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gen_qa_implicit_models.py
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gen_qa_implicit_models.py
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#!/usr/bin/env python3
# Copyright 2021-2024, 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
from typing import List, Tuple
import gen_ensemble_model_utils as emu
import numpy as np
from gen_common import (
np_to_model_dtype,
np_to_onnx_dtype,
np_to_torch_dtype,
np_to_trt_dtype,
)
FLAGS = None
np_dtype_string = np.dtype(object)
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 higher 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 higher 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"]
)
onnx_nodes = [
internal_input,
internal_input_state,
identity,
identity_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"]
)
onnx_nodes = [
internal_input,
internal_input_state,
add,
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(
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_libtorch_modelfile_wo_initial_state(
models_dir, model_version, max_batch, dtype, shape
):
if not tu.validate_for_libtorch_model(dtype, dtype, dtype, shape, shape, shape):
return
torch_dtype = np_to_torch_dtype(dtype)
# If input dtype is bool, then use bool type for control and
# int32 type for input/output
if torch_dtype == torch.bool:
torch_dtype = torch.int32
model_name = tu.get_sequence_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch", dtype
)
if torch_dtype == List[str]:
class SequenceNet(nn.Module):
def __init__(self):
super(SequenceNet, self).__init__()
def forward(
self, input0: List[str], input0_state: List[str], start0, ready0
) -> Tuple[List[str], List[str]]:
use_state = torch.logical_and(ready0, torch.logical_not(start0))
input0_state_int = torch.tensor(
[int("0" + i) for i in input0_state], device=use_state.device
)
input0_int = torch.tensor(
[int("0" + i) for i in input0], device=use_state.device
)
result_int = torch.mul(use_state, input0_state_int)
result_int += input0_int
result = [str(i.item()) for i in result_int.cpu()]
return result, result
else:
class SequenceNet(nn.Module):
def __init__(self):
super(SequenceNet, self).__init__()
def forward(self, input0, input0_state, start0, ready0):
use_state = torch.logical_and(ready0, torch.logical_not(start0))
result = torch.mul(use_state, input0_state)
result += input0
return result, result
traced = torch.jit.script(SequenceNet())
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
traced.save(model_version_dir + "/model.pt")
def create_libtorch_modelfile_with_initial_state(
models_dir, model_version, max_batch, dtype, shape
):
if not tu.validate_for_libtorch_model(dtype, dtype, dtype, shape, shape, shape):
return
torch_dtype = np_to_torch_dtype(dtype)
# If input dtype is bool, then use bool type for control and
# int32 type for input/output
if torch_dtype == torch.bool:
torch_dtype = torch.int32
model_name = tu.get_sequence_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch", dtype
)
# handle for -1 (when variable) since can't create tensor with shape of [-1]
if torch_dtype == List[str]:
class SequenceNet(nn.Module):
def __init__(self):
super(SequenceNet, self).__init__()
def forward(
self, input0: List[str], input0_state: List[str], start0, ready0
) -> Tuple[List[str], List[str]]:
input0_state_int = torch.tensor(
[int("0" + i) for i in input0_state], device=start0.device
)
input0_int = torch.tensor(
[int("0" + i) for i in input0], device=start0.device
)
result_int = (input0_state_int + input0_int).cpu()
result = [str(i.item()) for i in result_int]
return result, result
else:
class SequenceNet(nn.Module):
def __init__(self):
super(SequenceNet, self).__init__()
def forward(self, input0, input0_state, start0, ready0):
result = input0_state + input0
return result, result
sequenceModel = SequenceNet()
traced = torch.jit.script(sequenceModel)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
traced.save(model_version_dir + "/model.pt")
def create_libtorch_modelfile(
models_dir, model_version, max_batch, dtype, shape, initial_state
):
if initial_state is None:
create_libtorch_modelfile_wo_initial_state(
models_dir, model_version, max_batch, dtype, shape
)
else:
# This model assumes that the initial state contains correct data
create_libtorch_modelfile_with_initial_state(
models_dir, model_version, max_batch, dtype, shape
)
def create_libtorch_modelconfig(
models_dir, model_version, max_batch, dtype, shape, initial_state
):
if not tu.validate_for_libtorch_model(dtype, dtype, dtype, shape, shape, shape):
return
model_name = tu.get_sequence_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch", 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
}
]
"""
config = f"""
name: "{model_name}"
platform: "pytorch_libtorch"
max_batch_size: {max_batch}
input [
{{
name: "INPUT__0"
data_type: {emu.dtype_str(dtype)}
dims: [ {tu.shape_to_dims_str(shape)} ]
}}
]
output [
{{
name: "OUTPUT__0"
data_type: {emu.dtype_str(dtype)}
dims: [ {tu.shape_to_dims_str(shape)} ]
}}
]
"""
config += 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__2"
control [
{{
kind: CONTROL_SEQUENCE_START
{type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY__3"
control [
{{
kind: CONTROL_SEQUENCE_READY
{type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE__1"
output_name: "OUTPUT_STATE__1"
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__2"
control [
{{
kind: CONTROL_SEQUENCE_START
{control_type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY__3"
control [
{{
kind: CONTROL_SEQUENCE_READY
{control_type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE__1"
output_name: "OUTPUT_STATE__1"
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__2"
control [
{{
kind: CONTROL_SEQUENCE_START
{type}_false_true: [ 0, 1 ]
}}
]
}},
{{
name: "READY__3"
control [
{{
kind: CONTROL_SEQUENCE_READY
{type}_false_true: [ 0, 1 ]
}}
]
}}
]
state [
{{
input_name: "INPUT_STATE_1"
output_name: "OUTPUT_STATE_1"
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_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_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()
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)
# 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)
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)
constant_1_data = trt.Weights(np.ones(unit_shape, dtype=dtype))
constant_1 = network.add_constant(unit_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))
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.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 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: