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ggml : recurrent state rollback for ggml_ssm_scan (#26623)

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* Initial changes for Recurrent state rollback for nemotron for cpu and cuda

* Removing CPU RS rollback. Will enable it in subsequent PRs

* addition of test case

* Removing assert and calling runtime API to check if op is supported

* removing extra API and updating the call sites for K

* replace static cuda detection to runtime fused_op api

* address review comments and fallback when SSM rollback not supprted

* Adding changes for supporting RS-rollback in CPU. Also added test-backend-ops for cpu and cuda

* removing memory manipulation as rs rollback is now supported in CPU

* removing the static probe which is not needed now

* correcting the format

* address review comments

* enabling test for all the backends, unsupported backends will fallback to CPU

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* choose different graph based on the result of fused_ssm_op is supported or not and also handled memory->n_rs_seq >1 case incase of op is not supported

* Support K > 1 in ssm_scan for all backends

* Fix CI Issues

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
This commit is contained in:
lnigam
2026-08-14 17:20:40 +03:00
committed by GitHub
co-authored by Georgi Gerganov Gaurav Garg
parent 4c1a0af40d
commit 1692f9e50b
25 changed files with 291 additions and 43 deletions
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ggml/src/ggml-vulkan/ggml-vulkan.cpp
+5 -2
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@@ -1861,6 +1861,7 @@ struct vk_op_ssm_scan_push_constants {
uint32_t nb42, nb43, nb52, nb53;
uint32_t s_off;
uint32_t n_head, d_head, n_group, n_tok;
uint32_t n_seq, K;
};
struct vk_op_ssm_conv_push_constants {
uint32_t nb01, nb02;
@@ -12731,7 +12732,8 @@ static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx,
(uint32_t)src4->nb[2], (uint32_t)src4->nb[3],
(uint32_t)src5->nb[2], (uint32_t)src5->nb[3],
(uint32_t)s_off,
n_head, head_dim, n_group, n_tok
n_head, head_dim, n_group, n_tok,
n_seq, (uint32_t) ggml_get_op_params_i32(dst, 0)
};
vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
@@ -19417,8 +19419,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
} else if (tensor->op == GGML_OP_ADD_ID) {
tensor_clone = ggml_add_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]);
} else if (tensor->op == GGML_OP_SSM_SCAN) {
const int32_t K = ggml_get_op_params_i32(tensor, 0);
tensor_clone = ggml_ssm_scan(ggml_ctx, src_clone[0], src_clone[1], src_clone[2],
src_clone[3], src_clone[4], src_clone[5], src_clone[6]);
src_clone[3], src_clone[4], src_clone[5], src_clone[6], K);
} else if (tensor->op == GGML_OP_SSM_CONV) {
tensor_clone = ggml_ssm_conv(ggml_ctx, src_clone[0], src_clone[1]);
} else if (tensor->op == GGML_OP_ROLL) {