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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 15, 12:15 UTC

[Misc][ROCm] Exclude `cutlass_mla_decode` for ROCm build (#17289)

Signed-off-by: Tianyuan Wu <Tianyuan.Wu@amd.com>

TY-AMD committed Apr 30, 2025 at 01:26 UTC 06ffc7e1d35b3f754e46439babfed564822bbb75
1 file changed +7 -7
csrc/torch_bindings.cpp
+7 -7
@@ -130,13 +130,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
130 ") -> ()");
131 ops.impl("advance_step_flashinfer", torch::kCUDA, &advance_step_flashinfer);
132
133 - // Compute MLA decode using cutlass.
134 - ops.def(
135 - "cutlass_mla_decode(Tensor! out, Tensor q_nope, Tensor q_pe,"
136 - " Tensor kv_c_and_k_pe_cache, Tensor seq_lens,"
137 - " Tensor page_table, float scale) -> ()");
138 - ops.impl("cutlass_mla_decode", torch::kCUDA, &cutlass_mla_decode);
139 -
133 // Layernorm
134 // Apply Root Mean Square (RMS) Normalization to the input tensor.
135 ops.def(
@@ -450,6 +443,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
443 ops.def("cutlass_sparse_compress(Tensor a) -> Tensor[]");
444 ops.impl("cutlass_sparse_compress", &cutlass_sparse_compress);
445
446 + // CUTLASS MLA decode
447 + ops.def(
448 + "cutlass_mla_decode(Tensor! out, Tensor q_nope, Tensor q_pe,"
449 + " Tensor kv_c_and_k_pe_cache, Tensor seq_lens,"
450 + " Tensor page_table, float scale) -> ()");
451 + ops.impl("cutlass_mla_decode", torch::kCUDA, &cutlass_mla_decode);
452 +
453 // Mamba selective scan kernel
454 ops.def(
455 "selective_scan_fwd(Tensor! u, Tensor! delta,"