zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-07-17 22:51:19 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Test with CUDA 12.2 (#2375)

Browse Source 
* Test with CUDA 12.0

* try older image

* fix cpu sort
This commit is contained in:
Awni Hannun 2025-07-16 13:00:37 -07:00 committed by GitHub
parent f409b229a4
commit b2273733ea
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 18 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
.circleci/config.yml
View File

@ -201,7 +201,7 @@ jobs:
cuda_build_and_test:
machine:
image: linux-cuda-12:default
image: linux-cuda-12:2023.11.1
resource_class: gpu.nvidia.small.gen2
steps:
- checkout
@ -210,7 +210,7 @@ jobs:
command: |
sudo apt-get update
sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
python -m venv env
python3 -m venv env
source env/bin/activate
CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
pip install -e ".[dev]"

8
mlx/backend/cpu/sort.cpp
View File

@ -334,7 +334,9 @@ void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
auto& in = inputs[0];
// Copy input to output
CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
? CopyType::Vector
: CopyType::General;
copy_cpu(in, out, ctype, stream());
auto& encoder = cpu::get_command_encoder(stream());
@ -426,7 +428,9 @@ void Partition::eval_cpu(const std::vector<array>& inputs, array& out) {
auto& in = inputs[0];
// Copy input to output
CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
? CopyType::Vector
: CopyType::General;
copy_cpu(in, out, ctype, stream());
auto& encoder = cpu::get_command_encoder(stream());

16
mlx/backend/cuda/quantized.cu
View File

@ -36,7 +36,8 @@ affine_quantize(const T* w, uint8_t* out, T* scales, T* biases, size_t size) {
auto tidx = block_idx.x * block_size.x + idx_in_block.x;
auto tidy = block_idx.y * block_size.y + idx_in_block.y;
auto grid_dim = cg::this_grid().dim_threads();
auto grid_dim_x =
cg::this_grid().dim_blocks().x * cg::this_grid().block_index().x;
constexpr float eps = 1e-7;
constexpr int simd_size = WARP_SIZE;
constexpr float n_bins = (1 << bits) - 1;
@ -48,7 +49,7 @@ affine_quantize(const T* w, uint8_t* out, T* scales, T* biases, size_t size) {
writes_per_reduce > 1 ? 1 : values_per_reduce / pack_factor;
constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
size_t offset = tidx + grid_dim.x * size_t(tidy);
size_t offset = tidx + grid_dim_x * size_t(tidy);
size_t in_index = offset * values_per_reduce;
if (in_index >= size) {
return;
@ -153,12 +154,13 @@ __global__ void affine_dequantize(
auto tidx = block_idx.x * block_size.x + idx_in_block.x;
auto tidy = block_idx.y * block_size.y + idx_in_block.y;
auto grid_dim = cg::this_grid().dim_threads();
auto grid_dim_x =
cg::this_grid().dim_blocks().x * cg::this_grid().block_index().x;
constexpr int pack_factor = get_pack_factor<bits, 8>();
constexpr int bytes_per_pack = get_bytes_per_pack<bits>();
size_t offset = tidx + grid_dim.x * size_t(tidy);
size_t offset = tidx + grid_dim_x * size_t(tidy);
size_t oindex = offset * pack_factor;
if (oindex >= size) {
@ -349,7 +351,8 @@ void fast::AffineQuantize::eval_gpu(
dispatch_bits(bits_, [&](auto bits) {
using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
if (dequantize_) {
auto kernel = cu::affine_dequantize<DataType, group_size(), bits()>;
auto kernel =
cu::affine_dequantize<DataType, group_size.value, bits.value>;
auto [num_blocks, block_dims] =
get_launch_args(kernel, size, grid_shape, w.strides(), large);
enc.add_kernel_node(
@ -362,7 +365,8 @@ void fast::AffineQuantize::eval_gpu(
out.data<DataType>(),
out.size());
} else {
auto kernel = cu::affine_quantize<DataType, group_size(), bits()>;
auto kernel =
cu::affine_quantize<DataType, group_size.value, bits.value>;
auto [num_blocks, block_dims] =
get_launch_args(kernel, size, grid_shape, w.strides(), large);
enc.add_kernel_node(