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gen_qa_implicit_models.py
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gen_qa_implicit_models.py
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# Copyright 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import os
import numpy as np
import gen_ensemble_model_utils as emu
FLAGS = None
np_dtype_string = np.dtype(object)
def np_to_model_dtype(np_dtype):
if np_dtype == bool:
return "TYPE_BOOL"
elif np_dtype == np.int8:
return "TYPE_INT8"
elif np_dtype == np.int16:
return "TYPE_INT16"
elif np_dtype == np.int32:
return "TYPE_INT32"
elif np_dtype == np.int64:
return "TYPE_INT64"
elif np_dtype == np.uint8:
return "TYPE_UINT8"
elif np_dtype == np.uint16:
return "TYPE_UINT16"
elif np_dtype == np.float16:
return "TYPE_FP16"
elif np_dtype == np.float32:
return "TYPE_FP32"
elif np_dtype == np.float64:
return "TYPE_FP64"
elif np_dtype == np_dtype_string:
return "TYPE_STRING"
return None
def np_to_onnx_dtype(np_dtype):
if np_dtype == bool:
return onnx.TensorProto.BOOL
elif np_dtype == np.int8:
return onnx.TensorProto.INT8
elif np_dtype == np.int16:
return onnx.TensorProto.INT16
elif np_dtype == np.int32:
return onnx.TensorProto.INT32
elif np_dtype == np.int64:
return onnx.TensorProto.INT64
elif np_dtype == np.uint8:
return onnx.TensorProto.UINT8
elif np_dtype == np.uint16:
return onnx.TensorProto.UINT16
elif np_dtype == np.float16:
return onnx.TensorProto.FLOAT16
elif np_dtype == np.float32:
return onnx.TensorProto.FLOAT
elif np_dtype == np.float64:
return onnx.TensorProto.DOUBLE
elif np_dtype == np_dtype_string:
return onnx.TensorProto.STRING
def np_to_trt_dtype(np_dtype):
if np_dtype == bool:
return trt.bool
elif np_dtype == np.int8:
return trt.int8
elif np_dtype == np.int32:
return trt.int32
elif np_dtype == np.float16:
return trt.float16
elif np_dtype == np.float32:
return trt.float32
return None
def create_onnx_modelfile_wo_initial_state(models_dir, model_version, max_batch,
dtype, shape):
if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
return
model_name = tu.get_sequence_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
onnx_dtype = np_to_onnx_dtype(dtype)
onnx_control_dtype = onnx_dtype
onnx_input_shape, idx = tu.shape_to_onnx_shape(shape, 0)
onnx_output_shape, idx = tu.shape_to_onnx_shape(shape, idx)
# If the input is a string then use int32 for operation and just
# cast to/from string for input and output.
if onnx_dtype == onnx.TensorProto.STRING:
onnx_control_dtype = onnx.TensorProto.INT32
# If input dtype is bool, then use bool type for control and
# int32 type for input/output
if onnx_dtype == onnx.TensorProto.BOOL:
onnx_dtype = onnx.TensorProto.INT32
batch_dim = [] if max_batch == 0 else [None]
onnx_input = onnx.helper.make_tensor_value_info(
"INPUT", onnx_dtype, batch_dim + onnx_input_shape)
onnx_input_state = onnx.helper.make_tensor_value_info(
"INPUT_STATE", onnx_dtype, batch_dim + onnx_input_shape)
onnx_start = onnx.helper.make_tensor_value_info("START", onnx_control_dtype,
batch_dim + [1])
onnx_ready = onnx.helper.make_tensor_value_info("READY", onnx_control_dtype,
batch_dim + [1])
onnx_output = onnx.helper.make_tensor_value_info(
"OUTPUT", onnx_dtype, batch_dim + onnx_output_shape)
onnx_output_state = onnx.helper.make_tensor_value_info(
"OUTPUT_STATE", onnx_dtype, batch_dim + onnx_output_shape)
internal_input = onnx.helper.make_node("Identity", ["INPUT"], ["_INPUT"])
internal_input_state = onnx.helper.make_node("Identity", ["INPUT_STATE"],
["_INPUT_STATE"])
# cast int8, int16 input to higer precision int as Onnx Add/Sub operator doesn't support those type
if ((onnx_dtype == onnx.TensorProto.INT8) or
(onnx_dtype == onnx.TensorProto.INT16)):
internal_input = onnx.helper.make_node("Cast", ["INPUT"], ["_INPUT"],
to=onnx.TensorProto.INT32)
internal_input_state = onnx.helper.make_node("Cast", ["INPUT_STATE"],
["_INPUT_STATE"],
to=onnx.TensorProto.INT32)
# Convert boolean value to int32 value
if onnx_control_dtype == onnx.TensorProto.BOOL:
if onnx_dtype != onnx.TensorProto.STRING:
internal_input1 = onnx.helper.make_node("Cast", ["START"],
["_START"],
to=onnx.TensorProto.INT32)
internal_input2 = onnx.helper.make_node("Cast", ["READY"],
["_READY"],
to=onnx.TensorProto.INT32)
not_start_cast = onnx.helper.make_node("Not", ["START"],
["_NOT_START_CAST"])
not_start = onnx.helper.make_node("Cast", ["_NOT_START_CAST"],
["_NOT_START"],
to=onnx.TensorProto.INT32)
not_ready_cast = onnx.helper.make_node("Not", ["START"],
["_NOT_READY_CAST"])
not_ready = onnx.helper.make_node("Cast", ["_NOT_READY_CAST"],
["_NOT_READY"],
to=onnx.TensorProto.INT32)
input_state_cond = onnx.helper.make_node(
"And", ["READY", "_NOT_START_CAST"], ["input_state_cond"])
input_state_cond_cast = onnx.helper.make_node(
"Cast", ["input_state_cond"], ["input_state_cond_cast"],
to=onnx.TensorProto.INT32)
mul_state = onnx.helper.make_node(
"Mul", ["_INPUT_STATE", "input_state_cond_cast"], ["mul_state"])
add = onnx.helper.make_node("Add", ["_INPUT", "mul_state"],
["CAST"])
else:
if onnx_dtype != onnx.TensorProto.STRING:
start_cast = onnx.helper.make_node("Cast", ["START"],
["_START_CAST"],
to=onnx.TensorProto.BOOL)
not_start_cast = onnx.helper.make_node("Not", ["_START_CAST"],
["_NOT_START_CAST"])
not_start = onnx.helper.make_node("Cast", ["_NOT_START_CAST"],
["_NOT_START"],
to=onnx.TensorProto.INT32)
ready_cast = onnx.helper.make_node("Cast", ["READY"],
["_READY_CAST"],
to=onnx.TensorProto.BOOL)
not_ready_cast = onnx.helper.make_node("Not", ["_READY_CAST"],
["_NOT_READY_CAST"])
not_ready = onnx.helper.make_node("Cast", ["_NOT_READY_CAST"],
["_NOT_READY"],
to=onnx.TensorProto.INT32)
# Take advantage of knowledge that the READY false value is 0 and true is 1
input_state_cond = onnx.helper.make_node(
"And", ["_NOT_START_CAST", "_READY_CAST"], ["input_state_cond"])
input_state_cond_cast = onnx.helper.make_node(
"Cast", ["input_state_cond"], ["input_state_cond_cast"],
to=onnx.TensorProto.INT32)
mul_state = onnx.helper.make_node(
"Mul", ["_INPUT_STATE", "input_state_cond_cast"], ["mul_state"])
add = onnx.helper.make_node("Add", ["_INPUT", "mul_state"],
["CAST"])
if onnx_dtype == onnx.TensorProto.STRING:
cast = onnx.helper.make_node("Identity", ["_INPUT"], ["OUTPUT"])
cast_output_state = onnx.helper.make_node("Identity", ["_INPUT"],
["OUTPUT_STATE"])
elif onnx_dtype == onnx.TensorProto.FLOAT16:
# Avoid cast from float16 to float16
# (bug in Onnx Runtime, cast from float16 to float16 will become cast from float16 to float32)
cast = onnx.helper.make_node("Identity", ["CAST"], ["OUTPUT"])
cast_output_state = onnx.helper.make_node("Identity", ["CAST"],
["OUTPUT_STATE"])
else:
cast = onnx.helper.make_node("Cast", ["CAST"], ["OUTPUT"],
to=onnx_dtype)
cast_output_state = onnx.helper.make_node("Cast", ["CAST"],
["OUTPUT_STATE"],
to=onnx_dtype)
if onnx_control_dtype == onnx.TensorProto.BOOL:
if onnx_dtype != onnx.TensorProto.STRING:
onnx_nodes = [
internal_input, internal_input_state, internal_input1,
internal_input2, not_start_cast, not_start, not_ready_cast,
not_ready, input_state_cond, input_state_cond_cast, mul_state,
add, cast, cast_output_state
]
else:
onnx_nodes = [
internal_input, internal_input_state, cast, cast_output_state
]
else:
if onnx_dtype != onnx.TensorProto.STRING:
onnx_nodes = [
internal_input, internal_input_state, start_cast,
not_start_cast, not_start, ready_cast, not_ready_cast,
not_ready, input_state_cond, input_state_cond_cast, mul_state,
add, cast, cast_output_state
]
else:
onnx_nodes = [
internal_input, internal_input_state, cast, cast_output_state
]
onnx_inputs = [onnx_input_state, onnx_input, onnx_start, onnx_ready]
onnx_outputs = [onnx_output, onnx_output_state]
graph_proto = onnx.helper.make_graph(onnx_nodes, model_name, onnx_inputs,
onnx_outputs)
if FLAGS.onnx_opset > 0:
model_opset = onnx.helper.make_operatorsetid("", FLAGS.onnx_opset)
model_def = onnx.helper.make_model(graph_proto,
producer_name="triton",
opset_imports=[model_opset])
else:
model_def = onnx.helper.make_model(graph_proto, producer_name="triton")
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
onnx.save(model_def, model_version_dir + "/model.onnx")
def create_onnx_modelfile_with_initial_state(models_dir, model_version,
max_batch, dtype, shape):
if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
return
model_name = tu.get_sequence_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
onnx_dtype = np_to_onnx_dtype(dtype)
onnx_control_dtype = onnx_dtype
onnx_input_shape, idx = tu.shape_to_onnx_shape(shape, 0)
onnx_output_shape, idx = tu.shape_to_onnx_shape(shape, idx)
# If the input is a string then use int32 for operation and just
# cast to/from string for input and output.
if onnx_dtype == onnx.TensorProto.STRING:
onnx_control_dtype = onnx.TensorProto.INT32
# If input dtype is bool, then use bool type for control and
# int32 type for input/output
if onnx_dtype == onnx.TensorProto.BOOL:
onnx_dtype = onnx.TensorProto.INT32
batch_dim = [] if max_batch == 0 else [None]
onnx_input = onnx.helper.make_tensor_value_info(
"INPUT", onnx_dtype, batch_dim + onnx_input_shape)
onnx_input_state = onnx.helper.make_tensor_value_info(
"INPUT_STATE", onnx_dtype, batch_dim + onnx_input_shape)
onnx_start = onnx.helper.make_tensor_value_info("START", onnx_control_dtype,
batch_dim + [1])
onnx_ready = onnx.helper.make_tensor_value_info("READY", onnx_control_dtype,
batch_dim + [1])
onnx_output = onnx.helper.make_tensor_value_info(
"OUTPUT", onnx_dtype, batch_dim + onnx_output_shape)
onnx_output_state = onnx.helper.make_tensor_value_info(
"OUTPUT_STATE", onnx_dtype, batch_dim + onnx_output_shape)
internal_input = onnx.helper.make_node("Identity", ["INPUT"], ["_INPUT"])
internal_input_state = onnx.helper.make_node("Identity", ["INPUT_STATE"],
["_INPUT_STATE"])
# cast int8, int16 input to higer precision int as Onnx Add/Sub operator doesn't support those type
if ((onnx_dtype == onnx.TensorProto.INT8) or
(onnx_dtype == onnx.TensorProto.INT16)):
internal_input = onnx.helper.make_node("Cast", ["INPUT"], ["_INPUT"],
to=onnx.TensorProto.INT32)
internal_input_state = onnx.helper.make_node("Cast", ["INPUT_STATE"],
["_INPUT_STATE"],
to=onnx.TensorProto.INT32)
if onnx_dtype == onnx.TensorProto.STRING:
identity = onnx.helper.make_node("Identity", ["_INPUT"], ["OUTPUT"])
identity_output_state = onnx.helper.make_node("Identity", ["_INPUT"],
["OUTPUT_STATE"])
else:
add = onnx.helper.make_node("Add", ["_INPUT", "_INPUT_STATE"], ["CAST"])
cast = onnx.helper.make_node("Cast", ["CAST"], ["OUTPUT"],
to=onnx_dtype)
cast_output_state = onnx.helper.make_node("Cast", ["CAST"],
["OUTPUT_STATE"],
to=onnx_dtype)
# Avoid cast from float16 to float16
# (bug in Onnx Runtime, cast from float16 to float16 will become cast from float16 to float32)
if onnx_dtype == onnx.TensorProto.FLOAT16:
cast = onnx.helper.make_node("Identity", ["CAST"], ["OUTPUT"])
cast_output_state = onnx.helper.make_node("Identity", ["CAST"],
["OUTPUT_STATE"])
if onnx_dtype != onnx.TensorProto.STRING:
onnx_nodes = [
internal_input, internal_input_state, add, cast, cast_output_state
]
else:
onnx_nodes = [
internal_input, internal_input_state, identity,
identity_output_state
]
onnx_inputs = [onnx_input_state, onnx_input, onnx_start, onnx_ready]
onnx_outputs = [onnx_output, onnx_output_state]
graph_proto = onnx.helper.make_graph(onnx_nodes, model_name, onnx_inputs,
onnx_outputs)
if FLAGS.onnx_opset > 0:
model_opset = onnx.helper.make_operatorsetid("", FLAGS.onnx_opset)
model_def = onnx.helper.make_model(graph_proto,
producer_name="triton",
opset_imports=[model_opset])
else:
model_def = onnx.helper.make_model(graph_proto, producer_name="triton")
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
onnx.save(model_def, model_version_dir + "/model.onnx")
def create_onnx_modelfile(models_dir, model_version, max_batch, dtype, shape,
initial_state):
if initial_state is None:
create_onnx_modelfile_wo_initial_state(models_dir, model_version,
max_batch, dtype, shape)
else:
# This model assumes that the initial state contains correct data
create_onnx_modelfile_with_initial_state(models_dir, model_version,
max_batch, dtype, shape)
def create_onnx_modelconfig(models_dir, model_version, max_batch, dtype, shape,
initial_state):
if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
return
model_name = tu.get_sequence_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", dtype)
config_dir = models_dir + "/" + model_name
if dtype == np.float32:
control_type = "fp32"
elif dtype == bool:
control_type = "bool"
dtype = np.int32
else:
control_type = "int32"
instance_group_string = '''
instance_group [
{
kind: KIND_GPU
}
]
'''
# [TODO] move create_general_modelconfig() out of emu as it is general
# enough for all backends to use
config = emu.create_general_modelconfig(
model_name,
"onnxruntime_onnx",
max_batch, [dtype], [shape], [None], [dtype], [shape], [None], [None],
force_tensor_number_suffix=False,
instance_group_str=instance_group_string)
# Prepare the shapes for initial state initialization
shape_without_variable_dims = []
for dim in shape:
if dim == -1:
shape_without_variable_dims.append(1)
else:
shape_without_variable_dims.append(dim)
if initial_state is None:
config += '''
sequence_batching {{
max_sequence_idle_microseconds: 5000000
control_input [
{{
name: "START"
control [
{{
kind: CONTROL_SEQUENCE_START
{type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY"
control [
{{
kind: CONTROL_SEQUENCE_READY
{type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE"
output_name: "OUTPUT_STATE"
data_type: {dtype}
dims: {dims}
}}
]
}}
'''.format(type=control_type,
dims=tu.shape_to_dims_str(shape),
dtype=emu.dtype_str(dtype))
elif initial_state == 'zero':
config += f'''
sequence_batching {{
max_sequence_idle_microseconds: 5000000
control_input [
{{
name: "START"
control [
{{
kind: CONTROL_SEQUENCE_START
{control_type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY"
control [
{{
kind: CONTROL_SEQUENCE_READY
{control_type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE"
output_name: "OUTPUT_STATE"
data_type: {emu.dtype_str(dtype)}
dims: {tu.shape_to_dims_str(shape)}
initial_state: {{
name: "state init"
data_type: {emu.dtype_str(dtype)}
dims: {tu.shape_to_dims_str(shape_without_variable_dims)}
zero_data: true
}}
}}
]
}}
'''
elif initial_state == 'file':
config += '''
sequence_batching {{
max_sequence_idle_microseconds: 5000000
control_input [
{{
name: "START"
control [
{{
kind: CONTROL_SEQUENCE_START
{type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY"
control [
{{
kind: CONTROL_SEQUENCE_READY
{type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE"
output_name: "OUTPUT_STATE"
data_type: {dtype}
dims: {dims}
initial_state: {{
name: "state init"
data_type: {dtype}
dims: {shape_without_variable_dims}
data_file: input_state_data
}}
}}
]
}}
'''.format(type=control_type,
dims=tu.shape_to_dims_str(shape),
dtype=emu.dtype_str(dtype),
shape_without_variable_dims=tu.shape_to_dims_str(
shape_without_variable_dims))
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_plan_fixed_modelfile(models_dir, model_version, max_batch, dtype,
shape):
trt_dtype = np_to_trt_dtype(dtype)
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
in0 = network.add_input("INPUT", trt_dtype, shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, shape)
start0 = network.add_input("START", trt_dtype, [1 for i in shape])
network.add_input("READY", trt_dtype, [1 for i in shape])
constant_1_data = trt.Weights(np.ones([1 for i in shape], dtype=dtype))
constant_1 = network.add_constant([1 for i in shape], constant_1_data)
not_start = network.add_elementwise(constant_1.get_output(0), start0,
trt.ElementWiseOperation.SUB)
not_start.set_output_type(0, trt_dtype)
internal_state = network.add_elementwise(in_state0, not_start.get_output(0),
trt.ElementWiseOperation.PROD)
out0 = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0_state = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0.get_output(0).name = "OUTPUT"
network.mark_output(out0.get_output(0))
out0_state.get_output(0).name = "OUTPUT_STATE"
network.mark_output(out0_state.get_output(0))
config = builder.create_builder_config()
config.max_workspace_size = 1 << 20
builder.max_batch_size = max(1, max_batch)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
del network
model_name = tu.get_sequence_model_name(
"plan_nobatch" if max_batch == 0 else "plan", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_fixed_rf_modelfile(models_dir, model_version, max_batch, dtype,
shape):
trt_dtype = np_to_trt_dtype(dtype)
trt_memory_format = trt.TensorFormat.LINEAR
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
in0 = network.add_input("INPUT", trt_dtype, shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, shape)
start0 = network.add_input("START", trt_dtype, [1 for i in shape])
ready0 = network.add_input("READY", trt_dtype, [1 for i in shape])
constant_1_data = trt.Weights(np.ones([1 for i in shape], dtype=dtype))
constant_1 = network.add_constant([1 for i in shape], constant_1_data)
not_start = network.add_elementwise(constant_1.get_output(0), start0,
trt.ElementWiseOperation.SUB)
not_start.set_output_type(0, trt_dtype)
internal_state = network.add_elementwise(in_state0, not_start.get_output(0),
trt.ElementWiseOperation.PROD)
out0 = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0_state = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0.get_output(0).name = "OUTPUT"
network.mark_output(out0.get_output(0))
out0.get_output(0).dtype = trt_dtype
out0_state.get_output(0).name = "OUTPUT_STATE"
network.mark_output(out0_state.get_output(0))
out0_state.get_output(0).dtype = trt_dtype
in0.allowed_formats = 1 << int(trt_memory_format)
in_state0.allowed_formats = 1 << int(trt_memory_format)
start0.allowed_formats = 1 << int(trt_memory_format)
ready0.allowed_formats = 1 << int(trt_memory_format)
out0.get_output(0).allowed_formats = 1 << int(trt_memory_format)
out0_state.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if (trt_dtype == trt.int8):
in0.dynamic_range = (-128.0, 127.0)
in_state0.dynamic_range = (-128.0, 127.0)
out0.dynamic_range = (-128.0, 127.0)
out0_state.dynamic_range = (-128.0, 127.0)
start0.dynamic_range = (-128.0, 127.0)
ready0.dynamic_range = (-128.0, 127.0)
flags = 1 << int(trt.BuilderFlag.STRICT_TYPES)
if (trt_dtype == trt.int8):
flags |= 1 << int(trt.BuilderFlag.INT8)
elif (trt_dtype == trt.float16):
flags |= 1 << int(trt.BuilderFlag.FP16)
config = builder.create_builder_config()
config.flags = flags
config.max_workspace_size = 1 << 20
builder.max_batch_size = max(1, max_batch)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_sequence_model_name(
"plan_nobatch" if max_batch == 0 else "plan", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_dynamic_modelfile(models_dir, model_version, max_batch, dtype,
shape):
trt_dtype = np_to_trt_dtype(dtype)
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
# EXPLICIT_BATCH must be used when the dimension is variable
network = builder.create_network(
1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
unit_shape = ([1] * len(shape))
if max_batch != 0:
in0 = network.add_input("INPUT", trt_dtype, [-1] + shape)
start0 = network.add_input("START", trt_dtype, [-1] + unit_shape)
ready0 = network.add_input("READY", trt_dtype, [-1] + unit_shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, [-1] + shape)
else:
in0 = network.add_input("INPUT", trt_dtype, shape)
start0 = network.add_input("START", trt_dtype, unit_shape)
ready0 = network.add_input("READY", trt_dtype, unit_shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, shape)
# Append the dimension by 1 so that broadcasting works properly
constant_1_data = trt.Weights(np.ones(unit_shape + [1], dtype=dtype))
constant_1 = network.add_constant(unit_shape + [1], constant_1_data)
not_start = network.add_elementwise(constant_1.get_output(0), start0,
trt.ElementWiseOperation.SUB)
not_start.set_output_type(0, trt_dtype)
internal_state = network.add_elementwise(in_state0, not_start.get_output(0),
trt.ElementWiseOperation.PROD)
out0 = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0_state = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0.get_output(0).name = "OUTPUT"
network.mark_output(out0.get_output(0))
out0_state.get_output(0).name = "OUTPUT_STATE"
network.mark_output(out0_state.get_output(0))
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in shape:
if i == -1:
min_shape = min_shape + [1]
opt_shape = opt_shape + [8]
max_shape = max_shape + [32]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
profile.set_shape("INPUT", min_shape, opt_shape, max_shape)
profile.set_shape("INPUT_STATE", min_shape, opt_shape, max_shape)
if max_batch != 0:
profile.set_shape("START", [1] + unit_shape, [max_batch] + unit_shape,
[max_batch] + unit_shape)
profile.set_shape("READY", [1] + unit_shape, [max_batch] + unit_shape,
[max_batch] + unit_shape)
else:
profile.set_shape("START", unit_shape, unit_shape, unit_shape)
profile.set_shape("READY", unit_shape, unit_shape, unit_shape)
config = builder.create_builder_config()
config.add_optimization_profile(profile)
config.max_workspace_size = 1 << 20
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_sequence_model_name(
"plan_nobatch" if max_batch == 0 else "plan", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_dynamic_rf_modelfile(models_dir, model_version, max_batch,
dtype, shape):
trt_dtype = np_to_trt_dtype(dtype)
trt_memory_format = trt.TensorFormat.LINEAR
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
# EXPLICIT_BATCH must be used when the dimension is variable
network = builder.create_network(
1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
unit_shape = ([1] * len(shape))
if max_batch != 0:
in0 = network.add_input("INPUT", trt_dtype, [-1] + shape)
start0 = network.add_input("START", trt_dtype, [-1] + unit_shape)
ready0 = network.add_input("READY", trt_dtype, [-1] + unit_shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, [-1] + shape)
else:
in0 = network.add_input("INPUT", trt_dtype, shape)
start0 = network.add_input("START", trt_dtype, unit_shape)
ready0 = network.add_input("READY", trt_dtype, unit_shape)
in_state0 = network.add_input("INPUT_STATE", trt_dtype, [-1] + shape)
# Append the dimension by 1 so that broadcasting works properly
constant_1_data = trt.Weights(np.ones(unit_shape + [1], dtype=dtype))
constant_1 = network.add_constant(unit_shape + [1], constant_1_data)
not_start = network.add_elementwise(constant_1.get_output(0), start0,
trt.ElementWiseOperation.SUB)
not_start.set_output_type(0, trt_dtype)
internal_state = network.add_elementwise(in_state0, not_start.get_output(0),
trt.ElementWiseOperation.PROD)
out0 = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0_state = network.add_elementwise(internal_state.get_output(0), in0,
trt.ElementWiseOperation.SUM)
out0.get_output(0).name = "OUTPUT"
network.mark_output(out0.get_output(0))
out0_state.get_output(0).name = "OUTPUT_STATE"
network.mark_output(out0_state.get_output(0))
out0.get_output(0).dtype = trt_dtype
out0_state.get_output(0).dtype = trt_dtype
in0.allowed_formats = 1 << int(trt_memory_format)
start0.allowed_formats = 1 << int(trt_memory_format)
ready0.allowed_formats = 1 << int(trt_memory_format)
out0.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if (trt_dtype == trt.int8):
in0.dynamic_range = (-128.0, 127.0)
in_state0.dynamic_range = (-128.0, 127.0)
out0.dynamic_range = (-128.0, 127.0)
start0.dynamic_range = (-128.0, 127.0)
ready0.dynamic_range = (-128.0, 127.0)
flags = 1 << int(trt.BuilderFlag.STRICT_TYPES)
if (trt_dtype == trt.int8):
flags |= 1 << int(trt.BuilderFlag.INT8)
elif (trt_dtype == trt.float16):
flags |= 1 << int(trt.BuilderFlag.FP16)
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in shape:
if i == -1:
min_shape = min_shape + [1]
opt_shape = opt_shape + [8]
max_shape = max_shape + [32]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
profile.set_shape("INPUT", min_shape, opt_shape, max_shape)
profile.set_shape("INPUT_STATE", min_shape, opt_shape, max_shape)
if max_batch != 0:
profile.set_shape("START", [1] + unit_shape, [max_batch] + unit_shape,
[max_batch] + unit_shape)
profile.set_shape("READY", [1] + unit_shape, [max_batch] + unit_shape,
[max_batch] + unit_shape)
else:
profile.set_shape("START", unit_shape, unit_shape, unit_shape)
profile.set_shape("READY", unit_shape, unit_shape, unit_shape)
config = builder.create_builder_config()
config.flags = flags
config.add_optimization_profile(profile)
config.max_workspace_size = 1 << 20
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_sequence_model_name(
"plan_nobatch" if max_batch == 0 else "plan", dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_modelfile(models_dir, model_version, max_batch, dtype, shape):
if not tu.validate_for_trt_model(dtype, dtype, dtype, shape, shape, shape):
return
if dtype != np.float32:
if (not tu.shape_is_fixed(shape)):
create_plan_dynamic_rf_modelfile(models_dir, model_version,
max_batch, dtype, shape)
else:
create_plan_fixed_rf_modelfile(models_dir, model_version, max_batch,
dtype, shape)
else:
if (not tu.shape_is_fixed(shape)):
create_plan_dynamic_modelfile(models_dir, model_version, max_batch,
dtype, shape)
else:
create_plan_fixed_modelfile(models_dir, model_version, max_batch,
dtype, shape)
def create_plan_modelconfig(models_dir, model_version, max_batch, dtype, shape):
if not tu.validate_for_trt_model(dtype, dtype, dtype, shape, shape, shape):
return
model_name = tu.get_sequence_model_name(
"plan_nobatch" if max_batch == 0 else "plan", dtype)
config_dir = models_dir + "/" + model_name
config = '''
name: "{}"
platform: "tensorrt_plan"
max_batch_size: {}
sequence_batching {{
max_sequence_idle_microseconds: 5000000
control_input [
{{
name: "START"
control [
{{
kind: CONTROL_SEQUENCE_START
{}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY"
control [
{{
kind: CONTROL_SEQUENCE_READY
{}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE"
output_name: "OUTPUT_STATE"
data_type: {dtype}
dims: {shape}
}}
]
}}
input [
{{
name: "INPUT"
data_type: {dtype}
dims: [ {shape} ]
}}
]
output [
{{
name: "OUTPUT"
data_type: {dtype}
dims: [ {shape} ]
}}
]
instance_group [
{{
kind: KIND_GPU
}}
]
'''.format(model_name,
max_batch,
"int32" if dtype == np.int32 else "fp32",
"int32" if dtype == np.int32 else "fp32",
dtype=np_to_model_dtype(dtype),
shape=tu.shape_to_dims_str(shape))
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)