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An offline converter for TF-TRT transformation for TF 2.0 SavedModels.
tf.experimental.tensorrt.Converter( input_saved_model_dir=None, input_saved_model_tags=None, input_saved_model_signature_key=None, conversion_params=None )
Currently this is not available on Windows platform.
Note that in V2, is_dynamic_op=False is not supported, meaning TRT engines will be built only when the corresponding TRTEngineOp is executed. But we still provide a way to avoid the cost of building TRT engines during inference (see more below).
There are several ways to run the conversion:
- FP32/FP16 precision
params = tf.experimental.tensorrt.ConversionParams( precision_mode='FP16') converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) converter.convert() converter.save(output_saved_model_dir)
In this case, no TRT engines will be built or saved in the converted SavedModel. But if input data is available during conversion, we can still build and save the TRT engines to reduce the cost during inference (see option 2 below).
- FP32/FP16 precision with pre-built engines
params = tf.experimental.tensorrt.ConversionParams( precision_mode='FP16', # Set this to a large enough number so it can cache all the engines. maximum_cached_engines=16) converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) converter.convert() # Define a generator function that yields input data, and use it to execute # the graph to build TRT engines. # With TensorRT 5.1, different engines will be built (and saved later) for # different input shapes to the TRTEngineOp. def my_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.build(input_fn=my_input_fn) # Generate corresponding TRT engines converter.save(output_saved_model_dir) # Generated engines will be saved.
In this way, one engine will be built/saved for each unique input shapes of
the TRTEngineOp. This is good for applications that cannot afford building
engines during inference but have access to input data that is similar to
the one used in production (for example, that has the same input shapes).
Also, the generated TRT engines is platform dependent, so we need to run
build() in an environment that is similar to production (e.g. with
same type of GPU).
- INT8 precision and calibration with pre-built engines
params = tf.experimental.tensorrt.ConversionParams( precision_mode='INT8', # Currently only one INT8 engine is supported in this mode. maximum_cached_engines=1, use_calibration=True) converter = tf.experimental.tensorrt.Converter( input_saved_model_dir="my_dir", conversion_params=params) # Define a generator function that yields input data, and run INT8 # calibration with the data. All input data should have the same shape. # At the end of convert(), the calibration stats (e.g. range information) # will be saved and can be used to generate more TRT engines with different # shapes. Also, one TRT engine will be generated (with the same shape as # the calibration data) for save later. def my_calibration_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.convert(calibration_input_fn=my_calibration_input_fn) # (Optional) Generate more TRT engines offline (same as the previous # option), to avoid the cost of generating them during inference. def my_input_fn(): for _ in range(num_runs): inp1, inp2 = ... yield inp1, inp2 converter.build(input_fn=my_input_fn) # Save the TRT engine and the engines. converter.save(output_saved_model_dir)
input_saved_model_dir: the directory to load the SavedModel which contains the input graph to transforms. Used only when input_graph_def is None.
input_saved_model_tags: list of tags to load the SavedModel.
input_saved_model_signature_key: the key of the signature to optimize the graph for.
conversion_params: a TrtConversionParams instance.
ValueError: if the combination of the parameters is invalid.
build( input_fn )
Run inference with converted graph in order to build TensorRT engines.
input_fn: a generator function that yields input data as a list or tuple, which will be used to execute the converted signature to generate TRT engines. Example:
def input_fn(): # Let's assume a network with 2 input tensors. We generate 3 sets # of dummy input data: input_shapes = [[(1, 16), (2, 16)], # 1st input list [(2, 32), (4, 32)], # 2nd list of two tensors [(4, 32), (8, 32)]] # 3rd input list for shapes in input_shapes: # return a list of input tensors yield [np.zeros(x).astype(np.float32) for x in shapes]
NotImplementedError: build() is already called.
RuntimeError: the input_fx is None.
convert( calibration_input_fn=None )
Convert the input SavedModel in 2.0 format.
calibration_input_fn: a generator function that yields input data as a list or tuple, which will be used to execute the converted signature for calibration. All the returned input data should have the same shape. Example:
def input_fn(): yield input1, input2, input3
ValueError: if the input combination is invalid.
The TF-TRT converted Function.
save( output_saved_model_dir )
Save the converted SavedModel.
output_saved_model_dir: directory to saved the converted SavedModel.