llama-hparams.cpp 5.2 KB

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  1. #include "llama-hparams.h"
  2. #include "ggml.h"
  3. #include <cassert>
  4. void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
  5. if (dense_first) {
  6. for (uint32_t il = 0; il < n_layer; ++il) {
  7. swa_layers[il] = n_pattern == 0 || (il % n_pattern != 0);
  8. }
  9. } else {
  10. for (uint32_t il = 0; il < n_layer; ++il) {
  11. swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
  12. }
  13. }
  14. }
  15. bool llama_hparams::is_swa_any() const {
  16. for (uint32_t il = 0; il < n_layer; ++il) {
  17. if (swa_layers[il]) {
  18. return true;
  19. }
  20. }
  21. return false;
  22. }
  23. uint32_t llama_hparams::n_head(uint32_t il) const {
  24. if (il < n_layer) {
  25. return n_head_arr[il];
  26. }
  27. GGML_ABORT("fatal error");
  28. }
  29. uint32_t llama_hparams::n_head_kv(uint32_t il) const {
  30. if (il < n_layer) {
  31. return n_head_kv_arr[il];
  32. }
  33. GGML_ABORT("fatal error");
  34. }
  35. uint32_t llama_hparams::n_ff(uint32_t il) const {
  36. if (il < n_layer) {
  37. return n_ff_arr[il];
  38. }
  39. GGML_ABORT("fatal error");
  40. }
  41. uint32_t llama_hparams::n_gqa(uint32_t il) const {
  42. const uint32_t n_head = this->n_head(il);
  43. const uint32_t n_head_kv = this->n_head_kv(il);
  44. if (n_head_kv == 0) {
  45. return 0;
  46. }
  47. return n_head/n_head_kv;
  48. }
  49. uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
  50. const uint32_t n_head_kv = this->n_head_kv(il);
  51. return n_embd_head_k * n_head_kv;
  52. }
  53. uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
  54. const uint32_t n_head_kv = this->n_head_kv(il);
  55. return n_embd_head_v * n_head_kv;
  56. }
  57. bool llama_hparams::is_n_embd_k_gqa_variable() const {
  58. const uint32_t val = n_embd_k_gqa();
  59. for (uint32_t il = 0; il < n_layer; ++il) {
  60. if (val != n_embd_k_gqa(il)) {
  61. return true;
  62. }
  63. }
  64. return false;
  65. }
  66. bool llama_hparams::is_n_embd_v_gqa_variable() const {
  67. const uint32_t val = n_embd_v_gqa();
  68. for (uint32_t il = 0; il < n_layer; ++il) {
  69. if (val != n_embd_v_gqa(il)) {
  70. return true;
  71. }
  72. }
  73. return false;
  74. }
  75. uint32_t llama_hparams::n_embd_k_gqa_max() const {
  76. uint32_t val = n_embd_k_gqa();
  77. for (uint32_t il = 0; il < n_layer; ++il) {
  78. val = std::max(val, n_embd_k_gqa(il));
  79. }
  80. return val;
  81. }
  82. uint32_t llama_hparams::n_embd_v_gqa_max() const {
  83. uint32_t val = n_embd_v_gqa();
  84. for (uint32_t il = 0; il < n_layer; ++il) {
  85. val = std::max(val, n_embd_v_gqa(il));
  86. }
  87. return val;
  88. }
  89. uint32_t llama_hparams::n_embd_r() const {
  90. if (wkv_head_size != 0) {
  91. // for RWKV models
  92. return token_shift_count * n_embd;
  93. }
  94. if (n_shortconv_l_cache != 0) {
  95. // for LFM2 models
  96. return n_embd * (n_shortconv_l_cache - 1);
  97. }
  98. // TODO: maybe support other convolution strides than 1
  99. // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
  100. // Corresponds to Mamba's conv_states size
  101. return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * (ssm_d_inner + 2*ssm_n_group*ssm_d_state);
  102. }
  103. uint32_t llama_hparams::n_embd_s() const {
  104. if (wkv_head_size != 0) {
  105. // corresponds to RWKV's wkv_states size
  106. return n_embd * wkv_head_size;
  107. }
  108. // corresponds to Mamba's ssm_states size
  109. return ssm_d_state * ssm_d_inner;
  110. }
  111. bool llama_hparams::is_recurrent(uint32_t il) const {
  112. if (il < n_layer) {
  113. return recurrent_layer_arr[il];
  114. }
  115. GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);
  116. }
  117. uint32_t llama_hparams::n_pos_per_embd() const {
  118. return rope_type == LLAMA_ROPE_TYPE_MROPE || rope_type == LLAMA_ROPE_TYPE_IMROPE ? 4 : 1;
  119. }
  120. bool llama_hparams::is_swa(uint32_t il) const {
  121. if (il < n_layer) {
  122. return swa_layers[il];
  123. }
  124. GGML_ABORT("fatal error");
  125. }
  126. bool llama_hparams::has_kv(uint32_t il) const {
  127. if (n_layer_kv_from_start >= 0) {
  128. if (il < (uint32_t) n_layer_kv_from_start) {
  129. return true;
  130. }
  131. return false;
  132. }
  133. // by default, all layers have kv
  134. return true;
  135. }
  136. uint32_t llama_hparams::n_layer_kv() const {
  137. uint32_t res = 0;
  138. for (uint32_t il = 0; il < n_layer; ++il) {
  139. if (has_kv(il)) {
  140. res++;
  141. }
  142. }
  143. return res;
  144. }
  145. bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
  146. assert(p0 >= 0 && p1 >= 0);
  147. switch (swa_type) {
  148. case LLAMA_SWA_TYPE_NONE:
  149. {
  150. } break;
  151. case LLAMA_SWA_TYPE_STANDARD:
  152. {
  153. if (p1 - p0 >= (int32_t) n_swa) {
  154. return true;
  155. }
  156. } break;
  157. case LLAMA_SWA_TYPE_CHUNKED:
  158. {
  159. const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
  160. if (p0 < pos_chunk_start) {
  161. return true;
  162. }
  163. } break;
  164. case LLAMA_SWA_TYPE_SYMMETRIC:
  165. {
  166. const int32_t half_n_swa = (int32_t) n_swa / 2;
  167. const int32_t pos_diff = p1 - p0;
  168. // Mask if outside the symmetric window
  169. if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
  170. return true;
  171. }
  172. } break;
  173. }
  174. return false;
  175. }