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[chore](cuda): explicitly use ele_per_blk var for better readability (#1784)

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SCDESPERTATE
2026-01-23 11:11:08 +08:00
committed by GitHub
parent 779bf14556
commit b0f827d2a9
2 changed files with 14 additions and 14 deletions
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14
kt-kernel/cuda/custom_gguf/dequant.cu
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@@ -671,7 +671,7 @@ torch::Tensor dequantize_q8_0(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({ num_blocks, 32 }, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({ num_blocks, ele_per_blk }, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -705,7 +705,7 @@ torch::Tensor dequantize_q6_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -736,7 +736,7 @@ torch::Tensor dequantize_q5_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -768,7 +768,7 @@ torch::Tensor dequantize_q4_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -799,7 +799,7 @@ torch::Tensor dequantize_q3_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -830,7 +830,7 @@ torch::Tensor dequantize_q2_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -861,7 +861,7 @@ torch::Tensor dequantize_iq4_xs(const int8_t* data, const int num_bytes, const i
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:

14
kt-sft/csrc/ktransformers_ext/cuda/custom_gguf/dequant.cu
View File

@@ -671,7 +671,7 @@ torch::Tensor dequantize_q8_0(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({ num_blocks, 32 }, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({ num_blocks, ele_per_blk }, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -705,7 +705,7 @@ torch::Tensor dequantize_q6_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -736,7 +736,7 @@ torch::Tensor dequantize_q5_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -768,7 +768,7 @@ torch::Tensor dequantize_q4_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -799,7 +799,7 @@ torch::Tensor dequantize_q3_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -830,7 +830,7 @@ torch::Tensor dequantize_q2_k(const int8_t* data, const int num_bytes, const int
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16:
@@ -861,7 +861,7 @@ torch::Tensor dequantize_iq4_xs(const int8_t* data, const int num_bytes, const i
//data_gpu.copy_(data, false);
// Create output tensor
auto output = torch::zeros({num_blocks, 256}, torch::dtype(target_dtype).device(device));
auto output = torch::zeros({num_blocks, ele_per_blk}, torch::dtype(target_dtype).device(device));
switch (target_dtype) {
case torch::kFloat16: