f2ac76df5a
* [MLP]: create using core TensortRT python APIs * [MLP]: README.md added with chart * [MLP]: C++ TensorRT APIs added * [MLP]: Updated Docs * [MLP]: basic .wts added with APIs * [MLP]: Updated minor details * [MLP]: fix
249 lines
7.6 KiB
Python
249 lines
7.6 KiB
Python
import argparse
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import os
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import numpy as np
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import struct
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# required for the model creation
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import tensorrt as trt
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# required for the inference using TRT engine
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import pycuda.autoinit
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import pycuda.driver as cuda
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# Sizes of input and output for TensorRT model
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INPUT_SIZE = 1
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OUTPUT_SIZE = 1
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# path of .wts (weight file) and .engine (model file)
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WEIGHT_PATH = "./mlp.wts"
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ENGINE_PATH = "./mlp.engine"
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# input and output names are must for the TRT model
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INPUT_BLOB_NAME = 'data'
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OUTPUT_BLOB_NAME = 'out'
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# A logger provided by NVIDIA-TRT
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gLogger = trt.Logger(trt.Logger.INFO)
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################################
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# DEPLOYMENT RELATED ###########
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################################
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def load_weights(file_path):
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"""
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Parse the .wts file and store weights in dict format
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:param file_path:
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:return weight_map: dictionary containing weights and their values
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"""
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print(f"[INFO]: Loading weights: {file_path}")
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assert os.path.exists(file_path), '[ERROR]: Unable to load weight file.'
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weight_map = {}
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with open(file_path, "r") as f:
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lines = [line.strip() for line in f]
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# count for total # of weights
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count = int(lines[0])
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assert count == len(lines) - 1
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# Loop through counts and get the exact num of values against weights
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for i in range(1, count + 1):
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splits = lines[i].split(" ")
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name = splits[0]
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cur_count = int(splits[1])
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# len of splits must be greater than current weight counts
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assert cur_count + 2 == len(splits)
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# loop through all weights and unpack from the hexadecimal values
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values = []
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for j in range(2, len(splits)):
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# hex string to bytes to float
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values.append(struct.unpack(">f", bytes.fromhex(splits[j])))
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# store in format of { 'weight.name': [weights_val0, weight_val1, ..] }
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weight_map[name] = np.array(values, dtype=np.float32)
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return weight_map
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def create_mlp_engine(max_batch_size, builder, config, dt):
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"""
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Create Multi-Layer Perceptron using the TRT Builder and Configurations
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:param max_batch_size: batch size for built TRT model
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:param builder: to build engine and networks
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:param config: configuration related to Hardware
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:param dt: datatype for model layers
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:return engine: TRT model
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"""
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print("[INFO]: Creating MLP using TensorRT...")
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# load weight maps from the file
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weight_map = load_weights(WEIGHT_PATH)
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# build an empty network using builder
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network = builder.create_network()
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# add an input to network using the *input-name
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data = network.add_input(INPUT_BLOB_NAME, dt, (1, 1, INPUT_SIZE))
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assert data
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# add the layer with output-size (number of outputs)
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linear = network.add_fully_connected(input=data,
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num_outputs=OUTPUT_SIZE,
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kernel=weight_map['linear.weight'],
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bias=weight_map['linear.bias'])
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assert linear
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# set the name for output layer
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linear.get_output(0).name = OUTPUT_BLOB_NAME
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# mark this layer as final output layer
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network.mark_output(linear.get_output(0))
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# set the batch size of current builder
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builder.max_batch_size = max_batch_size
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# create the engine with model and hardware configs
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engine = builder.build_engine(network, config)
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# free captured memory
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del network
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del weight_map
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# return engine
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return engine
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def api_to_model(max_batch_size):
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"""
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Create engine using TensorRT APIs
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:param max_batch_size: for the deployed model configs
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:return:
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"""
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# Create Builder with logger provided by TRT
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builder = trt.Builder(gLogger)
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# Create configurations from Engine Builder
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config = builder.create_builder_config()
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# Create MLP Engine
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engine = create_mlp_engine(max_batch_size, builder, config, trt.float32)
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assert engine
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# Write the engine into binary file
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print("[INFO]: Writing engine into binary...")
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with open(ENGINE_PATH, "wb") as f:
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# write serialized model in file
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f.write(engine.serialize())
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# free the memory
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del engine
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del builder
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################################
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# INFERENCE RELATED ############
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################################
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def perform_inference(input_val):
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"""
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Get inference using the pre-trained model
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:param input_val: a number as an input
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:return:
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"""
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def do_inference(inf_context, inf_host_in, inf_host_out):
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"""
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Perform inference using the CUDA context
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:param inf_context: context created by engine
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:param inf_host_in: input from the host
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:param inf_host_out: output to save on host
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:return:
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"""
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inference_engine = inf_context.engine
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# Input and output bindings are required for inference
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assert inference_engine.num_bindings == 2
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# allocate memory in GPU using CUDA bindings
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device_in = cuda.mem_alloc(inf_host_in.nbytes)
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device_out = cuda.mem_alloc(inf_host_out.nbytes)
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# create bindings for input and output
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bindings = [int(device_in), int(device_out)]
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# create CUDA stream for simultaneous CUDA operations
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stream = cuda.Stream()
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# copy input from host (CPU) to device (GPU) in stream
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cuda.memcpy_htod_async(device_in, inf_host_in, stream)
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# execute inference using context provided by engine
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inf_context.execute_async(bindings=bindings, stream_handle=stream.handle)
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# copy output back from device (GPU) to host (CPU)
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cuda.memcpy_dtoh_async(inf_host_out, device_out, stream)
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# synchronize the stream to prevent issues
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# (block CUDA and wait for CUDA operations to be completed)
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stream.synchronize()
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# create a runtime (required for deserialization of model) with NVIDIA's logger
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runtime = trt.Runtime(gLogger)
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assert runtime
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# read and deserialize engine for inference
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with open(ENGINE_PATH, "rb") as f:
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engine = runtime.deserialize_cuda_engine(f.read())
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assert engine
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# create execution context -- required for inference executions
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context = engine.create_execution_context()
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assert context
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# create input as array
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data = np.array([input_val], dtype=np.float32)
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# capture free memory for input in GPU
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host_in = cuda.pagelocked_empty((INPUT_SIZE), dtype=np.float32)
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# copy input-array from CPU to Flatten array in GPU
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np.copyto(host_in, data.ravel())
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# capture free memory for output in GPU
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host_out = cuda.pagelocked_empty(OUTPUT_SIZE, dtype=np.float32)
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# do inference using required parameters
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do_inference(context, host_in, host_out)
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print(f'\n[INFO]: Predictions using pre-trained model..\n\tInput:\t{input_val}\n\tOutput:\t{host_out[0]:.4f}')
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def get_args():
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"""
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Parse command line arguments
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:return arguments: parsed arguments
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"""
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-s', action='store_true')
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arg_parser.add_argument('-d', action='store_true')
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arguments = vars(arg_parser.parse_args())
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# check for the arguments
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if not (arguments['s'] ^ arguments['d']):
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print("[ERROR]: Arguments not right!\n")
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print("\tpython mlp.py -s # serialize model to engine file")
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print("\tpython mlp.py -d # deserialize engine file and run inference")
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exit()
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return arguments
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if __name__ == "__main__":
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args = get_args()
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if args['s']:
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api_to_model(max_batch_size=1)
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print("[INFO]: Successfully created TensorRT engine...")
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print("\n\tRun inference using `python mlp.py -d`\n")
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else:
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perform_inference(input_val=4.0)
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