leavelet/cutlass
1
0
Fork 0
Code Issues Pull Requests Actions 25 Packages Projects Releases Wiki Activity

CUTLASS 3.4.0 (#1286)

Browse Source 
* CUTLASS 3.4.0

* Update CHANGELOG.md

---------

Co-authored-by: Pradeep Ramani <prramani@nvidia.com>
This commit is contained in:
Pradeep Ramani
2023-12-29 15:21:31 -05:00
committed by GitHub
co-authored by Pradeep Ramani
parent b7508e3379
commit 8236f30675
211 changed files with 11409 additions and 2763 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+10 -12
View File
@@ -1,8 +1,8 @@
![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
# CUTLASS 3.3
# CUTLASS 3.4
_CUTLASS 3.3 - October 2023_
_CUTLASS 3.4 - December 2023_
CUTLASS is a collection of CUDA C++ template abstractions for implementing
high-performance matrix-matrix multiplication (GEMM) and related computations at all levels
@@ -41,17 +41,14 @@ and improves code composability and readability. More documentation specific to
In addition to GEMMs, CUTLASS implements high-performance convolution via the implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution operation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline. This allows CUTLASS to build convolutions by reusing highly-optimized GEMM components.
# What's New in CUTLASS 3.3
# What's New in CUTLASS 3.4
CUTLASS 3.3.0 is an update to CUTLASS adding:
CUTLASS 3.4.0 is an update to CUTLASS adding:
- New [Mixed-input Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input types with optimal performance.
- New [Mixed-input Ampere GEMMs](https://github.com/NVIDIA/cutlass/pull/1084) with support for canonical layouts (TN). The implementation supports upcast on operandB {fp16, bf16} x {s8, u8} and upcast on operandA {s8, u8} x {fp16, bf16}. They also include fast numeric conversion recipes and warp level shuffles to achieve optimal performance.
- New [Copy Async based Hopper GEMMs](/test/unit/gemm/device/sm90_gemm_bf16_bf16_bf16_alignx_tensor_op_f32_warpspecialized_cooperative.cu) - which support lower than 16B aligned input tensors (across s8/fp8/fp16/bf16/tf32 types) with optimal performance. As a part of this, new kernel schedules, and Copy Ops [SM80\_CP\_ASYNC\_CACHE\_\*](/include/cute/arch/copy_sm80.hpp) were also added.
- EVT Support for RELU with Aux bitmap tensor store (used in dRELU). See [SM90 EVT fusions](/include/cutlass/epilogue/fusion/sm90_visitor_compute_tma_warpspecialized.hpp) for details.
- Various subbyte enhancements like tagged device ptrs, support for vectorized copy, various operators to treat subbyte iterators as pointers, and full-fledged CuTe Tensor support.
- Support for Clang as a host compiler.
- Support for void-C kernels and SM80 mixed-input GEMMs in the CUTLASS Python interface
- Improved [Mixed-input Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) supporting {16-bit, 8-bit} x {8-bit, 4-bit} input types with fast numerical converters and group scaling factors tuned for optimal performance on Hopper H100.
- Beta release of [Pointer-Array Batched GEMMs](/examples/56_hopper_ptr_array_batched_gemm) utilizing TMA and Hopper H100 tensor cores now available. (Requires CUDA 12.3 or above)
- Beta release of [Group-GEMM](/examples/57_hopper_grouped_gemm) - commonly used in optimization of Mixture-Of-Expert models, is now available on Hopper GPUs taking advantage of TMA and Hopper H100 tensor cores. (Requires CUDA 12.3 or above)
- Impovements to NamedBarriers including details of [ReservedNamedBarriers](/include/cutlass/arch/barrier.h) used within the CUTLASS library.
Minimum requirements:
@@ -95,7 +92,7 @@ as shown in the above figure. Tensor Core operations are implemented using CUDA
CUTLASS requires a C++17 host compiler and
performs best when built with the [**CUDA 12.2.2 Toolkit**](https://developer.nvidia.com/cuda-toolkit-archive).
It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, CUDA 12.0 and CUDA 12.1.
It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, CUDA 12.0, CUDA 12.1, CUDA 12.2.2 and CUDA 12.3.1
## Operating Systems
We have tested the following environments.
@@ -107,6 +104,7 @@ We have tested the following environments.
| Ubuntu 22.04 | GCC 11.2.0 |
| Ubuntu 22.04 | Clang 10.0.0 |
| Ubuntu 22.04 | Clang 14.0.6 |
| Ubuntu 22.04 | Clang 17.0.6 |
| Windows 10.0 | Visual Studio 2019 v16.11.27 |
Note: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC >= 9 is recommended.