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CUTLASS 3.0.0 (#786)

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* CUTLASS 3.0.0
This commit is contained in:
Vijay Thakkar
2023-01-23 20:55:28 -05:00
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parent 66d9cddc83
commit 277bd6e537
377 changed files with 76396 additions and 1186 deletions
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media/docs/code_organization.md
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@@ -7,6 +7,7 @@
This document describes the layout of the CUTLASS repository. The main components are:
* **CUTLASS Template Library** - CUDA Templates for Linear Algebra Subroutines and Solvers (header only)
* **CuTe Template Library** - CUTLASS's core vocabulary layout type and associated algebra (header only)
* **CUTLASS Utilities** - Additional templates
* **CUTLASS Instance Library** - instantiations of CUTLASS templates covering the design space
* **CUTLASS Profiler** - CUTLASS Library, Profiler, and Utilities
@@ -29,7 +30,6 @@ CUTLASS Templates are implemented by header files in the following directory str
```
include/ # Top-level include directory. Client applications should target this path.
cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only
arch/ # direct exposure of architecture features (including instruction-level GEMMs)
@@ -37,10 +37,11 @@ include/ # Top-level include directory. Client applications
gemm/ # code specialized for general matrix product computations
thread/ # thread-level operators
warp/ # warp-level operators
collective/ # 3.x API operators for all threads a tiled mma/copy are built over
threadblock/ # CTA-level operators
kernel/ # CUDA kernel entry points
device/ # launches kernel(s) over a full device
* # scope-agnostic components and basic vocabular type definitions for GEMM
* # scope-agnostic components and basic vocabulary type definitions for GEMM
layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory
*
@@ -51,7 +52,7 @@ include/ # Top-level include directory. Client applications
threadblock/ # CTA-level operators
kernel/ # CUDA kernel entry points
device/ # launches kernel(s) over a full device
* # scope-agnostic components and basic vocabular type definitions
* # scope-agnostic components and basic vocabulary type definitions
transform/ # code specialized for layout, type, and domain transformations
thread/ # thread-level operators
@@ -64,11 +65,27 @@ include/ # Top-level include directory. Client applications
util/ # miscellaneous CUTLASS components
*
* # core vocabulary types and fundamental arithmetic operators
cute / # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy
algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples
arch/ # Bare bones PTX wrapper structs for copy and math instructions
atom/ # Meta-information either link to or built from arch/ operators
mma_atom.hpp # cute::Mma_Atom and cute::TiledMma
copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy
*sm*.hpp # Arch specific meta-information for copy and math operations
container/ # Core container types used across CuTe, namely, cute::tuple
numeric/ # CuTe's internal numerics implementation
* # Core library types such as Shape, Stride, Layout, Tensor, and associated operations
```
See [Programming Guidelines](/media/docs/programming_guidelines.md) for further details about
conventions and design patterns used throughout CUTLASS.
## CuTe
CuTe is a collection of C++ CUDA template abstractions for defining and operating on hierarchically multidimensional layouts of threads and data. CuTe provides `Layout` and `Tensor` objects that compactly packages the type, shape, memory space, and layout of data, while performing the complicated indexing for the user. This lets programmers focus on the logical descriptions of their algorithms while CuTe does the mechanical bookkeeping for them. With these tools, we can quickly design, implement, and modify all dense linear algebra operations. More documentation
for CuTe can be found in [`/media/docs/cute/`](/media/docs/cute/).
## Tools
The `tools/` directory contains clients of the CUTLASS Template library and includes the following.
@@ -181,9 +198,9 @@ examples/
11_planar_complex_array/ # example demonstrating planar complex kernels with batch-specific problem sizes
12_gemm_bias_relu/ # example demonstrating GEMM fused with bias and relu
12_gemm_bias_relu/ # example demonstrating GEMM fused with bias and relu activation function
13_fused_two_gemms/ # example demonstrating two GEMms fused in one kernel
13_fused_two_gemms/ # example demonstrating two GEMMs fused into one kernel
```
## Media