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Jul 31, 2025
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graph : reduce splits for recurrent and hybrid models #14825

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ac14979
graph : avoid creating redundant s_copy views
compilade Jul 23, 2025
7b660a9
graph : comment the s_copy views
compilade Jul 24, 2025
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38 changes: 23 additions & 15 deletions src/llama-graph.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1561,56 +1561,62 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif

ggml_tensor * llm_graph_context::build_rs(
ggml_tensor * s,
ggml_tensor * state_copy,
ggml_tensor * state_copy_main,
ggml_tensor * state_copy_extra,
int32_t state_size,
int32_t n_seqs,
uint32_t n_kv,
uint32_t kv_head,
uint32_t kv_size,
uint32_t n_rs,
uint32_t rs_head,
uint32_t rs_size,
int32_t rs_zero,
const llm_graph_get_rows_fn & get_state_rows) const {

ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_size);
ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, rs_size);

// Clear a single state which will then be copied to the other cleared states.
// Note that this is a no-op when the view is zero-sized.
ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));

// copy states
// NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
// {state_size, kv_size} -> {state_size, n_seqs}
ggml_tensor * output_states = get_state_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
// NOTE: assuming the copy destinations are ALL contained between rs_head and rs_head + n_rs
// {state_size, rs_size} -> {state_size, n_seqs}
ggml_tensor * output_states = get_state_rows(ctx0, states, state_copy_main);
ggml_build_forward_expand(gf, output_states);

// copy extra states which won't be changed further (between n_seqs and n_kv)
ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_kv - n_seqs, n_seqs*state_copy->nb[0]));
// copy extra states which won't be changed further (between n_seqs and n_rs)
ggml_tensor * states_extra = ggml_get_rows(ctx0, states, state_copy_extra);
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
states_extra,
ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
ggml_view_1d(ctx0, s, state_size*(n_rs - n_seqs), (rs_head + n_seqs)*state_size*ggml_element_size(s))));

return output_states;
}

static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
ggml_context * ctx0,
const llama_ubatch & ubatch,
const llama_memory_recurrent_context * mctx_cur) {

auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);

const auto n_rs = mctx_cur->get_n_rs();
const int64_t n_rs = mctx_cur->get_n_rs();
const int64_t n_seqs = ubatch.n_seqs;

inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);

inp->s_copy_main = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);

return inp;
}

llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);

auto inp = build_rs_inp_impl(ctx0, mctx_cur);
auto inp = build_rs_inp_impl(ctx0, ubatch, mctx_cur);

return (llm_graph_input_rs *) res->add_input(std::move(inp));
}
Expand All @@ -1623,7 +1629,9 @@ ggml_tensor * llm_graph_context::build_rs(
const llm_graph_get_rows_fn & get_state_rows) const {
const auto * kv_state = inp->mctx;

return build_rs(s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
return build_rs(s, inp->s_copy_main, inp->s_copy_extra, state_size, n_seqs,
kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(),
get_state_rows);
}

ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
Expand Down Expand Up @@ -1670,7 +1678,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);

auto inp_rs = build_rs_inp_impl(ctx0, mctx_cur->get_recr());
auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
auto inp_attn = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());

auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
Expand Down
17 changes: 11 additions & 6 deletions src/llama-graph.h
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Original file line number Diff line number Diff line change
Expand Up @@ -214,7 +214,12 @@ class llm_graph_input_rs : public llm_graph_input_i {

void set_input(const llama_ubatch * ubatch) override;

ggml_tensor * s_copy; // I32 [kv_size]
ggml_tensor * s_copy; // I32 [n_rs]

// views of s_copy, computed once per graph
// and shared across layers which use build_rs
ggml_tensor * s_copy_main; // I32 [n_seqs]
ggml_tensor * s_copy_extra; // I32 [n_rs - n_seqs]

const llama_memory_recurrent_context * mctx;
};
Expand Down Expand Up @@ -715,20 +720,20 @@ struct llm_graph_context {
// recurrent
//

// TODO: avoid notion of "kv"
// TODO: move this implementation to llama_memory_recurrent.
// this is analogous to llama_kv_cache_unified::cpy_k / cpy_v
// when moving, avoid passing `ggml_cgraph` - only pass `ggml_context`. would likely need to split the
// implementation in 2 separate methods. the goal is to avoid calling `ggml_build_forward_expand` in
// `llama_memory_recurrent`
ggml_tensor * build_rs(
ggml_tensor * s,
ggml_tensor * state_copy,
ggml_tensor * state_copy_main,
ggml_tensor * state_copy_extra,
int32_t state_size,
int32_t n_seqs,
uint32_t n_kv,
uint32_t kv_head,
uint32_t kv_size,
uint32_t n_rs,
uint32_t rs_head,
uint32_t rs_size,
int32_t rs_zero,
const llm_graph_get_rows_fn & get_state_rows = ggml_get_rows) const;

Expand Down
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