1#include "llama-memory-hybrid.h"
2
3#include "llama-impl.h"
4#include "llama-model.h"
5#include "llama-context.h"
6
7//
8// llama_memory_hybrid
9//
10
11llama_memory_hybrid::llama_memory_hybrid(
12 const llama_model & model,
13 /* attn */
14 ggml_type type_k,
15 ggml_type type_v,
16 bool v_trans,
17 uint32_t kv_size,
18 uint32_t n_pad,
19 uint32_t n_swa,
20 llama_swa_type swa_type,
21 /* recurrent */
22 ggml_type type_r,
23 ggml_type type_s,
24 uint32_t rs_size,
25 /* common */
26 uint32_t n_seq_max,
27 bool offload,
28 bool unified,
29 /* layer filters */
30 const layer_filter_cb & filter_attn,
31 const layer_filter_cb & filter_recr) :
32 hparams(model.hparams),
33 mem_attn(new llama_kv_cache(
34 model,
35 type_k,
36 type_v,
37 v_trans,
38 offload,
39 unified,
40 kv_size,
41 n_seq_max,
42 n_pad,
43 n_swa,
44 swa_type,
45 filter_attn == nullptr ?
46 [&](int32_t il) { return !hparams.is_recurrent(il); }
47 : filter_attn,
48 nullptr
49 )),
50 mem_recr(new llama_memory_recurrent(
51 model,
52 type_r,
53 type_s,
54 offload,
55 rs_size,
56 n_seq_max,
57 filter_recr == nullptr ?
58 [&](int32_t il) { return hparams.is_recurrent(il); }
59 : filter_recr
60 )) {}
61
62llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
63 do {
64 balloc.split_reset();
65
66 // follow the recurrent pattern for creating the ubatch splits
67 std::vector<llama_ubatch> ubatches;
68
69 while (true) {
70 llama_ubatch ubatch;
71
72 if (embd_all) {
73 // if all tokens are output, split by sequence
74 ubatch = balloc.split_seq(n_ubatch);
75 } else {
76 // TODO: non-sequential equal split can be done if using unified KV cache
77 // for simplicity, we always use sequential equal split for now
78 ubatch = balloc.split_equal(n_ubatch, true);
79 }
80
81 if (ubatch.n_tokens == 0) {
82 break;
83 }
84
85 ubatches.push_back(std::move(ubatch)); // NOLINT
86 }
87
88 if (balloc.get_n_used() < balloc.get_n_tokens()) {
89 // failed to find a suitable split
90 break;
91 }
92
93 // prepare the recurrent batches first
94 if (!mem_recr->prepare(ubatches)) {
95 // TODO: will the recurrent cache be in an undefined context at this point?
96 LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
97 return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
98 }
99
100 // prepare the attention cache
101 auto heads_attn = mem_attn->prepare(ubatches);
102 if (heads_attn.empty()) {
103 LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
104 return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
105 }
106
107 return std::make_unique<llama_memory_hybrid_context>(
108 this, std::move(heads_attn), std::move(ubatches));
109 } while(false);
110
111 return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
112}
113
114llama_memory_context_ptr llama_memory_hybrid::init_full() {
115 return std::make_unique<llama_memory_hybrid_context>(this);
116}
117
118llama_memory_context_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
119 return std::make_unique<llama_memory_hybrid_context>(this, lctx, optimize);
120}
121
122bool llama_memory_hybrid::get_can_shift() const {
123 // Shifting is trivially supported for recurrent
124 return mem_attn->get_can_shift();
125}
126
127void llama_memory_hybrid::clear(bool data) {
128 mem_attn->clear(data);
129 mem_recr->clear(data);
130}
131
132bool llama_memory_hybrid::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
133 // Try removing from the recurrent cache first since it may fail. If it does
134 // fail, the cache will not have been mutated.
135 if (!mem_recr->seq_rm(seq_id, p0, p1)) {
136 return false;
137 }
138 return mem_attn->seq_rm(seq_id, p0, p1);
139}
140
141void llama_memory_hybrid::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
142 mem_attn->seq_cp(seq_id_src, seq_id_dst, p0, p1);
143 mem_recr->seq_cp(seq_id_src, seq_id_dst, p0, p1);
144}
145
146void llama_memory_hybrid::seq_keep(llama_seq_id seq_id) {
147 mem_attn->seq_keep(seq_id);
148 mem_recr->seq_keep(seq_id);
149}
150
151void llama_memory_hybrid::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
152 mem_attn->seq_add(seq_id, p0, p1, shift);
153 mem_recr->seq_add(seq_id, p0, p1, shift);
154}
155
156void llama_memory_hybrid::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
157 mem_attn->seq_div(seq_id, p0, p1, d);
158 mem_recr->seq_div(seq_id, p0, p1, d);
159}
160
161llama_pos llama_memory_hybrid::seq_pos_min(llama_seq_id seq_id) const {
162 // the min of the total cache is the max of the two caches' min values
163 return std::max(mem_attn->seq_pos_min(seq_id), mem_recr->seq_pos_min(seq_id));
164}
165
166llama_pos llama_memory_hybrid::seq_pos_max(llama_seq_id seq_id) const {
167 // the max of the total cache is the min of the two caches' max values
168 return std::min(mem_attn->seq_pos_max(seq_id), mem_recr->seq_pos_max(seq_id));
169}
170
171std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid::memory_breakdown() const {
172 std::map<ggml_backend_buffer_type_t, size_t> mb = mem_attn->memory_breakdown();
173 for (const auto & buft_size : mem_recr->memory_breakdown()) {
174 mb[buft_size.first] += buft_size.second;
175 }
176 return mb;
177}
178
179void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
180 if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
181 mem_attn->state_write(io, seq_id, flags);
182 }
183 mem_recr->state_write(io, seq_id, flags);
184}
185
186void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
187 if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
188 mem_attn->state_read(io, seq_id, flags);
189 }
190 mem_recr->state_read(io, seq_id, flags);
191}
192
193llama_kv_cache * llama_memory_hybrid::get_mem_attn() const {
194 return mem_attn.get();
195}
196
197llama_memory_recurrent * llama_memory_hybrid::get_mem_recr() const {
198 return mem_recr.get();
199}
200
201llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_status status) : status(status) {}
202
203llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_hybrid * mem) :
204 ctx_attn(mem->get_mem_attn()->init_full()),
205 ctx_recr(mem->get_mem_recr()->init_full()),
206 status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
207}
208
209llama_memory_hybrid_context::llama_memory_hybrid_context(
210 llama_memory_hybrid * mem,
211 llama_context * lctx,
212 bool optimize) :
213 ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
214 ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
215 status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
216}
217
218llama_memory_hybrid_context::llama_memory_hybrid_context(
219 llama_memory_hybrid * mem,
220 slot_info_vec_t sinfos_attn,
221 std::vector<llama_ubatch> ubatches) :
222 ubatches(std::move(ubatches)),
223 // note: here we copy the ubatches. not sure if this is ideal
224 ctx_attn(new llama_kv_cache_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
225 ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
226 status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
227}
228
229bool llama_memory_hybrid_context::next() {
230 assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
231
232 ctx_attn->next();
233 ctx_recr->next();
234
235 if (++i_next >= ubatches.size()) {
236 return false;
237 }
238
239 return true;
240}
241
242bool llama_memory_hybrid_context::apply() {
243 assert(!llama_memory_status_is_fail(status));
244
245 bool res = true;
246
247 res = res & ctx_attn->apply();
248 res = res & ctx_recr->apply();
249
250 return res;
251}
252
253llama_memory_status llama_memory_hybrid_context::get_status() const {
254 return status;
255}
256
257const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const {
258 assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
259 return ubatches[i_next];
260}
261
262const llama_kv_cache_context * llama_memory_hybrid_context::get_attn() const {
263 return static_cast<const llama_kv_cache_context *>(ctx_attn.get());
264}
265
266const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const {
267 return static_cast<const llama_memory_recurrent_context *>(ctx_recr.get());
268}