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[Easy] Eliminate c10::optional usage in vllm/csrc (#17819)

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Lu Fang 2025-05-08 03:05:10 -07:00 committed by GitHub
parent a1e19b635d
commit f50dcb7c21
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GPG Key ID: B5690EEEBB952194
4 changed files with 7 additions and 7 deletions
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4
csrc/quantization/gptq_allspark/allspark_qgemm_w8a16.cu
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@ -9,7 +9,7 @@ at::Tensor as_g_workspace;
torch::Tensor allspark_w8a16_gemm(
torch::Tensor const& a, torch::Tensor const& b_qweight,
torch::Tensor const& b_scales, c10::optional<torch::Tensor> const& b_qzeros,
torch::Tensor const& b_scales, std::optional<torch::Tensor> const& b_qzeros,
int64_t n, int64_t group_size, int64_t sm_count, int64_t sm_version,
int64_t CUBLAS_M_THRESHOLD, bool has_zp, bool n32k16_reorder) {
TORCH_CHECK_NOT_IMPLEMENTED(
@ -918,7 +918,7 @@ void allspark_qgemm_w8a16_perc_ampere(
torch::Tensor allspark_w8a16_gemm(
torch::Tensor const& a, torch::Tensor const& b_qweight,
torch::Tensor const& b_scales, c10::optional<torch::Tensor> const& b_qzeros,
torch::Tensor const& b_scales, std::optional<torch::Tensor> const& b_qzeros,
int64_t n, int64_t group_size, int64_t sm_count, int64_t sm_version,
int64_t CUBLAS_M_THRESHOLD, bool has_zp, bool n32k16_reorder) {
// Verify device and strides

4
csrc/quantization/gptq_allspark/allspark_repack.cu
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@ -100,9 +100,9 @@ void rearrange_kn_weight_as_n32k16_order_ldg16(
void rearrange_kn_weight_as_n32k16_order(
torch::Tensor const& b_qweight, torch::Tensor const& b_scales,
c10::optional<torch::Tensor> const& b_zeros, bool has_zp,
std::optional<torch::Tensor> const& b_zeros, bool has_zp,
torch::Tensor& b_qweight_reorder, torch::Tensor& b_scales_reorder,
c10::optional<torch::Tensor> const& b_zeros_reorder, const int64_t K,
std::optional<torch::Tensor> const& b_zeros_reorder, const int64_t K,
const int64_t N, const int64_t N_32align) {
// Verify device and strides
TORCH_CHECK(b_qweight.device().is_cuda(), "b_qweight is not on GPU");

4
csrc/rocm/attention.cu
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@ -1597,7 +1597,7 @@ void paged_attention_custom_launcher(
torch::Tensor& block_tables, torch::Tensor& context_lens,
const std::optional<torch::Tensor>& query_start_loc, int max_context_len,
const std::optional<torch::Tensor>& alibi_slopes, torch::Tensor& k_scale,
torch::Tensor& v_scale, const c10::optional<torch::Tensor>& fp8_out_scale) {
torch::Tensor& v_scale, const std::optional<torch::Tensor>& fp8_out_scale) {
int num_seqs = block_tables.size(0);
int num_heads = query.size(1);
int head_size = query.size(2);
@ -1825,7 +1825,7 @@ void paged_attention(
const std::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, torch::Tensor& k_scale,
torch::Tensor& v_scale,
const c10::optional<torch::Tensor>& fp8_out_scale) {
const std::optional<torch::Tensor>& fp8_out_scale) {
// clang-format on
const int head_size = query.size(2);
if (kv_cache_dtype == "auto") {

2
csrc/rocm/ops.h
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@ -19,4 +19,4 @@ void paged_attention(
const std::optional<torch::Tensor>& query_start_loc, int64_t block_size,
int64_t max_context_len, const std::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, torch::Tensor& k_scale,
torch::Tensor& v_scale, const c10::optional<torch::Tensor>& fp8_out_scale);
torch::Tensor& v_scale, const std::optional<torch::Tensor>& fp8_out_scale);