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ggml-cpu: rename all fp16<->fp32 macros to prefix with ggml_cpu

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ref: https://github.com/ggml-org/llama.cpp/pull/14317#discussion_r2164449406

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
This commit is contained in:
Aaron Teo
2025-06-25 01:07:58 +08:00
parent 64568ffb2d
commit a02b360f2c
20 changed files with 666 additions and 735 deletions
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30
ggml/src/ggml-cpu/ggml-cpu.c
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@@ -736,7 +736,7 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_CPU_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_BF16:
@@ -795,7 +795,7 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_CPU_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_BF16:
@@ -846,7 +846,7 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
case GGML_TYPE_F16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
}
case GGML_TYPE_BF16:
{
@@ -891,7 +891,7 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
case GGML_TYPE_F16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
((ggml_fp16_t *)(tensor->data))[i] = GGML_CPU_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
@@ -920,7 +920,7 @@ int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i
case GGML_TYPE_I32:
return ((int32_t *) data)[0];
case GGML_TYPE_F16:
return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
case GGML_TYPE_BF16:
return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
case GGML_TYPE_F32:
@@ -947,7 +947,7 @@ void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
((ggml_fp16_t *)(data))[0] = GGML_CPU_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
@@ -985,7 +985,7 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
}
case GGML_TYPE_F16:
{
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
}
case GGML_TYPE_BF16:
{
@@ -1024,7 +1024,7 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
((ggml_fp16_t *)(tensor->data))[i] = GGML_CPU_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
@@ -1051,7 +1051,7 @@ float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
case GGML_TYPE_I32:
return ((int32_t *) data)[0];
case GGML_TYPE_F16:
return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
case GGML_TYPE_BF16:
return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
case GGML_TYPE_F32:
@@ -1078,7 +1078,7 @@ void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
((ggml_fp16_t *)(data))[0] = GGML_CPU_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
@@ -3158,7 +3158,7 @@ void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
}
#endif
for (; i < n; ++i) {
y[i] = GGML_FP32_TO_FP16(x[i]);
y[i] = GGML_CPU_FP32_TO_FP16(x[i]);
}
}
@@ -3200,7 +3200,7 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
#endif
for (; i < n; ++i) {
y[i] = GGML_FP16_TO_FP32(x[i]);
y[i] = GGML_CPU_FP16_TO_FP32(x[i]);
}
}
@@ -3478,9 +3478,9 @@ void ggml_cpu_init(void) {
uint16_t u16;
ggml_fp16_t fp16;
} u = {i};
float f = GGML_FP16_TO_FP32(u.fp16);
ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
float f = GGML_CPU_FP16_TO_FP32(u.fp16);
ggml_table_gelu_f16[i] = GGML_CPU_FP32_TO_FP16(ggml_gelu_f32(f));
ggml_table_gelu_quick_f16[i] = GGML_CPU_FP32_TO_FP16(ggml_gelu_quick_f32(f));
}
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);