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986 lines
33 KiB
C++
986 lines
33 KiB
C++
#include "ggml.h"
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#define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "falcon_impl.h"
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#include "llama.h"
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#include "llama-util.h"
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#include "utils.h"
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#include "llmodel_shared.h"
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#include <cassert>
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#include <cinttypes>
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#include <iostream>
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#include <sstream>
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namespace {
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const char *modelType_ = "Falcon";
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}
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// commented out 40B support as it presently would require forking ggml/llama.cpp
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// can re-add once mainline ggml supports it
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#define FALCON_MAGIC 0x67676a74
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// default hparams (Falcon 7B)
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struct falcon_hparams {
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int32_t n_vocab = 65024;
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int32_t n_embd = 4544;
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int32_t n_head = 71;
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int32_t n_head_kv = 1;
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int32_t n_layer = 32;
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int32_t falcon_version = 7; // 7 for Falcon-7B, 40 for Falcon-40B
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int32_t ftype = 1;
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int32_t n_ctx = 2048;
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};
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struct falcon_layer {
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// normalization
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struct ggml_tensor* input_layernorm;
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struct ggml_tensor* input_layernorm_b;
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//struct ggml_tensor* attention_norm; // Falcon-40B only
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//struct ggml_tensor* attention_norm_b; // Falcon-40B only
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// attention
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struct ggml_tensor* query_key_value;
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struct ggml_tensor* wo;
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// ff
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struct ggml_tensor* ffn_up;
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struct ggml_tensor* ffn_down;
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};
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struct falcon_model {
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falcon_hparams hparams;
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struct ggml_tensor* tok_embeddings;
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struct ggml_tensor* output_norm;
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struct ggml_tensor* output_norm_b;
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struct ggml_tensor* lm_head;
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std::vector<falcon_layer> layers;
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// key + value memory
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llm_kv_cache kv_self;
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struct ggml_context* ctx;
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std::map<std::string, struct ggml_tensor*> tensors;
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llm_buffer eval_buf;
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llm_buffer work_buf;
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llm_buffer scr0_buf;
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llm_buffer scr1_buf;
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};
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static bool kv_cache_init(
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const struct falcon_hparams & hparams,
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struct llm_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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const int n_embd = hparams.n_embd;
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const int dim_head = n_embd / hparams.n_head;
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const int dim_kv = dim_head * hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*n_ctx;
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const int64_t n_elements = dim_kv * n_mem;
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cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size;
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params.mem_buffer = cache.buf.addr;
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params.no_alloc = false;
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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// load the model's weights from a file
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bool falcon_model_load(const std::string & fname, falcon_model & model, gpt_vocab & vocab, size_t *mem_req) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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if (mem_req) {
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*mem_req = 0;
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}
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic != FALCON_MAGIC) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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}
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uint32_t format_version;
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fin.read((char *) &format_version, sizeof(format_version));
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_head_kv, sizeof(hparams.n_head_kv));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.falcon_version, sizeof(hparams.falcon_version));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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if (hparams.falcon_version != 7) { // && hparams.falcon_version != 40) {
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fprintf(stderr, "%s: invalid model file '%s' (bad Falcon version: %d)\n", __func__, fname.c_str(), hparams.falcon_version);
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return false;
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}
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: ftype = %d\n", __func__, hparams.ftype);
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printf("%s: qntvr = %d\n", __func__, qntvr);
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hparams.ftype %= GGML_QNT_VERSION_FACTOR;
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}
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// load vocab
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{
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const int32_t n_vocab = model.hparams.n_vocab;
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std::string word;
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std::vector<char> buf(128);
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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buf.resize(len);
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fin.read((char *) buf.data(), len);
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word.assign(buf.data(), len);
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uint32_t dummy;
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fin.read((char *) &dummy, sizeof(dummy));
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
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if (wtype == GGML_TYPE_COUNT) {
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fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
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__func__, fname.c_str(), model.hparams.ftype);
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return false;
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}
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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{
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const auto& hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_head = hparams.n_head;
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const int n_head_kv = hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_ff = 4 * model.hparams.n_embd;
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const int n_vocab = hparams.n_vocab;
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const int head_dim = hparams.n_embd / hparams.n_head;
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ctx_size += ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // tok_embeddings
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ctx_size += ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm
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ctx_size += ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm_b
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ctx_size += ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // lm_head
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// if (hparams.version == 40) { // Falcon-40B
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// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm
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// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm_b
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// }
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm_b
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * (n_head_kv * 2 + n_head) * head_dim); // query_key_value
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_embd); // wo
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_ff); // ffn_up
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ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_ff * n_embd); // ffn_down
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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if (mem_req) {
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const int n_embd = model.hparams.n_embd;
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const int dim_head = n_embd / model.hparams.n_head;
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const int dim_kv = dim_head * model.hparams.n_head_kv;
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const int n_layer = model.hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx;
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const int64_t n_elements = dim_kv * n_mem;
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size_t kv_cache_size = 2u*n_elements*ggml_type_size(wtype) + 2_MiB;
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*mem_req = ctx_size + kv_cache_size;
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return false;
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}
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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model.ctx = ggml_init(params);
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if (!model.ctx) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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return false;
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}
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}
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// prepare memory for the weights
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{
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const auto& hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_head = hparams.n_head;
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const int n_head_kv = hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int n_ff = 4 * model.hparams.n_embd;
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const int n_vocab = hparams.n_vocab;
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const int head_dim = hparams.n_embd / hparams.n_head;
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model.layers.resize(n_layer);
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model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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// map by name
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model.tensors["transformer.word_embeddings.weight"] =
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model.tok_embeddings;
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model.tensors["transformer.ln_f.weight"] = model.output_norm;
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model.tensors["transformer.ln_f.bias"] = model.output_norm_b;
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model.tensors["lm_head.weight"] = model.lm_head;
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for (int i = 0; i < n_layer; ++i) {
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auto& layer = model.layers[i];
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layer.input_layernorm =
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ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.input_layernorm_b =
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ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// if (hparams.version == 40) { // for Falcon-40B only
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// layer.attention_norm =
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// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// layer.attention_norm_b =
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// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// }
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// query_key_value shape for config.multi_query == True:
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layer.query_key_value = ggml_new_tensor_2d(
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ctx, wtype, n_embd, (n_head_kv * 2 + n_head) * head_dim);
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layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.ffn_up = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
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layer.ffn_down = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
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// map by name
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// if (hparams.version == 40) {
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// // Falcon-40B:
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// model.tensors["transformer.h." + std::to_string(i) +
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// ".ln_mlp.weight"] = layer.input_layernorm;
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// model.tensors["transformer.h." + std::to_string(i) +
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// ".ln_mlp.bias"] = layer.input_layernorm_b;
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// model.tensors["transformer.h." + std::to_string(i) +
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// ".ln_attn.weight"] = layer.attention_norm;
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// model.tensors["transformer.h." + std::to_string(i) +
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// ".ln_attn.bias"] = layer.attention_norm_b;
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// } else {
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// Falcon-7B:
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model.tensors["transformer.h." + std::to_string(i) +
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".input_layernorm.weight"] = layer.input_layernorm;
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model.tensors["transformer.h." + std::to_string(i) +
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".input_layernorm.bias"] = layer.input_layernorm_b;
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//}
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model.tensors["transformer.h." + std::to_string(i) +
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".self_attention.query_key_value.weight"] =
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layer.query_key_value;
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model.tensors["transformer.h." + std::to_string(i) +
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".self_attention.dense.weight"] = layer.wo;
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model.tensors["transformer.h." + std::to_string(i) +
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".mlp.dense_h_to_4h.weight"] = layer.ffn_up;
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model.tensors["transformer.h." + std::to_string(i) +
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".mlp.dense_4h_to_h.weight"] = layer.ffn_down;
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_head_kv = hparams.n_head_kv;
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const int head_dim = hparams.n_embd / hparams.n_head;
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const int64_t n_mem = n_layer*n_ctx;
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const int64_t n_elements = head_dim*n_mem;
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if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, model.hparams.n_ctx)) {
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fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
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ggml_free(ctx);
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return false;
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}
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const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
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printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
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}
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// load weights
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{
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int n_tensors = 0;
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size_t total_size = 0;
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printf("%s: ", __func__);
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while (true) {
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int32_t n_dims;
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int32_t length;
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int32_t ttype;
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fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
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fin.read(reinterpret_cast<char *>(&length), sizeof(length));
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fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
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if (fin.eof()) {
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break;
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}
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int32_t nelements = 1;
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int32_t ne[2] = { 1, 1 };
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for (int i = 0; i < n_dims; ++i) {
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fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
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nelements *= ne[i];
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}
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std::string name(length, 0);
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fin.read(&name[0], length);
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fin.seekg(-static_cast<ptrdiff_t>(fin.tellg()) & 31, std::ios_base::cur);
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if (model.tensors.find(name.data()) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
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return false;
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}
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auto tensor = model.tensors[name.data()];
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if (ggml_nelements(tensor) != nelements) {
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fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
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return false;
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}
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if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
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fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n",
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__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
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return false;
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}
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// for debugging
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if (0) {
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printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
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}
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const size_t bpe = ggml_type_size(ggml_type(ttype));
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if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
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fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
|
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
|
return false;
|
|
}
|
|
|
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
|
|
|
total_size += ggml_nbytes(tensor);
|
|
if (++n_tensors % 8 == 0) {
|
|
printf(".");
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
printf(" done\n");
|
|
|
|
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
|
|
}
|
|
|
|
fin.close();
|
|
|
|
model.eval_buf.resize(1280u * 1024 * 1024);
|
|
model.scr0_buf.resize(256u * 1024 * 1024);
|
|
model.scr1_buf.resize(256u * 1024 * 1024);
|
|
return true;
|
|
}
|
|
|
|
|
|
// evaluate the transformer
|
|
//
|
|
// - model: the model
|
|
// - n_threads: number of threads to use
|
|
// - n_past: the context size so far
|
|
// - embd_inp: the embeddings of the tokens in the context
|
|
// - embd_w: the predicted logits for the next token
|
|
//
|
|
bool falcon_eval(
|
|
falcon_model & model,
|
|
const int n_threads,
|
|
const int n_past,
|
|
const std::vector<gpt_vocab::id> & embd_inp,
|
|
std::vector<float> & embd_w,
|
|
size_t & mem_per_token) {
|
|
const int N = embd_inp.size();
|
|
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_head = hparams.n_head;
|
|
const int n_head_kv = hparams.n_head_kv;
|
|
const int n_vocab = hparams.n_vocab;
|
|
const int version = hparams.falcon_version;
|
|
const size_t head_dim = n_embd / n_head;
|
|
|
|
struct ggml_init_params eval_ctx_params = {
|
|
.mem_size = model.eval_buf.size,
|
|
.mem_buffer = model.eval_buf.addr,
|
|
.no_alloc = false,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(eval_ctx_params);
|
|
struct ggml_cgraph gf = {};
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
|
|
|
|
// wte
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
|
|
struct ggml_tensor* repeat_dummy = ggml_new_tensor_3d(ctx0, inpL->type, head_dim, N + n_past, n_head);
|
|
|
|
ggml_type wtype = GGML_TYPE_F32;
|
|
const int sizeof_wtype = ggml_type_sizef(wtype);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * layernorm_output;
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
|
|
// self-attention
|
|
{
|
|
layernorm_output = ggml_norm(ctx0, inpL);
|
|
|
|
layernorm_output = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].input_layernorm, layernorm_output),
|
|
layernorm_output),
|
|
ggml_repeat(ctx0, model.layers[il].input_layernorm_b, layernorm_output));
|
|
|
|
// if (version == 40) { // Falcon-40B only
|
|
// cur = ggml_norm(ctx0, inpL);
|
|
|
|
// cur = ggml_add(ctx0,
|
|
// ggml_mul(ctx0,
|
|
// ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
|
|
// cur),
|
|
// ggml_repeat(ctx0, model.layers[il].attention_norm_b, cur));
|
|
// }
|
|
// else {
|
|
cur = layernorm_output;
|
|
// }
|
|
|
|
// compute QKV
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].query_key_value, cur);
|
|
|
|
// Note that the strides for Kcur, Vcur are set up so that the
|
|
// resulting views are misaligned with the tensor's storage
|
|
// (by applying the K/V offset we shift the tensor's original
|
|
// view to stick out behind the viewed QKV tensor's allocated
|
|
// memory, so to say). This is ok because no actual accesses
|
|
// happen to that out-of-range memory, but it can require some
|
|
// trickery when trying to accurately dump these views for
|
|
// debugging.
|
|
|
|
struct ggml_tensor * Qcur = ggml_view_3d(
|
|
ctx0, cur, head_dim, n_head, N,
|
|
head_dim * sizeof_wtype,
|
|
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
|
|
0);
|
|
|
|
struct ggml_tensor * Kcur = ggml_view_3d(
|
|
ctx0, cur, head_dim, n_head_kv, N,
|
|
head_dim * sizeof_wtype,
|
|
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
|
|
head_dim * n_head * sizeof_wtype);
|
|
|
|
struct ggml_tensor * Vcur = ggml_view_3d(
|
|
ctx0, cur, head_dim, n_head_kv, N,
|
|
head_dim * sizeof_wtype,
|
|
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
|
|
head_dim * (n_head + n_head_kv) * sizeof_wtype);
|
|
|
|
// using mode = 2 for neox mode
|
|
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, head_dim, 2, n_ctx);
|
|
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, n_ctx);
|
|
|
|
// store key and value to memory
|
|
{
|
|
struct ggml_tensor* k = ggml_view_1d(
|
|
ctx0, model.kv_self.k, N * n_head_kv * head_dim,
|
|
(ggml_element_size(model.kv_self.k) * n_head_kv * head_dim) *
|
|
(il * n_ctx + n_past));
|
|
struct ggml_tensor* v = ggml_view_1d(
|
|
ctx0, model.kv_self.v, N * n_head_kv * head_dim,
|
|
(ggml_element_size(model.kv_self.v) * n_head_kv * head_dim) *
|
|
(il * n_ctx + n_past));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * K = ggml_permute(
|
|
ctx0,
|
|
ggml_view_3d(
|
|
ctx0,
|
|
model.kv_self.k,
|
|
head_dim, n_head_kv, n_past + N,
|
|
head_dim * sizeof_wtype,
|
|
head_dim * n_head_kv * sizeof_wtype,
|
|
il * n_ctx * ggml_element_size(model.kv_self.k) * n_head_kv * head_dim),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
|
|
// changed from repeat2 back to repeat, will not support 40B!
|
|
K = ggml_cont(ctx0, ggml_repeat(ctx0, K, repeat_dummy));
|
|
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale_inplace(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrt(float(head_dim)))
|
|
);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
|
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
|
struct ggml_tensor* V = ggml_permute(
|
|
ctx0,
|
|
ggml_view_3d(
|
|
ctx0,
|
|
model.kv_self.v,
|
|
head_dim, n_head_kv, n_past + N,
|
|
head_dim * sizeof_wtype,
|
|
head_dim * n_head_kv * sizeof_wtype,
|
|
il * n_ctx * ggml_element_size(model.kv_self.v) * n_head_kv * head_dim),
|
|
0, 2, 1, 3);
|
|
|
|
// changed from repeat2 back to repeat, will not support 40B!
|
|
V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat(ctx0, V, repeat_dummy)));
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection
|
|
{
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
}
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
|
|
|
|
struct ggml_tensor* inpFF = layernorm_output;
|
|
struct ggml_tensor* attn_out = ggml_cpy(
|
|
ctx0, cur, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up, inpFF);
|
|
cur = ggml_gelu(ctx0, cur);
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, attn_out);
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
|
|
// inpL = ln_f_g*inpL + ln_f_b
|
|
inpL = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.output_norm, inpL),
|
|
inpL),
|
|
ggml_repeat(ctx0, model.output_norm_b, inpL));
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
|
|
// lm_head
|
|
{
|
|
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
|
|
|
|
//inpL = ggml_add(ctx0,
|
|
// ggml_repeat(ctx0, model.lmh_b, inpL),
|
|
// inpL);
|
|
}
|
|
|
|
// logits -> probs
|
|
//inpL = ggml_soft_max_inplace(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
ggml_graph_compute_g4a(model.work_buf, &gf, n_threads);
|
|
|
|
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_print (&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
|
//}
|
|
|
|
//embd_w.resize(n_vocab*N);
|
|
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
|
|
|
// return result for just the last token
|
|
embd_w.resize(n_vocab);
|
|
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
|
|
|
if (mem_per_token == 0) {
|
|
mem_per_token = ggml_used_mem(ctx0)/N;
|
|
}
|
|
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return true;
|
|
}
|
|
|
|
|
|
#define MAX_RNG_STATE 64*1024
|
|
size_t falcon_get_state_size(const falcon_model &model) {
|
|
const size_t s_rng_size = sizeof(size_t);
|
|
const size_t s_rng = MAX_RNG_STATE;
|
|
const size_t s_kv_size = sizeof(size_t);
|
|
const size_t s_kv_ntok = sizeof(int);
|
|
const size_t s_kv = model.kv_self.buf.size;
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_kv_size
|
|
+ s_kv_ntok
|
|
+ s_kv
|
|
);
|
|
return s_total;
|
|
}
|
|
|
|
size_t falcon_copy_state_data(const falcon_model &model, const std::mt19937 &rng, uint8_t *dest)
|
|
{
|
|
uint8_t * out = dest;
|
|
// copy rng
|
|
{
|
|
std::stringstream rng_ss;
|
|
rng_ss << rng;
|
|
|
|
const size_t rng_size = rng_ss.str().size();
|
|
char rng_buf[MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, MAX_RNG_STATE);
|
|
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
|
|
|
|
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
|
|
memcpy(out, &rng_buf[0], MAX_RNG_STATE); out += MAX_RNG_STATE;
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const size_t kv_size = model.kv_self.buf.size;
|
|
const int kv_ntok = model.kv_self.n;
|
|
|
|
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
|
|
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
|
|
}
|
|
}
|
|
|
|
const size_t written = out - dest;
|
|
assert(written == falcon_get_state_size(model));
|
|
fflush(stdout);
|
|
return written;
|
|
}
|
|
|
|
size_t falcon_set_state_data(falcon_model *model, std::mt19937 *rng, const uint8_t *src)
|
|
{
|
|
const uint8_t * in = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], in, MAX_RNG_STATE); in += MAX_RNG_STATE;
|
|
|
|
std::stringstream rng_ss;
|
|
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
|
rng_ss >> *rng;
|
|
|
|
assert(rng_ss.fail() == false);
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
size_t kv_size;
|
|
int kv_ntok;
|
|
|
|
memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
|
|
memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
assert(model->kv_self.buf.size == kv_size);
|
|
|
|
void * k_data = model->kv_self.k->data; // remember data pointers
|
|
void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
|
|
|
|
memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
|
|
|
|
model->kv_self.k->data = k_data; // restore correct data pointers
|
|
model->kv_self.v->data = v_data;
|
|
|
|
}
|
|
|
|
model->kv_self.n = kv_ntok;
|
|
}
|
|
|
|
const size_t nread = in - src;
|
|
assert(nread == falcon_get_state_size(*model));
|
|
fflush(stdout);
|
|
return nread;
|
|
}
|
|
|
|
struct FalconPrivate {
|
|
const std::string modelPath;
|
|
bool modelLoaded;
|
|
gpt_vocab vocab;
|
|
falcon_model *model = nullptr;
|
|
int64_t n_threads = 0;
|
|
size_t mem_per_token = 0;
|
|
std::mt19937 rng;
|
|
};
|
|
|
|
Falcon::Falcon() : d_ptr(new FalconPrivate) {
|
|
d_ptr->model = new falcon_model;
|
|
d_ptr->model->ctx = nullptr;
|
|
d_ptr->modelLoaded = false;
|
|
}
|
|
|
|
Falcon::~Falcon() {
|
|
if(d_ptr->model->ctx) {
|
|
ggml_free(d_ptr->model->ctx);
|
|
d_ptr->model->ctx = nullptr;
|
|
}
|
|
delete d_ptr->model;
|
|
}
|
|
|
|
bool Falcon::loadModel(const std::string &modelPath)
|
|
{
|
|
std::mt19937 rng(time(NULL));
|
|
d_ptr->rng = rng;
|
|
|
|
// load the model
|
|
if (!falcon_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) {
|
|
std::cerr << "FALCON ERROR: failed to load model from " << modelPath;
|
|
return false;
|
|
}
|
|
|
|
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
|
d_ptr->modelLoaded = true;
|
|
fflush(stdout);
|
|
return true;
|
|
}
|
|
|
|
bool Falcon::isModelLoaded() const
|
|
{
|
|
return d_ptr -> modelLoaded;
|
|
}
|
|
|
|
size_t Falcon::requiredMem(const std::string &modelPath)
|
|
{
|
|
falcon_model dummy_model;
|
|
gpt_vocab dummy_vocab;
|
|
size_t mem_req;
|
|
auto fin = std::ifstream(modelPath, std::ios::binary);
|
|
falcon_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
|
|
return mem_req;
|
|
}
|
|
|
|
size_t Falcon::stateSize() const
|
|
{
|
|
return falcon_get_state_size(*d_ptr->model);
|
|
}
|
|
|
|
size_t Falcon::saveState(uint8_t *dest) const
|
|
{
|
|
return falcon_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
|
|
}
|
|
|
|
size_t Falcon::restoreState(const uint8_t *src)
|
|
{
|
|
return falcon_set_state_data(d_ptr->model, &d_ptr->rng, src);
|
|
}
|
|
|
|
void Falcon::setThreadCount(int32_t n_threads)
|
|
{
|
|
d_ptr->n_threads = n_threads;
|
|
}
|
|
|
|
int32_t Falcon::threadCount() const
|
|
{
|
|
return d_ptr->n_threads;
|
|
}
|
|
|
|
std::vector<LLModel::Token> Falcon::tokenize(PromptContext &, const std::string &str) const
|
|
{
|
|
return ::gpt_tokenize(d_ptr->vocab, str);
|
|
}
|
|
|
|
LLModel::Token Falcon::sampleToken(PromptContext &promptCtx) const
|
|
{
|
|
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
|
|
return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
|
|
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
|
|
n_prev_toks,
|
|
promptCtx.logits,
|
|
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
|
|
promptCtx.repeat_penalty,
|
|
d_ptr->rng);
|
|
}
|
|
|
|
std::string Falcon::tokenToString(Token id) const
|
|
{
|
|
return d_ptr->vocab.id_to_token[id];
|
|
}
|
|
|
|
bool Falcon::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
|
|
{
|
|
// determine the required inference memory per token:
|
|
static bool initialized = false;
|
|
if (!initialized) {
|
|
falcon_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
|
|
d_ptr->mem_per_token);
|
|
initialized = true;
|
|
}
|
|
|
|
return falcon_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
|
|
}
|
|
|
|
int32_t Falcon::contextLength() const
|
|
{
|
|
return d_ptr->model->hparams.n_ctx;
|
|
}
|
|
|
|
const std::vector<LLModel::Token> &Falcon::endTokens() const
|
|
{
|
|
static const std::vector<LLModel::Token> out = { 11 };
|
|
return out;
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
#define DLL_EXPORT __declspec(dllexport)
|
|
#else
|
|
#define DLL_EXPORT __attribute__ ((visibility ("default")))
|
|
#endif
|
|
|
|
extern "C" {
|
|
DLL_EXPORT bool is_g4a_backend_model_implementation() {
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_model_type() {
|
|
return modelType_;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_build_variant() {
|
|
return GGML_BUILD_VARIANT;
|
|
}
|
|
|
|
DLL_EXPORT bool magic_match(std::istream& f) {
|
|
uint32_t magic = 0;
|
|
f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
|
|
uint32_t version = 0;
|
|
f.read(reinterpret_cast<char*>(&version), sizeof(version));
|
|
if (magic != FALCON_MAGIC) {
|
|
return false;
|
|
}
|
|
falcon_hparams hparams;
|
|
f.read(reinterpret_cast<char*>(&hparams), sizeof(hparams));
|
|
// we're matching the file format of existing pre-converted models
|
|
// compatible with ctransformers llama.cpp based format, which also
|
|
// unfortunately shares its magic number what llama uses, so we now
|
|
// differentiate by n_vocab
|
|
// give some wiggle room over the max to allow for finetunes that expand the
|
|
// vocabulary
|
|
if (!(hparams.n_vocab >= 65024 && hparams.n_vocab <= 65100)) {
|
|
return false;
|
|
}
|
|
if (hparams.falcon_version != 7) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
return new Falcon;
|
|
}
|
|
}
|