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ggml: move fp16/bf16 conversion optimizations to CPU backend + export conversion APIs (#13107)

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* ggml: dynamic x86_64 feature detection for FP32 <-> FP16/BF16 conversion

* move fp converter to ggml-cpu

* Switch ggml_compute_forward_get_rows_f16/bf16 to new ggml_cpu_fp16/bf16_to_fp32
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SXX 2025-04-26 22:05:31 +08:00 • committed by GitHub
parent d5fe4e81bd
commit 77d5e9a76a
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4 changed files with 101 additions and 50 deletions
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91
ggml/src/ggml-cpu/ggml-cpu.c
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@ -215,7 +215,7 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
.nrows = 1,
},
[GGML_TYPE_F16] = {
.from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row,
.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_fp16,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16,
.vec_dot_type = GGML_TYPE_F16,
.nrows = 1,
@ -356,7 +356,7 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
.from_float = quantize_row_q8_K,
},
[GGML_TYPE_BF16] = {
.from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row,
.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_bf16,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16,
.vec_dot_type = GGML_TYPE_BF16,
.nrows = 1,
@ -3166,6 +3166,93 @@ enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct g
return ggml_graph_compute(cgraph, &cplan);
}
void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
int64_t i = 0;
#if defined(__F16C__)
#if defined(__AVX512F__)
for (; i + 15 < n; i += 16) {
__m512 x_vec = _mm512_loadu_ps(x + i);
__m256i y_vec = _mm512_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm256_storeu_si256((__m256i *)(y + i), y_vec);
}
#endif
for (; i + 7 < n; i += 8) {
__m256 x_vec = _mm256_loadu_ps(x + i);
__m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storeu_si128((__m128i *)(y + i), y_vec);
}
for (; i + 3 < n; i += 4) {
__m128 x_vec = _mm_loadu_ps(x + i);
__m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storel_epi64((__m128i *)(y + i), y_vec);
}
#endif
for (; i < n; ++i) {
y[i] = GGML_FP32_TO_FP16(x[i]);
}
}
void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
int64_t i = 0;
#if defined(__F16C__)
#if defined(__AVX512F__)
for (; i + 15 < n; i += 16) {
__m256i x_vec = _mm256_loadu_si256((const __m256i *)(x + i));
__m512 y_vec = _mm512_cvtph_ps(x_vec);
_mm512_storeu_ps(y + i, y_vec);
}
#endif
for (; i + 7 < n; i += 8) {
__m128i x_vec = _mm_loadu_si128((const __m128i *)(x + i));
__m256 y_vec = _mm256_cvtph_ps(x_vec);
_mm256_storeu_ps(y + i, y_vec);
}
for (; i + 3 < n; i += 4) {
__m128i x_vec = _mm_loadl_epi64((const __m128i *)(x + i));
__m128 y_vec = _mm_cvtph_ps(x_vec);
_mm_storeu_ps(y + i, y_vec);
}
#endif
for (; i < n; ++i) {
y[i] = GGML_FP16_TO_FP32(x[i]);
}
}
void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) {
int64_t i = 0;
for (; i < n; ++i) {
y[i] = GGML_FP32_TO_BF16(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__)
#if defined(__AVX512F__)
for (; i + 15 < n; i += 16) {
_mm512_storeu_ps(y + i,
_mm512_castsi512_ps(
_mm512_slli_epi32(
_mm512_cvtepu16_epi32(
_mm256_loadu_si256(
(const __m256i *)(x + i))),
16)));
}
#endif
for (; i + 7 < n; i += 8) {
_mm256_storeu_ps(y + i,
_mm256_castsi256_ps(
_mm256_slli_epi32(
_mm256_cvtepu16_epi32(
_mm_loadu_si128(
(const __m128i *)(x + i))),
16)));
}
#endif
for (; i < n; i++) {
y[i] = GGML_BF16_TO_FP32(x[i]);
}
}
int ggml_cpu_has_avx(void) {
#if defined(__AVX__)