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ggml: Backport scale kernel fixes 

The GGML scale kernel uses signed 32-bit ints to represent
the number of elements in the tensor. For large images,
mistral-small3.2 overflows this, triggering CUDA errors due
to negative arguments.

Currently, this can happen when the user passes a large image
to mistral-small3.2. However, with upcoming changes to reserve
CUDA memory, it happens every time mistral-small is loaded as
we reserve using a worst case batch.

This patch is part of an upstream GGML commit and should be removed
after GGML is updated past 0a1b398 "ggml: add ops for WAN video model
(cuda && cpu) (#15669)".

Fixes #10388
This commit is contained in:
Jesse Gross 2025-09-23 12:13:39 -07:00 committed by Jesse Gross
parent 734b57da0e
commit efaee8c2d6
2 changed files with 67 additions and 9 deletions
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57
llama/patches/0026-ggml-Backport-scale-kernel-fixes.patch Normal file
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@ -0,0 +1,57 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Jesse Gross <jesse@ollama.com>
Date: Tue, 23 Sep 2025 15:41:58 -0700
Subject: [PATCH] ggml: Backport scale kernel fixes
The GGML scale kernel uses signed 32-bit ints to represent
the number of elements in the tensor. For large images,
mistral-small3.2 overflows this, triggering CUDA errors due
to negative arguments.
Currently, this can happen when the user passes a large image
to mistral-small3.2. However, with upcoming changes to reserve
CUDA memory, it happens every time mistral-small is loaded as
we reserve using a worst case batch.
This patch is part of an upstream GGML commit and should be removed
after GGML is updated past 0a1b398 "ggml: add ops for WAN video model
(cuda && cpu) (#15669)".
Fixes #10388
---
ggml/src/ggml-cuda/scale.cu | 19 ++++++++++---------
1 file changed, 10 insertions(+), 9 deletions(-)
diff --git a/ggml/src/ggml-cuda/scale.cu b/ggml/src/ggml-cuda/scale.cu
index 2ee9e5889..0ddeff6a1 100644
--- a/ggml/src/ggml-cuda/scale.cu
+++ b/ggml/src/ggml-cuda/scale.cu
@@ -1,18 +1,19 @@
#include "scale.cuh"
-static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int k) {
- const int i = blockDim.x*blockIdx.x + threadIdx.x;
+#define MAX_GRIDDIM_X 0x7FFFFFFF
- if (i >= k) {
- return;
- }
+static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int64_t nelements) {
+ int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
+ int64_t stride = (int64_t)blockDim.x * (int64_t)gridDim.x;
- dst[i] = scale * x[i] + bias;
+ for (int64_t i = tid; i < nelements; i += stride) {
+ dst[i] = scale * x[i] + bias;
+ }
}
-static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int k, cudaStream_t stream) {
- const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
- scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, k);
+static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int64_t nelements, cudaStream_t stream) {
+ const int64_t num_blocks = (nelements + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
+ scale_f32<<<MIN(MAX_GRIDDIM_X, num_blocks), CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, nelements);
}
void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

19
ml/backend/ggml/ggml/src/ggml-cuda/scale.cu vendored
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@ -1,18 +1,19 @@
#include "scale.cuh"
static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
#define MAX_GRIDDIM_X 0x7FFFFFFF
if (i >= k) {
return;
static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int64_t nelements) {
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t stride = (int64_t)blockDim.x * (int64_t)gridDim.x;
for (int64_t i = tid; i < nelements; i += stride) {
dst[i] = scale * x[i] + bias;
}
dst[i] = scale * x[i] + bias;
}
static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, k);
static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int64_t nelements, cudaStream_t stream) {
const int64_t num_blocks = (nelements + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
scale_f32<<<MIN(MAX_GRIDDIM_X, num_blocks), CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, nelements);
}
void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {