zhangyiss/gguf-tools
1
0
Fork 0
mirror of https://github.com/antirez/gguf-tools.git synced 2025-09-16 17:48:08 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Quantization functions refactoring.

Browse Source
This commit is contained in:
antirez
2024-01-03 21:02:17 +01:00
parent ff16bc3dcf
commit b1f32c4088
1 changed files with 193 additions and 169 deletions
Download Patch File Download Diff File Expand all files Collapse all files

98
gguflib.c
View File

@@ -500,45 +500,34 @@ int gguf_append_tensor_data(gguf_ctx *ctx, void *tensor, uint64_t tensor_size) {
/* ============================ GGUF dequantization ========================= */
/* Convert the specified tensor (quantized or not) into an array of
* floats. The array is allocated with malloc(). If the tensor is already
* in FP32 floats format, it is just memcpy()-ed to the destination array.
*
* On OOM, NULL is returned. If the tensor format is not yet supported,
* NULL is returned as well, but errno is set to EINVAL. */
float *gguf_tensor_to_float(gguf_tensor *tensor) {
/* G8_0 blocks dequantization to floats.
* 'y' is supposed to have enough space for 'count' weights. */
void gguf_q8_0_to_float(void *weights_data, float *y, uint64_t count) {
struct gguf_tensor_type_features *tf =
gguf_get_tensor_type_features(tensor->type);
uint64_t block_size = tf->bytes_per_block;
float *f = malloc(tensor->num_weights*sizeof(float));
if (tensor->type == GGUF_TYPE_F32) {
memcpy(f, tensor->weights_data, tensor->num_weights*sizeof(float));
} else if (tensor->type == GGUF_TYPE_F16) {
uint64_t i = 0; // i-th weight to dequantize.
uint16_t *w16 = (uint16_t*) tensor->weights_data;
while(i < tensor->num_weights) {
f[i] = from_half(w16[i]);
i++;
}
} else if (tensor->type == GGUF_TYPE_Q8_0) {
gguf_get_tensor_type_features(GGUF_TYPE_Q8_0);
/* Very simple layout: |16 bit scale|32 x 8bit weights|
* Each weight is scale * quantized_weight[0..31] */
int8_t *block = (int8_t*)tensor->weights_data;
int8_t *block = weights_data;
uint64_t i = 0; // i-th weight to dequantize.
while(i < tensor->num_weights) {
while(i < count) {
/* For each block get the scale and convert all the
* weights in the block. */
float scale = from_half(*((uint16_t*)block));
for (uint32_t j = 0; j < tf->items_per_block; j++) {
f[i++] = block[j+2] * scale; // j+2 to skip the scale bytes.
if (i == tensor->num_weights) break;
y[i++] = block[j+2] * scale; // j+2 to skip the scale bytes.
if (i == count) break;
}
block += block_size; // Go to the next block.
block += tf->bytes_per_block; // Go to the next block.
}
} else if (tensor->type == GGUF_TYPE_Q4_K) {
uint8_t *block = (uint8_t*)tensor->weights_data;
}
/* G4_K blocks dequantization to floats.
* 'y' is supposed to have enough space for 'count' weights. */
void gguf_q4_k_to_float(void *weights_data, float *y, uint64_t count) {
uint8_t *block = weights_data;
uint64_t i = 0; // i-th weight to dequantize.
while(i < tensor->num_weights) {
while(i < count) {
/* Q4_K super-blocks have 256 total weights, split in 8 sub-block.
* Each 8 sub-blocks have a different set of scales/mins, so
* there are 16 total values for scales/mins, but the scales/mins
@@ -600,22 +589,26 @@ float *gguf_tensor_to_float(gguf_tensor *tensor) {
/* First set: higher bits. */
for (uint32_t j = 0; j < 32; j++) {
uint8_t w = block[j] & 0xf;
f[i++] = w * scale - min;
if (i == tensor->num_weights) return f;
y[i++] = w * scale - min;
if (i == count) return;
}
/* Second set: lower bits. */
for (uint32_t j = 0; j < 32; j++) {
uint8_t w = block[j] >> 4;
f[i++] = w * scale - min;
if (i == tensor->num_weights) return f;
y[i++] = w * scale - min;
if (i == count) return;
}
block += 32; // Skip the two processed blocks.
}
}
} else if (tensor->type == GGUF_TYPE_Q6_K) {
uint8_t *block = (uint8_t*)tensor->weights_data;
}
/* G6_K blocks dequantization to floats.
* 'y' is supposed to have enough space for 'count' weights. */
void gguf_q6_k_to_float(void *weights_data, float *y, uint64_t count) {
uint8_t *block = weights_data;
uint64_t i = 0; // i-th weight to dequantize.
while(i < tensor->num_weights) {
while(i < count) {
/* Q6_K super-blocks have 256 total weights, split in 16 sub-block
* of 16 elements. There are no mins, just scales. Each sub-block
* have a block-specific scale quantized at 8 bits via a single
@@ -670,12 +663,12 @@ float *gguf_tensor_to_float(gguf_tensor *tensor) {
int8_t *scales = (int8_t*)block+128+64;
for (int cluster = 0; cluster < 2; cluster++) {
for (uint64_t j = 0; j < 128; j++) {
f[i] = (super_scale * scales[j/16]) *
y[i] = (super_scale * scales[j/16]) *
((int8_t)
((((L[j%64] >> (j/64*4)) & 0xF) |
(((H[j%32] >> (j/32*2)) & 3) << 4)))-32);
i++;
if (i == tensor->num_weights) return f;
if (i == count) return;
}
L += 64;
H += 32;
@@ -683,6 +676,37 @@ float *gguf_tensor_to_float(gguf_tensor *tensor) {
}
block += 128+64+16+2; // Go to the next block.
}
}
/* FP16 blocks dequantization to floats.
* 'y' is supposed to have enough space for 'count' weights. */
void gguf_f16_to_float(void *weights_data, float *y, uint64_t count) {
uint64_t i = 0; // i-th weight to dequantize.
uint16_t *w16 = weights_data;
while(i < count) {
y[i] = from_half(w16[i]);
i++;
}
}
/* Convert the specified tensor (quantized or not) into an array of
* floats. The array is allocated with malloc(). If the tensor is already
* in FP32 floats format, it is just memcpy()-ed to the destination array.
*
* On OOM, NULL is returned. If the tensor format is not yet supported,
* NULL is returned as well, but errno is set to EINVAL. */
float *gguf_tensor_to_float(gguf_tensor *tensor) {
float *f = malloc(tensor->num_weights*sizeof(float));
if (tensor->type == GGUF_TYPE_F32) {
memcpy(f, tensor->weights_data, tensor->num_weights*sizeof(float));
} else if (tensor->type == GGUF_TYPE_F16) {
gguf_f16_to_float(tensor->weights_data, f, tensor->num_weights);
} else if (tensor->type == GGUF_TYPE_Q8_0) {
gguf_q8_0_to_float(tensor->weights_data, f, tensor->num_weights);
} else if (tensor->type == GGUF_TYPE_Q4_K) {
gguf_q4_k_to_float(tensor->weights_data, f, tensor->num_weights);
} else if (tensor->type == GGUF_TYPE_Q6_K) {
gguf_q6_k_to_float(tensor->weights_data, f, tensor->num_weights);
} else {
errno = EINVAL;
return NULL;