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Cuda bug fixes 2 (#2298)

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* more bug fixes

* more bug fixes

* format
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Awni Hannun 2025-06-16 13:14:46 -07:00 committed by GitHub
parent c552ff2451
commit bc53f8293f
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11 changed files with 143 additions and 107 deletions
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14
mlx/backend/cuda/binary.cu
View File

@ -101,10 +101,12 @@ constexpr bool supports_binary_op() {
return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
}
if (std::is_same_v<Op, NaNEqual>) {
return std::is_same_v<Out, bool> &&
(is_floating_v<In> || std::is_same_v<In, complex64_t>);
return std::is_same_v<Out, bool> && is_inexact_v<In>;
}
if (std::is_same_v<Op, LogAddExp> || std::is_same_v<Op, ArcTan2>) {
if (std::is_same_v<Op, LogAddExp>) {
return std::is_same_v<In, Out> && is_inexact_v<In>;
}
if (std::is_same_v<Op, ArcTan2>) {
return std::is_same_v<In, Out> && is_floating_v<In>;
}
if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
@ -150,10 +152,10 @@ void binary_op_gpu_inplace(
auto [shape, strides] = collapse_contiguous_dims(a, b, out);
auto& a_strides = strides[0];
auto& b_strides = strides[1];
bool large = a.data_size() > UINT32_MAX ||
b.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
bool large = a.data_size() > INT32_MAX ||
b.data_size() > INT32_MAX || out.data_size() > INT32_MAX;
MLX_SWITCH_BOOL(large, LARGE, {
using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
using IdxT = std::conditional_t<LARGE, int64_t, int32_t>;
int ndim = shape.size();
if (ndim <= 3) {
MLX_SWITCH_1_2_3(ndim, NDIM, {

2
mlx/backend/cuda/compiled.cpp
View File

@ -130,11 +130,13 @@ struct FusedKernelBuilder {
constexpr const char* g_jit_includes = R"(
#include "mlx/backend/cuda/device/binary_ops.cuh"
#include "mlx/backend/cuda/device/ternary_ops.cuh"
#include "mlx/backend/cuda/device/unary_ops.cuh"
#include "mlx/backend/cuda/device/utils.cuh"
#include <cooperative_groups.h>
#define inf cuda::std::numeric_limits<float>::infinity()
)";
void Compiled::eval_gpu(

22
mlx/backend/cuda/device/binary_ops.cuh
View File

@ -1,6 +1,8 @@
// Copyright © 2025 Apple Inc.
#include "mlx/backend/cuda/device/cucomplex_math.cuh"
#include "mlx/backend/cuda/device/fp16_math.cuh"
#include "mlx/backend/cuda/device/utils.cuh"
#include <cuComplex.h>
#include <cuda/std/array>
@ -122,6 +124,26 @@ struct LogAddExp {
? maxval
: T(float(maxval) + log1p(expf(minval - maxval)));
};
__device__ cuComplex operator()(cuComplex x, cuComplex y) {
if (isnan(cuCrealf(x)) || isnan(cuCimagf(x)) || isnan(cuCrealf(y)) ||
isnan(cuCimagf(y))) {
return {
cuda::std::numeric_limits<float>::quiet_NaN(),
cuda::std::numeric_limits<float>::quiet_NaN()};
}
constexpr float inf = cuda::std::numeric_limits<float>::infinity();
auto maxval = x > y ? x : y;
auto minval = x < y ? x : y;
if (cuCrealf(minval) == -inf || cuCrealf(maxval) == inf)
return maxval;
float m = exp(cuCrealf(minval) - cuCrealf(maxval));
cuComplex dexp{
m * cos(cuCimagf(minval) - cuCimagf(maxval)),
m * sin(cuCimagf(minval) - cuCimagf(maxval)),
};
return maxval + log1p(dexp);
}
};
struct Maximum {

1
mlx/backend/cuda/device/ternary_ops.cuh
View File

@ -1,4 +1,5 @@
// Copyright © 2025 Apple Inc.
#pragma once
namespace mlx::core::cu {

33
mlx/backend/cuda/device/utils.cuh
View File

@ -187,8 +187,8 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_nd(
template <typename IdxT = int64_t>
inline __host__ __device__ IdxT
elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
IdxT loc = elem_to_loc_nd<3>(elem, shape, strides);
for (int i = ndim - 1; i >= 3; --i) {
IdxT loc = 0;
for (int i = ndim - 1; i >= 0; --i) {
loc += (elem % shape[i]) * IdxT(strides[i]);
elem /= shape[i];
}
@ -202,8 +202,9 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
const int64_t* a_strides,
const int64_t* b_strides,
int ndim) {
auto [a_loc, b_loc] = elem_to_loc_nd<3>(elem, shape, a_strides, b_strides);
for (int i = ndim - 1; i >= 3; --i) {
IdxT a_loc = 0;
IdxT b_loc = 0;
for (int i = ndim - 1; i >= 0; --i) {
int dim_idx = elem % shape[i];
a_loc += dim_idx * a_strides[i];
b_loc += dim_idx * b_strides[i];
@ -220,9 +221,10 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_4d(
const int64_t* b_strides,
const int64_t* c_strides,
int ndim) {
auto [a_loc, b_loc, c_loc] =
elem_to_loc_nd<3>(elem, shape, a_strides, b_strides, c_strides);
for (int i = ndim - 1; i >= 3; --i) {
IdxT a_loc = 0;
IdxT b_loc = 0;
IdxT c_loc = 0;
for (int i = ndim - 1; i >= 0; --i) {
int dim_idx = elem % shape[i];
a_loc += dim_idx * a_strides[i];
b_loc += dim_idx * b_strides[i];
@ -336,4 +338,21 @@ struct LoopedElemToLoc<1, false, OffsetT> {
}
};
inline __device__ cuComplex log1p(cuComplex in) {
float x = cuCrealf(in);
float y = cuCimagf(in);
float zabs = sqrt(x * x + y * y);
float theta = atan2f(y, x + 1);
if (zabs < 0.5f) {
float r = x * (2 + x) + y * y;
if (r == 0) { // handle underflow
return {x, theta};
}
return {0.5f * log1pf(r), theta};
} else {
auto z0 = sqrt((x + 1) * (x + 1) + y * y);
return {log(z0), theta};
}
}
} // namespace mlx::core::cu

50
mlx/backend/cuda/indexing.cpp
View File

@ -65,8 +65,8 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
bool large = (nidx > 0 && inputs[1].size() > UINT32_MAX) ||
(src.size() > UINT32_MAX) || (out.size() > UINT32_MAX);
bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
(src.size() > INT32_MAX) || (out.size() > INT32_MAX);
uint32_t slice_size = std::accumulate(
slice_sizes_.begin(), slice_sizes_.end(), 1, std::multiplies<uint32_t>());
@ -88,7 +88,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
dtype_to_cuda_type(idx_dtype),
nidx,
ndim,
large ? "int64_t" : "uint32_t"));
large ? "int64_t" : "int32_t"));
}
}
return std::make_pair(jit_source_gather, std::move(kernel_names));
@ -99,7 +99,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
if (large) {
mod.append_arg<int64_t>(out.size());
} else {
mod.append_arg<uint32_t>(out.size());
mod.append_arg<int32_t>(out.size());
}
mod.append_ndim_arg(src.shape());
mod.append_ndim_arg(src.strides());
@ -115,7 +115,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
dtype_to_cuda_type(idx_dtype),
nidx,
idx_ndim,
large ? "int64_t" : "uint32_t");
large ? "int64_t" : "int32_t");
auto& encoder = cu::get_command_encoder(s);
for (const auto& in : inputs) {
@ -152,14 +152,14 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
bool large = (nidx > 0 && inputs[1].size() > UINT32_MAX) ||
(upd.size() > UINT32_MAX) || (out.size() > UINT32_MAX);
bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
(upd.size() > INT32_MAX) || (out.size() > INT32_MAX);
uint32_t upd_post_idx_size = std::accumulate(
int32_t upd_post_idx_size = std::accumulate(
upd.shape().begin() + idx_ndim,
upd.shape().end(),
1,
std::multiplies<uint32_t>());
std::multiplies<int32_t>());
const char* op = g_scatter_ops[reduce_type_];
std::string module_name = fmt::format(
@ -181,7 +181,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
op,
nidx,
ndim,
large ? "int64_t" : "uint32_t"));
large ? "int64_t" : "int32_t"));
}
}
return std::make_pair(jit_source_scatter, std::move(kernel_names));
@ -192,7 +192,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
if (large) {
mod.append_arg<int64_t>(upd.size());
} else {
mod.append_arg<uint32_t>(upd.size());
mod.append_arg<int32_t>(upd.size());
}
mod.append_ndim_arg(upd.shape());
mod.append_ndim_arg(upd.strides());
@ -200,7 +200,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
if (large) {
mod.append_arg<int64_t>(upd_post_idx_size);
} else {
mod.append_arg<uint32_t>(upd_post_idx_size);
mod.append_arg<int32_t>(upd_post_idx_size);
}
mod.append_ndim_arg(out.shape());
mod.append_ndim_arg(out.strides());
@ -215,7 +215,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
op,
nidx,
idx_ndim,
large ? "int64_t" : "uint32_t");
large ? "int64_t" : "int32_t");
auto& encoder = cu::get_command_encoder(s);
for (const auto& in : inputs) {
@ -238,7 +238,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
return;
}
bool large = idx.size() > UINT32_MAX || src.size() > UINT32_MAX;
bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
std::string module_name = fmt::format(
"gather_axis_{}_{}",
@ -258,7 +258,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
ndim,
contiguous & 1 ? true : false,
contiguous & 2 ? true : false,
large ? "int64_t" : "uint32_t"));
large ? "int64_t" : "int32_t"));
}
}
}
@ -283,9 +283,9 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
mod.append_arg<int64_t>(idx_size_axis);
mod.append_arg<int64_t>(idx_size_post);
} else {
mod.append_arg<uint32_t>(idx_size_pre);
mod.append_arg<uint32_t>(idx_size_axis);
mod.append_arg<uint32_t>(idx_size_post);
mod.append_arg<int32_t>(idx_size_pre);
mod.append_arg<int32_t>(idx_size_axis);
mod.append_arg<int32_t>(idx_size_post);
}
mod.append_arg(remove_index(idx.shape(), axis_));
mod.append_arg(remove_index(src.strides(), axis_));
@ -302,7 +302,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
src.ndim() - 1,
src.flags().row_contiguous,
idx.flags().row_contiguous,
large ? "int64_t" : "uint32_t");
large ? "int64_t" : "int32_t");
auto& encoder = cu::get_command_encoder(s);
for (const auto& in : inputs) {
@ -337,7 +337,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
return;
}
bool large = idx.size() > UINT32_MAX || src.size() > UINT32_MAX;
bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
const char* op = reduce_type_ == ScatterAxis::Sum ? "Sum" : "Assign";
std::string module_name = fmt::format(
@ -360,7 +360,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
ndim,
contiguous & 1 ? true : false,
contiguous & 2 ? true : false,
large ? "int64_t" : "uint32_t"));
large ? "int64_t" : "int32_t"));
}
}
}
@ -385,9 +385,9 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
mod.append_arg<int64_t>(idx_size_axis);
mod.append_arg<int64_t>(idx_size_post);
} else {
mod.append_arg<uint32_t>(idx_size_pre);
mod.append_arg<uint32_t>(idx_size_axis);
mod.append_arg<uint32_t>(idx_size_post);
mod.append_arg<int32_t>(idx_size_pre);
mod.append_arg<int32_t>(idx_size_axis);
mod.append_arg<int32_t>(idx_size_post);
}
mod.append_arg(remove_index(idx.shape(), axis_));
mod.append_arg(remove_index(upd.strides(), axis_));
@ -405,7 +405,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
idx.ndim() - 1,
upd.flags().row_contiguous,
idx.flags().row_contiguous,
large ? "int64_t" : "uint32_t");
large ? "int64_t" : "int32_t");
auto& encoder = cu::get_command_encoder(s);
for (const auto& in : inputs) {

6
mlx/backend/cuda/ternary.cu
View File

@ -101,10 +101,10 @@ void ternary_op_gpu_inplace(
auto& a_strides = strides[0];
auto& b_strides = strides[1];
auto& c_strides = strides[2];
bool large = a.data_size() > UINT32_MAX || b.data_size() > UINT32_MAX ||
c.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
bool large = a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
c.data_size() > INT32_MAX || out.data_size() > INT32_MAX;
MLX_SWITCH_BOOL(large, LARGE, {
using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
using IdxT = std::conditional_t<LARGE, int64_t, int32_t>;
int ndim = shape.size();
if (ndim <= 3) {
MLX_SWITCH_1_2_3(ndim, NDIM, {

7
mlx/backend/cuda/unary.cu
View File

@ -27,13 +27,12 @@ constexpr bool supports_unary_op() {
std::is_same_v<Op, ArcSin> || std::is_same_v<Op, ArcSinh> ||
std::is_same_v<Op, ArcTan> || std::is_same_v<Op, ArcTanh> ||
std::is_same_v<Op, Erf> || std::is_same_v<Op, ErfInv> ||
std::is_same_v<Op, Expm1> || std::is_same_v<Op, Log1p> ||
std::is_same_v<Op, Sigmoid> || std::is_same_v<Op, Sqrt> ||
std::is_same_v<Op, Rsqrt>) {
std::is_same_v<Op, Expm1> || std::is_same_v<Op, Sigmoid> ||
std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Rsqrt>) {
return std::is_same_v<In, Out> && is_floating_v<In>;
}
if (std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
std::is_same_v<Op, Log10>) {
std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p>) {
return std::is_same_v<In, Out> && is_inexact_v<In>;
}
if (std::is_same_v<Op, BitwiseInvert>) {

3
mlx/backend/cuda/utils.cpp
View File

@ -31,6 +31,9 @@ const char* dtype_to_cuda_type(const Dtype& dtype) {
if (dtype == bfloat16) {
return "__nv_bfloat16";
}
if (dtype == complex64) {
return "cuComplex";
}
#define SPECIALIZE_DtypeToString(CPP_TYPE, DTYPE) \
if (dtype == DTYPE) { \
return #CPP_TYPE; \

108
python/tests/cuda_skip.py
View File

@ -1,24 +1,50 @@
cuda_skip = {
"TestArray.test_api",
"TestAutograd.test_cumprod_grad",
"TestAutograd.test_slice_grads",
"TestAutograd.test_split_against_slice",
"TestAutograd.test_stop_gradient",
"TestAutograd.test_topk_grad",
"TestAutograd.test_update_state",
"TestAutograd.test_vjp",
"TestBF16.test_arg_reduction_ops",
"TestBF16.test_binary_ops",
"TestBF16.test_reduction_ops",
"TestBlas.test_block_masked_matmul",
"TestBlas.test_complex_gemm",
"TestCompile.test_compile_dynamic_dims",
"TestEinsum.test_ellipses",
"TestEinsum.test_opt_einsum_test_cases",
"TestLoad.test_load_f8_e4m3",
"TestMemory.test_memory_info",
"TestLayers.test_group_norm",
"TestLayers.test_pooling",
"TestLayers.test_quantized_embedding",
"TestLayers.test_sin_pe",
"TestLayers.test_upsample",
"TestOps.test_array_equal",
"TestOps.test_complex_ops",
"TestOps.test_dynamic_slicing",
"TestOps.test_softmax",
"TestOps.test_sort",
"TestOps.test_tile",
"TestReduce.test_axis_permutation_sums",
"TestReduce.test_dtypes",
"TestReduce.test_expand_sums",
"TestReduce.test_many_reduction_axes",
"TestUpsample.test_torch_upsample",
# DivMod NYI
"TestOps.test_divmod",
"TestEval.test_multi_output_eval_during_transform",
# Partition NYI
"TestAutograd.test_topk_grad",
"TestOps.test_argpartition",
"TestOps.test_partition",
# Block masked matmul NYI
"TestBlas.test_block_masked_matmul",
# Gather matmul NYI
"TestBlas.test_gather_matmul",
"TestBlas.test_gather_matmul_grad",
"TestBlas.test_matmul_batched",
"TestBlas.test_matrix_vector_attn",
"TestCompile.test_compile_dynamic_dims",
"TestCompile.test_compile_inf",
"TestCompile.test_inf_constant",
# Scan NYI
"TestAutograd.test_cumprod_grad",
"TestOps.test_scans",
"TestOps.test_logcumsumexp",
# Hadamard NYI
"TestOps.test_hadamard",
"TestOps.test_hadamard_grad_vmap",
# Convolutions NYI
"TestConv.test_1d_conv_with_2d",
"TestConv.test_asymmetric_padding",
"TestConv.test_basic_grad_shapes",
@ -45,11 +71,11 @@ cuda_skip = {
"TestConvTranspose.test_torch_conv_transpose_3D",
"TestConvTranspose.test_torch_conv_transpose_3D_grad",
"TestConvTranspose.test_torch_conv_transpose_3d_output_padding",
"TestEinsum.test_attention",
"TestEinsum.test_ellipses",
"TestEinsum.test_opt_einsum_test_cases",
"TestEval.test_multi_output_eval_during_transform",
"TestExportImport.test_export_conv",
"TestLayers.test_conv1d",
"TestLayers.test_conv2d",
"TestVmap.test_vmap_conv",
# FFTs NYI
"TestFFT.test_fft",
"TestFFT.test_fft_big_powers_of_two",
"TestFFT.test_fft_contiguity",
@ -59,52 +85,22 @@ cuda_skip = {
"TestFFT.test_fft_large_numbers",
"TestFFT.test_fft_shared_mem",
"TestFFT.test_fftn",
"TestInit.test_orthogonal",
# Lapack ops NYI
"TestLinalg.test_cholesky",
"TestLinalg.test_cholesky_inv",
"TestLinalg.test_eig",
"TestLinalg.test_eigh",
"TestLinalg.test_inverse",
"TestVmap.test_vmap_inverse",
"TestLinalg.test_lu",
"TestLinalg.test_lu_factor",
"TestLinalg.test_pseudo_inverse",
"TestLinalg.test_qr_factorization",
"TestInit.test_orthogonal",
"TestLinalg.test_svd_decomposition",
"TestVmap.test_vmap_svd",
"TestLinalg.test_tri_inverse",
"TestLoad.test_load_f8_e4m3",
"TestLosses.test_binary_cross_entropy",
"TestMemory.test_memory_info",
"TestLayers.test_conv1d",
"TestLayers.test_conv2d",
"TestLayers.test_elu",
"TestLayers.test_group_norm",
"TestLayers.test_hard_shrink",
"TestLayers.test_pooling",
"TestLayers.test_quantized_embedding",
"TestLayers.test_sin_pe",
"TestLayers.test_softshrink",
"TestLayers.test_upsample",
"TestOps.test_argpartition",
"TestOps.test_array_equal",
"TestOps.test_as_strided",
"TestOps.test_binary_ops",
"TestOps.test_bitwise_grad",
"TestOps.test_complex_ops",
"TestOps.test_divmod",
"TestOps.test_dynamic_slicing",
"TestOps.test_hadamard",
"TestOps.test_hadamard_grad_vmap",
"TestOps.test_irregular_binary_ops",
"TestOps.test_kron",
"TestOps.test_log1p",
"TestOps.test_logaddexp",
"TestOps.test_logcumsumexp",
"TestOps.test_partition",
"TestOps.test_scans",
"TestOps.test_softmax",
"TestOps.test_sort",
"TestOps.test_tensordot",
"TestOps.test_tile",
# Quantization NYI
"TestQuantized.test_gather_matmul_grad",
"TestQuantized.test_gather_qmm",
"TestQuantized.test_gather_qmm_sorted",
@ -120,12 +116,4 @@ cuda_skip = {
"TestQuantized.test_small_matrix",
"TestQuantized.test_throw",
"TestQuantized.test_vjp_scales_biases",
"TestReduce.test_axis_permutation_sums",
"TestReduce.test_dtypes",
"TestReduce.test_expand_sums",
"TestReduce.test_many_reduction_axes",
"TestUpsample.test_torch_upsample",
"TestVmap.test_vmap_conv",
"TestVmap.test_vmap_inverse",
"TestVmap.test_vmap_svd",
}

4
python/tests/test_losses.py
View File

@ -83,14 +83,14 @@ class TestLosses(mlx_tests.MLXTestCase):
logits, targets, reduction="mean"
)
expected_mean = mx.mean(expected_none)
self.assertEqual(losses_mean, expected_mean)
self.assertTrue(mx.allclose(losses_mean, expected_mean))
# Test with reduction 'sum'
losses_sum = nn.losses.binary_cross_entropy(
logits, targets, reduction="sum"
)
expected_sum = mx.sum(expected_none)
self.assertEqual(losses_sum, expected_sum)
self.assertTrue(mx.allclose(losses_sum, expected_sum))
# With weights, no label smoothing
weights = mx.array([1.0, 2.0, 1.0, 2.0])