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https://github.com/ggml-org/llama.cpp.git
synced 2026-08-06 10:00:48 -05:00
Merge remote-tracking branch 'upstream/master' into backend-sampling
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@@ -255,6 +255,24 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
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}
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}
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bool llm_graph_input_rs::can_reuse(const llm_graph_params & params) {
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const auto * mctx = static_cast<const llama_memory_recurrent_context *>(params.mctx);
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this->mctx = mctx;
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bool res = true;
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res &= s_copy->ne[0] == mctx->get_n_rs();
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res &= s_copy_main->ne[0] == params.ubatch.n_seqs;
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res &= s_copy_extra->ne[0] == mctx->get_n_rs() - params.ubatch.n_seqs;
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res &= head == mctx->get_head();
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res &= rs_z == mctx->get_rs_z();
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return res;
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}
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void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
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GGML_UNUSED(ubatch);
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@@ -462,8 +480,46 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
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}
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void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
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inp_attn->set_input(ubatch);
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inp_rs->set_input(ubatch);
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mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
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mctx->get_attn()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);
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mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
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const int64_t n_rs = mctx->get_recr()->get_n_rs();
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if (inp_rs->s_copy) {
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GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
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int32_t * data = (int32_t *) inp_rs->s_copy->data;
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// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
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for (uint32_t i = 0; i < n_rs; ++i) {
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data[i] = mctx->get_recr()->s_copy(i);
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}
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}
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}
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bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
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const auto * mctx = static_cast<const llama_memory_hybrid_context *>(params.mctx);
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this->mctx = mctx;
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bool res = true;
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res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
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//res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
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res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
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res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
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res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
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res &= inp_rs->s_copy_main->ne[0] == params.ubatch.n_seqs;
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res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
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res &= inp_rs->head == mctx->get_recr()->get_head();
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res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
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return res;
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}
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void llm_graph_input_sampling::set_input(const llama_ubatch * ubatch) {
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@@ -1164,6 +1220,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
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cur = ggml_relu(ctx0, cur);
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cb(cur, "ffn_moe_relu", il);
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} break;
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case LLM_FFN_RELU_SQR:
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if (gate_exps) {
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// TODO: add support for gated squared relu
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GGML_ABORT("fatal error: gated squared relu not implemented");
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} else {
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cur = ggml_relu(ctx0, cur);
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cur = ggml_sqr(ctx0, cur);
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cb(cur, "ffn_moe_relu_sqr", il);
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} break;
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default:
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GGML_ABORT("fatal error");
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}
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@@ -1920,6 +1985,9 @@ static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
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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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inp->head = mctx_cur->get_head();
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inp->rs_z = mctx_cur->get_rs_z();
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return inp;
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}
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@@ -1988,10 +2056,10 @@ 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, ubatch, 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_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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auto inp = std::make_unique<llm_graph_input_mem_hybrid>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
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return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
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}
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