llama-hparams.cpp 5.1 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. return recurrent_layer_arr[il];
  113. }
  114. uint32_t llama_hparams::n_pos_per_embd() const {
  115. return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
  116. }
  117. bool llama_hparams::is_swa(uint32_t il) const {
  118. if (il < n_layer) {
  119. return swa_layers[il];
  120. }
  121. GGML_ABORT("fatal error");
  122. }
  123. bool llama_hparams::has_kv(uint32_t il) const {
  124. if (n_layer_kv_from_start >= 0) {
  125. if (il < (uint32_t) n_layer_kv_from_start) {
  126. return true;
  127. }
  128. return false;
  129. }
  130. // by default, all layers have kv
  131. return true;
  132. }
  133. uint32_t llama_hparams::n_layer_kv() const {
  134. uint32_t res = 0;
  135. for (uint32_t il = 0; il < n_layer; ++il) {
  136. if (has_kv(il)) {
  137. res++;
  138. }
  139. }
  140. return res;
  141. }
  142. bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
  143. assert(p0 >= 0 && p1 >= 0);
  144. switch (swa_type) {
  145. case LLAMA_SWA_TYPE_NONE:
  146. {
  147. } break;
  148. case LLAMA_SWA_TYPE_STANDARD:
  149. {
  150. if (p1 - p0 >= (int32_t) n_swa) {
  151. return true;
  152. }
  153. } break;
  154. case LLAMA_SWA_TYPE_CHUNKED:
  155. {
  156. const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
  157. if (p0 < pos_chunk_start) {
  158. return true;
  159. }
  160. } break;
  161. case LLAMA_SWA_TYPE_SYMMETRIC:
  162. {
  163. const int32_t half_n_swa = (int32_t) n_swa / 2;
  164. const int32_t pos_diff = p1 - p0;
  165. // Mask if outside the symmetric window
  166. if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
  167. return true;
  168. }
  169. } break;
  170. }
  171. return false;
  172. }