mirror of
https://github.com/ravenscroftj/turbopilot.git
synced 2024-07-08 03:51:59 +00:00
647 lines
23 KiB
C++
647 lines
23 KiB
C++
#include <turbopilot/gptj.hpp>
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#include <spdlog/spdlog.h>
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#include <ggml/ggml.h>
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#include <iostream>
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#include <fstream>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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// evaluate the transformer
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//
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// - model: the model
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// - n_threads: number of threads to use
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// - n_past: the context size so far
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// - embd_inp: the embeddings of the tokens in the context
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// - embd_w: the predicted logits for the next token
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//
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// The GPT-J model requires about 16MB of memory per input token.
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//
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bool gptj_eval(
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const gptj_model & model,
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const int n_threads,
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const int n_past,
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const std::vector<gpt_vocab::id> & embd_inp,
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std::vector<float> & embd_w,
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size_t & mem_per_token) {
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const int N = embd_inp.size();
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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_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_head = hparams.n_head;
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const int n_vocab = hparams.n_vocab;
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const int n_rot = hparams.n_rot;
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static size_t buf_size = 256u*1024*1024;
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static void * buf = malloc(buf_size);
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if (mem_per_token > 0 && mem_per_token*N > buf_size) {
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const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
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//printf("\n{}: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
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// reallocate
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buf_size = buf_size_new;
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buf = realloc(buf, buf_size);
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if (buf == nullptr) {
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spdlog::error("{}: failed to allocate {} bytes\n", __func__, buf_size);
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return false;
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}
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}
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ buf,
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/*.no_alloc =*/ false,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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// wte
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struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * cur;
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// norm
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{
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cur = ggml_norm(ctx0, inpL);
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// cur = ln_1_g*cur + ln_1_b
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
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cur),
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ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
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}
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struct ggml_tensor * inpSA = cur;
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// self-attention
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{
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struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
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struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
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// store key and value to memory
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{
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur));
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struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
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struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd,
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( n_ctx)*ggml_element_size(model.memory_v),
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(il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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}
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// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
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struct ggml_tensor * Q =
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ggml_permute(ctx0,
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Qcur,
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0, 2, 1, 3);
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// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
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struct ggml_tensor * K =
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ggml_permute(ctx0,
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ggml_reshape_3d(ctx0,
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ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
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n_embd/n_head, n_head, n_past + N),
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0, 2, 1, 3);
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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// KQ_scaled = KQ / sqrt(n_embd/n_head)
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struct ggml_tensor * KQ_scaled =
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ggml_scale_inplace(ctx0,
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KQ,
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ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
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);
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
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// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, model.memory_v,
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n_past + N, n_embd/n_head, n_head,
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n_ctx*ggml_element_size(model.memory_v),
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n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
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il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
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// KQV = transpose(V) * KQ_soft_max
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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// cur = KQV_merged.contiguous().view(n_embd, N)
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cur = ggml_cpy(ctx0,
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KQV_merged,
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ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
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// projection (no bias)
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cur = ggml_mul_mat(ctx0,
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model.layers[il].c_attn_proj_w,
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cur);
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}
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struct ggml_tensor * inpFF = cur;
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// feed-forward network
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// this is independent of the self-attention result, so it could be done in parallel to the self-attention
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{
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// note here we pass inpSA instead of cur
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cur = ggml_mul_mat(ctx0,
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model.layers[il].c_mlp_fc_w,
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inpSA);
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cur = ggml_add(ctx0,
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ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
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cur);
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// GELU activation
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cur = ggml_gelu(ctx0, cur);
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// projection
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// cur = proj_w*cur + proj_b
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cur = ggml_mul_mat(ctx0,
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model.layers[il].c_mlp_proj_w,
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cur);
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cur = ggml_add(ctx0,
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ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
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cur);
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}
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// self-attention + FF
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cur = ggml_add(ctx0, cur, inpFF);
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// input for next layer
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inpL = ggml_add(ctx0, cur, inpL);
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}
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// norm
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{
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inpL = ggml_norm(ctx0, inpL);
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// inpL = ln_f_g*inpL + ln_f_b
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inpL = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.ln_f_g, inpL),
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inpL),
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ggml_repeat(ctx0, model.ln_f_b, inpL));
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}
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// lm_head
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{
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inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
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inpL = ggml_add(ctx0,
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ggml_repeat(ctx0, model.lmh_b, inpL),
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inpL);
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}
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// logits -> probs
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//inpL = ggml_soft_max_inplace(ctx0, inpL);
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// run the computation
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ggml_build_forward_expand(&gf, inpL);
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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//if (n_past%100 == 0) {
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// ggml_graph_print (&gf);
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// ggml_graph_dump_dot(&gf, NULL, "gpt-j.dot");
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//}
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//embd_w.resize(n_vocab*N);
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//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
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// return result for just the last token
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embd_w.resize(n_vocab);
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memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
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if (mem_per_token == 0) {
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mem_per_token = ggml_used_mem(ctx0)/N;
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}
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//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
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ggml_free(ctx0);
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return true;
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}
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GPTJModel::~GPTJModel(){
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ggml_free(model->ctx);
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free(model);
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free(vocab);
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}
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bool GPTJModel::load_model(std::string fname) {
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spdlog::info("{}: loading model from '{}' - please wait ...\n", __func__, fname.c_str());
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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spdlog::error("{}: failed to open '{}'\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 != GGML_FILE_MAGIC) {
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spdlog::error("{}: invalid model file '{}' (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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// 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_ctx, sizeof(hparams.n_ctx));
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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_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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spdlog::info("{}: n_vocab = {}\n", __func__, hparams.n_vocab);
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spdlog::info("{}: n_ctx = {}\n", __func__, hparams.n_ctx);
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spdlog::info("{}: n_embd = {}\n", __func__, hparams.n_embd);
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spdlog::info("{}: n_head = {}\n", __func__, hparams.n_head);
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spdlog::info("{}: n_layer = {}\n", __func__, hparams.n_layer);
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spdlog::info("{}: n_rot = {}\n", __func__, hparams.n_rot);
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spdlog::info("{}: ftype = {}\n", __func__, hparams.ftype);
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spdlog::info("{}: qntvr = {}\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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int32_t n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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if (n_vocab != model->hparams.n_vocab) {
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spdlog::error("{}: invalid model file '{}' (bad vocab size {} != {})\n",
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__func__, fname.c_str(), n_vocab, model->hparams.n_vocab);
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return false;
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}
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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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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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spdlog::error("{}: invalid model file '{}' (bad ftype value {})\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_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
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ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
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ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g
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ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
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ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_v
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ctx_size += (5 + 10*n_layer)*512; // object overhead
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spdlog::info("{}: ggml ctx size = {} MB\n", __func__, ctx_size/(1024.0*1024.0));
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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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spdlog::error("{}: 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_layer = hparams.n_layer;
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const int n_vocab = hparams.n_vocab;
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model->layers.resize(n_layer);
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model->wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model->ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model->ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model->lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model->lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
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// map by name
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model->tensors["transformer.wte.weight"] = model->wte;
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model->tensors["transformer.ln_f.weight"] = model->ln_f_g;
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model->tensors["transformer.ln_f.bias"] = model->ln_f_b;
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|
|
|
model->tensors["lm_head.weight"] = model->lmh_g;
|
|
model->tensors["lm_head.bias"] = model->lmh_b;
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = model->layers[i];
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|
|
|
layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
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layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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|
|
|
layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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|
layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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|
|
|
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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|
|
|
layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
|
|
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
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|
|
|
layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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|
layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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|
|
|
// map by name
|
|
model->tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
|
|
model->tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;
|
|
|
|
model->tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
|
|
model->tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
|
|
model->tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
|
|
|
|
model->tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
|
|
|
|
model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
|
|
model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
|
|
|
|
model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
|
|
model->tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"] = layer.c_mlp_proj_b;
|
|
}
|
|
}
|
|
|
|
// key + value memory
|
|
{
|
|
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_mem = n_layer*n_ctx;
|
|
const int n_elements = n_embd*n_mem;
|
|
|
|
model->memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
|
|
model->memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
|
|
|
|
const size_t memory_size = ggml_nbytes(model->memory_k) + ggml_nbytes(model->memory_v);
|
|
|
|
spdlog::info("{}: memory_size = {} MB, n_mem = {}\n", __func__, memory_size/1024.0/1024.0, n_mem);
|
|
}
|
|
|
|
// load weights
|
|
{
|
|
int n_tensors = 0;
|
|
size_t total_size = 0;
|
|
|
|
spdlog::info("{}: ", __func__);
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ttype;
|
|
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
|
|
|
|
if (fin.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
fin.read(&name[0], length);
|
|
|
|
if (model->tensors.find(name.data()) == model->tensors.end()) {
|
|
spdlog::error("{}: unknown tensor '{}' in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
auto tensor = model->tensors[name.data()];
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
spdlog::error("{}: tensor '{}' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
|
spdlog::error("{}: tensor '{}' has wrong shape in model file: got [{}, {}], expected [{}, {}]\n",
|
|
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
|
|
// for debugging
|
|
if (0) {
|
|
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));
|
|
}
|
|
|
|
const size_t bpe = ggml_type_size(ggml_type(ttype));
|
|
|
|
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
|
spdlog::error("{}: tensor '{}' 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));
|
|
|
|
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ttype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
|
total_size += ggml_nbytes(tensor);
|
|
if (++n_tensors % 8 == 0) {
|
|
printf(".");
|
|
fflush(stdout);
|
|
}
|
|
|
|
}
|
|
|
|
printf("\n");
|
|
spdlog::info(" done\n");
|
|
|
|
spdlog::info("{}: model size = {:06.2f} MB / num tensors = {}\n", __func__, total_size/1024.0/1024.0, n_tensors);
|
|
}
|
|
|
|
fin.close();
|
|
|
|
return true;
|
|
}
|
|
|
|
std::stringstream GPTJModel::predict(std::string prompt, int max_length, bool include_prompt) {
|
|
|
|
std::stringstream result;
|
|
// tokenize the prompt
|
|
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize((*vocab), prompt);
|
|
|
|
int n_past = 0;
|
|
|
|
int64_t t_sample_us = 0;
|
|
int64_t t_predict_us = 0;
|
|
|
|
int n_predict = std::min(max_length, model->hparams.n_ctx - (int) embd_inp.size());
|
|
|
|
spdlog::debug("{}: number of tokens in prompt = {}", __func__, embd_inp.size());
|
|
|
|
std::vector<gpt_vocab::id> embd;
|
|
|
|
// determine the required inference memory per token:
|
|
size_t mem_per_token = 0;
|
|
|
|
std::vector<float> logits;
|
|
|
|
gptj_eval((*model), config.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
|
|
|
|
for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
|
|
// predict
|
|
if (embd.size() > 0) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!gptj_eval((*model), config.n_threads, n_past, embd, logits, mem_per_token)) {
|
|
throw std::runtime_error("Failed to predict");
|
|
}
|
|
|
|
t_predict_us += ggml_time_us() - t_start_us;
|
|
}
|
|
|
|
n_past += embd.size();
|
|
embd.clear();
|
|
|
|
if (i >= embd_inp.size()) {
|
|
// sample next token
|
|
const int top_k = config.top_k;
|
|
const float top_p = config.top_p;
|
|
const float temp = config.temp;
|
|
|
|
const int n_vocab = model->hparams.n_vocab;
|
|
|
|
gpt_vocab::id id = 0;
|
|
|
|
{
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
id = gpt_sample_top_k_top_p((*vocab), logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
|
|
|
|
t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
// add it to the context
|
|
embd.push_back(id);
|
|
|
|
if(id != 50256){
|
|
result << vocab->id_to_token[id].c_str();
|
|
}
|
|
|
|
} else {
|
|
// if here, it means we are still processing the input prompt
|
|
for (int k = i; k < embd_inp.size(); k++) {
|
|
embd.push_back(embd_inp[k]);
|
|
|
|
if(include_prompt){
|
|
result << vocab->id_to_token[embd_inp[k]].c_str();
|
|
}
|
|
|
|
if (embd.size() > config.n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
i += embd.size() - 1;
|
|
}
|
|
|
|
// end of text token
|
|
if (embd.back() == 50256) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|