From 95709dc4bb456d3224e832beef519b89c630504f Mon Sep 17 00:00:00 2001 From: Sivanantham Chinnaiyan Date: Wed, 13 Nov 2024 09:22:29 +0530 Subject: [PATCH] Add Binary tensor data extension docs Signed-off-by: Sivanantham Chinnaiyan --- .../binary_tensor_data_extension.md | 274 ++++++++++++++++++ mkdocs.yml | 2 + 2 files changed, 276 insertions(+) create mode 100644 docs/modelserving/data_plane/binary_tensor_data_extension.md diff --git a/docs/modelserving/data_plane/binary_tensor_data_extension.md b/docs/modelserving/data_plane/binary_tensor_data_extension.md new file mode 100644 index 000000000..9b4292b9b --- /dev/null +++ b/docs/modelserving/data_plane/binary_tensor_data_extension.md @@ -0,0 +1,274 @@ +# Binary Tensor Data Extension + +The Binary Tensor Data Extension allows clients to send and receive tensor data in a binary format in +the body of an HTTP/REST request. This extension is particularly useful for sending and receiving FP16 data as +there is no specific data type for a 16-bit float type in the Open Inference Protocol and large tensors +for high-throughput scenarios. + +## Overview + +Tensor data represented as binary data is organized in little-endian byte order, row major, without stride or +padding between elements. All tensor data types are representable as binary data in the native size of the data type. +For BOOL type element true is a single byte with value 1 and false is a single byte with value 0. +For BYTES type an element is represented by a 4-byte unsigned integer giving the length followed by the actual bytes. +The binary data for a tensor is delivered in the HTTP body after the JSON object (see Examples). + +The binary tensor data extension uses parameters to indicate that an input or output tensor is communicated as binary data. + +The `binary_data_size` parameter is used in `$request_input` and `$response_output` to indicate that the input or output tensor is communicated as binary data: + +- "binary_data_size" : int64 parameter indicating the size of the tensor binary data, in bytes. + +The `binary_data` parameter is used in `$request_output` to indicate that the output should be returned from KServe runtime +as binary data. + +- "binary_data" : bool parameter that is true if the output should be returned as binary data and false (or not given) if the + tensor should be returned as JSON. + +The `binary_data_output` parameter is used in `$inference_request` to indicate that all outputs should be returned from KServe runtime as binary data, unless overridden by "binary_data" on a specific output. + +- "binary_data_output" : bool parameter that is true if all outputs should be returned as binary data and false + (or not given) if the outputs should be returned as JSON. If "binary_data" is specified on an output it overrides this setting. + +When one or more tensors are communicated as binary data, the HTTP body of the request or response +will contain the JSON inference request or response object followed by the binary tensor data in the same order as the +order of the input or output tensors are specified in the JSON. + +- If any binary data is present in the request or response the `Inference-Header-Content-Length` header must be provided to + give the length of the JSON object, and Content-Length continues to give the full body length (as HTTP requires). + + +## Examples + +### Sending and Receiving Binary Data + +For the following request the input tensors `input0` and `input2` are sent as binary data while `input1` is sent as non-binary data. Note that the `input0` and `input2` input tensors have a parameter `binary_data_size` which represents the size of the binary data. + +The output tensor `output0` must be returned as binary data as that is what is requested by setting the `binary_data` parameter to true. Also note that the size of the JSON part is provided in the `Inference-Header-Content-Length` and the total size of the binary data is reflected in the `Content-Length` header. + +```shell +POST /v2/models/mymodel/infer HTTP/1.1 +Host: localhost:8000 +Content-Type: application/octet-stream +Inference-Header-Content-Length: # Json length +Content-Length: # Json length + binary data length (In this case 16 + 3 = 19) +{ + "model_name" : "mymodel", + "inputs" : [ + { + "name" : "input0", + "shape" : [ 2, 2 ], + "datatype" : "FP16", + "parameters" : { + "binary_data_size" : 16 + } + }, + { + "name" : "input1", + "shape" : [ 2, 2 ], + "datatype" : "UINT32", + "data": [[1, 2], [3, 4]] + }, + { + "name" : "input2", + "shape" : [ 3 ], + "datatype" : "BOOL", + "parameters" : { + "binary_data_size" : 3 + } + } + ], + "outputs" : [ + { + "name" : "output0", + "parameters" : { + "binary_data" : true + } + }, + { + "name" : "output1" + } + ] +} +<16 bytes of data for input0 tensor> +<3 bytes of data for input2 tensor> +``` + +Assuming the model returns a [ 3, 2 ] tensor of data type FP16 and a [2, 2] tensor of data type FP32 the following response would be returned. + +```shell +HTTP/1.1 200 OK +Content-Type: application/octet-stream +Inference-Header-Content-Length: # Json length +Content-Length: # Json length + binary data length (In this case 16) +{ + "outputs" : [ + { + "name" : "output0", + "shape" : [ 3, 2 ], + "datatype" : "FP16", + "parameters" : { + "binary_data_size" : 16 + } + }, + { + "name" : "output1", + "shape" : [ 2, 2 ], + "datatype" : "FP32", + "data" : [[1.203, 5.403], [3.434, 34.234]] + } + ] +} +<16 bytes of data for output0 tensor> +``` + +=== "Inference Client Example" + +```python +from kserve import ModelServer, InferenceRESTClient, InferRequest, InferInput +from kserve.protocol.infer_type import RequestedOutput +from kserve.inference_client import RESTConfig + +fp16_data = np.array([[1.1, 2.22], [3.345, 4.34343]], dtype=np.float16) +uint32_data = np.array([[1, 2], [3, 4]], dtype=np.uint32) +bool_data = np.array([True, False, True], dtype=np.bool) + +# Create input tensor with binary data +input_0 = InferInput(name="input_0", datatype="FP16", shape=[2, 2]) +input_0.set_data_from_numpy(fp16_data, binary_data=True) +input_1 = InferInput(name="input_1", datatype="UINT32", shape=[2, 2]) +input_1.set_data_from_numpy(uint32_data, binary_data=False) +input_2 = InferInput(name="input_2", datatype="BOOL", shape=[3]) +input_2.set_data_from_numpy(bool_data, binary_data=True) + +# Create request output +output_0 = RequestedOutput(name="output_0", binary_data=True) +output_1 = RequestedOutput(name="output_1", binary_data=False) + +# Create inference request +infer_request = InferRequest( + model_name="mymodel", + request_id="2ja0ls9j1309", + infer_inputs=[input_0, input_1, input_2], + requested_outputs=[output_0, output_1], +) + +# Create the REST client +config = RESTConfig(verbose=True, protocol="v2") +rest_client = InferenceRESTClient(config=config) + +# Send the request +infer_response = await rest_client.infer( + "http://localhost:8000", + model_name="TestModel", + data=infer_request, + headers={"Host": "test-server.com"}, + timeout=2, + ) + +# Read the binary data from the response +output_0 = infer_response.outputs[0] +fp16_output = output_0.as_numpy() + +# Read the non-binary data from the response +output_1 = infer_response.outputs[1] +fp32_output = output_1.data # This will return the data as a list +fp32_output_arr = output_1.as_numpy() +``` + +### Requesting All The Outputs To Be In Binary Format + +For the following request, `binary_data_output` is set to true to receive all the outputs as binary data. Note that the +`binary_data_output` is set in the `$inference_request` parameters field, not in the `$inference_input` parameters field. This parameter can be overridden for a specific output by setting `binary_data` parameter to false in the `$request_output`. + +```shell +POST /v2/models/mymodel/infer HTTP/1.1 +Host: localhost:8000 +Content-Type: application/json +Content-Length: 75 +{ + "model_name": "my_model", + "inputs": [ + { + "name": "input_tensor", + "datatype": "FP32", + "shape": [1, 2], + "data": [[32.045, 399.043]], + } + ], + "parameters": { + "binary_data_output": true + } +} +``` +Assuming the model returns a [ 3, 2 ] tensor of data type FP16 and a [2, 2] tensor of data type FP32 the following response would be returned. + +```shell +HTTP/1.1 200 OK +Content-Type: application/octet-stream +Inference-Header-Content-Length: # Json length +Content-Length: # Json length + binary data length (In this case 16 + 32) +{ + "outputs" : [ + { + "name" : "output_tensor0", + "shape" : [ 3, 2 ], + "datatype" : "FP16", + "parameters" : { + "binary_data_size" : 16 + } + }, + { + "name" : "output_tensor1", + "shape" : [ 2, 2 ], + "datatype" : "FP32", + "parameters": { + "binary_data_size": 32 + } + } + ] +} +<16 bytes of data for output_tensor0 tensor> +<32 bytes of data for output_tensor1 tensor> +``` + +=== "Inference Client Example" + +```python +from kserve import ModelServer, InferenceRESTClient, InferRequest, InferInput +from kserve.protocol.infer_type import RequestedOutput +from kserve.inference_client import RESTConfig + +fp32_data = np.array([[32.045, 399.043]], dtype=np.float32) + +# Create the input tensor +input_0 = InferInput(name="input_0", datatype="FP32", shape=[1, 2]) +input_0.set_data_from_numpy(fp16_data, binary_data=False) + +# Create inference request with binary_data_output set to True +infer_request = InferRequest( + model_name="mymodel", + request_id="2ja0ls9j1309", + infer_inputs=[input_0], + parameters={"binary_data_output": True} +) + +# Create the REST client +config = RESTConfig(verbose=True, protocol="v2") +rest_client = InferenceRESTClient(config=config) + +# Send the request +infer_response = await rest_client.infer( + "http://localhost:8000", + model_name="TestModel", + data=infer_request, + headers={"Host": "test-server.com"}, + timeout=2, + ) + +# Read the binary data from the response +output_0 = infer_response.outputs[0] +fp16_output = output_0.as_numpy() +output_1 = infer_response.outputs[1] +fp32_output_arr = output_1.as_numpy() +``` \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 51269abd6..5631409e6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -21,6 +21,8 @@ nav: - Model Serving Data Plane: modelserving/data_plane/data_plane.md - V1 Inference Protocol: modelserving/data_plane/v1_protocol.md - Open Inference Protocol (V2 Inference Protocol): modelserving/data_plane/v2_protocol.md + - Open Inference Protocol Extensions: + - Binary Tensor Data Extension: modelserving/data_plane/binary_tensor_data_extension.md - Serving Runtimes: modelserving/servingruntimes.md - Model Serving Runtimes: - Supported Model Frameworks/Formats: