"data": req.get_next_token(), "last": req.is_last_token()}
             results.append(res)
 
-        for i in range(1, batch_size + 1):
-            if self.pending_requests[batch_size - i].is_last_token():
-                self.pending_requests.pop(batch_size - i)
+        self.pending_requests = [
+            req for req in self.pending_requests if not req.is_last_token()
+        ]
 
         if len(self.pending_requests) == 0:
             self.req_id_counter = 0

engines/python/setup/djl_python/rolling_batch/scheduler_rolling_batch.py

@@ -35,20 +35,22 @@ def __init__(self, model_id_or_path, device, properties, **kwargs):
         """
 
         super().__init__(device, **kwargs)
-        self._init_model_and_tokenizer(kwargs, model_id_or_path)
+        self._init_model_and_tokenizer(model_id_or_path,
+                                       device=device,
+                                       **kwargs)
         self._init_scheduler(properties)
 
     @stop_on_any_exception
-    def inference(self, input_data, parameters):
+    def inference(self, input_text, parameters):
         """
         Performs prefill and decode operations for the batch.
 
-        :param input_data: List of input texts for each request in a batch
+        :param input_text: List of input texts for each request in a batch
         :param parameters: List of kwargs for each request in a batch
         :return: generated batch decoded tokens
         """
-        batch_size = len(input_data)
-        new_requests = self.get_new_requests(input_data, parameters,
+        batch_size = len(input_text)
+        new_requests = self.get_new_requests(input_text, parameters,
                                              batch_size)
 
         preprocessed_new_requests = self.preprocess_requests(new_requests)
@@ -86,18 +88,21 @@ def preprocess_requests(self, requests):
 
         return new_requests
 
-    def _init_model_and_tokenizer(self, kwargs, model_id_or_path):
+    def _init_model_and_tokenizer(self,
+                                  model_id_or_path,
+                                  device=None,
+                                  **kwargs):
         self.config = AutoConfig.from_pretrained(model_id_or_path, **kwargs)
         architectures = self.config.architectures
         if architectures and architectures[0].endswith(
                 "ForConditionalGeneration"):
             raise ValueError('Seq2Seq model is not supported by scheduler')
         else:
             self.model = AutoModelForCausalLM.from_pretrained(
-                model_id_or_path, **kwargs)
-
-        if self.device:
-            self.model.to(self.device)
+                model_id_or_path,
+                device_map="auto"
+                if device and device.type == "cuda" else "cpu",
+                **kwargs)
 
         self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path,
                                                        padding_side="left")
@@ -155,7 +160,7 @@ def _prefill_and_decode(self, new_requests):
         for request_id, generated_token, request in zip(
                 request_ids, generated_tokens, self.pending_requests):
             is_last_token = (request_id in exit_req_ids)
-            request.set_next_token(generated_token,
+            request.set_next_token(f" {generated_token}",
                                    self.output_formatter,
                                    last_token=is_last_token)
 

engines/python/setup/djl_python/tests/rolling_batch_test_scripts/rolling_batch_scheduler.py

@@ -0,0 +1,63 @@
+from collections import defaultdict
+import torch
+from djl_python.rolling_batch import SchedulerRollingBatch
+import torch.distributed as dist
+
+
+def print_rank0(content):
+    rank = 0
+    if dist.is_initialized():
+        rank = dist.get_rank()
+    if rank == 0:
+        print(content)
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+properties = {
+    "tensor_parallel_degree": 1,
+    "dtype": "fp16",
+    "max_rolling_batch_size": 8,
+    "model_loading_timeout": 7200,
+    "max_rolling_batch_prefill_tokens": 10000,
+    "paged_attention": "True"
+}
+
+model_id = "huggyllama/llama-7b"
+"""
+{"inputs":"write a program to add two numbers in python","parameters":{"max_new_tokens":1000, "do_sample":true, "temperature":0.7}}
+"""
+input_str = [
+    "write a program to add two numbers in python",
+    "write a program to add two numbers in python\n"
+]
+
+params = [{
+    "max_new_tokens": 50,
+    "do_sample": False,
+    "temperature": 0.7
+}, {
+    "max_new_tokens": 50,
+    "do_sample": False,
+    "temperature": 0.7
+}]
+
+# ===================== scheduler ============================
+print("=========== scheduler ==========")
+rolling_batch = SchedulerRollingBatch(model_id, device, properties)
+rolling_batch.output_formatter = None
+print("reach here")
+
+output_all = defaultdict(list)
+result = rolling_batch.inference(input_str, params)
+for i, res in enumerate(result):
+    output_all[i].append(res['data'])
+
+for _ in range(50):
+    result = rolling_batch.inference([], [])
+    for i, res in enumerate(result):
+        output_all[i].append(res['data'])
+
+for i, out in enumerate(output_all.values()):
+    print_rank0(input_str[i] + ''.join(out))
+    print_rank0('\n====')

engines/python/setup/djl_python/tests/run_rolling_batch_alone.py → engines/python/setup/djl_python/tests/rolling_batch_test_scripts/run_rolling_batch_alone.py

@@ -30,8 +30,7 @@ def get_rolling_batch_class_from_str(rolling_batch_type: str):
     raise ValueError(f"Invalid rolling batch type: {rolling_batch_type}")
 
 
-def init_rolling_batch(rolling_batch_type: str,
-                       model_id: str,
+def init_rolling_batch(rolling_batch_type: str, model_id: str,
                        properties: dict):
     rolling_batch_type = rolling_batch_type.lower()
     device = 0
@@ -140,7 +139,11 @@ def simulator(batcher,
     if args.properties:
         properties = json.loads(args.properties)
     else:
-        properties = {"tensor_parallel_degree": 1, "trust_remote_code": True, "engine": "Python"}
+        properties = {
+            "tensor_parallel_degree": 1,
+            "trust_remote_code": True,
+            "engine": "Python"
+        }
     if args.rollingbatch == "lmi-dist":
         dist.init_process_group("nccl")
         properties["engine"] = "MPI"