llama + spec: MTP Support (#22673)
* spec: support MTP * fix batch size * rename files * cont : simplify (#7) * MTP: clean-up (#9) * MTP: clean-up * review: use llama_context_type instead of llama_graph_type * review: remove llama_model_has_mtp * review: fix convert issues * convert: fix pycheck * review: formatting * use `mtp-` for identifying mtp models * convert: fix mtp conversion * mtp -> draft-mtp * remove unused llama_arch * add need_embd in speculative * llama: allow partial seq_rm for GDN models for speculative decoding Currently speculative checkpoint needs to restart from a checkpoint after some draft tokens are not accepted, this leads to some wastage in running the target again. This PR adds the ability to rollback upto `draft_max` by storing the GDN intermediates. * fix pending state * vulkan: add GDN partial rollback * meta: extend check to axis 1 * metal: add GDN partial rollback Extend the gated delta net kernel to store intermediate states for partial rollback support on the Metal backend. - Add K (snapshot slot count) as a function constant - Read input state from slot 0 of the 3D state tensor - Write intermediate states to different slots during token loop - For K=1, maintain backward-compatible single-slot behavior Ref: https://github.com/ggml-org/llama.cpp/commit/8c05923630110223669f069af2000e9cf10c02bc Assisted-by: llama.cpp:local pi * delta_net_base: use ggml_pad instead of new_tensor * review: add need_rs_seq * review: rename part_bounded to n_rs * review: deslop comments * review: rename, add asserts * server : adjust checkpoint logic (#11) * server : adjust checkpoint logic * cont : rm asserts * server-context: fix early exit * spec : fix compatibility with n-gram and add TODOs (#13) * metal : cleanup * llama : fix faulty bitwise check in recurrent memory * server : disable RS-based MTP in combination with other spec types * spec : add TODOs * cont : fix comment * cont : update comment * common : fix logic for ngram + mtp compat * llama-memory: enable checkpointing with partial rollback * cont: add test-case for loading into a dirty ctx * llama-memory-recurrent: clear rs_idx in clear * download: fix mtp path * llama-arch: fix enorm op * docs: update docs * conversion: fix type annotations --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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co-authored by
Georgi Gerganov
parent
b81c2cdd74
commit
255582687b
@@ -10513,19 +10513,30 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
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const bool kda = (neg0 == S_v);
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// scratch layout per thread: [delta(S_v)]
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const int64_t scratch_per_thread = S_v;
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// state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
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const int64_t K = src_state->ne[1];
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GGML_ASSERT(K >= 1);
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// per-seq stride in floats (slot 0 of seq s lives at state + s * seq_stride)
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const int64_t state_seq_stride = src_state->nb[2] / sizeof(float);
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const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
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const int ith = params->ith;
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float * delta = (float *)params->wdata + ith * scratch_per_thread + CACHE_LINE_SIZE_F32;
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float * delta = (float *)params->wdata + ith * per_thread + CACHE_LINE_SIZE_F32;
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float * state_work = K > 1 ? (delta + S_v) : nullptr;
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// output layout: [attn_scores | new_states]
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// attn_scores: S_v * H * n_tokens * n_seqs floats
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// new_states: S_v * S_v * H * n_seqs floats
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const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
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// attn_scores: S_v * H * n_tokens * n_seqs floats
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// new_states: S_v * S_v * H * n_seqs * K floats (K snapshot slots; last min(n_tokens, K))
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const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
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const int64_t state_size_per_snap = S_v * S_v * H * n_seqs;
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float * attn_out_base = (float *)dst->data;
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float * state_out_base = (float *)dst->data + attn_score_elems;
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// snapshot slot mapping: target_slot = t - shift. When n_tokens < K only the last
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// n_tokens slots are written; earlier slots are left untouched (caller-owned).
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const int64_t shift = n_tokens - K;
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const float * state_in_base = (const float *)src_state->data;
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//const int64_t rq1 = nev1 / neq1;
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@@ -10545,10 +10556,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
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const int64_t iq3 = iv3 / rq3;
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const int64_t ik3 = iv3 / rk3;
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float * s_out = state_out_base + (iv3 * H + iv1) * S_v * S_v;
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// For K=1, write directly to the single output slot to avoid an extra memcpy at the end.
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// For K>1, work in scratch and copy out per-token when the slot is in range.
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float * s_out = (K > 1)
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? state_work
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: state_out_base + (iv3 * H + iv1) * S_v * S_v;
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// copy input state into output buffer and operate in-place
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const float * s_in = state_in_base + (iv3 * H + iv1) * S_v * S_v;
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// copy input state into the working buffer and operate in-place
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// state layout (D, K, n_seqs): slot 0 of seq iv3 starts at iv3 * state_seq_stride.
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const float * s_in = state_in_base + iv3 * state_seq_stride + iv1 * S_v * S_v;
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memcpy(s_out, s_in, S_v * S_v * sizeof(float));
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// attn output pointer for first token of this (head, seq)
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@@ -10598,6 +10614,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
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}
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attn_data += S_v * H; // advance to next token
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if (K > 1) {
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const int64_t target_slot = t - shift;
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if (target_slot >= 0 && target_slot < K) {
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float * curr_state_o = state_out_base + target_slot * state_size_per_snap +
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(iv3 * H + iv1) * S_v * S_v;
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memcpy(curr_state_o, s_out, S_v * S_v * sizeof(float));
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}
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}
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}
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}
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}
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