Developer Guide

Abstract

This cuDNN 8.9.1 Developer Guide explains how to use the NVIDIA cuDNN library. While the NVIDIA cuDNN API Reference provides per-function API documentation, the Developer Guide gives a more informal end-to-end story about cuDNN’s key capabilities and how to use them.

NVIDIA^® CUDA^® Deep Neural Network LIbrary (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. It provides highly tuned implementations of operations arising frequently in DNN applications:

• Convolution forward and backward, including cross-correlation
• Matrix multiplication
• Pooling forward and backward
• Softmax forward and backward
• Neuron activations forward and backward: relu, tanh, sigmoid, elu, gelu, softplus, swish
• Arithmetic, mathematical, relational, and logical pointwise operations (including various flavors of forward and backward neuron activations)
• Tensor transformation functions
• LRN, LCN, batch normalization, instance normalization, and layer normalization forward and backward

Beyond just providing performant implementations of individual operations, the library also supports a flexible set of multi-operation fusion patterns for further optimization. The goal is to achieve the best available performance on NVIDIA GPUs for important deep learning use cases.

In cuDNN version 7 and older, the API was designed to support a fixed set of operations and fusion patterns. We informally call this the “legacy API”. Starting in cuDNN version 8, to address the quickly expanding set of popular fusion patterns, we added a graph API, which allows the user to express a computation by defining an operation graph, rather than by selecting from a fixed set of API calls. This offers better flexibility versus the legacy API, and for most use cases, is the recommended way to use cuDNN.

Note that while the cuDNN library exposes a C API, we also provide an open source C++ layer which wraps the C API and is considered more convenient for most users. It is, however, limited to just the graph API, and does not support the legacy API.

Before we discuss the details of the graph and legacy APIs, this section introduces the key concepts that are common to both.

2.1. cuDNN Handle

The cuDNN library exposes a host API but assumes that for operations using the GPU, the necessary data is directly accessible from the device.

An application using cuDNN must initialize a handle to the library context by calling cudnnCreate(). This handle is explicitly passed to every subsequent library function that operates on GPU data. Once the application finishes using cuDNN, it can release the resources associated with the library handle using cudnnDestroy(). This approach allows the user to explicitly control the library's functioning when using multiple host threads, GPUs, and CUDA streams.

For example, an application can use cudaSetDevice (prior to creating a cuDNN handle) to associate different devices with different host threads, and in each of those host threads, create a unique cuDNN handle that directs the subsequent library calls to the device associated with it. In this case, the cuDNN library calls made with different handles would automatically run on different devices.

The device associated with a particular cuDNN context is assumed to remain unchanged between the corresponding cudnnCreate() and cudnnDestroy() calls. In order for the cuDNN library to use a different device within the same host thread, the application must set the new device to be used by calling cudaSetDevice() and then create another cuDNN context, which will be associated with the new device, by calling cudnnCreate().

2.2. Tensors and Layouts

Whether using the graph API or the legacy API, cuDNN operations take tensors as input and produce tensors as output.

2.2.1. Tensor Descriptor

The cuDNN library describes data with a generic n-D tensor descriptor defined with the following parameters:

• a number of dimensions from 3 to 8
• a data type (32-bit floating-point, 64 bit-floating point, 16-bit floating-point...)
• an integer array defining the size of each dimension
• an integer array defining the stride of each dimension (for example, the number of elements to add to reach the next element from the same dimension)

This tensor definition allows, for example, to have some dimensions overlapping each other within the same tensor by having the stride of one dimension smaller than the product of the dimension and the stride of the next dimension. In cuDNN, unless specified otherwise, all routines will support tensors with overlapping dimensions for forward-pass input tensors, however, dimensions of the output tensors cannot overlap. Even though this tensor format supports negative strides (which can be useful for data mirroring), cuDNN routines do not support tensors with negative strides unless specified otherwise.

2.2.1.1. WXYZ Tensor Descriptor

Tensor descriptor formats are identified using acronyms, with each letter referencing a corresponding dimension. In this document, the usage of this terminology implies:

• all the strides are strictly positive
• the dimensions referenced by the letters are sorted in decreasing order of their respective strides

2.2.1.2. 3-D Tensor Descriptor

A 3-D tensor is commonly used for matrix multiplications, with three letters: B, M, and N. B represents the batch size (for batch GEMM, set to 1 for single GEMM), M represents the number of rows, and N represents the number of columns. Refer to the CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR operation for more information.

2.2.1.3. 4-D Tensor Descriptor

A 4-D tensor descriptor is used to define the format for batches of 2D images with 4 letters: N,C,H,W for respectively the batch size, the number of feature maps, the height and the width. The letters are sorted in decreasing order of the strides. The commonly used 4-D tensor formats are:

• NCHW
• NHWC
• CHWN

2.2.1.4. 5-D Tensor Descriptor

A 5-D tensor descriptor is used to define the format of the batch of 3D images with 5 letters: N,C,D,H,W for respectively the batch size, the number of feature maps, the depth, the height, and the width. The letters are sorted in decreasing order of the strides. The commonly used 5-D tensor formats are called:

• NCDHW
• NDHWC
• CDHWN

2.2.1.5. Fully-Packed Tensors

A tensor is defined as XYZ-fully-packed if, and only if:

• the number of tensor dimensions is equal to the number of letters preceding the fully-packed suffix
• the stride of the i-th dimension is equal to the product of the (i+1)-th dimension by the (i+1)-th stride
• the stride of the last dimension is 1

2.2.1.6. Partially-Packed Tensors

The partially XYZ-packed terminology only applies in the context of a tensor format described with a superset of the letters used to define a partially-packed tensor. A WXYZ tensor is defined as XYZ-packed if, and only if:

• the strides of all dimensions NOT referenced in the -packed suffix are greater or equal to the product of the next dimension by the next stride
• the stride of each dimension referenced in the -packed suffix in position i is equal to the product of the (i+1)-st dimension by the (i+1)-st stride
• if the last tensor's dimension is present in the -packed suffix, its stride is 1

For example, an NHWC tensor WC-packed means that the c_stride is equal to 1 and w_stride is equal to c_dim x c_stride. In practice, the -packed suffix is usually applied to the minor dimensions of a tensor but can be applied to only the major dimensions; for example, an NCHW tensor that is only N-packed.

2.2.1.7. Spatially Packed Tensors

Spatially-packed tensors are defined as partially-packed in spatial dimensions. For example, a spatially-packed 4D tensor would mean that the tensor is either NCHW HW-packed or CNHW HW-packed.

2.2.1.8. Overlapping Tensors

A tensor is defined to be overlapping if iterating over a full range of dimensions produces the same address more than once. In practice an overlapped tensor will have stride[i-1] < stride[i]*dim[i] for some of the i from [1,nbDims] interval.

2.2.2. Data Layout Formats

This section describes how cuDNN tensors are arranged in memory according to several data layout formats.

The recommended way to specify the layout format of a tensor is by setting its strides accordingly. For compatibility with the v7 API, a subset of the layout formats can also be configured through the cudnnTensorFormat_t enum. The enum is only supplied for legacy reasons and is deprecated.

2.2.2.1. Example Tensor

Consider a batch of images with the following dimensions:

• N is the batch size; 1
• C is the number of feature maps (that is,, number of channels); 64
• H is the image height; 5
• W is the image width; 4

To keep the example simple, the image pixel elements are expressed as a sequence of integers, 0, 1, 2, 3, and so on. Refer to Figure 1.

Figure 1. Example with N=1, C=64, H=5, W=4

In the following subsections, we’ll use the above example to demonstrate the different layout formats.

2.2.2.2. Convolution Layouts

cuDNN supports several layouts for convolution, as described in the following sections.

2.2.2.2.1. NCHW Memory Layout

The above 4D tensor is laid out in the memory in the NCHW format as below:

1. Beginning with the first channel (c=0), the elements are arranged contiguously in row-major order.
2. Continue with second and subsequent channels until the elements of all the channels are laid out. Refer to Figure 2.
3. Proceed to the next batch (if N is > 1).

Figure 2. NCHW Memory Layout

2.2.2.2.2. NHWC Memory Layout

For the NHWC memory layout, the corresponding elements in all the C channels are laid out first, as below:

1. Begin with the first element of channel 0, then proceed to the first element of channel 1, and so on, until the first elements of all the C channels are laid out.
2. Next, select the second element of channel 0, then proceed to the second element of channel 1, and so on, until the second element of all the channels are laid out.
3. Follow the row-major order of channel 0 and complete all the elements. Refer to Figure 3.
4. Proceed to the next batch (if N is > 1).

Figure 3. NHWC Memory Layout

2.2.2.2.3. NC/32HW32 Memory Layout

The NC/32HW32 is similar to NHWC, with a key difference. For the NC/32HW32 memory layout, the 64 channels are grouped into two groups of 32 channels each - first group consisting of channels c0 through c31, and the second group consisting of channels c32 through c63. Then each group is laid out using the NHWC format. Refer to Figure 4.

Figure 4. NC/32HW32 Memory Layout

For the generalized NC/xHWx layout format, the following observations apply:

• Only the channel dimension, C, is grouped into x channels each.
• When x = 1, each group has only one channel. Hence, the elements of one channel (that is, one group) are arranged contiguously (in the row-major order), before proceeding to the next group (that is, next channel). This is the same as NCHW format.
• When x = C, then NC/xHWx is identical to NHWC, that is, the entire channel depth C is considered as a single group. The case x = C can be thought of as vectorizing the entire C dimension as one big vector, laying out all the Cs, followed by the remaining dimensions, just like NHWC.
• The tensor format cudnnTensorFormat_t can also be interpreted in the following way: The NCHW INT8x32 format is really N x (C/32) x H x W x 32 (32 Cs for every W), just as the NCHW INT8x4 format is N x (C/4) x H x W x 4 (4 Cs for every W). Hence the VECT_C name - each W is a vector (4 or 32) of Cs.

2.2.2.3. MatMul Layouts

As discussed in 3-D Tensor Descriptor, matmul uses 3D tensors, described using BMN dimensions. The layout can be specified through the following strides. The following are two examples of recommended layouts:

• Packed Row-major: dim [B,M,N] with stride [MN, N, 1], or
• Packed Column-major: dim [B,M,N] with stride [MN, 1, M]

Unpacked layouts for 3-D tensors are supported as well, but their support surface is more ragged.

2.3. Tensor Core Operations

The cuDNN v7 library introduced the acceleration of compute-intensive routines using Tensor Core hardware on supported GPU SM versions. Tensor Core operations are supported beginning with the NVIDIA Volta GPU.

Tensor Core operations accelerate matrix math operations; cuDNN uses Tensor Core operations that accumulate into FP16, FP32, and INT32 values. Setting the math mode to CUDNN_TENSOR_OP_MATH via the cudnnMathType_t enumerator indicates that the library will use Tensor Core operations. This enumerator specifies the available options to enable the Tensor Core and should be applied on a per-routine basis.

The default math mode is CUDNN_DEFAULT_MATH, which indicates that the Tensor Core operations will be avoided by the library. Because the CUDNN_TENSOR_OP_MATH mode uses the Tensor Cores, it is possible that these two modes generate slightly different numerical results due to different sequencing of the floating-point operations.

For example, the result of multiplying two matrices using Tensor Core operations is very close, but not always identical, to the result achieved using a sequence of scalar floating-point operations. For this reason, the cuDNN library requires an explicit user opt-in before enabling the use of Tensor Core operations.

However, experiments with training common deep learning models show negligible differences between using Tensor Core operations and scalar floating point paths, as measured by both the final network accuracy and the iteration count to convergence. Consequently, the cuDNN library treats both modes of operation as functionally indistinguishable and allows for the scalar paths to serve as legitimate fallbacks for cases in which the use of Tensor Core operations is unsuitable. Kernels using Tensor Core operations are available for:

• Convolutions
• RNNs
• Multi-Head Attention

For more information, refer to NVIDIA Training with Mixed Precision. For a deep learning compiler, the following are the key guidelines:

• Make sure that the convolution operation is eligible for Tensor Cores by avoiding any combinations of large padding and large filters.
• Transform the inputs and filters to NHWC, pre-pad channel and batch size to be a multiple of 8.
• Make sure that all user-provided tensors, workspace, and reserve space are aligned to 128-bit boundaries. Note that 1024-bit alignment may deliver better performance.

2.3.1. Notes on Tensor Core Precision

For FP16 data, Tensor Cores operate on FP16 input, output in FP16, and may accumulate in FP16 or FP32. The FP16 multiply leads to a full-precision result that is accumulated in FP32 operations with the other products in a given dot product for a matrix with m x n x k dimensions. Refer to Figure 5.

For an FP32 accumulation, with FP16 output, the output of the accumulator is down-converted to FP16. Generally, the accumulation type is of greater or equal precision to the output type.

Figure 5. Tensor operation with FP16 inputs. The accumulation is in FP32, which could be the input for other kernel features (for example, activation/bias, beta blending, etc). The final output in this example would be FP16.

The cuDNN library provides a declarative programming model for describing computation as a graph of operations. This graph API was introduced in cuDNN 8.0 to provide a more flexible API, especially with the growing importance of operation fusion.

The user starts by building a graph of operations. At a high level, the user is describing a dataflow graph of operations on tensors. Given a finalized graph, the user then selects and configures an engine that can execute that graph. There are several methods for selecting and configuring engines, which have tradeoffs with respect to ease-of-use, runtime overhead, and engine performance. The graph API has two entry points:

• NVIDIA cuDNN Backend API (lowest level entry point into the graph API)
• NVIDIA cuDNN Frontend API (convenience layer on top of the C backend API)

We expect that most users prefer the cuDNN frontend API because:

• It is less verbose without loss of control - all functionality accessible through the backend API is also accessible through the frontend API.
• It adds functionality on top of the backend API, like errata filters and autotuning.
• It is open source.

In either case (that is, the backend or frontend API), the high level concepts are the same.

6.1. Key Concepts

As mentioned previously, the key concepts in the graph API are:

• Operations and Operation Graphs
• Engines and Engine Configurations
• Heuristics

3.1.1. Operations and Operation Graphs

An operation graph is a dataflow graph of operations on tensors. It is meant to be a mathematical specification and is decoupled from the underlying engines that can implement it, as there may be more than one engine available for a given graph.

I/O tensors connect the operations implicitly, for example, an operation A may produce a tensor X, which is then consumed by operation B, implying that operation B depends on operation A.

3.1.2. Engines and Engine Configurations

For a given operation graph, there are some number of engines that are candidates for implementing that graph. The typical way to query for a list of candidate engines is through a heuristics query, covered below.

An engine has knobs for configuring properties of the engine, like tile size (refer to cudnnBackendKnobType_t).

3.1.3. Heuristics

A heuristic is a way to get a list of engine configurations that are intended to be sorted from the most performant to least performant for the given operation graph. There are three modes:

CUDNN_HEUR_MODE_A

Intended to be fast and be able to handle most operation graph patterns. It returns a list of engine configs ranked by the expected performance.

CUDNN_HEUR_MODE_B

Intended to be more generally accurate than mode A, but with the tradeoff of higher CPU latency to return the list of engine configs. The underlying implementation may fall back to the mode A heuristic in cases where we know mode A can do better.

CUDNN_HEUR_MODE_FALLBACK

Intended to be fast and provide functional fallbacks without expectation of optimal performance.

The recommended workflow is to query either mode A or B and check for support. The first engine config with support is expected to have the best performance.

You can “auto-tune”, that is, iterate over the list and time for each engine config and choose the best one for a particular problem on a particular device. The cuDNN frontend API provides a convenient function, cudnnFindPlan(), which does this.

If all the engine configs are not supported, then use the mode fallback to find the functional fallbacks.

Expert users may also want to filter engine configs based on properties of the engine, such as numerical notes, behavior notes, or adjustable knobs. Numerical notes inform the user about the numerical properties of the engine such as whether it does datatype down conversion at the input or during output reduction. The behavior notes can signal something about the underlying implementation like whether or not it uses runtime compilation. The adjustable knobs allow fine grained control of the engine’s behavior and performance.

3.2. Graph API Example with Operation Fusion

The following example implements a fusion of convolution, bias, and activation.

3.2.1. Creating Operation and Tensor Descriptors to Specify the Graph Dataflow

First, create three cuDNN backend operation descriptors.

As can be seen in Figure 6, the user specified one forward convolution operation (using CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR), a pointwise operation for the bias addition (using CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR with mode CUDNN_POINTWISE_ADD), and a pointwise operation for the ReLU activation (using CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR with mode CUDNN_POINTWISE_RELU_FWD). Refer to the NVIDIA cuDNN Backend API for more details on setting the attributes of these descriptors. For an example of how a forward convolution can be set up, refer to the Setting Up An Operation Graph For A Grouped Convolution use case in the cuDNN backend API. You should also create tensor descriptors for the inputs and outputs of all of the operations in the graph. The graph dataflow is implied by the assignment of tensors (refer to Figure 6), for example, by specifying the backend tensor Tmp0 as both the output of the convolution operation and the input of the bias operation, cuDNN infers that the dataflow runs from the convolution into the bias. The same applies to tensor Tmp1. If the user doesn’t need the intermediate results Tmp0 and Tmp1 for any other use, then the user can specify them to be virtual tensors, so the memory I/Os can later be optimized out.

• Note that graphs with more than one operation node do not support in-place operations (that is, where any of the input UIDs matches any of the output UIDs). Such in-place operations are considered cyclic in later graph analysis and deemed unsupported. In-place operations are supported for single-node graphs.
• Also note that the operation descriptors can be created and passed into cuDNN in any order, as the tensor UIDs are enough to determine the dependencies in the graph.

Figure 6. A set of operation descriptors the user passes to the operation graph

3.2.2. Finalizing The Operation Graph

Second, the user finalizes the operation graph. As part of finalization, cuDNN performs the dataflow analysis to establish the dependency relationship between operations and connect the edges, as illustrated in the following figure. In this step, cuDNN performs various checks to confirm the validity of the graph.

Figure 7. The operation graph after finalization

3.2.3. Configuring An Engine That Can Execute The Operation Graph

Third, given the finalized operation graph, the user must select and configure an engine to execute that graph, which results in an execution plan. As mentioned in Heuristics, the typical way to do this is:

1. Query heuristics mode A or B.
2. Look for the first engine config with functional support (or auto-tune all the engine configs with functional support).
3. If no engine config was found in (2), try querying the fallback heuristic for more options.

3.2.4. Executing The Engine

Finally, with the execution plan constructed and when it comes time to run it, the user should construct the backend variant pack by providing the workspace pointer, an array of UIDs, and an array of device pointers. The UIDs and the pointers should be in the corresponding order. With the handle, the execution plan and variant pack, the execution API can be called and the computation is carried out on the GPU.

3.3. Supported Graph Patterns

The cuDNN Graph API supports a set of graph patterns. These patterns are supported by a large number of engines, each with their own support surfaces. These engines are grouped into four different classes, as reflected by the following four subsections: pre-compiled single operation engines, generic runtime fusion engines, specialized runtime fusion engines, and specialized pre-compiled fusion engines. The specialized engines, whether they use runtime compilation or pre-compilation, are targeted to a set of important use cases, and thus have a fairly limited set of patterns they currently support. Over time, we expect to support more of those use cases with the generic runtime fusion engines, whenever practical.

Since these engines have some overlap in the patterns they support, a given pattern may result in zero, one, or more engines.

3.3.1. Pre-compiled Single Operation Engines

One basic class of engines includes pre-compiled engines that support an operation graph with just one operation; specifically: ConvolutionFwd, ConvolutionBwFilter, ConvolutionBwData, or ConvolutionBwBias. Their more precise support surface can be found in the NVIDIA cuDNN API Reference.

3.3.1.1. ConvolutionFwd

ConvolutionFwd computes the convolution of X with filter data W. In addition, it uses scaling factors

Figure 8. ConvolutionFwd Engine

3.3.1.2. ConvolutionBwFilter

ConvolutionBwFilter computes the convolution filter gradient of the tensor dy. In addition, it uses scaling factors

Figure 9. ConvolutionBwFilter Engine

3.3.1.3. ConvolutionBwData

ConvolutionBwData computes the convolution data gradient of the tensor dy. In addition, it uses scaling factors

Figure 10. ConvolutionBwData Engine

3.3.1.4. NormalizationForward

NormalizationForward computes the normalization output Y from the input X. This operation is used in both the inference and training phase. The phases are distinguished by the attribute CUDNN_ATTR_OPERATION_NORM_FWD_PHASE.

Figure 11. NormalizationForward Engine

This operation supports different normalization modes which are set by the attribute CUDNN_ATTR_OPERATION_NORM_FWD_MODE. The dashed lines indicate optional inputs, which are typically used in the batch norm mode of this operation. Currently, the precompiled engines support instance and layer norm while batch norm is supported using a specialized runtime compiled engine (refer to BnAddRelu).

3.3.1.5. NormalizationBackward

NormalizationBackward computes the gradient dX and the scale and bias gradients dScale and dBias. This operation supports multiple modes which are set by the attribute CUDNN_ATTR_OPERATION_NORM_BWD_MODE. The precompiled engines support instance and layer norm backward while batch norm backward is supported by a specialized runtime compiled engine (refer to DReluForkDBn). The mean and variance saved during the forward training pass is passed as input to theNormBackward operation.

Figure 12. NormalizationBackward Engine

3.3.2. Generic Runtime Fusion Engines

The engines documented in the previous section support single-op patterns. Of course, for fusion to be interesting, the graph needs to support multiple operations. And ideally, we want the supported patterns to be flexible to cover a diverse set of use cases. To accomplish this generality, cuDNN has runtime fusion engines that generate the kernel (or kernels) at runtime based on the graph pattern. This section outlines the patterns supported by these runtime fusion engines (that is, engines with CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION behavioral note).
We can think of the support surface as covering the follwing generic patterns:

1. ConvolutionFwd fusions

   $g_{\text{2}}\left(\text{Y}=\text{convolutionFwd}\left(\text{X}=g_{\text{1}}\left(\text{inputs}\right)\text{,}\text{W}\right)\text{,}\text{inputs}\right)$

2. ConvolutionBwFilter fusions

   $g_{\text{2}}\left(\text{dw}=\text{convolutionBwFilter}\left(\text{dy}=g_{\text{1}}\left(\text{inputs}\right)\text{,}\text{X}\right)\text{,}\text{inputs}\right)$

3. ConvolutionBwData fusions

   $g_{\text{2}}\left(\text{dx}=\text{convolutionBwData}\left(\text{dy, W}\right)\text{,}\text{inputs}\right)$

4. MatMul fusions

   $g_{\text{2}}\left(\text{C}=\text{matmul}\left(\text{A}={\text{g}}_{\text{1}}\left(\text{inputs}\right)\text{,}\text{B}\right)\text{,}\text{inputs}\right)$

5. Pointwise fusions

   $g_{\text{2}}\left(\text{inputs}\right)$

Figure 13. Graphical Representation of the Generic Patterns Supported by the Runtime Fusion Engines

g₁ is a directed acyclic graph (DAG) that can consist of zero or any number of the following operation:

• CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR

g₂ is a DAG that can consist of zero or any number of the following operations:

• CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR
• CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR

3.3.2.1. Limitations

While the generic patterns listed previously are widely applicable, there are some cases where we do not have full support.

Limitations Common to all Generic Patterns

Limitations to g₁:

• Concatenation or signaling operations, if present, should be before any pointwise operations.
• For compute capability < 8.0, g₁ is not supported.

Limitations to g₂:

• For compute capability < 7.0, g₂ is not supported.
• As specified in the previous section, g₂ can include only Pointwise operations, ResampleFwd, ResampleBwd, GenStats, and Reduction.
• The I/O (that is, non-virtual) tensor data type can be any of {FP32, FP16, BF16, INT8, packed-BOOLEAN}.
• For pointwise operations, non-virtual tensors need to be either all NCHW (or row-major), or all NHWC (or column-major).
• The intermediate virtual tensor data type can be any of {FP32, FP16, BF16, INT8, BOOLEAN}, and this intermediate storage type is obeyed by the code-generator. Generally, FP32 is recommended.
• The input tensor to a ResampleFwd or ResampleBwd operation should not be produced by another operation within this graph, but should come from global memory.
  • The two operations cannot be used in the ConvolutionBwFilter, ConvolutionBwData, and MatMul fusion patterns.
  • Only compute capability >= 7.5 is supported.
• There can be at most one reduction operation, and it needs to be at the final node of g₂.
• Signaling operations, if present, must be the final nodes in g₂. Hence, signaling operations cannot be used in conjunction with reduction operations.

Limitations per Generic Pattern

Tensor Layout Requirements

Lastly, there are some layout requirements to the I/O tensors involved in fusion graphs. For more information, refer to the Tensor Descriptor and Data Layout Formats sections. The following table describes the requirements per fusion pattern:

3.3.2.2. Examples of Supported Patterns

The following sections provide examples of supported patterns, in order of increasing complexity. We employ the same color scheme as in the overall pattern to aid in identifying the structure of g₁ (blue) and g₂ (purple).

For illustration purposes, we abbreviated the operations used. For a full mapping to the actual backend descriptors, refer to the Mapping with Backend Descriptors.

3.3.2.2.1. Single Operation

The following example illustrates a convolution operation without any operations before or after it. This means, g₁ and g₂, are empty graphs.

Figure 14. This example illustrates the Runtime Fusion Engines with a Single Operation

3.3.2.2.2. Pointwise Operations After Convolution 1

In this example, g₂ consists of a sequential set of two pointwise operations after the convolution.

Figure 15. ConvolutionFwd Followed by a DAG with Two Operations

3.3.2.2.3. Pointwise Operations After Convolution 2

Similar to the previous example, g₂ consists of a sequential set of multiple pointwise operations.

Figure 16. ConvolutionFwd Followed by a DAG with Three Operations

3.3.2.2.4. Pointwise Operations Before Matrix Multiplication

Pointwise operations can also precede a convolution or matrix multiplication, that is, g₁ is composed of pointwise operations.

Figure 17. MatMul Preceded by a DAG with Two Operations

3.3.2.2.5. Convolution Producer Node in Middle of DAG

The following pattern shows g₁ as a DAG of pointwise operations feeding into a convolution. In addition, g₂ is a DAG consisting of two pointwise operations. Note that the convolution is being consumed in the middle of g₂ as opposed to g₂’s first node. This is a valid pattern.

Figure 18. This example illustrates fusion of operations before and after the ConvolutionFwdoperation. In addition we observe that the output of ConvolutionFwdcan feed anywhere in g₂.

3.3.2.3. Operation specific Constraints for the Runtime Fusion Engines

Every operation in the supported generic patterns of the runtime fusion engines is subject to a few specific constraints regarding their parameter surface. The following subsections document these.

Note that these constraints are in addition to (1) any constraints mentioned in the NVIDIA cuDNN Backend API, and (2) limitations in relation to other operations in the directed acyclic graph (DAG), as mentioned in the Limitations section.

3.3.2.3.1. Convolutions

There are three operation nodes that represent different types of convolutions namely:

ConvolutionFwd

This operation represents forward convolution, that is, computing the response tensor of image tensor convoluted with filter tensor. For complete details on the interface, as well as general constraints, refer to the CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR section.

ConvolutionBwFilter

This operation represents convolution backward filters, that is, computing filter gradients from a response and an image tensor. For complete details on the interface, as well as general constraints, refer to the CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR section.

ConvolutionBwData

This operation represents convolution backward data, that is, computing input data gradients from a response and a filter tensor. For complete details on the interface, as well as general constraints, refer to the CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR section.

The following tables list the constraints for all three operations, in addition to any constraints mentioned in the NVIDIA cuDNN Backend API, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when these operations are used in the runtime fusion engines.

Lastly, there are some batch size requirements per operation:

The FP8 data type since Hopper architecture has two variants; CUDNN_DATA_FP8_E4M3 and CUDNN_DATA_FP8_E5M2 as I/O data types. It also has two possible compute types; CUDNN_DATA_FLOAT and CUDNN_DATA_FAST_FLOAT_FOR_FP8, which is a faster, but less accurate option for FP8 Tensor Core operations. It is sufficiently accurate for inference or the forward pass of training. However, for FP8 training backward pass computations (that is, computing weight and activation gradients), we recommend choosing the more accurate CUDNN_DATA_FLOAT compute type to preserve a higher level of accuracy which can be necessary for some models.

3.3.2.3.2. MatMul

This operation represents matrix-matrix multiplication: A * B = C. For complete details on the interface, refer to the CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR section.

The following two tables list the constraints for MatMul operations, in addition to any general constraints as listed in the NVIDIA cuDNN Backend API, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when MatMul is used in the runtime fusion engines.

3.3.2.3.3. Pointwise

Represents a pointwise operation that implements the equation Y = op (alpha1 * X) or Y = op (alpha1 * X, alpha2 * B). Refer to the CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR and CUDNN_BACKEND_POINTWISE_DESCRIPTOR sections for more information and general constraints.

The following table lists the constraints for pointwise operations, in addition to the general constraints listed above, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when these operations are used in the runtime fusion engines.

3.3.2.3.4. GenStats

Represents an operation that generates per-channel statistics. Refer to the CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR section for more information and general constraints.

The following table lists the constraints for GenStats operations, in addition to the general constraints listed above, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when GenStats operations are used in the runtime fusion engines.

3.3.2.3.5. Reduction

This operation represents reducing values of a tensor in one or more dimensions. Refer to the CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR section for more information and general constraints.

The following two tables are constraints for Reduction forward operations, in addition to the general constraints listed above, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when Reduction operations are used in the runtime fusion engines.

3.3.2.3.6. ResampleFwd

This operation represents resampling of the spatial dimensions of an image to a desired value. Resampling is supported in both directions, upsampling and downsampling. Downsampling represents the standard operation of pooling, commonly used in convolutional neural networks. Refer to the CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR section for more information and general constraints.

The following are constraints for Resample operations, in addition to the general constraints listed above, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when Resample forward operations are used in the runtime fusion engines. We allow a choice amongst four modes for resample. All modes have the following common support specifications:

• Supported layout: NHWC or NDHWC, NCHW or NCDHW
• Spatial dimensions supported: 2 or 3
• Input dimensions supported: 4 or 5
• Packed boolean data type is not supported.
• If specified, the index tensor dimension should be equal to the response tensor dimension.

When the tensor format is NCHW/NCDHW, the following additional restrictions apply:

• Upsampling is not supported.
• Int64_t indices are not supported.
• Only supports symmetric padding using the prepadding backend API.

There are some mode specific restrictions also. The following tables list the values that are allowed for particular parameters. For the parameters not listed, we allow any value which is mathematically correct. The following downsampling modes are supported:

• CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING
• CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING
• CUDNN_RESAMPLE_MAXPOOL

For the upsampling modes, CUDNN_RESAMPLE_NEAREST is not supported for any combination of parameters. CUDNN_RESAMPLE_BILINEAR has the following support specifications.

3.3.2.3.6.1. Resampling Index Tensor Dump for Training

For max-pooling resampling mode, an index tensor can be provided to be used as a mask for backpropagation.
Values in the index tensors are:

• Zero-indexed row-major position of maximum value of input tensor in the resampling window.
• In case of multiple input pixels with maximum value, the first index in a left-to-right top-to-bottom scan is selected.

Example of index element selection:

Figure 19. Values In the Index Tensors

Select an appropriate element size for the index tensor. As a reference, any element size such that the maximum zero-indexed window position fits should be sufficient.

3.3.2.3.7. ResampleBwd

This operation represents backward resampling of the spatial dimensions of an output response to a desired value. Resampling is supported in both directions, upsampling and downsampling. Backwards downsampling represents the standard operation of backward pooling, commonly used in convolutional neural networks. Refer to the CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR section for more information and general constraints.

The following are constraints for Resample backward operations, in addition to the general constraints listed above, and any constraints listed in the Limitations section, in relation to other operations. Note that these additional constraints only apply when Resample backward operations are used in the runtime fusion engines. We allow a choice amongst four modes for resample. All modes have the following common support specifications:

• Supported layout: NHWC or NDHWC, NCHW or NCDHW
• Spatial dimensions supported: 2 or 3
• Input dimensions supported: 4 or 5

For layout NHWC or NDHWC:

• The index tensor should be provided for only max pooling mode, and should adhere to the format described in the resampling forward index dump section.
• The index tensor dimensions should be equal to the input gradient tensor dimensions.

For layout NCHW or NCDHW:

• X, Y, and DY are required when max pooling mode is used.
• Int64_t indices are not supported.

There are some mode specific restrictions also. The following tables list the values that are allowed for particular parameters. For the parameters not listed, we allow any value which is mathematically correct. The following backward downsampling modes are supported:

• CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING
• CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING
• CUDNN_RESAMPLE_MAXPOOL

Backward upsampling modes are currently not supported.

3.3.3. Specialized Runtime Fusion Engines

The specialized runtime fusion engines target and optimize specialized graph patterns that commonly occur in popular deep learning models. These engines offer limited flexibility regarding supported fusion patterns, supported data types, and supported tensor layouts. Long term, these patterns are expected to be more generic.

The following sections highlight the supported patterns.

3.3.3.1. BnAddRelu

In ResNet-like vision models, batch normalization followed by ReLU activation is a commonly occurring pattern. The BNAddRelu fusion pattern, supported using a runtime compiled engine, aims to optimize this recurring operation graph. It also supports single node multi-GPU batch normalization for speeding up batch norm computation in multi-GPU systems. The pattern is intended for use in the forward pass during the training phase. The full pattern BNAddRelu with the add node is used in cases where there are skip connections in the model.
The pattern is illustrated in the following diagram and its options and limitations include:

• The pointwise nodes: Add, ReLU, and GT (greater than) are optional.
• All tensors should be in NHWC packed layout format.
• Both 4D and 5D tensors are supported.
• Only ReLU activation is supported.
• The attribute CUDNN_ATTR_OPERATION_NORM_FWD_MODE for the norm forward operation must be set to CUDNN_BATCH_NORM.
• The attribute CUDNN_ATTR_OPERATION_NORM_FWD_PHASE for the norm forward operation must be set to CUDNN_NORM_FWD_TRAINING.
• The batch norm input tensors: Scale, Bias, Input_running_mean, and Input_running_var must be of float data type.
• The batch norm output tensors: output_running_mean, output_running var, mean, and InvVariance must be of float data type.
• The batch norm input tensor BN_x, residual input Z and output tensor Y can be any of {FP32, FP16, BF16} data types. For FP16 and BF16 data types, the channel count C for the tensors must be a multiple of 8 while for float data type the channel count must be a multiple of 4.
• These patterns are supported on devices with compute capability >= 8.0.

Figure 20. BnAddRelu cuDNN Operation Graph

In case of single node multi-GPU batch norm, each GPU computes the local statistics based on its input data and writes out the local statistics to the peerTensors. Each peerTensor resides on a separate GPU on the node and is used for reading and writing local statistics from the peer GPUs. This is followed by a global stats computation phase where each GPU aggregates the statistics from the peers and computes the global mean and variance for the batch norm output computation on its local data. Apart from the options and limitations listed above, the following additional restrictions apply for using multi-GPU batch norm:

• The attribute CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS of the NormForward operation must be set.
• The size of the peerTensors vector should be equal to the number of GPUs in the node participating in the batch norm computation.
• The maximum size of the peerTensors vector is 32.
• Each GPU should operate on the same size of input data [N,C,H,W].
• The size of each peerTensor in the peerTensors vector should be equal to num_gpu * 4 * C where C is the channel count of the BN_x tensor and num_gpu is the number of GPUs in the node participating in the batch norm computation.
• All the elements of each tensor in the peerTensors vector should be memset to 0 before passing that tensor in the variant pack.

Figure 21. Single Node Multi-GPU Batch Norm

3.3.3.2. DReluForkDBn

Similar to the BnAddRelu pattern, the DReluForkDBn pattern also targets ResNet-like vision networks. It is intended to be used in backpropagation during the training phase. The DReluForkDBn pattern is supported through a runtime compiled engine that usually complements the BnAddRelu pattern. It also supports single node multi-GPU batch normalization for speeding up batch norm backward computation in multi-GPU systems.
The pattern is illustrated in the following diagram and its options and limitations include:

• The pointwise node dRelu is optional.
• The intermediate tensor dZ can be virtual or non-virtual.
• All tensors should be in NHWC packed layout format.
• Both 4D and 5D tensors are supported.
• Only dRelu activation is supported.
• Bitmask tensor input is needed for the dRelu node.
• The attribute CUDNN_ATTR_OPERATION_NORM_BWD_MODE for the norm backward operation must be set to CUDNN_BATCH_NORM.
• The batch norm backward input tensors: Scale, Mean, InvVariance and the output tensors dScale and dBias must be of float data type.
• dRelu input tensor dY, batch norm backward input BN_x, bias gradient dZ, and output tensor dX can be any of {FP32, FP16, BF16} data types. For FP16 and BF16 data types, the channel count C for the tensors must be a multiple of 8 while for float data type the channel count must be a multiple of 4.
• These patterns are supported on devices with compute capability >= 8.0.

Figure 22. DReluForkDBn cuDNN Operation Graph

The single node multi-GPU version of this pattern is typically used for dScale and dBias gradient aggregation across GPUs. For using the multi-GPU version, the attribute CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS of the NormBackward operation must be set. Other restrictions for the peerTensors vector listed in the previous section apply for this pattern as well.

3.3.3.3. Fused Attention fprop

Mha-Fprop fusions

1. Input sizes supported contain small sequence lengths (<= 512).
2. The operation graph is flexible to switch between different types of masks, different operations between the two matrix multiplications, and so on.

Figure 23. Mha-fprop cuDNN Operation Graph

g₃ can be an empty graph or a single scale operation with the scale being a scalar value (CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR with mode CUDNN_POINTWISE_MUL).

g₄ can be empty or the combination of the following DAGs of cuDNN operations. Each of these DAGs is optional, as shown by the dotted line.

Figure 24. DAGs of cuDNN operations

The combination has to obey the order in which we present them. For example, if you want to use the padding mask and softmax, the padding mask has to appear before softmax.

These operations are commonly used in attention. In the following diagram, we depict how to create a DAG for each of the operations. In later versions, we will be expanding the possible DAGs for g₃ and g₄.

Padding Mask

Figure 25. cuDNN graph depicting DAG:Padding Mask

Causal Mask

Figure 26. cuDNN graph depicting DAG:Causal Mask

Softmax

Figure 27. cuDNN graph depicting DAG:Softmax

Dropout

Figure 28. cuDNN graph depicting DAG:Dropout

g₄ is capable of storing an intermediate tensor to global memory marked as S, which can be used for fused multi-head attention bprop. Both DAG:Softmax and DAG:Dropout have this capability. Set S as the output from the last DAG in the graph.

The tensor descriptor marked as S must have the CUDNN_ATTR_TENSOR_REORDERING_MODE set to CUDNN_TENSOR_REORDERING_F16x16. This is because the tensor is stored in a special format and can only be consumed by fused attention bprop.

Layout requirements of Mha-fprop fusions include:

• All I/O tensors must have 4 dimensions, with the first two denoting the batch dimensions. The usage of rank-4 tensors in MatMul ops can be read from the NVIDIA cuDNN Backend API documentation.
• The contracting dimension (dimension K) for the first MatMul must be 64.
• The non-contracting dimension (dimensions M and N) for the first MatMul must be less than or equal to 512. In inference mode, any sequence length is functional. For training, support exists only for multiples of 64.
• The last dimension (corresponding to hidden dimensions) in Q, V, and O is expected to have stride 1.
• For the K tensor, the stride is expected to be 1 for the 2nd last dimension.
• The S tensor is expected to have CUDNN_ATTR_TENSOR_REORDERING_MODE set to CUDNN_TENSOR_REORDERING_F16x16.

3.3.3.4. Fused Attention bprop

Mha-Bprop fusions are executed in a fused pattern in a single kernel.

$\text{dV}=\text{matmul}\left({\text{g}}_{\text{5}}\left(\text{S}\right)\text{,}\text{dO}\right)$

$\text{dS}=\text{matmul}\left(\text{dO}\text{,}{V}^T\right)$

$\text{dQ}=\text{matmul}\left({\text{g}}_{\text{6}}\left(\text{dS}\right)\text{,}\text{K}\right)$

$\text{dK}=\text{matmul}\left(\text{Q}\text{,}{\text{g}}_{\text{7}}\left(\text{dS}\right)\right)$

cuDNN supports the corresponding backpropagation graph for fused attention. This can be used together with the fused attention fprop graph to perform training on models that have similar architectures to BERT and T5. This is not compatible with the flash fused attention bprop operation graph.

Figure 29. Mha-bprop cuDNN Operation Graph

g₅, g₆, and g₇ can only support a fixed DAG. We are working towards generalizing these graphs.

Figure 30. cuDNN Graph Depicting g₅

g₆ represents the backward pass of softmax and masking, to get dP.

Figure 31. cuDNN Graph Depicting g₆

There are options for the Mask DAG that you can opt-in. You can either use the padding/causal mask, general mask as an input, or not do any masking.

Figure 32. cuDNN Graph Depicting Mask DAG

dBias DAG is useful to calculate the bprop of the relative positional encoding and is optional and available for you to opt-in.

Figure 33. cuDNN Graph Depicting dBias DAG

g₇ is the transpose of dP the output of g₆.

Figure 34. cuDNN Graph Depicting g₇

Layout requirements of Mha-bprop fusions include:

• All I/O tensors must have 4 dimensions, with the first two denoting the batch dimensions. The usage of rank-4 tensors in MatMul ops can be read from the NVIDIA cuDNN Backend API documentation.
• The contracting dimension (dimension K) for the second MatMul must be 64.
• The contracting dimension (dimension K) for the 1st, 2nd, and 3rd MatMul must be less than or equal to 512 and a multiple of 64.
• The last dimension (corresponding to hidden dimensions) in Q, K, V, O, and dO is expected to have stride 1.
• The S and dP tensors are expected to have CUDNN_ATTR_TENSOR_REORDERING_MODE set to CUDNN_TENSOR_REORDERING_F16x16.

3.3.3.5. Fused Flash Attention fprop

cuDNN supports flash fused to perform scale dot product attention commonly used in models like GPT, BERT, and so on. Currently, support is limited to the following graph depicting forward propagation and inference with limitations on the support size and data types. Support will be expanded in future versions. The graph supports a pattern of BMM-Scale-Causal Mask-Softmax-Dropout-Matmul.

Figure 35. Flash fprop cuDNN Operation Graph

The compound operations for example: Causal Mask, Softmax, and so on, can be represented using the following operation graphs in cuDNN.

Figure 36. Flash fprop Causal Mask Operation Graph

Figure 37. Flash fprop Softmax Operation Graph

Figure 38. Flash fprop Dropout Operation Graph

Inference mode can be turned on by passing the Softmax stats as a virtual tensor and setting the RNG node probability to 0.0f. Currently, the pattern is only supported on A100 and H100 GPUs.

3.3.3.6. Fused Flash Attention bprop

cuDNN supports the corresponding backpropagation graph for fused flash attention. This can be used together with the fprop graph to perform training on Large Language Models (LLMs).
For the input and output tensors, the limitations from the fprop graph are carried over. For the bprop specific tensors, the limitations are as follows:

Figure 39. Flash bprop cuDNN Operation Graph

Currently, the pattern is only supported on A100 and H100 GPUs.

3.3.4. Specialized Pre-Compiled Engines

The pre-compiled specialized engines target and optimize for a specialized graph pattern with a ragged support surface. Because of this targeting, these graphs do not require runtime compilation.

In most cases, the specialized patterns are just special cases of the generic patterns used in the runtime fusion engines, but there are some cases where the specialized pattern does not fit any of the generic patterns. If your graph pattern matches a specialized pattern, you will get at least a pattern matching engine, and you might also get runtime fusion engines as another option.

Currently, the following patterns are supported by the pattern matching engines. Some nodes are optional. Optional nodes are indicated by dashed outlines.

3.3.4.1. ConvBNfprop

In Figure 40, the ConvBNfprop pattern is illustrated. Its restrictions and options include:

• The three pointwise nodes scale, bias, and ReLU are optional.
• X, Z, W, s₁, b₁ must all be of FP16 data type.
• Z needs to be of shape [N, C, H, W] with NHWC packed layout.
• W needs to be of shape [K, C, R, S] with KRSC packed layout.
• s₁, b₁ need to be of shape [1, C, 1, 1] with NHWC packed layout.
• Only ReLU activation is supported.
• All of the intermediate tensors need to be virtual, except Y needs to be non-virtual.
• I/O pointers should be 16 bytes aligned.
• These patterns are only supported on devices with compute capability >=8.0.
• On devices with compute capability >= 9.0, we only support two patterns:
  • the full pattern: scale + bias + ReLU + Conv + GenStats, and
  • the partial pattern: Conv + GenStats.

Figure 40. ConvBNfprop, A Pre-Compiled Engine, Fuses ConvolutionFwd and GenStats With Several Pointwise Operations

Skip connections are commonly observed in ResNet-like models. To support fusions in skip connections, we support a variant of the pattern above, the DBARCS pattern (short for Dual, Scale, Bias, Add, ReLU, Conv genStats). The limitations and options of the DBARCS pattern include:

• The pointwise dual scale and dual bias nodes are either both present or not. This is indicated by the dashed block encircling the dual scale and dual bias nodes. In case both the nodes are missing, the dual_X tensor is directly fed as input to the add node.
• The pointwise nodes scale, bias, add, and ReLU are required nodes.
• Currently, only supported on Hopper GPUs.
• For all the other data types, layout and virtualness restrictions of the ConvBNfprop pattern apply to this pattern as well.
• dual_X, dual_scale, and dual_bias must all be of FP16 data type.
• dual_scale and dual_bias must be of shape [1,C,1,1] with NHWC packed layout.
• Intermediate outputs of the ReLU and Conv nodes: Relu_Y and Y are non-virtual. All the other intermediate outputs are virtual.
• The weight tensor W for the convolution needs to be of shape [K,C,1,1]. Only 1x1 filters with padding 0 are supported for the convolution in the DBARCS pattern.

Figure 41. DBARCS In The convBNfprop Series For Supporting Fusions Across Skip Connections

3.3.4.2. ConvBNwgrad

In Figure 42, the ConvBNwgrad pattern is illustrated. Its restrictions and options include:

• The three pointwise operations are all optional, as indicated by the dashed outlines.
• Only ReLU activation is supported.
• X, s₁, b₁, and dy must all be of FP16 datatype.
• I/O pointers should be 16 bytes aligned.
• X, s₁, b₁, and dy must all have NHWC packed layouts.
• All the intermediate tensors need to be virtual.
• These patterns are only supported on devices with compute capability >=8.0.
• On devices with compute capability >= 9.0, support is restricted to:
  • the full pattern: scale + bias + ReLU + wgrad.

Figure 42. ConvBNwgrad, A Pre-Compiled Engine, Fuses ConvolutionBwFilter With Several (Optional) Pointwise Operations

3.3.4.3. ConvBiasAct

In the following figure, the ConvBiasAct pattern is illustrated. Its restrictions and options include:

• ${\alpha }_{\text{1}}$ and ${\alpha }_{\text{2}}$ need to be scalars.
• The activation node is optional.
• The size of the bias tensor should be [1, K, 1, 1].
• Internal conversions are not supported. That is, the virtual output between nodes need to have the same data type as the node’s compute type, which should be the same as the epilog type of the convolution node.
• There are some restrictions on the supported combination of data types, which can be found in the API Reference (refer to cudnnConvolutionBiasActivationForward()).

Figure 43. ConvBiasAct, A Pre-Compiled Engine, Fuses ConvolutionFwd With Several Pointwise Operations

3.3.4.4. ConvScaleBiasAct

In the following figure, the ConvScaleBiasAct pattern is illustrated. Its restrictions and options include:

• ${\alpha }_{\text{1}}$ and ${\alpha }_{\text{2}}$ and $b_{\text{2}}$ should have the same data type/layout and can only be FP32.
• X, W, and Z can only be INT8x4 or INT8x32.
• The size of the bias tensor should be [1, K, 1, 1].
• Internal conversions are not supported. Meaning, "virtual output" between nodes needs to be the same as their compute type.
• Currently, Pointwise:ReLU is the only optional pointwise node.

Figure 44. ConvScaleBiasAct, A Pre-Compiled Engine

This pattern is very similar as ConvBiasAct. The difference is that here, the scales ${\alpha }_{\text{1}}$ and ${\alpha }_{\text{2}}$ are tensors, not scalars. If they are scalars, this pattern becomes a normal ConvBiasAct.

3.3.4.5. DgradDreluBNBwdWeight

In Figure 45, the DgradDreluBNBwdWeight pattern is illustrated. Its restrictions and options include:

• Dgrad input dy and W are of FP16 datatypes.
• Batch norm fwd inputs, X_bn is of FP16 datatype while the other tensors mean_bn, invstd_dev_bn, scale_bn, and bias_bn are FP32.
• Outputs: dScale, dBias, A,B,C are of FP32 data type.
• All pointers are 16 byte aligned.
• This pattern is only supported on devices with compute capability >= 8.0.

DgradDreluBNBwdWeight is a pre-compiled engine that can be used in conjunction with the dBNApply pattern to compute the backwards path of batch norm.

Figure 45. DgradDreluBNBwdWeight Pattern For Fusions In The Backward Pass

The BNBwdWeight operation takes in five inputs: X_bn, mean_bn, invstddev_bn, scale_bn, and dy_bn (that is, the output from the ReLUBwd node).

It produces five outputs: gradients of the batch norm scale and bias params, dScale, dBias, and coefficients A,B,C. Note that for illustration purposes, the inputs are duplicated. The inputs on the left and right are however exactly the same.

This pattern is typically used in the computation of the Batch Norm Backward Pass.

When computing the backward pass of batch norm, dScale, dBias, and dX_bn are needed. The DgradDreluBnBwdWeight pattern computes the former two. Using the generated A, B, and C we can use the following dBNApply pattern to compute dX, the input gradient, as follows dx_bn = A*dy_bn + B*X_bn +C.

Figure 46. dBNApply Pattern For Final Gradient Computation

The dBNApply pattern was initially supported by a pre-compiled static engine but is now supported by the generic runtime fusion engine.

Note that the DgradDreluBNBwdWeight pattern is used in combination with the forward pass pattern ConvBNfprop. Because of performance reasons, the output of batch norm Y_bn, which was calculated in ConvBNfprop (output of scale-bias), needs to be recalculated by DgradDreluBnBwdWeight. The pointwise add node subtracts mean_bn from X_bn, hence the alpha2 parameter for that node should be set to -1.

3.3.4.6. FP8 Fused Flash Attention

cuDNN supports fused flash attention with input and output data types being in FP8 format. This FP8-specific graph pattern is supported only on Hopper (H100) GPUs.

Support exists for both training (forward and backward pass) and inference in FP8 format. The training forward pass is slightly different from the inference forward pass regarding whether some intermediate tensors are output or not.

Within the NVIDIA Hopper architecture, there are two new FP8 formats: E4M3 and E5M2. Currently, for forward pass, only when all the inputs and outputs are in E4M3 format is supported. For the backward pass, the support is only when some of the inputs and outputs are in E4M3 and some in E5M2. More general support for the FP8 formats will be added in future releases.

Due to the limited numerical precision of FP8 data type, for practical use cases, you must scale values computed in FP32 format before storing them in FP8 format, and descale the values stored in FP8 format before performing computations on them. For more information, refer to the Transformer Engine FP8 Primer. The following notation is used in this section.

• b - number of batches
• h - number of heads
• d - maximum length of sequences in a batch
• d - embedding dimension size of a word in a sequence

Scaling and Descaling

In the context of FP8, scaling refers to multiplying each element of a FP32 tensor by a quantization factor.

The quantization factor is computed as: (Max representable value in the fp8 format) / (Max absolute value seen in the tensor).

For the E4M3 format, the quantization factor is 448.f/ tensor_amax (rounded to the nearest lower power of two).

For the E5M2 format, the quantization factor is 57344.f / tensor_amax (rounded to the nearest lower power of two).

The meaning behind scaling is to spawn the full range of the FP8 format when computing on FP8 values and storing FP8 values, thereby, minimizing the precision loss. True values in FP32 format are multiplied by the quantization factor before storing them as scaled values in FP8 format. Computations on scaled values in FP8 format are descaled by multiplying with the dequantization factor to convert them back to their true values in FP32 format.

Scaling and descaling are critical for convergence with the FP8 data type, hence cuDNN only supports graph patterns for FP8 fused attention with the scaling and descaling nodes present.

Unpadded Tensors

In fused flash attention, the length of different sequences in a batch can be different. The cuDNN operation graph supports an unpadded layout where all the sequences of different lengths in a batch are tightly packed. All the word embeddings after the useful length of the sequence are pushed towards the end of all sequences in the layout.

Forward Pass

The following figure shows the cuDNN operation graph for the fused attention forward pass. The same graph supports forward pass for both training and inference. The operation graph pattern is identified as training when M and Zinv tensors are non-virtual. When M and Zinv tensors are virtual, the operation graph pattern is identified as inference.

The FP8 tensors are expected to be scaled and the matrix multiplication computation is performed on the FP8 tensors in the scaled format. All non matrix multiplication computations are performed in FP32 precision. The output of the FP8 matrix multiplication is converted to real values in FP32 by format multiplying with the descale values.

Figure 47. FP8 Fused Attention Forward Pass Operation Graph

Backward Pass

Figure 48. FP8 Fused Attention Backward Pass Operation Graph

3.3.5. Mapping with Backend Descriptors

For readability, the operations used in this section are abbreviated. The mapping with the actual backend descriptors can be found in this table:

            

            
CUDNN_POINTWISE_RELU_FWD, CUDNN_POINTWISE_TANH_FWD, CUDNN_POINTWISE_SIGMOID_FWD,
        CUDNN_POINTWISE_ELU_FWD
        

4.1. Convolution Functions

4.1.1. Prerequisites

For the supported GPUs, the Tensor Core operations will be triggered for convolution functions only when cudnnSetConvolutionMathType() is called on the appropriate convolution descriptor by setting the mathType to CUDNN_TENSOR_OP_MATH or CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION.

4.1.2. Supported Algorithms

When the prerequisite is met, the below convolution functions can be run as Tensor Core operations:

• cudnnConvolutionForward()
• cudnnConvolutionBackwardData()
• cudnnConvolutionBackwardFilter()

Refer to the following table for a list of supported algorithms:

4.1.3. Data and Filter Formats

The cuDNN library may use padding, folding, and NCHW-to-NHWC transformations to call the Tensor Core operations. For more information, refer to Tensor Transformations.
For algorithms other than *_ALGO_WINOGRAD_NONFUSED, when the following requirements are met, the cuDNN library will trigger the Tensor Core operations:

• Input, filter, and output descriptors (xDesc, yDesc, wDesc, dxDesc, dyDesc and dwDesc as applicable) are of the dataType = CUDNN_DATA_HALF (that is, FP16). For FP32 dataType, refer to Conversion Between FP32 and FP16.
• The number of input and output feature maps (that is, channel dimension C) is a multiple of 8. When the channel dimension is not a multiple of 8, refer to Padding.
• The filter is of type CUDNN_TENSOR_NCHW or CUDNN_TENSOR_NHWC.
• If using a filter of type CUDNN_TENSOR_NHWC, then the input, filter, and output data pointers (X, Y, W, dX, dY, and dW as applicable) are aligned to 128-bit boundaries.

4.2. RNN Functions

4.2.1. Prerequisites

Tensor Core operations are triggered for these RNN functions only when cudnnSetRNNMatrixMathType() is called on the appropriate RNN descriptor setting mathType to CUDNN_TENSOR_OP_MATH or CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION.

4.2.2. Supported Algorithms

When the above prerequisites are met, the RNN functions below can be run as Tensor Core operations:

• cudnnRNNForwardInference()
• cudnnRNNForwardTraining()
• cudnnRNNBackwardData()
• cudnnRNNBackwardWeights()
• cudnnRNNForwardInferenceEx()
• cudnnRNNForwardTrainingEx()
• cudnnRNNBackwardDataEx()
• cudnnRNNBackwardWeightsEx()
• cudnnRNNForward()
• cudnnRNNBackwardData_v8()
• cudnnRNNBackwardWeights_v8()

Refer to the following table for a list of supported algorithms:

4.2.3. Data and Filter Formats

When the following requirements are met, then the cuDNN library triggers the Tensor Core operations:

• For algo = CUDNN_RNN_ALGO_STANDARD:
  • The hidden state size, input size, and the batch size is a multiple of 8.
  • All user-provided tensors, workspace, and reserve space are aligned to 128-bit boundaries.
  • For FP16 input/output, the CUDNN_TENSOR_OP_MATH or CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is selected.
  • For FP32 input/output, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is selected.
• For algo = CUDNN_RNN_ALGO_PERSIST_STATIC:
  • The hidden state size and the input size is a multiple of 32.
  • The batch size is a multiple of 8.
  • If the batch size exceeds 96 (for forward training or inference) or 32 (for backward data), then the batch size constraints may be stricter, and large power-of-two batch sizes may be needed.
  • All user-provided tensors, workspace, and reserve space are aligned to 128-bit boundaries.
  • For FP16 input/output, CUDNN_TENSOR_OP_MATH or CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is selected.
  • For FP32 input/output, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION is selected.

For more information, refer to Features of RNN Functions.

4.2.4. Features of RNN Functions

Refer to the following table for a list of features supported by each RNN function.

The following table provides the features supported by the algorithms referred in the above table: CUDNN_RNN_ALGO_STANDARD, CUDNN_RNN_ALGO_PERSIST_STATIC, and CUDNN_RNN_ALGO_PERSIST_DYNAMIC.

4.3. Tensor Transformations

A few functions in the cuDNN library will perform transformations such as folding, padding, and NCHW-to-NHWC conversion while performing the actual function operation.

4.3.1. Conversion Between FP32 and FP16

The cuDNN API Reference allows you to specify that FP32 input data may be copied and converted to FP16 data internally to use Tensor Core operations for potentially improved performance. This can be achieved by selecting CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION enum for cudnnMathType_t. In this mode, the FP32 tensors are internally down-converted to FP16, the Tensor Op math is performed, and finally up-converted to FP32 as outputs. For more information, refer to Figure 49.

Figure 49. Tensor Operation with FP32 Inputs

For Convolutions

For convolutions, the FP32-to-FP16 conversion can be achieved by passing the CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION enum value to the cudnnSetConvolutionMathType() call.

            

            
// Set the math type to allow cuDNN to use Tensor Cores:
checkCudnnErr(cudnnSetConvolutionMathType(cudnnConvDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION));
        

For RNNs

For RNNs, the FP32-to-FP16 conversion can be achieved by passing the CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION enum value to the cudnnSetRNNMatrixMathType() call to allow FP32 data to be converted for use in RNNs.

            

            
// Set the math type to allow cuDNN to use Tensor Cores:
checkCudnnErr(cudnnSetRNNMatrixMathType(cudnnRnnDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION));
        

4.3.2. Padding

For packed NCHW data, when the channel dimension is not a multiple of 8, then the cuDNN library will pad the tensors as needed to enable Tensor Core operations. This padding is automatic for packed NCHW data in both the CUDNN_TENSOR_OP_MATH and the CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION cases.

4.3.3. Folding

In the folding operation, the cuDNN library implicitly performs the formatting of input tensors and saves the input tensors in an internal workspace. This can lead to an acceleration of the call to Tensor Cores.

With folding or channel-folding, cuDNN can implicitly format the input tensors within an internal workspace to accelerate the overall calculation. Performing this transformation for the user often allows cuDNN to use kernels with restrictions on convolution stride to support a strided convolution problem.

4.3.4. Conversion Between NCHW And NHWC

Tensor Cores require that the tensors be in the NHWC data layout. Conversion between NCHW and NHWC is performed when the user requests Tensor Op math. However, a request to use Tensor Cores is just that, a request and Tensor Cores may not be used in some cases. The cuDNN library converts between NCHW and NHWC if and only if Tensor Cores are requested and are actually used.

If your input (and output) are NCHW, then expect a layout change.

Non-Tensor Op convolutions will not perform conversions between NCHW and NHWC.

In very rare and difficult-to-qualify cases that are a complex function of padding and filter sizes, it is possible that Tensor Ops is not enabled. In such cases, users can pre-pad to enable the Tensor Ops path.

4.4. Mixed Precision Numerical Accuracy

When the computation precision and the output precision are not the same, it is possible that the numerical accuracy will vary from one algorithm to the other.

For example, when the computation is performed in FP32 and the output is in FP16, the CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0 (ALGO_0) has lower accuracy compared to the CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 (ALGO_1). This is because ALGO_0 does not use extra workspace, and is forced to accumulate the intermediate results in FP16, that is, half precision float, and this reduces the accuracy. The ALGO_1, on the other hand, uses additional workspace to accumulate the intermediate values in FP32, that is, full precision float.

This section includes a random set of topics and concepts.

5.1. Thread Safety

The cuDNN library is thread-safe. Its functions can be called from multiple host threads, so long as the threads do not share the same cuDNN handle simultaneously.

When creating a per-thread cuDNN handle, it is recommended that a single synchronous call of cudnnCreate() be made first before each thread creates its own handle asynchronously.

Per cudnnCreate(), for multi-threaded applications that use the same device from different threads, the recommended programming model is to create one (or a few, as is convenient) cuDNN handles per thread and use that cuDNN handle for the entire life of the thread.

5.2. Reproducibility (Determinism)

By design, most of cuDNN's routines from a given version generate the same bit-wise results across runs when executed on GPUs with the same architecture. There are some exceptions. For example, the following routines do not guarantee reproducibility across runs, even on the same architecture, because they use atomic operations in a way that introduces truly random floating point rounding errors:

• cudnnConvolutionBackwardFilter when CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0 or CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3 is used
• cudnnConvolutionBackwardData when CUDNN_CONVOLUTION_BWD_DATA_ALGO_0 is used
• cudnnPoolingBackward when CUDNN_POOLING_MAX is used
• cudnnSpatialTfSamplerBackward
• cudnnCTCLoss and cudnnCTCLoss_v8 when CUDNN_CTC_LOSS_ALGO_NON_DETERMINSTIC is used

Across different architectures, no cuDNN routines guarantee bit-wise reproducibility. For example, there is no guarantee of bit-wise reproducibility when comparing the same routine run on NVIDIA Volta™ and NVIDIA Turing™, NVIDIA Turing, and NVIDIA Ampere architecture.

5.3. Scaling Parameters

Many cuDNN routines like cudnnConvolutionForward() accept pointers in host memory to scaling factors alpha and beta. These scaling factors are used to blend the computed values with the prior values in the destination tensor as follows (refer to Figure 50):

            

            
dstValue = alpha*computedValue + beta*priorDstValue
        

Figure 50. Scaling Parameters for Convolution

When beta is zero, the output is not read and may contain uninitialized data (including NaN). These parameters are passed using a host memory pointer. The storage data types for alpha and beta are:

• float for HALF and FLOAT tensors, and
• double for DOUBLE tensors.

When the data input x, the filter input w and the output y are all in INT8 data type, the function cudnnConvolutionBiasActivationForward() will perform the type conversion as shown in Figure 51:

Figure 51. INT8 for cudnnConvolutionBiasActivationForward

5.4. cuDNN API Compatibility

Beginning in cuDNN 7, the binary compatibility of a patch and minor releases is maintained as follows:

• Any patch release x.y.z is forward or backward-compatible with applications built against another cuDNN patch release x.y.w (meaning, of the same major and minor version number, but having w!=z).
• cuDNN minor releases are binary backward-compatible with applications built against the same or earlier minor release (meaning, cuDNN x.y is binary compatible with an app built against cuDNN x.z, where z<=y).
• Applications compiled with a cuDNN version x.z are not guaranteed to work with x.y release when z>y.

5.5. Deprecation Policy

cuDNN version 8 introduces a new API deprecation policy to enable a faster pace of innovation.

The old deprecation policy required three major library releases to complete an API update. During this process, the original function name was first assigned to the legacy API, and then to the revised API, depending on the library version. The user wishing to migrate to the new API version had to update his or her code twice. In the first update, the original call foo() had to be changed to foo_vN(), where N is the new major cuDNN version. After the next major cuDNN release, the foo_vN() function had to be renamed back as foo(). Clearly, the above process could be difficult for code maintenance, especially when many functions are upgraded. A streamlined, two-step, deprecation policy will be used for all API changes starting with cuDNN version 8. Let us explain the process using two subsequent, major cuDNN releases, version 8 and 9:

If the existing API needs to be updated, a new function flavor is introduced with the _v tag followed by the current, major cuDNN version. In the next major release, the deprecated function is removed, and its name is never reused. A brand-new API is first introduced without the _v tag.

The revised depreciation scheme allows us to retire the legacy API in just one major release. Similarly to the previous API deprecation policy, the user is able to compile the legacy code without any changes using the next major release of the cuDNN library. The backward compatibility ends when another major cuDNN release is introduced.

The updated function name embeds the information in which the cuDNN version of the API call was modified. As a result, the API changes will be easier to track and document.

The new deprecation policy is applied also to pending API changes from previous cuDNN releases. For example, according to the old deprecation policy, cudnnSetRNNDescriptor_v6() should be removed in cuDNN version 8 and the upgraded call cudnnSetRNNDescriptor() with the same arguments and behavior should be kept. Instead, the new deprecation policy is applied to this case and the tagged function is kept. Prototypes of deprecated functions will be prepended in cuDNN version 8 headers using the CUDNN_DEPRECATED macro. When the -DCUDNN_WARN_DEPRECATED switch is passed to the compiler, any deprecated function call in the user's code will emit a compiler warning, for example:

            

            
warning: ‘cudnnStatus_t cudnnSetRNNMatrixMathType(cudnnRNNDescriptor_t, cudnnMathType_t)’ is deprecated [-Wdeprecated-declarations]
        

Or

            

            
warning C4996: 'cudnnSetRNNMatrixMathType': was declared deprecated
        

The above warnings are disabled by default to avoid potential build breaks in software setups where compiler warnings are treated as errors.

Note that the simple swapping of older cuDNN version 7 shared library files will not work with the cuDNN version 8 release. The user source code needs to be recompiled from scratch with the cuDNN version 8 headers and linked with the version 8 libraries.

5.6. GPU And Driver Requirements

For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, refer to the NVIDIA cuDNN Support Matrix.

5.7. Convolutions

The convolution functions are:

• cudnnConvolutionBackwardData()
• cudnnConvolutionBiasActivationForward()
• cudnnConvolutionForward()
• cudnnConvolutionBackwardBias()
• cudnnConvolutionBackwardFilter()

5.7.1. Convolution Formulas

This section describes the various convolution formulas implemented in cuDNN convolution functions for the cudnnConvolutionForward() path.
The convolution terms described in the table below apply to all the convolution formulas that follow.

Convolution (convolution mode set to CUDNN_CROSS_CORRELATION)

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; c,\; p+r,\; q+s}}\times w_{\mathit{k,c,r,s}}$

Convolution with Padding

$x_{\mathit{<0,\; <0}}=0$

$x_{\mathit{>H,\; >W}}=0$

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; c,\; p+r-pad,\; q+s-pad}}\times w_{\mathit{k,c,r,s}}$

Convolution with Subsample-Striding

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; c,\; (p*u)\; +\; r,\; (q*v)\; +\; s}}\times w_{\mathit{k,c,r,s}}$

Convolution with Dilation

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; c,\; p\; +\; (r*dilh),\; q\; +\; (s*dilw)}}\times w_{\mathit{k,c,r,s}}$

Convolution (convolution mode set to CUDNN_CONVOLUTION)

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; c,\; p\; +\; r,\; q\; +\; s}}\times w_{\mathit{k,\; c,\; R-r-1,\; S-s-1}}$

Convolution using Grouped Convolution

$C_g=\frac{C}{G}$

$K_g=\frac{K}{G}$

$y_{\mathit{n,\; k,\; p,\; q}}=\underset{c}{\overset{C_g}{\sum }}\underset{r}{\overset{R}{\sum }}\underset{s}{\overset{S}{\sum }}{x}_{\mathit{n,\; Cg*floor(k/Kg)+c,\; p+r,\; q+s}}\times w_{\mathit{k,c,r,s}}$

5.7.2. Grouped Convolutions

cuDNN supports grouped convolutions by setting groupCount > 1 for the convolution descriptor convDesc, using cudnnSetConvolutionGroupCount().

Basic Idea

Conceptually, in grouped convolutions, the input channels and the filter channels are split into a groupCount number of independent groups, with each group having a reduced number of channels. The convolution operation is then performed separately on these input and filter groups.

For example, consider the following: if the number of input channels is 4, and the number of filter channels of 12. For a normal, ungrouped convolution, the number of computation operations performed are 12*4.

If the groupCount is set to 2, then there are now two input channel groups of two input channels each, and two filter channel groups of six filter channels each.

As a result, each grouped convolution will now perform 2*6 computation operations, and two such grouped convolutions are performed. Hence the computation savings are 2x: (12*4)/(2*(2*6)).

cuDNN Grouped Convolution

• When using groupCount for grouped convolutions, you must still define all tensor descriptors so that they describe the size of the entire convolution, instead of specifying the sizes per group.
• Grouped convolutions are supported for all formats that are currently supported by the functions cudnnConvolutionForward(), cudnnConvolutionBackwardData() and cudnnConvolutionBackwardFilter().
• The tensor stridings that are set for groupCount of 1 are also valid for any group count.
• By default, the convolution descriptor convDesc is set to groupCount of 1.

Example

Below is an example showing the dimensions and strides for grouped convolutions for NCHW format, for 2D convolution.

xDesc or dxDesc

• Dimensions: [batch_size, input_channel, x_height, x_width]
• Strides: [input_channels*x_height*x_width, x_height*x_width, x_width, 1]

wDesc or dwDesc

• Dimensions: [output_channels, input_channels/groupCount, w_height, w_width]
• Format: NCHW

convDesc

• Group Count: groupCount

yDesc or dyDesc

• Dimensions: [batch_size, output_channels, y_height, y_width]
• Strides: [output_channels*y_height*y_width, y_height*y_width, y_width, 1]

5.7.3. Best Practices for 3D Convolutions

The following guidelines are for setting the cuDNN library parameters to enhance the performance of 3D convolutions. Specifically, these guidelines are focused on settings such as filter sizes, padding and dilation settings. Additionally, an application-specific use-case, namely, medical imaging, is presented to demonstrate the performance enhancement of 3D convolutions with these recommended settings. Specifically, these guidelines are applicable to the following functions and their associated data types:

• cudnnConvolutionForward()
• cudnnConvolutionBackwardData()
• cudnnConvolutionBackwardFilter()

For more information, refer to the NVIDIA cuDNN API Reference.

5.7.3.1. Recommended Settings

The following table shows the recommended settings while performing 3D convolutions for cuDNN.

5.7.3.2. Limitations

Your application will be functional but could be less performant if the model has channel counts lower than 32 (gets worse the lower it is).

If the above is in the network, use cuDNNFind to get the best option.

5.8. Environment Variables

cuDNN’s behavior can be influenced through a set of environment variables. The following environment variables are officially supported by cuDNN:

• NVIDIA_TF32_OVERRIDE
• NVIDIA_LICENSE_FILE
• CUDNN_LOGDEST_DBG
• CUDNN_LOGINFO_DBG
• CUDNN_LOGWARN_DBG
• CUDNN_LOGERR_DBG

For more information about these variables, refer to the NVIDIA cuDNN API Reference.

The following sections help answer the most commonly asked questions regarding typical use cases.

Error Reporting And API Logging

The cuDNN error reporting and API logging is a utility for recording the cuDNN API execution and error information. For each cuDNN API function call, all input parameters are reported in the API logging. If errors occur during the execution of the cuDNN API, a traceback of the error conditions can also be reported to help troubleshooting. This functionality is disabled by default, and can be enabled using the methods described in the later part of this section through three logging severity levels: CUDNN_LOGINFO_DBG, CUDNN_LOGWARN_DBG and CUDNN_LOGERR_DBG.

The log output contains variable names, data types, parameter values, device pointers, process ID, thread ID, cuDNN handle, CUDA stream ID, and metadata such as time of the function call in microseconds.

For example, when the severity level CUDNN_LOGINFO_DBG is enabled, the user will receive the API loggings, such as:

            

            
cuDNN (v8300) function cudnnSetActivationDescriptor() called:
    mode: type=cudnnActivationMode_t; val=CUDNN_ACTIVATION_RELU (1);
    reluNanOpt: type=cudnnNanPropagation_t; val=CUDNN_NOT_PROPAGATE_NAN (0);
    coef: type=double; val=1000.000000;
Time: 2017-11-21T14:14:21.366171 (0d+0h+1m+5s since start)
Process: 21264, Thread: 21264, cudnn_handle: NULL, cudnn_stream: NULL.
        

Starting in cuDNN 8.3.0, when the severity level CUDNN_LOGWARN_DBG or CUDNN_LOGERR_DBG are enabled, the log output additionally reports an error traceback such as the example below (currently only cuDNN version 8 graph APIs and legacy convolution APIs are using this error reporting feature). This traceback reports the relevant error/warning conditions, aiming to provide the user hints for troubleshooting purposes. Within the traceback, each message may have their own severity and will only be reported when the respective severity level is enabled. The traceback messages are printed in the reverse order of the execution so the messages at the top will be the root cause and tend to be more helpful for debugging.

            

            
cuDNN (v8300) function cudnnBackendFinalize() called:
    Info: Traceback contains 5 message(s)
        Error: CUDNN_STATUS_BAD_PARAM; reason: out <= 0
        Error: CUDNN_STATUS_BAD_PARAM; reason: is_valid_spacial_dim(xSpatialDimA[dim], wSpatialDimA[dim], ySpatialDimA[dim], cDesc.getPadLowerA()[dim], cDesc.getPadUpperA()[dim], cDesc.getStrideA()[dim], cDesc.getDilationA()[dim])
        Error: CUDNN_STATUS_BAD_PARAM; reason: is_valid_convolution(xDesc, wDesc, cDesc, yDesc)
        Error: CUDNN_STATUS_BAD_PARAM; reason: convolution.init(xDesc, wDesc, cDesc, yDesc)
        Error: CUDNN_STATUS_BAD_PARAM; reason: finalize_internal()
Time: 2021-10-05T17:11:07.935640 (0d+0h+0m+15s since start)
Process=87720; Thread=87720; GPU=NULL; Handle=NULL; StreamId=NULL.
        

There are two methods, as described below, to enable the error/warning reporting and API logging. For convenience, the log output can be handled by the built-in default callback function, which will direct the output to a log file or the standard I/O as designated by the user. The user may also write their own callback function to handle this information programmably, and use the to pass in the function pointer of their own callback function.

Method 1: Using Environment Variables

To enable API logging using environment variables, follow these steps:

• Decide which logging severity levels to include from these three options: CUDNN_LOGINFO_DBG, CUDNN_LOGWARN_DBG, or CUDNN_LOGERR_DBG. The logging severity levels are independent of each other. Any combination of them is valid.
• Set the environment variables CUDNN_LOGINFO_DBG, CUDNN_LOGWARN_DBG, or CUDNN_LOGERR_DBG to 1, and
• Set the environment variable CUDNN_LOGDEST_DBG to one of the following:
  • stdout, stderr, or a user-desired file path, for example, /home/userName1/log.txt.
• Include the conversion specifiers in the file name. For example:
  • To include date and time in the file name, use the date and time conversion specifiers: log_%Y_%m_%d_%H_%M_%S.txt. The conversion specifiers will be automatically replaced with the date and time when the program is initiated, resulting in log_2017_11_21_09_41_00.txt.
  • To include the process id in the file name, use the %i conversion specifier: log_%Y_%m_%d_%H_%M_%S_%i.txt for the result: log_2017_11_21_09_41_00_21264.txt when the process id is 21264. When you have several processes running, using the process id conversion specifier will prevent these processes from writing to the same file at the same time.
  Note:

  The supported conversion specifiers are similar to the strftime function.

  
  

  If the file already exists, the log will overwrite the existing file.

  Note:

  These environmental variables are only checked once at the initialization. Any subsequent changes in these environmental variables will not be effective in the current run. Also note that these environment settings can be overridden by Method 2 below.

Refer to Table 33 for the impact on the performance of API logging using environment variables. The CUDNN_LOG{INFO,WARN,ERR}_DBGnotation in the table header means the conclusion is applicable to either one of the environment variables.

Method 2: Using the API

To use API function calls to enable API logging, refer to the API description of cudnnSetCallback() and cudnnGetCallback().

6.2. FAQs

A: The following graphic shows how cuDNN relates to other software in the stack.

Figure 52. Software Stack With cuDNN

A: cuDNN provides the building blocks for common routines such as convolution, matmul, normalization, attention, pooling, activation and RNN/LSTMs. You can use cuDNN for both training and inference. However, where it differs from TensorRT is that the latter (TensorRT) is a programmable inference accelerator; just like a framework. TensorRT sees the whole graph and optimizes the network by fusing/combining layers and optimizing kernel selection for improved latency, throughout, power efficiency and for reducing memory requirements.

A rule of thumb you can apply is to check out TensorRT, see if it meets your inference needs, if it doesn't, then look at cuDNN for a closer, more in-depth perspective.

A: NVIDIA actively monitors the Deep Learning space for important problem specifications such as commonly used models. The heuristics are produced by sampling a portion of these problem specifications with available computational choices. Over time, more models are discovered and incorporated into the heuristics.

A: No, we don’t plan to become a framework and execute the whole graph one op at a time. At this time, we are focused on a subgraph given by the user, where we try to produce an optimized fusion kernel. We will document the rules regarding what can be fused and what cannot. The goal is to support general and flexible fusion, however, it will take time and there will be limits in what it can do in the cuDNN version 8.0.0 launch.

A: TensorRT and TensorFlow are frameworks; they see the whole graph and can do global optimization, however, they generally only fuse pointwise ops together or pattern match to a limited set of pre-compiled fixed fusion patterns like conv-bias-relu. On the other hand, cuDNN targets a subgraph, but can fuse convolutions with pointwise ops, thus providing potentially better performance. CuDNN fusion kernels can be utilized by TensorRT and TensorFlow/XLA as part of their global graph optimization.

A: Yes, you can call the C/C++ API directly. Usually, data scientists would wait for framework integration and use the Python API which is more convenient. However, if your use case requires better performance, you can target the cuDNN API directly.

A: Several components need to work together to make mixed precision training possible. CuDNN needs to support the layers with the required datatype config and have optimized kernels that run very fast. In addition, there is a module called automatic mixed precision (AMP) in frameworks which intelligently decides which op can run in a lower precision without affecting convergence and minimize the number of type conversions/transposes in the entire graph. These work together to give you speed up. For more information, refer to Mixed Precision Numerical Accuracy.

A: In the API introduced in cuDNN v8, convolution kernels are grouped by similar computation and numerical properties into engines. Every engine has a queryable set of performance tuning knobs. A computation case such as a convolution operation graph can be computed using different valid combinations of engines and their knobs, known as an engine configuration. Users can query an array of engine configurations for any given computation case ordered by performance, from fastest to slowest according to cuDNN’s own heuristics. Alternately, users can generate all possible engine configurations by querying the engine count and available knobs for each engine. This generated list could be used for auto-tuning or the user could create their own heuristics.

A: Due to the library split, cuDNN version 8.0 API will only load the necessary kernels on the first API call that requires it. In previous versions, this load would have been observed in the first cuDNN API call that triggers CUDA context initialization, typically cudnnCreate(). In version 8.0, this is delayed until the first sub-library call that triggers CUDA context initialization. Users who desire to have CUDA context preloaded can call the new cudnnCnnInferVersionCheck() API (or its related cousins), which has the side effect of initializing a CUDA context. This will reduce the run time for all subsequent API calls.

A: cuDNN v8.0 library is split into multiple sub-libraries. Each library contains a subset of the API. Users can link directly against the individual libraries or link with a dlopen layer which follows a plugin architecture.

To link against an individual library, users can directly specify it and its dependencies on the linker command line. For example, for infer libraries: -lcudnn_adv_infer, -lcudnn_cnn_infer, or -lcudnn_ops_infer.

For all libraries, -lcudnn_adv_train, -lcudnn_cnn_train, -lcudnn_ops_train, -lcudnn_adv_infer, -lcudnn_cnn_infer, and -lcudnn_ops_infer.

The dependency order is documented in the NVIDIA cuDNN 8.0.0 Preview Release Notes and the NVIDIA cuDNN API Reference.

Alternatively, the user can continue to link against a shim layer (-libcudnn) which can dlopen the correct library that provides the implementation of the function. When the function is called for the first time, the dynamic loading of the library takes place. Linker argument:

            

            
-lcudnn
        

A: The new cuDNN APIs are listed in the cuDNN 8.0.0 Release Notes as well as in the API changes for cuDNN 8.0.0.

6.3. Support

Support, resources, and information about cuDNN can be found online at https://developer.nvidia.com/cudnn. This includes downloads, webinars, NVIDIA Developer Forums, and more.

We appreciate all types of feedback. Consider posting on the forums with questions, comments, and suspected bugs that are appropriate to discuss publicly. cuDNN-related posts are reviewed by the cuDNN engineering team, and internally we will file bugs where appropriate. It’s helpful if you can paste or attach an API log to help us reproduce. External users can also file bugs directly by following these steps:

1. Register for the NVIDIA Developer website.
2. Log in to the developer site.
3. Click on your name in the upper right corner.
4. Click My account > My Bugs and select Submit a New Bug.
5. Fill out the bug reporting page. Be descriptive and if possible, provide the steps that you are following to help reproduce the problem. If possible, paste or attach an API log.
6. Click Submit a bug.

Some of the cuDNN library routines were derived from code developed by others and are subject to the following:

7.1. University of Tennessee

            

            
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