added HTTP interface around the router

deepsparse/main.py

@@ -1,42 +1,60 @@
-import uvicorn, fastapi
+import fastapi, uvicorn
 from threading import Thread
 from queue import Queue
-
 from router import DeepSparseRouter, batching_task
 from utils import GenerateRequest
 
 TOKENIZER_PATH = "/home/robertgshaw/.cache/sparsezoo/neuralmagic/codegen_mono-350m-bigpython_bigquery_thepile-base/deployment"
 MODEL_PATH = "/home/robertgshaw/.cache/sparsezoo/neuralmagic/codegen_mono-350m-bigpython_bigquery_thepile-base/model.onnx/model.onnx"
 
-# setup router
-router = DeepSparseRouter(
+def serve(
     model_path=MODEL_PATH,
-    tokenizer_path=TOKENIZER_PATH
-)
+    tokenizer_path=TOKENIZER_PATH,
+    host="0.0.0.0",
+    port=5543
+):
+    # setup router
+    print("\n--------------------       Building Router               --------------------\n")
+    router = DeepSparseRouter(
+        model_path=model_path,
+        tokenizer_path=tokenizer_path
+    )
 
-# start background routing task
-batching_thread = Thread(target=batching_task, args=[router])
-batching_thread.start()
+    # start background routing task
+    print("\n--------------------       Starting Batching Task        --------------------\n")
+    batching_thread = Thread(target=batching_task, args=[router])
+    batching_thread.start()
 
-app = fastapi.FastAPI()
+    print("\n--------------------       Launching App                 --------------------\n")
+    app = fastapi.FastAPI()
 
-@app.post("/generate")
-def generate(prompt:str, max_generated_tokens:int):
-    response_stream = Queue()
-
-    # submit request to the router
-    router.submit_request(
-        generate_request=GenerateRequest(
-            prompt=prompt,
-            max_generated_tokens=max_generated_tokens,
-            response_stream=response_stream
+    @app.get("/generate/{prompt}")
+    async def generate(prompt:str):
+        response_stream = Queue()
+        router.submit_request(
+            GenerateRequest(
+                prompt=prompt,
+                max_generated_tokens=100, 
+                response_stream=response_stream
+            )
         )
+        
+        response_string = prompt
+        generation = response_stream.get()
+        while not generation.stopped:
+            response_string += generation.token
+            generation = response_stream.get()
+
+        return response_string
+
+    uvicorn.run(
+        app,
+        host=host,
+        port=port,
+        workers=1
     )
     
-    response_string = prompt
-    generation = response_stream.get()
-    while not generation.stopped:
-        response_string += generation.token
-        generation = response_stream.get()
+    batching_thread.join()
 
-    return generation
+if __name__ == "__main__":
+    serve()

deepsparse/router.py

@@ -1,8 +1,8 @@
 from queue import Queue
 from typing import List, Dict, Optional, Tuple
-from server.deepsparse.service.service import DeepSparseService
-from server.deepsparse.service.causal_lm import DeepSparseCausalLM
-from server.deepsparse.utils import CachedBatch, Batch, Generation, GenerateRequest, Request
+from service.service import DeepSparseService
+from service.causal_lm import DeepSparseCausalLM
+from utils import CachedBatch, Batch, Generation, GenerateRequest, Request
 
 # TODO: implement logic for maximum size of the queue based on memory usage
 class DeepSparseQueue:
@@ -50,9 +50,9 @@ class DeepSparseQueue:
 class DeepSparseRouter:
     def __init__(
         self, 
-        service: Optional[DeepSparseService],
-        model_path: Optional[str],
-        tokenizer_path: Optional[str]
+        service: Optional[DeepSparseService] = None,
+        model_path: Optional[str] = None,
+        tokenizer_path: Optional[str] = None
     ):        
         assert (
             service is not None or 

deepsparse/service/causal_lm.py

@@ -3,8 +3,8 @@ from typing import List, Dict, Optional
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 import numpy as np
 
-from server.deepsparse.service.model import DeepSparsePastKeyValues, DeepSparseDecoderModel
-from server.deepsparse.utils import Request, Batch, CachedBatch, Generation
+from service.model import DeepSparsePastKeyValues, DeepSparseDecoderModel
+from utils import Request, Batch, CachedBatch, Generation
 
 DEEPSPARSE_SEQUENCE_LENGTH = 128
 DEEPSPARSE_MULTITOKEN_LENGTH = 4

deepsparse/service/service.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Tuple
-from server.deepsparse.service.causal_lm import DeepSparseCausalLM, DeepSparseCausalLMBatch
-from server.deepsparse.utils import Generation, CachedBatch, Batch
+from service.causal_lm import DeepSparseCausalLM, DeepSparseCausalLMBatch
+from utils import Generation, CachedBatch, Batch
 
 class BatchCache:
     def __init__(self):

server-dev.ipynb

@@ -11,6 +11,24 @@
     "%autoreload 2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6948b65b-8851-40f3-bf67-d7db2cf1955e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"def fib(n):\\n    if n == 0:\\n        return 0\\n    elif n == 1:\\n        return 1\\n    else:\\n        return fib(n-1) + fib(n-2)\\n\\ndef fib2(n):\\n    if n == 0:\\n        return 0\\n    elif n == 1:\\n        return 1\\n    else:\\n        return fib2(n-1) + fib2(n-2)\\n\\ndef fib3(n):\\n    if n == 0:\\n        return 0\\n    elif n == 1\""
+     ]
+    }
+   ],
+   "source": [
+    "!curl http://127.0.0.1:5543/generate/def%20fib%28n%29%3A"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a19786b8-e72c-43c1-964f-45d92fd171e9",