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Merged
merged 9 commits into from
Sep 8, 2025
+247 −3
Merged

ggml: allow casting between f32 and i32 #15783

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f3b489b
ggml: allow casting between f32 and i32
ngxson Sep 4, 2025
d4f78de
fix cuda
ngxson Sep 4, 2025
4f57dda
add vulkan
ngxson Sep 4, 2025
fee65fa
fix CPU non-cont
ngxson Sep 4, 2025
01af2c2
add non-cont test case
ngxson Sep 4, 2025
60e8f20
add note
ngxson Sep 4, 2025
9f5d5cd
extend test number range
ngxson Sep 4, 2025
0a2b23c
correct note
ngxson Sep 4, 2025
65b19a0
add cont version for vulkan
ngxson Sep 4, 2025
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1 change: 1 addition & 0 deletions ggml/include/ggml-cpu.h
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Expand Up @@ -135,6 +135,7 @@ extern "C" {
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);

GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *, float *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp32_to_i32 (const float *, int32_t *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t);
Expand Down
1 change: 1 addition & 0 deletions ggml/include/ggml.h
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Expand Up @@ -1403,6 +1403,7 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);

// note: casting from f32 to i32 will discard the fractional part
GGML_API struct ggml_tensor * ggml_cast(
struct ggml_context * ctx,
struct ggml_tensor * a,
Expand Down
15 changes: 14 additions & 1 deletion ggml/src/ggml-cpu/ggml-cpu.c
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Expand Up @@ -373,6 +373,9 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_I32] = {
.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_i32,
},
};

const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
Expand Down Expand Up @@ -2691,7 +2694,10 @@ struct ggml_cplan ggml_graph_plan(
if (ggml_is_quantized(node->type) ||
// F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
(node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16) ||
// conversion between F32 and I32
(node->src[0]->type == GGML_TYPE_F32 && node->src[1] && node->src[1]->type == GGML_TYPE_I32) ||
(node->src[0]->type == GGML_TYPE_I32 && node->src[1] && node->src[1]->type == GGML_TYPE_F32)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
}
} break;
Expand Down Expand Up @@ -3283,6 +3289,13 @@ void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) {
}
}

void ggml_cpu_fp32_to_i32(const float * x, int32_t * y, int64_t n) {
int64_t i = 0;
for (; i < n; ++i) {
y[i] = x[i];
}
}

void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
int64_t i = 0;
#if defined(__AVX2__)
Expand Down
160 changes: 160 additions & 0 deletions ggml/src/ggml-cpu/ops.cpp
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Expand Up @@ -776,6 +776,24 @@ static void ggml_compute_forward_dup_f32(
id += ne00 * (ne01 - ir1);
}
}
} else if (dst->type == GGML_TYPE_I32) {
size_t id = 0;
int32_t * dst_ptr = (int32_t *) dst->data;

for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
id += ne00 * ir0;
for (int i01 = ir0; i01 < ir1; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);

dst_ptr[id] = *src0_ptr;
id++;
}
}
id += ne00 * (ne01 - ir1);
}
}
Comment on lines +779 to +796
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Should we merge this into the F32 branch above?

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Yes and indeed I also want to migrate some of these codes into template function. WDYT?

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Yes, refactoring this code is welcome.

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I'll merge this PR as-is and will open another PR to refactor this code

} else {
GGML_ABORT("fatal error"); // TODO: implement
}
Expand Down Expand Up @@ -947,6 +965,144 @@ static void ggml_compute_forward_dup_f32(
}
}
}
} else if (dst->type == GGML_TYPE_I32) {
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
i10 += ne00 * ir0;
while (i10 >= ne0) {
i10 -= ne0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
for (int64_t i01 = ir0; i01 < ir1; i01++) {
for (int64_t i00 = 0; i00 < ne00; i00++) {
const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);

*(int32_t *) dst_ptr = *(const float *) src0_ptr;

if (++i10 == ne0) {
i10 = 0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
}
}
i10 += ne00 * (ne01 - ir1);
while (i10 >= ne0) {
i10 -= ne0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
}
}
} else {
GGML_ABORT("fatal error"); // TODO: implement
}
}

static void ggml_compute_forward_dup_i32(
const ggml_compute_params * params,
ggml_tensor * dst) {

const ggml_tensor * src0 = dst->src[0];

GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));

GGML_TENSOR_UNARY_OP_LOCALS

const int ith = params->ith; // thread index
const int nth = params->nth; // number of threads

// parallelize by rows
const int nr = ne01;
// number of rows per thread
const int dr = (nr + nth - 1) / nth;
// row range for this thread
const int ir0 = dr * ith;
const int ir1 = MIN(ir0 + dr, nr);

// dst counters

int64_t i10 = 0;
int64_t i11 = 0;
int64_t i12 = 0;
int64_t i13 = 0;

// TODO: not optimal, but works
if (dst->type == GGML_TYPE_F32) {
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
i10 += ne00 * ir0;
while (i10 >= ne0) {
i10 -= ne0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
for (int64_t i01 = ir0; i01 < ir1; i01++) {
for (int64_t i00 = 0; i00 < ne00; i00++) {
const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);

*(float *) dst_ptr = *(const int32_t *) src0_ptr;

if (++i10 == ne0) {
i10 = 0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
}
}
i10 += ne00 * (ne01 - ir1);
while (i10 >= ne0) {
i10 -= ne0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
i12 = 0;
if (++i13 == ne3) {
i13 = 0;
}
}
}
}
}
}
} else {
GGML_ABORT("fatal error"); // TODO: implement
}
Expand Down Expand Up @@ -1177,6 +1333,10 @@ void ggml_compute_forward_dup(
{
ggml_compute_forward_dup_f32(params, dst);
} break;
case GGML_TYPE_I32:
{
ggml_compute_forward_dup_i32(params, dst);
} break;
default:
{
if (ggml_is_quantized(src0->type) && dst->type == GGML_TYPE_F32) {
Expand Down
2 changes: 2 additions & 0 deletions ggml/src/ggml-cuda/convert.cuh
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Expand Up @@ -38,6 +38,8 @@ template<typename dst_t, typename src_t>
return __float2bfloat16(float(x));
} else if constexpr(std::is_same_v<src_t, nv_bfloat16>) {
return __bfloat162float(x);
} else if constexpr(std::is_same_v<dst_t, int32_t>) {
return int32_t(x);
} else {
return float(x);
}
Expand Down
4 changes: 4 additions & 0 deletions ggml/src/ggml-cuda/cpy.cu
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Expand Up @@ -374,6 +374,10 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
ggml_cpy_flt_cuda<nv_bfloat16, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) {
ggml_cpy_flt_cuda<nv_bfloat16, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) {
ggml_cpy_flt_cuda<float, int32_t> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) {
ggml_cpy_flt_cuda<int32_t, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else {
GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
Expand Down
6 changes: 6 additions & 0 deletions ggml/src/ggml-cuda/ggml-cuda.cu
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Expand Up @@ -3458,6 +3458,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) {
return true;
}
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_I32) {
return true;
}
if (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32) {
return true;
}
if (src0_type == src1_type && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) {
return true;
}
Expand Down
15 changes: 15 additions & 0 deletions ggml/src/ggml-metal/ggml-metal.m
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Expand Up @@ -582,6 +582,8 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
GGML_METAL_KERNEL_TYPE_CPY_BF16_F32,
GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16,
GGML_METAL_KERNEL_TYPE_CPY_F32_I32,
GGML_METAL_KERNEL_TYPE_CPY_I32_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
Expand Down Expand Up @@ -1614,6 +1616,8 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, cpy_bf16_f32, use_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, cpy_bf16_bf16, use_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_I32, cpy_f32_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_I32_F32, cpy_i32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
Expand Down Expand Up @@ -1940,6 +1944,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_I32:
return true;
default:
return false;
Expand Down Expand Up @@ -1972,6 +1977,8 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
default:
return false;
}
case GGML_TYPE_I32:
return op->type == GGML_TYPE_F32;
default:
return false;
};
Expand Down Expand Up @@ -5674,6 +5681,7 @@ static int ggml_metal_encode_node(

switch (dstt) {
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_I32].pipeline; break;
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_BF16].pipeline; break;
case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
Expand All @@ -5685,6 +5693,13 @@ static int ggml_metal_encode_node(
default: GGML_ABORT("not implemented");
};
} break;
case GGML_TYPE_I32:
{
switch (dstt) {
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_I32_F32].pipeline; break;
default: GGML_ABORT("not implemented");
};
} break;
case GGML_TYPE_F16:
{
switch (dstt) {
Expand Down
2 changes: 2 additions & 0 deletions ggml/src/ggml-metal/ggml-metal.metal
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Expand Up @@ -5338,6 +5338,8 @@ typedef decltype(kernel_cpy<float, float>) kernel_cpy_t;

template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy<float, float>;
template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy<float, half>;
template [[host_name("kernel_cpy_f32_i32")]] kernel kernel_cpy_t kernel_cpy<float, int32_t>;
template [[host_name("kernel_cpy_i32_f32")]] kernel kernel_cpy_t kernel_cpy<int32_t, float>;
#if defined(GGML_METAL_USE_BF16)
template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy<float, bfloat>;
#endif
Expand Down
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