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functional.py
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functional.py
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# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from collections import OrderedDict
from enum import IntEnum
from functools import partial
from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import tensorrt as trt
import torch
from . import graph_rewriting as gw
from ._common import default_net, default_trtnet, precision
from ._utils import (dim_resolve_negative, dim_to_trt_axes, fp16_array,
fp32_array, int32_array, np_dtype_to_trt, str_dtype_to_np,
str_dtype_to_trt, torch_to_numpy, trt_dtype_to_torch)
from .plugin import TRT_LLM_PLUGIN_NAMESPACE
from .quantization import QuantMode
class DimRange(object):
'''
One DimRange object stores the ranges of all the dimensions of one tensor in one optimization profile.
For example, tensor has 2 dimensions. Then the data members are:
self.min = [dim 0 min, dim 1 min]
self.opt = [dim 0 opt, dim 1 opt]
self.max = [dim 0 max, dim 1 max]
For static dimension, it has min==opt==max, thus the \p shape param in the ctor can be an integer
'''
def __init__(self, shape: List[Union[int, List[int], Tuple[int, int, int]]],
names: List[str]):
'''
Parameters:
shape: a list with length N, each element is an integer or a 3-elements tuple/list of int,
where N is the number of dimensions for a tensor.
When one element is an integer, it means that dimension is static.
Otherwise, when one element is a tuple/list, it means the dimension is dynamic.
The 3 elements in one tuple/list is ordered by (min, opt, max), and this function asserts
0 <= min <= opt <= max.
Example, for a 3 rank tensor, with 1st dimension being static and has value 16, and second dimension being dynamic with
min/opt/max values being 1/8/32, and 3rd dimension being static and has value 8.
The shape parameter could be:
[16, (1, 8, 32), 8]
It has same semantics of
[(16, 16, 16), (1, 8, 32), (8, 8, 8)]
'''
self.min = []
self.opt = []
self.max = []
self.dimension_names = names
assert len(names) == len(
shape
), "Expecting shape list and name list must have same length, got {shape=}, {name=}"
for dim in shape:
if isinstance(dim, (list, tuple)):
assert len(dim) == 3 and 0 <= dim[0] <= dim[1] <= dim[2], \
"Each dimension must specify a 3-elements tuple or list in the oder of (min,opt,max), got {dim=}"
self.min.append(dim[0])
self.opt.append(dim[1])
self.max.append(dim[2])
elif isinstance(dim, int):
self.min.append(dim)
self.opt.append(dim)
self.max.append(dim)
else:
raise AttributeError(
f'Dimension should be [min, opt, max] (dynamic shape) or int (specific value). Got {type(dim)}'
)
def __eq__(self, __value: object) -> bool:
return isinstance(__value, DimRange) and \
self.dimension_names == __value.dimension_names and \
self.min == __value.min and self.opt == __value.opt and self.max == __value.max
def __repr__(self) -> str:
return str(self)
def __str__(self) -> str:
return f"{self.dimension_names=} {self.min=}, {self.opt=}, {self.max=})"
def __hash__(self) -> int:
return hash(str(self))
class Tensor(object):
'''
The class to represent dense tensors.
A dense tensor is named, has a shape and contains typed elements. Each
dimension of a tensor can either be static or dynamic. Static dimensions
are known at engine compilation by TensorRT. Dynamic dimensions can take
values determined at runtime. The tensor can be located on the host (CPU)
or the device (GPU).
'''
def __init__(self,
name=None,
dtype=None,
shape=None,
dim_range=None,
is_network_input=True,
location=trt.TensorLocation.DEVICE,
network=None,
trt_tensor=None):
'''
Parameters:
name : str
The name of the tensor.
dtype : tensorrt.DataType
The type of the elements of the tensor. See the TensorRT
documentation for list of supported data types.
shape : tensorrt.Dims
The dimensions of the tensor. In TensorRT-LLM, tensors can have
static or dynamic dimensions (it is possible to mix static and
dynamic dimensions). A static dimension is known when the
TensorRT engine is built. A dynamic dimension can be set when
the engine is executed. Use -1 for dynamic dimensions.
dim_range : OrderedDict
An ordered dictionary (the positions of the elements matter)
that associates a name and a range of values to the dimensions.
For a static dimension, the range must be limited to a single
value. For a dynamic dimension, the range is defined by three
values [min, opt, max] where min and max are, respectively, the
smallest and largest possible values of that dimension. The
opt value is used by TensorRT to optimize the engine for the
most common case.
Assume there is N optimization profiles, each item dim_range dict is ordered by:
(dynamic dimension name : [profile 0 (min, opt, max), profile 1 (min, opt, max), ... profile N(min, opt, max)])
or it's following when the dimension is static (can think as min==opt==max):
(static dimension name : [profile 0 value, profile 1 value, ... profile N value])
For static dimension the profile 0-N value must be same, (TODO: can it be simplified to be only 1 value?)
And number of keys is equal to number of optimization profiles.
is_network_input : bool
A boolean indicating if that tensor is an input of the network.
Inputs must be provided by the user to run the engine.
location : tensorrt.TensorLocation
A flag to indicate where the tensor will be located. It can be
on the host (CPU) or the device (GPU).
network: Network
A parent Network instance, that helps to fine the users of this tensor.
trt_tensor: trt.ITensor
Construct with the ITensor instance directly, and no shape profiles are required.
'''
# Layout of self.profiles
# Opt profile 0: dim 0 (min, opt, max), dim 1 (min, opt, max) ... dim M
# Opt profile 1: dim 0 (min, opt, max), dim 1 (min, opt, max) ... dim M
# ...
# Opt profile N: dim 0 ... dim M
# So from the dim_range arg to self.profiles conversion, there is a layout transpose
# dim_range arg is: {M dimension x N profiles}, while self.profiles layout is {N profiles x M dimensions}
self.profiles = []
self.is_tensor_wrapper = False # specially for the graph rewriter
# work as a wrapper for a trt.ITensor, this is used specially in the graph rewriter
if trt_tensor is not None:
self.is_tensor_wrapper = True
assert network is not None
self.trt_tensor = trt_tensor
self.network = network
assert not is_network_input, "is_network_input should be False when trt_tensor is not None"
return
# defining an input placeholder for the network
self.network = default_net()
if is_network_input:
if dim_range is not None:
assert isinstance(dim_range, OrderedDict)
assert len(
dim_range
) >= 1, f"Each input tensor shall have at least one dimension, tensor '{name}' found {dim_range=}"
found_profiles = [
len(ranges) for _, ranges in dim_range.items()
]
assert all(
[x == found_profiles[0] for x in found_profiles]
), f"Expecting all the dimensions in the dim_range has same number of profiles, tensor '{name}' got {dim_range=}"
num_opt_profile = len(list(dim_range.items())[0][1])
assert num_opt_profile >= 1
for i in range(num_opt_profile):
range_shape = []
dimension_names = []
for dim, ranges in dim_range.items():
assert isinstance(ranges, (list, tuple))
range_shape.append(ranges[i])
dimension_names.append(dim)
self.profiles.append(DimRange(range_shape, dimension_names))
default_net()._add_input(self, name, dtype, shape, dim_range)
self.name = name
self.dtype = dtype
self.shape = shape
self.location = location
@property
def name(self):
'''
The name of the tensor.
'''
return self.trt_tensor.name
@name.setter
def name(self, name):
'''
Set the name of the tensor.
'''
if name is not None:
self.trt_tensor.name = name
@property
def dtype(self):
'''
The type of the elements in the tensor.
'''
return self.trt_tensor.dtype
@dtype.setter
def dtype(self, dtype):
'''
Set the type of the elements in the tensor.
'''
if dtype is not None:
self.trt_tensor.dtype = dtype
@property
def shape(self):
'''
The shape of the tensor.
'''
return self.size()
@shape.setter
def shape(self, shape):
'''
Set the shape of the tensor. See __init__.
'''
if shape is not None:
self.trt_tensor.shape = shape
@property
def location(self):
'''
The physical location of the tensor (on the host or the device).
'''
return self.trt_tensor.location
@location.setter
def location(self, location):
'''
Set the physical location of the tensor (on the host or the device). See __init__.
'''
if location is not None:
self.trt_tensor.location = location
def mark_output(self, name, dtype):
'''
Mark a tensor as a network output.
When a tensor is marked as an output, its content can be obtained after
the execution of the TensorRT engine. The user is responsible for
allocating buffers to store the output tensors when preparing the
execution of the TensorRT engine.
'''
default_net()._mark_output(self, name, dtype)
def __add__(self, b):
'''
See functional.add.
'''
return add(self, b)
def __radd__(self, b):
'''
See functional.add.
'''
return add(b, self)
def __sub__(self, b):
'''
See functional.sub.
'''
return sub(self, b)
def __rsub__(self, b):
'''
See functional.sub.
'''
return sub(b, self)
def __mul__(self, b):
'''
See functional.mul.
'''
return mul(self, b)
def __rmul__(self, b):
'''
See functional.mul.
'''
return mul(b, self)
def __truediv__(self, b):
'''
See functional.div.
'''
return div(self, b)
def __lt__(self, b):
'''
See functional.lt.
'''
return lt(self, b)
def __gt__(self, b):
'''
See functional.gt.
'''
return gt(self, b)
def __eq__(self, b):
'''
See functional.eq.
'''
if self.is_tensor_wrapper:
# for graph rewriter
return hash(self) == hash(b)
else:
# for creating the network
return eq(self, b)
def __ge__(self, b):
'''
Maps to functional.gt or functional.eq.
'''
return op_or(self.__gt__(b), self.__eq__(b))
def __le__(self, b):
'''
Maps to functional.lt or functional.eq.
'''
return op_or(self.__lt__(b), self.__eq__(b))
def view(self, shape, zero_is_placeholder=True):
'''
See functional.view.
'''
return view(self, shape, zero_is_placeholder)
def permute(self, dims):
'''
See functional.permute.
'''
return permute(self, dims)
def transpose(self, dim0, dim1):
'''
See functional.transpose.
'''
return transpose(self, dim0, dim1)
def mean(self, dim, keepdim=False):
'''
See functional.mean.
'''
return mean(self, dim, keepdim)
def max(self, dim, keepdim=False):
'''
See functional.max.
'''
return max(self, dim, keepdim)
def abs(self):
'''
See functional.abs.
'''
return abs(self)
def sqrt(self):
'''
See functional.sqrt.
'''
return sqrt(self)
def cast(self, dtype):
'''
See functional.cast.
'''
return cast(self, dtype)
def size(self, dim=None):
'''
Returns the shape of the tensor if the dim parameter is None.
Otherwise, returns a size of the dimension indicated by dim. The
behavior is undefined if dim is negative or exceeds the rank of the
tensor.
'''
if dim is None:
return self.trt_tensor.shape
return self.trt_tensor.shape[dim]
def rank(self):
'''
Returns the rank (i.e. the number of dimensions) of the tensor.
'''
return len(self.trt_tensor.shape)
def ndim(self):
'''
Returns the rank (i.e. the number of dimensions) of the tensor.
'''
return self.rank()
def split(self, split_size_or_sections, dim=0):
'''
See functional.split.
'''
return split(self, split_size_or_sections, dim)
def is_dynamic(self, dim=None):
'''
If the argument 'dim' is None, that function returns a boolean that
indicates if the tensor contains a dynamic dimension (True) or not
(False). In that case, the first dimension is excluded (as it usually
corresponds to the batch size). If the argument is an integer, that
functions returns a boolean that indicates if the dimension 'dim' is
dynamic (True) or not (False).
'''
if dim is not None:
return self.trt_tensor.shape[dim] == -1
for i, s in enumerate(self.trt_tensor.shape):
if i != 0 and s == -1:
return True
return False
# graph writer related functions
def get_parent(self):
''' Get the layer that produces this tensor. '''
return self.network.get_tensor_parent(self)
def get_users(self):
''' Get the layers that use this tensor as an input. '''
return self.network.get_tensor_users(self)
def replace_all_uses_with(self, new_tensor):
'''
Replace all uses of this tensor as an input to consumer layers
'''
self.network.is_graph_altered = True
users = self.get_users()
for user in users:
inputs_changed = 0
for i in range(user.num_inputs):
if user.get_inputs(i)[0].trt_tensor is self.trt_tensor:
inputs_changed += 1
user.set_input(i, new_tensor.trt_tensor)
assert inputs_changed >= 1, "Tensor not found in layer inputs"
# update the FLayerMetadata as well
flayer = gw.FLayerInfoMemo.instance().get(user.name)
flayer and flayer.replace_input_with(self, new_tensor)
def is_trt_wrapper(self):
'''
Check if there is a trt.ITensor member inside, which is required for
graph rewriter. In order to differentiate usages, it may be necessary
to have an inheritance hierarachy.
'''
if hasattr(self, 'trt_tensor'):
return True
else:
return False
def __hash__(self):
if self.is_trt_wrapper():
return id(self.trt_tensor)
else:
return id(None)
def _create_tensor(trt_tensor: trt.ITensor,
producer: trt.ILayer = None) -> Tensor:
'''
A helper function to create a TensorRT-LLM Tensor object that encapsulates
the connection between the TensorRT tensor (trt.ITensor) and the layer
(trt.ILayer) that produces it.
That function is expected to be used as:
# Insert a new layer in the network using the TensorRT API:
layer = default_trtnet().add_<some_layer>(...)
# Extract the first output of that layer and connect it to the layer.
return _create_tensor(layer.get_output(0), layer)
That function also sets the precision of the layer/producer to the default
precision of the network.
Parameters:
trt_tensor : trt.ITensor
The TensorRT tensor to connect to its producer (the layer).
producer : trt.ILayer = None
The producer.
Returns:
The TensorRT-LLM tensor (functional.Tensor) that encapsulates the
TensorRT tensor and the layer that produces it. The former is
accessible through the attribute 'trt_tensor' and the latter using the
attribute 'producer'.
'''
assert trt_tensor is not None
tensor = Tensor(name=trt_tensor.name,
dtype=trt_tensor.dtype,
shape=trt_tensor.shape,
is_network_input=False)
tensor.trt_tensor = trt_tensor
tensor.producer = producer
# Set the layer name since this is the only
# centralized location to pass the name from
# module space to the TRT IR
default_net()._set_layer_name(producer)
if default_net().dtype is not None and not default_net().strongly_typed:
if producer.type not in [
trt.LayerType.CONSTANT, trt.LayerType.GATHER,
trt.LayerType.CONCATENATION
]:
producer.precision = default_net().dtype
assert tensor is not None
if gw.FLayerInfoMemo.instance().cur_flayer is not None:
gw.FLayerInfoMemo.instance().cur_flayer.layer_name = producer.name
return tensor
class RotaryScalingType(IntEnum):
none = 0
linear = 1
dynamic = 2
class PositionEmbeddingType(IntEnum):
learned_absolute = 0
rope_gptj = 1
rope_gpt_neox = 2
alibi = 3
alibi_with_scale = 4
relative = 5
def is_rope(self) -> bool:
return self in [self.rope_gptj, self.rope_gpt_neox]
def is_alibi(self) -> bool:
return self in [self.alibi, self.alibi_with_scale]
@staticmethod
def choices() -> List[str]:
return [embedding.name for embedding in PositionEmbeddingType]
class AttentionMaskType(IntEnum):
padding = 0
causal = 1
bidirectional = 2
class LayerNormType(IntEnum):
LayerNorm = 0
RmsNorm = 1
GroupNorm = 2
class LayerNormPositionType(IntEnum):
pre_layernorm = 0
post_layernorm = 1
def activation(input: Tensor, act_type: trt.ActivationType) -> Tensor:
'''
Add an activation function.
Parameters:
input : Tensor
The input tensor on which the activation function is applied.
act_type : trt.ActivationType
The type of the activation (RELU, TANH, SIGMOID, ...).
The following closures are defined in functional.*:
relu for op=trt.ActivationType.RELU
tanh for op=trt.ActivationType.TANH
sigmoid for op=trt.ActivationType.SIGMOID
Returns:
The tensor produced by the activation layer.
'''
layer = default_trtnet().add_activation(input.trt_tensor, act_type)
return _create_tensor(layer.get_output(0), layer)
def clip(input: Tensor, alpha: float, beta: float) -> Tensor:
'''
Add a CLIP operation that sets the range to [alpha, beta].
Parameters:
input : Tensor
The input tensor on which the activation function is applied.
alpha : float
The lower bound of the CLIP function.
beta : float
The upper bound of the CLIP function.
Returns:
The tensor produced by the activation layer.
'''
layer = default_trtnet().add_activation(input.trt_tensor,
trt.ActivationType.CLIP)
layer.alpha = alpha
layer.beta = beta
return _create_tensor(layer.get_output(0), layer)
relu = partial(activation, act_type=trt.ActivationType.RELU)
tanh = partial(activation, act_type=trt.ActivationType.TANH)
sigmoid = partial(activation, act_type=trt.ActivationType.SIGMOID)
def silu(input: Tensor) -> Tensor:
'''
Add a SiLU (`x * sigmoid(x)`) operation.
Parameters:
input : Tensor
The input tensor on which the activation function is applied.
Returns:
The tensor produced by the activation layer.
'''
return input * sigmoid(input)
def swiglu(input: Tensor) -> Tensor:
'''
Add a SwiGLU (`x * SiLU(gate)`) operation.
That function takes a tensor, splits it into two halves along the last
dimension, applies SiLU to the second half and multiply the results. The
behaviour is undefined if the last dimension is not even.
Parameters:
input : Tensor
The input tensor on which the activation function is applied.
Returns:
The tensor produced by the activation layer.
'''
x, gate = chunk(input, 2, dim=-1)
return silu(gate) * x
def squared_relu(x: Tensor) -> Tensor:
'''
Add a Squared ReLU operation.
This function applies ReLU and squares the output.
Parameters:
input : Tensor
The input tensor on which the activation function is applied.
Returns:
The tensor produced by the activation layer.
'''
return pow(relu(x), 2.0)
def cast(input: Tensor, dtype: Union[str, trt.DataType]):
'''
Add a cast operation.
For an input tensor of type INT8, this function sets the dynamic range of
the input to [-127, 127] for automatic dequantization. For a cast into
INT8, that function sets the dynamic range of the output to [-127, 127] for
automatic quantization.
Parameters:
input : Tensor
The input tensor on which the cast is applied.
dtype : str or trt.DataType
The data type of the output tensor after the cast. When 'dtype' is
provided as a string, it must be a name amongst the valid names.
See _str_to_trt_dtype_dict in _utils.py for a list of supported
types and type names.
Returns:
The tensor produced by the inserted layer.
'''
if isinstance(dtype, str):
cvt_dtype = str_dtype_to_trt(dtype)
elif isinstance(dtype, trt.DataType):
cvt_dtype = dtype
else:
raise TypeError("%s is not supported" % type(dtype))
layer = default_trtnet().add_cast(input.trt_tensor, cvt_dtype)
if not default_net().strongly_typed:
layer.set_output_type(0, cvt_dtype)
output = _create_tensor(layer.get_output(0), layer)
if input.dtype == str_dtype_to_trt('int8'):
layer.get_input(0).set_dynamic_range(-127, 127)
if cvt_dtype == str_dtype_to_trt('int8'):
layer.get_output(0).set_dynamic_range(-127, 127)
return output
def flip(input: Tensor, dims: Sequence[int]) -> Tensor:
'''
Reverses the order of an n-D tensor along given axis in dims.
That flip operation maps to a TensorRT ISliceLayer. For the dimensions
listed in dims it copies the elements from the last one to the first one
(from (N-1) down to 0 with a step of -1). For the dimensions not in 'dims',
it copies the elements from the first one to the last one (from 0 to N-1
with a step of 1).
Parameters:
input : Tensor
The input tensor on which the cast is applied.
dims : list or tuple
The axes to flip. Negative indices are supported.
Returns:
The tensor produced by the inserted layer.
'''
assert not input.is_dynamic()
ndim = input.ndim()
for index, value in enumerate(dims):
assert -ndim <= value < ndim
if -ndim <= value < 0:
dims[index] += ndim
assert len(dims) == len(set(dims))
start_values = [
input.size()[i] - 1 if i in dims else 0 for i in range(ndim)
]
stride_values = [-1 if i in dims else 1 for i in range(ndim)]
layer = default_trtnet().add_slice(input.trt_tensor,
start=start_values,
shape=input.size(),
stride=stride_values)
return _create_tensor(layer.get_output(0), layer)
def interpolate(input: Tensor,
size: Union[int, List[int]] = None,
scale_factor: Union[float, List[float]] = None,
mode: str = 'nearest',
align_corners: bool = False,
recompute_scale_factor: bool = False,
antialias: bool = False) -> Tensor:
##
## TODO: Document that function!
##
assert not input.is_dynamic()
input_ndim = input.ndim()
assert 2 < input_ndim < 6, "Only 3D, 4D and 5D input Tensors supported"
assert (size is not None) ^ (
scale_factor
is not None), "Only one of out_shape or scales should be defined"
assert mode in ('nearest', 'linear', 'bilinear', 'bicubic', 'trilinear',
'nearest-exact')
if mode == 'trilinear' and input_ndim != 5:
raise ValueError("trilinear only supports 5D tensor")
if mode == "bilinear" and input_ndim != 4:
raise ValueError("bilinear only supports 4D tensor")
if mode == "linear" and input_ndim != 3:
raise ValueError("linear only supports 3D tensor")
layer = default_trtnet().add_resize(input.trt_tensor)
input_shape = input.size()
updated_shape = []
if scale_factor:
scale_len = 1 if isinstance(scale_factor,
(float, int)) else len(scale_factor)
if scale_len == 1 and isinstance(scale_factor, (float, int)):
updated_scale = [scale_factor for _ in range(input_ndim - 2)]
else:
updated_scale = scale_factor
updated_shape = [
int(math.floor(updated_scale[i - 2] *
input_shape[i])) if i > 1 else input_shape[i]
for i in range(input_ndim)
]
else:
size_len = 1 if isinstance(size, int) else len(size)
assert size_len == input_ndim - 2
if size_len == 1 and isinstance(size, int):
updated_size = [size for _ in range(input_ndim - 2)]
else:
updated_size = size
updated_shape = [
input_shape[i] if i < 2 else updated_size[i - 2]
for i in range(input_ndim)
]
layer.shape = updated_shape
if mode in ['nearest', 'nearest-exact'] or mode is None:
layer.resize_mode = trt.ResizeMode.NEAREST
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.ASYMMETRIC
elif mode in ['linear', 'bilinear', 'trilinear']:
layer.resize_mode = trt.ResizeMode.LINEAR
if align_corners:
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.ALIGN_CORNERS
else:
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.HALF_PIXEL
# TODO, need to confirm the align_corners effect on bilinear mode.
if mode == 'bilinear':
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.HALF_PIXEL
elif mode in ['bicubic']:
layer.resize_mode = trt.ResizeMode.CUBIC
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.HALF_PIXEL
else:
layer.resize_mode = trt.ResizeMode.NEAREST
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.ASYMMETRIC
return _create_tensor(layer.get_output(0), layer)
def matmul(input: Tensor,
mat2: Tensor,
transa: bool = False,
transb: bool = False) -> Tensor:
'''
Add a matrix multiplication.
That operation maps to a tensorrt.IMatrixMultiplyLayer layer. As explained
in the TensorRT documentation, it computes the inner product between the
two inputs after applying an optional transposition on the inputs.
Parameters:
input : Tensor
The first tensor (often called A).
mat2 : Tensor
The second tensor (often called B).
transa : bool
Is the first input transposed? Set to 'True' if you want the first
input to be transposed, 'False' otherwise.
transb : bool
Is the second input transposed? Set to 'True' if you want the
second input to be transposed, 'False' otherwise.
Returns:
The tensor produced by the inserted layer.
'''
input, mat2 = broadcast_helper(input, mat2)
op0 = trt.MatrixOperation.TRANSPOSE if transa \
else trt.MatrixOperation.NONE
op1 = trt.MatrixOperation.TRANSPOSE if transb \
else trt.MatrixOperation.NONE
layer = default_trtnet().add_matrix_multiply(input.trt_tensor, op0,
mat2.trt_tensor, op1)
return _create_tensor(layer.get_output(0), layer)
def constant(ndarray: np.ndarray) -> Tensor:
'''
Add a constant layer.
TensorRT graphs encapsulate constant values in the form of constant layers
(tensorrt.IConstantLayer). That function creates such a layer from a Numpy
array of values. After compilation of the network by TensorRT, those
weights are stored in the serialized TensorRT engine.
Parameters:
ndarray : numpy.ndarray
The array of values (weights) encapsulated by this constant layer.
Returns:
The tensor produced by the inserted layer.
'''
weights = trt.Weights(np_dtype_to_trt(ndarray.dtype), ndarray.ctypes.data,
ndarray.size)
# Prevent underlying numpy array from going out of scope
default_net().register_ndarray(ndarray)
layer = default_trtnet().add_constant(trt.Dims(ndarray.shape), weights)
if not default_net()._strongly_typed:
layer.set_output_type(0, np_dtype_to_trt(ndarray.dtype))
return _create_tensor(layer.get_output(0), layer)
# TODO: TensorRT uses sizes of the output dimensions.
# DL framework uses ends usually. Will change it to ends.
def slice(input: Tensor, starts: Union[Tensor, Sequence[int]],
sizes: Union[Tensor, Sequence[int]]) -> Tensor:
'''
Add an operation to extract a slice from a tensor.
As described in the TensorRT documentation of the ISliceLayer, the slice
layer has two variants: Static and dynamic.
For static slicing, this function takes the starts and sizes values in the
different dimensions to slice at layer creation time via a sequence of
integers. For dynamic slicing, it accepts starts and sizes as
tensorrt.ITensor`s.
The slice layer selects for each dimension a start location from within the
input tensor, and copies elements to the output tensor using a stride of 1
across the input tensor. Start and size tensors must be 1-D int32 shape
tensors if not specified as a sequence of integers.
As an example, on input = [[0, 2, 4], [1, 3, 5]], the call to
slice(input, start=[1, 0], size=[1, 2])
will produce the tensor [[1, 3]] as output. The slice operator when
executed by TensorRT will copy one row (because size[0] == 1) starting from
the 2nd row (because start[0] == 1) and two columns (size[1] == 2) starting
from the 1st column (because start[1] == 0).
In pseudo-code the behaviour of that operation can be described as follows
for a 2D tensor (and easily be extended to more dimensions):
output = Tensor(shape=sizes)
for ii in range(sizes[0]):
for jj in range(sizes[1]):
output[ii][jj] = input[starts[0]+ii][starts[1]+jj]
Note that it is common in deep-learning frameworks to use ranges
[start:end] for similar operations. It can be emulated by setting the sizes
argument such that in each dimension [start:start+size] == [start:end] i.e.
size = end-start.
TensorRT supports different slice modes but that function restricts that
choice to `mode == tensorrt.SliceMode.STRICT_BOUNDS`.
Parameters: