mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-08-15 04:33:06 -04:00
graph : reduce splits for recurrent and hybrid models (#14825)
* graph : avoid creating redundant s_copy views * graph : comment the s_copy views
This commit is contained in:
@@ -1644,16 +1644,17 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
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ggml_tensor * llm_graph_context::build_rs(
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ggml_tensor * s,
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ggml_tensor * state_copy,
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ggml_tensor * state_copy_main,
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ggml_tensor * state_copy_extra,
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int32_t state_size,
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int32_t n_seqs,
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uint32_t n_kv,
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uint32_t kv_head,
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uint32_t kv_size,
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uint32_t n_rs,
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uint32_t rs_head,
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uint32_t rs_size,
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int32_t rs_zero,
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const llm_graph_get_rows_fn & get_state_rows) const {
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ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_size);
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ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, rs_size);
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// Clear a single state which will then be copied to the other cleared states.
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// Note that this is a no-op when the view is zero-sized.
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@@ -1661,39 +1662,44 @@ ggml_tensor * llm_graph_context::build_rs(
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ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
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// copy states
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// NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
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// {state_size, kv_size} -> {state_size, n_seqs}
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ggml_tensor * output_states = get_state_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
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// NOTE: assuming the copy destinations are ALL contained between rs_head and rs_head + n_rs
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// {state_size, rs_size} -> {state_size, n_seqs}
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ggml_tensor * output_states = get_state_rows(ctx0, states, state_copy_main);
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ggml_build_forward_expand(gf, output_states);
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// copy extra states which won't be changed further (between n_seqs and n_kv)
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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]));
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// copy extra states which won't be changed further (between n_seqs and n_rs)
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ggml_tensor * states_extra = ggml_get_rows(ctx0, states, state_copy_extra);
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ggml_build_forward_expand(gf,
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ggml_cpy(ctx0,
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states_extra,
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ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
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ggml_view_1d(ctx0, s, state_size*(n_rs - n_seqs), (rs_head + n_seqs)*state_size*ggml_element_size(s))));
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return output_states;
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}
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static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
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ggml_context * ctx0,
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const llama_ubatch & ubatch,
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const llama_memory_recurrent_context * mctx_cur) {
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auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
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const auto n_rs = mctx_cur->get_n_rs();
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const int64_t n_rs = mctx_cur->get_n_rs();
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const int64_t n_seqs = ubatch.n_seqs;
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inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
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ggml_set_input(inp->s_copy);
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inp->s_copy_main = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
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inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);
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return inp;
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}
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llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
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const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
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auto inp = build_rs_inp_impl(ctx0, mctx_cur);
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auto inp = build_rs_inp_impl(ctx0, ubatch, mctx_cur);
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return (llm_graph_input_rs *) res->add_input(std::move(inp));
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}
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@@ -1706,7 +1712,9 @@ ggml_tensor * llm_graph_context::build_rs(
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const llm_graph_get_rows_fn & get_state_rows) const {
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const auto * kv_state = inp->mctx;
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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);
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return build_rs(s, inp->s_copy_main, inp->s_copy_extra, state_size, n_seqs,
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kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(),
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get_state_rows);
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}
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ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
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@@ -1753,7 +1761,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
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llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
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const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
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auto inp_rs = build_rs_inp_impl(ctx0, mctx_cur->get_recr());
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auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
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auto inp_attn = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
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auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
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@@ -214,7 +214,12 @@ public:
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void set_input(const llama_ubatch * ubatch) override;
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ggml_tensor * s_copy; // I32 [kv_size]
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ggml_tensor * s_copy; // I32 [n_rs]
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// views of s_copy, computed once per graph
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// and shared across layers which use build_rs
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ggml_tensor * s_copy_main; // I32 [n_seqs]
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ggml_tensor * s_copy_extra; // I32 [n_rs - n_seqs]
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const llama_memory_recurrent_context * mctx;
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};
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@@ -730,7 +735,6 @@ struct llm_graph_context {
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// recurrent
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//
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// TODO: avoid notion of "kv"
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// TODO: move this implementation to llama_memory_recurrent.
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// this is analogous to llama_kv_cache_unified::cpy_k / cpy_v
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// when moving, avoid passing `ggml_cgraph` - only pass `ggml_context`. would likely need to split the
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@@ -738,12 +742,13 @@ struct llm_graph_context {
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// `llama_memory_recurrent`
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ggml_tensor * build_rs(
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ggml_tensor * s,
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ggml_tensor * state_copy,
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ggml_tensor * state_copy_main,
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ggml_tensor * state_copy_extra,
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int32_t state_size,
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int32_t n_seqs,
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uint32_t n_kv,
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uint32_t kv_head,
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uint32_t kv_size,
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uint32_t n_rs,
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uint32_t rs_head,
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uint32_t rs_size,
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int32_t rs_zero,
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const llm_graph_get_rows_fn & get_state_rows = ggml_get_rows) const;
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