Streaming first pass

language/llama2-70b/SUT.py

@@ -112,7 +112,7 @@ def __init__(self,
 
         if not batch_size:
             if device == "cpu":
-                batch_size = 300
+                batch_size = 512
             else:
                 batch_size = 32  # Reduce to 8 if using 4 GPUs, 16 for 8.
         self.batch_size = batch_size
@@ -455,10 +455,28 @@ def stream_api(self, input, response_ids):
                                 token_cache.append(token)
         return token_cache
 
+    def stream_api_grpc(self, input, response_ids):
+        token_cache = []
+        first = True
+        resps = self.grpc_client.make_request_stream(input, model_id=self.api_model_name)
+        for resp in resps:
+            if resp.text:
+                tokens = self.tokenizer(resp.text)["input_ids"][1:]
+                if first:
+                    self.first_token_queue.put((tokens[0], response_ids[0]))
+                    token_cache.extend(tokens[1:])
+                    first = False
+                else:
+                    token_cache.extend(tokens)
+        return token_cache
+
     def async_process_query(self, input_ids_tensor, qitem_id):
         decoded = self.tokenizer.decode(input_ids_tensor[0])
         response_ids = [qitem_id]
-        output_tokens = self.stream_api(decoded, response_ids)
+        if self.grpc:
+            output_tokens = self.stream_api_grpc(decoded, response_ids)
+        else:
+            output_tokens = self.stream_api(decoded, response_ids)
 
         response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
         bi = response_array.buffer_info()

language/llama2-70b/api-endpoint-artifacts/benchmark.yaml

@@ -6,7 +6,7 @@ spec:
   restartPolicy: Never
   containers:
   - name: mlperf-env
-    image: quay.io/meyceoz/mlperf-inference:grpc-batch
+    image: quay.io/meyceoz/mlperf-inference:grpc-stream
     resources:
       requests:
         memory: 20000Mi

language/llama2-70b/api-endpoint-artifacts/serving-tgis.yaml

@@ -24,11 +24,11 @@ spec:
           #  value: float16
         # Dynamic batch size changes
         - name: MAX_BATCH_SIZE
-          value: "256"
+          value: "512"
         - name: MAX_CONCURRENT_REQUESTS
-          value: "300"
+          value: "5000"
         - name: MAX_BATCH_WEIGHT
-          value: "550000"
+          value: "1000000"
         - name: MAX_SEQUENCE_LENGTH
           value: "2048"
         - name: MAX_PREFILL_WEIGHT
@@ -43,8 +43,8 @@ spec:
           value: "5"
       resources: # configure as required
         requests:
-          cpu: 64
-          memory: 900Gi
+          cpu: 96
+          memory: 1000Gi
           nvidia.com/gpu: 8
         limits:
           nvidia.com/gpu: 8

language/llama2-70b/inference.py

@@ -71,6 +71,21 @@ def make_request(self, texts: str, model_id: str = "flan-t5-small"):
         )
         result = self.generation_service_stub.Generate(request=request)
         return result
+
+    def make_request_stream(self, text: str, model_id: str = "flan-t5-small"):
+        request = generation_pb2_grpc.generation__pb2.SingleGenerationRequest(
+            model_id=model_id,
+            request=generation_pb2_grpc.generation__pb2.GenerationRequest(text=text),
+            params=generation_pb2_grpc.generation__pb2.Parameters(
+                   method=generation_pb2_grpc.generation__pb2.GREEDY,
+                   stopping=generation_pb2_grpc.generation__pb2.StoppingCriteria(
+                       max_new_tokens=1024,
+                       min_new_tokens=1
+                   )
+            )
+        )
+        result = self.generation_service_stub.GenerateStream(request=request)
+        return result
 
     def __enter__(self):
         return self