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

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Merged
ovowei merged 1 commit into from
Jan 23, 2026
Merged

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

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14 changes: 7 additions & 7 deletions kt-kernel/cuda/custom_gguf/dequant.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down
14 changes: 7 additions & 7 deletions kt-sft/csrc/ktransformers_ext/cuda/custom_gguf/dequant.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down