Support query params, headers, and non-json payloads (bytes and forms)

docs/deployments/predictors.md

@@ -55,11 +55,13 @@ class PythonPredictor:
         """
         pass
 
-    def predict(self, payload):
+    def predict(self, payload, query_params, headers):
         """Called once per request. Preprocesses the request payload (if necessary), runs inference, and postprocesses the inference output (if necessary).
 
         Args:
-            payload: The parsed JSON request payload.
+            payload: The request payload (see below for the possible payload types) (optional).
+            query_params: A dictionary of the query parameters used in the request (optional).
+            headers: A dictionary of the headers sent in the request (optional).
 
         Returns:
             Prediction or a batch of predictions.
@@ -69,6 +71,8 @@ class PythonPredictor:
 
 For proper separation of concerns, it is recommended to use the constructor's `config` paramater for information such as from where to download the model and initialization files, or any configurable model parameters. You define `config` in your [API configuration](api-configuration.md), and it is passed through to your Predictor's constructor.
 
+The `payload` parameter is parsed according to the `Content-Type` header in the request. For `Content-Type: application/json`, `payload` will be the parsed JSON body. For `Content-Type: multipart/form` or `Content-Type: application/x-www-form-urlencoded`, `payload` will be `starlette.datastructures.FormData` (key-value pairs where the value is a `string` for form data, or `starlette.datastructures.UploadFile` for file uploads, see [Starlette's documentation](https://www.starlette.io/requests/#request-files)). For all other `Content-Type` values, `payload` will the the raw `bytes` of the request body.
+
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
@@ -173,11 +177,13 @@ class TensorFlowPredictor:
         self.client = tensorflow_client
         # Additional initialization may be done here
 
-    def predict(self, payload):
+    def predict(self, payload, query_params, headers):
         """Called once per request. Preprocesses the request payload (if necessary), runs inference (e.g. by calling self.client.predict(model_input)), and postprocesses the inference output (if necessary).
 
         Args:
-            payload: The parsed JSON request payload.
+            payload: The request payload (see below for the possible payload types) (optional).
+            query_params: A dictionary of the query parameters used in the request (optional).
+            headers: A dictionary of the headers sent in the request (optional).
 
         Returns:
             Prediction or a batch of predictions.
@@ -190,6 +196,8 @@ Cortex provides a `tensorflow_client` to your Predictor's constructor. `tensorfl
 
 For proper separation of concerns, it is recommended to use the constructor's `config` paramater for information such as configurable model parameters or download links for initialization files. You define `config` in your [API configuration](api-configuration.md), and it is passed through to your Predictor's constructor.
 
+The `payload` parameter is parsed according to the `Content-Type` header in the request. For `Content-Type: application/json`, `payload` will be the parsed JSON body. For `Content-Type: multipart/form` or `Content-Type: application/x-www-form-urlencoded`, `payload` will be `starlette.datastructures.FormData` (key-value pairs where the value is a `string` for form data, or `starlette.datastructures.UploadFile` for file uploads, see [Starlette's documentation](https://www.starlette.io/requests/#request-files)). For all other `Content-Type` values, `payload` will the the raw `bytes` of the request body.
+
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
@@ -249,11 +257,13 @@ class ONNXPredictor:
         self.client = onnx_client
         # Additional initialization may be done here
 
-    def predict(self, payload):
+    def predict(self, payload, query_params, headers):
         """Called once per request. Preprocesses the request payload (if necessary), runs inference (e.g. by calling self.client.predict(model_input)), and postprocesses the inference output (if necessary).
 
         Args:
-            payload: The parsed JSON request payload.
+            payload: The request payload (see below for the possible payload types) (optional).
+            query_params: A dictionary of the query parameters used in the request (optional).
+            headers: A dictionary of the headers sent in the request (optional).
 
         Returns:
             Prediction or a batch of predictions.
@@ -266,6 +276,8 @@ Cortex provides an `onnx_client` to your Predictor's constructor. `onnx_client`
 
 For proper separation of concerns, it is recommended to use the constructor's `config` paramater for information such as configurable model parameters or download links for initialization files. You define `config` in your [API configuration](api-configuration.md), and it is passed through to your Predictor's constructor.
 
+The `payload` parameter is parsed according to the `Content-Type` header in the request. For `Content-Type: application/json`, `payload` will be the parsed JSON body. For `Content-Type: multipart/form` or `Content-Type: application/x-www-form-urlencoded`, `payload` will be `starlette.datastructures.FormData` (key-value pairs where the value is a `string` for form data, or `starlette.datastructures.UploadFile` for file uploads, see [Starlette's documentation](https://www.starlette.io/requests/#request-files)). For all other `Content-Type` values, `payload` will the the raw `bytes` of the request body.
+
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->

pkg/workloads/cortex/lib/type/predictor.py

@@ -57,10 +57,12 @@ def initialize_client(self, model_dir=None, tf_serving_host=None, tf_serving_por
     def initialize_impl(self, project_dir, client=None):
         class_impl = self.class_impl(project_dir)
         try:
-            if self.type == "python":
-                return class_impl(self.config)
+            if self.type == "onnx":
+                return class_impl(onnx_client=client, config=self.config)
+            elif self.type == "tensorflow":
+                return class_impl(tensorflow_client=client, config=self.config)
             else:
-                return class_impl(client, self.config)
+                return class_impl(config=self.config)
         except Exception as e:
             raise UserRuntimeException(self.path, "__init__", str(e)) from e
         finally:
@@ -128,55 +130,90 @@ def _load_module(self, module_name, impl_path):
 
 PYTHON_CLASS_VALIDATION = {
     "required": [
-        {"name": "__init__", "args": ["self", "config"]},
-        {"name": "predict", "args": ["self", "payload"]},
+        {"name": "__init__", "required_args": ["self", "config"]},
+        {
+            "name": "predict",
+            "required_args": ["self"],
+            "optional_args": ["payload", "query_params", "headers"],
+        },
     ]
 }
 
 TENSORFLOW_CLASS_VALIDATION = {
     "required": [
-        {"name": "__init__", "args": ["self", "tensorflow_client", "config"]},
-        {"name": "predict", "args": ["self", "payload"]},
+        {"name": "__init__", "required_args": ["self", "tensorflow_client", "config"]},
+        {
+            "name": "predict",
+            "required_args": ["self"],
+            "optional_args": ["payload", "query_params", "headers"],
+        },
     ]
 }
 
 ONNX_CLASS_VALIDATION = {
     "required": [
-        {"name": "__init__", "args": ["self", "onnx_client", "config"]},
-        {"name": "predict", "args": ["self", "payload"]},
+        {"name": "__init__", "required_args": ["self", "onnx_client", "config"]},
+        {
+            "name": "predict",
+            "required_args": ["self"],
+            "optional_args": ["payload", "query_params", "headers"],
+        },
     ]
 }
 
 
 def _validate_impl(impl, impl_req):
-    for optional_func in impl_req.get("optional", []):
-        _validate_optional_fn_args(impl, optional_func["name"], optional_func["args"])
+    for optional_func_signature in impl_req.get("optional", []):
+        _validate_optional_fn_args(impl, optional_func_signature)
 
-    for required_func in impl_req.get("required", []):
-        _validate_required_fn_args(impl, required_func["name"], required_func["args"])
+    for required_func_signature in impl_req.get("required", []):
+        _validate_required_fn_args(impl, required_func_signature)
 
 
-def _validate_optional_fn_args(impl, fn_name, args):
-    if fn_name in vars(impl):
-        _validate_required_fn_args(impl, fn_name, args)
+def _validate_optional_fn_args(impl, func_signature):
+    if getattr(impl, func_signature["name"], None):
+        _validate_required_fn_args(impl, func_signature)
 
 
-def _validate_required_fn_args(impl, fn_name, args):
-    fn = getattr(impl, fn_name, None)
+def _validate_required_fn_args(impl, func_signature):
+    fn = getattr(impl, func_signature["name"], None)
     if not fn:
-        raise UserException('required function "{}" is not defined'.format(fn_name))
+        raise UserException(f'required function "{func_signature["name"]}" is not defined')
 
     if not callable(fn):
-        raise UserException('"{}" is defined, but is not a function'.format(fn_name))
+        raise UserException(f'"{func_signature["name"]}" is defined, but is not a function')
 
     argspec = inspect.getfullargspec(fn)
 
-    if argspec.args != args:
-        raise UserException(
-            'invalid signature for function "{}": expected arguments ({}) but found ({})'.format(
-                fn_name, ", ".join(args), ", ".join(argspec.args)
+    required_args = func_signature.get("required_args", [])
+    optional_args = func_signature.get("optional_args", [])
+    fn_str = f'{func_signature["name"]}({", ".join(argspec.args)})'
+
+    for arg_name in required_args:
+        if arg_name not in argspec.args:
+            raise UserException(
+                f'invalid signature for function "{fn_str}": "{arg_name}" is a required argument, but was not provided'
+            )
+
+        if arg_name == "self":
+            if argspec.args[0] != "self":
+                raise UserException(
+                    f'invalid signature for function "{fn_str}": "self" must be the first argument'
+                )
+
+    seen_args = []
+    for arg_name in argspec.args:
+        if arg_name not in required_args and arg_name not in optional_args:
+            raise UserException(
+                f'invalid signature for function "{fn_str}": "{arg_name}" is not a supported argument'
             )
-        )
+
+        if arg_name in seen_args:
+            raise UserException(
+                f'invalid signature for function "{fn_str}": "{arg_name}" is duplicated'
+            )
+
+        seen_args.append(arg_name)
 
 
 tf_expected_dir_structure = """tensorflow model directories must have the following structure:

pkg/workloads/cortex/serve/requirements.txt

@@ -4,6 +4,7 @@ dill==0.3.1.1
 fastapi==0.54.1
 msgpack==1.0.0
 numpy==1.18.4
+python-multipart==0.0.5
 pyyaml==5.3.1
 requests==2.23.0
 uvicorn==0.11.5

pkg/workloads/cortex/serve/serve.py

@@ -15,6 +15,7 @@
 import sys
 import os
 import argparse
+import inspect
 import time
 import json
 import msgpack
@@ -154,11 +155,31 @@ async def register_request(request: Request, call_next):
     return response
 
 
-def predict(request: Any = Body(..., media_type="application/json")):
+@app.middleware("http")
+async def parse_payload(request: Request, call_next):
+    if "payload" not in local_cache["predict_fn_args"]:
+        return await call_next(request)
+
+    content_type = request.headers.get("content-type", "").lower()
+
+    if content_type.startswith("multipart/form") or content_type.startswith(
+        "application/x-www-form-urlencoded"
+    ):
+        request.state.payload = await request.form()
+    elif content_type.startswith("application/json"):
+        request.state.payload = await request.json()
+    else:
+        request.state.payload = await request.body()
+
+    return await call_next(request)
+
+
+def predict(request: Request):
     api = local_cache["api"]
     predictor_impl = local_cache["predictor_impl"]
+    args = build_predict_args(request)
 
-    prediction = predictor_impl.predict(request)
+    prediction = predictor_impl.predict(**args)
 
     if isinstance(prediction, bytes):
         response = Response(content=prediction, media_type="application/octet-stream")
@@ -194,6 +215,19 @@ def predict(request: Any = Body(..., media_type="application/json")):
     return response
 
 
+def build_predict_args(request: Request):
+    args = {}
+
+    if "payload" in local_cache["predict_fn_args"]:
+        args["payload"] = request.state.payload
+    if "headers" in local_cache["predict_fn_args"]:
+        args["headers"] = request.headers
+    if "query_params" in local_cache["predict_fn_args"]:
+        args["query_params"] = request.query_params
+
+    return args
+
+
 def get_summary():
     response = {"message": API_SUMMARY_MESSAGE}
 
@@ -230,6 +264,7 @@ def start():
         storage = LocalStorage(os.getenv("CORTEX_CACHE_DIR"))
     else:
         storage = S3(bucket=os.environ["CORTEX_BUCKET"], region=os.environ["AWS_REGION"])
+
     try:
         raw_api_spec = get_spec(provider, storage, cache_dir, spec_path)
         api = API(provider=provider, storage=storage, cache_dir=cache_dir, **raw_api_spec)
@@ -243,13 +278,15 @@ def start():
         local_cache["provider"] = provider
         local_cache["client"] = client
         local_cache["predictor_impl"] = predictor_impl
+        local_cache["predict_fn_args"] = inspect.getfullargspec(predictor_impl.predict).args
         predict_route = "/"
         if provider != "local":
             predict_route = "/predict"
         local_cache["predict_route"] = predict_route
     except:
         cx_logger().exception("failed to start api")
         sys.exit(1)
+
     if (
         provider != "local"
         and api.monitoring is not None