tf.experimental.tensorrt.Converter

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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:

  1. 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).

  1. 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).

  1. 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.

Methods

build

View source

build(
    input_fn
)

Run inference with converted graph in order to build TensorRT engines.

Args
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]`

Raises
NotImplementedError build() is already called.
RuntimeError the input_fx is None.

convert

View source

convert(
    calibration_input_fn=None
)

Convert the input SavedModel in 2.0 format.

Args
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

Raises
ValueError if the input combination is invalid.

Returns
The TF-TRT converted Function.

save

View source

save(
    output_saved_model_dir
)

Save the converted SavedModel.

Args
output_saved_model_dir directory to saved the converted SavedModel.