commit e7e2ce551cfbb10d71554fd4ed45a96138a20372 Author: Yaossg Date: Thu Jul 18 21:38:25 2024 +0800 first commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c3d8f61 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +gpt +gpt2_124M.bin \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..98af70c --- /dev/null +++ b/README.md @@ -0,0 +1,44 @@ +# GPT2 + +原作者：Andrej Karpathy @ https://github.com/karpathy/llm.c + +## 背景 + +GPT 很酷，能不能在我自己的电脑上跑一个呢？当然可以！ + +![](show.gif) + +现在给你提供 GPT2 的预训练模型：[点击这里](https://alist.yaossg.com/share/model/gpt2_124M.bin)，请把该模型放在本仓库代码的同目录下，按照下面的指示即可运行该程序。 + +## 依赖 + +需要安装 GCC 和 Python3 以及下面的 Python 包 + +```bash +pip3 install tiktoken +``` + +## 编译 + +```bash +bash build.sh +``` + +## 运行 + +```bash +python3 chat.py +``` + +## 目标 + +你可能已经发现了，你的程序可能并没有我演示的跑的那么快（~~神机请忽略~~）。 + +你的目标就是优化该程序的性能，在保证结果不变的情况下更快的完成文本的补全。 + +我会使用一些测试点来评测你的程序的正确性和执行时间。期待更高的效率和更多样的优化方案。 + +此外，请在 wp 中回答下面的问题： + +- 什么是阿姆达尔定律？根据阿姆达尔定律，我们应该把优化的重点放在哪里？ +- 你的优化方案和思路是什么？优化的效果受到哪些因素影响？ diff --git a/build.sh b/build.sh new file mode 100644 index 0000000..fd20f0d --- /dev/null +++ b/build.sh @@ -0,0 +1 @@ +gcc gpt.c -lm -O3 -std=gnu11 -ggdb -Wall -Werror -Wno-unused-result -Wno-unused-value -Wno-unused-variable -o gpt diff --git a/chat.py b/chat.py new file mode 100644 index 0000000..c608c8d --- /dev/null +++ b/chat.py @@ -0,0 +1,30 @@ +import tiktoken +import subprocess +import time + +length = input("Completion length: ") +length = str(int(length)) # ensure input a valid integer + +text = input("Text to complete: ") +enc = tiktoken.get_encoding("gpt2") + +tokens = [ + str(tok) for tok in enc.encode(text) +] + +start = time.time() + +proc = subprocess.Popen( + ["./gpt", length, *tokens], + stdout=subprocess.PIPE, + text=True +) + +while (line := proc.stdout.readline()): + token = int(line) + print(enc.decode([token]), end='', flush=True) + +print() + +end = time.time() +print(f"It took {end - start:.2f}s to complete the text.") \ No newline at end of file diff --git a/gpt.c b/gpt.c new file mode 100644 index 0000000..f2fd27e --- /dev/null +++ b/gpt.c @@ -0,0 +1,600 @@ +// Original Author: Andrej Karpathy +// https://github.com/karpathy/llm.c + +#include +#include +#include +#include +#include +#include +#include + +// ---------------------------------------------------------------------------- +// all the individual layers' forward passes +// B = batch_size, T = sequence_length, C = channels, V = vocab_size + +void encoder_forward(float* out, + int* inp, float* wte, float* wpe, + int B, int T, int C) { + // out is (B,T,C). At each position (b,t), a C-dimensional vector summarizing token & position + // inp is (B,T) of integers, holding the token ids at each (b,t) position + // wte is (V,C) of token embeddings, short for "weight token embeddings" + // wpe is (maxT,C) of position embeddings, short for "weight positional embedding" + for (int b = 0; b < B; b++) { + for (int t = 0; t < T; t++) { + // seek to the output position in out[b,t,:] + float* out_bt = out + b * T * C + t * C; + // get the index of the token at inp[b, t] + int ix = inp[b * T + t]; + // seek to the position in wte corresponding to the token + float* wte_ix = wte + ix * C; + // seek to the position in wpe corresponding to the position + float* wpe_t = wpe + t * C; + // add the two vectors and store the result in out[b,t,:] + for (int i = 0; i < C; i++) { + out_bt[i] = wte_ix[i] + wpe_t[i]; + } + } + } +} + +void layernorm_forward(float* out, float* mean, float* rstd, + float* inp, float* weight, float* bias, + int B, int T, int C) { + // reference: https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html + // both inp and out are (B,T,C) of the activations + // mean and rstd are (B,T) buffers, to be used later in backward pass + // at each position (b,t) of the input, the C-dimensional vector + // of activations gets normalized, then scaled and shifted + float eps = 1e-5f; + for (int b = 0; b < B; b++) { + for (int t = 0; t < T; t++) { + // seek to the input position inp[b,t,:] + float* x = inp + b * T * C + t * C; + // calculate the mean + float m = 0.0f; + for (int i = 0; i < C; i++) { + m += x[i]; + } + m = m/C; + // calculate the variance (without any bias correction) + float v = 0.0f; + for (int i = 0; i < C; i++) { + float xshift = x[i] - m; + v += xshift * xshift; + } + v = v/C; + // calculate the rstd (reciprocal standard deviation) + float s = 1.0f / sqrtf(v + eps); + // seek to the output position in out[b,t,:] + float* out_bt = out + b * T * C + t * C; + for (int i = 0; i < C; i++) { + float n = (s * (x[i] - m)); // normalize + float o = n * weight[i] + bias[i]; // scale and shift + out_bt[i] = o; // write + } + // cache the mean and rstd for the backward pass later + mean[b * T + t] = m; + rstd[b * T + t] = s; + } + } +} + +void matmul_forward(float* out, + float* inp, float* weight, float* bias, + int B, int T, int C, int OC) { + // most of the running time is spent here and in matmul_backward + // OC is short for "output channels" + // inp is (B,T,C), weight is (OC, C), bias is (OC) + // out will be (B,T,OC) + for (int b = 0; b < B; b++) { + for (int t = 0; t < T; t++) { + float* out_bt = out + b * T * OC + t * OC; + float* inp_bt = inp + b * T * C + t * C; + for (int o = 0; o < OC; o++) { + float val = (bias != NULL) ? bias[o] : 0.0f; + float* wrow = weight + o*C; + for (int i = 0; i < C; i++) { + val += inp_bt[i] * wrow[i]; + } + out_bt[o] = val; + } + } + } +} + +void attention_forward(float* out, float* preatt, float* att, + float* inp, + int B, int T, int C, int NH) { + // input is (B, T, 3C) holding the query, key, value (Q, K, V) vectors + // preatt, att are (B, NH, T, T). NH = number of heads, T = sequence length + // that holds the pre-attention and post-attention scores (used in backward) + // output is (B, T, C) + // attention is the only layer that mixes information across time + // every other operation is applied at every (b,t) position independently + // (and of course, no layer mixes information across batch) + int C3 = C*3; + int hs = C / NH; // head size + float scale = 1.0 / sqrtf(hs); + + for (int b = 0; b < B; b++) { + for (int t = 0; t < T; t++) { + for (int h = 0; h < NH; h++) { + float* query_t = inp + b * T * C3 + t * C3 + h * hs; + float* preatt_bth = preatt + b*NH*T*T + h*T*T + t*T; + float* att_bth = att + b*NH*T*T + h*T*T + t*T; + + // pass 1: calculate query dot key and maxval + float maxval = -10000.0f; // TODO something better + for (int t2 = 0; t2 <= t; t2++) { + float* key_t2 = inp + b * T * C3 + t2 * C3 + h * hs + C; // +C because it's key + + // (query_t) dot (key_t2) + float val = 0.0f; + for (int i = 0; i < hs; i++) { + val += query_t[i] * key_t2[i]; + } + val *= scale; + if (val > maxval) { + maxval = val; + } + + preatt_bth[t2] = val; + } + + // pass 2: calculate the exp and keep track of sum + // maxval is being calculated and subtracted only for numerical stability + float expsum = 0.0f; + for (int t2 = 0; t2 <= t; t2++) { + float expv = expf(preatt_bth[t2] - maxval); + expsum += expv; + att_bth[t2] = expv; + } + float expsum_inv = expsum == 0.0f ? 0.0f : 1.0f / expsum; + + // pass 3: normalize to get the softmax + for (int t2 = 0; t2 < T; t2++) { + if (t2 <= t) { + att_bth[t2] *= expsum_inv; + } else { + // causal attention mask. not strictly necessary to set to zero here + // only doing this explicitly for debugging and checking to PyTorch + att_bth[t2] = 0.0f; + } + } + + // pass 4: accumulate weighted values into the output of attention + float* out_bth = out + b * T * C + t * C + h * hs; + for (int i = 0; i < hs; i++) { out_bth[i] = 0.0f; } + for (int t2 = 0; t2 <= t; t2++) { + float* value_t2 = inp + b * T * C3 + t2 * C3 + h * hs + C*2; // +C*2 because it's value + float att_btht2 = att_bth[t2]; + for (int i = 0; i < hs; i++) { + out_bth[i] += att_btht2 * value_t2[i]; + } + } + } + } + } +} + +#define GELU_SCALING_FACTOR sqrtf(2.0f / M_PI) +void gelu_forward(float* out, float* inp, int N) { + // (approximate) GeLU elementwise non-linearity in the MLP block of Transformer + for (int i = 0; i < N; i++) { + float x = inp[i]; + float cube = 0.044715f * x * x * x; + out[i] = 0.5f * x * (1.0f + tanhf(GELU_SCALING_FACTOR * (x + cube))); + } +} + +void residual_forward(float* out, float* inp1, float* inp2, int N) { + for (int i = 0; i < N; i++) { + out[i] = inp1[i] + inp2[i]; + } +} + +void softmax_forward(float* probs, float* logits, int B, int T, int V) { + // output: probs are (B,T,V) of the probabilities (sums to 1.0 in each b,t position) + // input: logits is (B,T,V) of the unnormalized log probabilities + for (int b = 0; b < B; b++) { + for (int t = 0; t < T; t++) { + // probs <- softmax(logits) + float* logits_bt = logits + b * T * V + t * V; + float* probs_bt = probs + b * T * V + t * V; + + // maxval is only calculated and subtracted for numerical stability + float maxval = -10000.0f; // TODO something better + for (int i = 0; i < V; i++) { + if (logits_bt[i] > maxval) { + maxval = logits_bt[i]; + } + } + float sum = 0.0f; + for (int i = 0; i < V; i++) { + probs_bt[i] = expf(logits_bt[i] - maxval); + sum += probs_bt[i]; + } + for (int i = 0; i < V; i++) { + probs_bt[i] /= sum; + } + } + } +} + +// ---------------------------------------------------------------------------- +// GPT-2 model definition + +// the parameters of the model +#define NUM_PARAMETER_TENSORS 16 +typedef struct { + float* wte; // (V, C) + float* wpe; // (maxT, C) + float* ln1w; // (L, C) + float* ln1b; // (L, C) + float* qkvw; // (L, 3*C, C) + float* qkvb; // (L, 3*C) + float* attprojw; // (L, C, C) + float* attprojb; // (L, C) + float* ln2w; // (L, C) + float* ln2b; // (L, C) + float* fcw; // (L, 4*C, C) + float* fcb; // (L, 4*C) + float* fcprojw; // (L, C, 4*C) + float* fcprojb; // (L, C) + float* lnfw; // (C) + float* lnfb; // (C) +} ParameterTensors; + +// allocate memory for the parameters and point the individual tensors to the right places +float* malloc_and_point_parameters(ParameterTensors* params, size_t* param_sizes) { + size_t num_parameters = 0; + for (size_t i = 0; i < NUM_PARAMETER_TENSORS; i++) { + num_parameters += param_sizes[i]; + } + // malloc all parameters all at once + float* params_memory = (float*)malloc(num_parameters * sizeof(float)); + // assign all the tensors + float** ptrs[] = { + ¶ms->wte, ¶ms->wpe, ¶ms->ln1w, ¶ms->ln1b, ¶ms->qkvw, ¶ms->qkvb, + ¶ms->attprojw, ¶ms->attprojb, ¶ms->ln2w, ¶ms->ln2b, ¶ms->fcw, ¶ms->fcb, + ¶ms->fcprojw, ¶ms->fcprojb, ¶ms->lnfw, ¶ms->lnfb + }; + float* params_memory_iterator = params_memory; + for (size_t i = 0; i < NUM_PARAMETER_TENSORS; i++) { + *(ptrs[i]) = params_memory_iterator; + params_memory_iterator += param_sizes[i]; + } + return params_memory; +} + +#define NUM_ACTIVATION_TENSORS 23 +typedef struct { + float* encoded; // (B, T, C) + float* ln1; // (L, B, T, C) + float* ln1_mean; // (L, B, T) + float* ln1_rstd; // (L, B, T) + float* qkv; // (L, B, T, 3*C) + float* atty; // (L, B, T, C) + float* preatt; // (L, B, NH, T, T) + float* att; // (L, B, NH, T, T) + float* attproj; // (L, B, T, C) + float* residual2; // (L, B, T, C) + float* ln2; // (L, B, T, C) + float* ln2_mean; // (L, B, T) + float* ln2_rstd; // (L, B, T) + float* fch; // (L, B, T, 4*C) + float* fch_gelu; // (L, B, T, 4*C) + float* fcproj; // (L, B, T, C) + float* residual3; // (L, B, T, C) + float* lnf; // (B, T, C) + float* lnf_mean; // (B, T) + float* lnf_rstd; // (B, T) + float* logits; // (B, T, V) + float* probs; // (B, T, V) + float* losses; // (B, T) +} ActivationTensors; + +float* malloc_and_point_activations(ActivationTensors* acts, size_t* act_sizes) { + size_t num_activations = 0; + for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) { + num_activations += act_sizes[i]; + } + float* acts_memory = (float*)malloc(num_activations * sizeof(float)); + float** ptrs[] = { + &acts->encoded, &acts->ln1, &acts->ln1_mean, &acts->ln1_rstd, &acts->qkv, &acts->atty, + &acts->preatt, &acts->att, &acts->attproj, &acts->residual2, &acts->ln2, &acts->ln2_mean, + &acts->ln2_rstd, &acts->fch, &acts->fch_gelu, &acts->fcproj, &acts->residual3, &acts->lnf, + &acts->lnf_mean, &acts->lnf_rstd, &acts->logits, &acts->probs, &acts->losses + }; + float* acts_memory_iterator = acts_memory; + for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) { + *(ptrs[i]) = acts_memory_iterator; + acts_memory_iterator += act_sizes[i]; + } + return acts_memory; +} + +typedef struct { + int max_seq_len; // max sequence length, e.g. 1024 + int vocab_size; // vocab size, e.g. 50257 + int num_layers; // number of layers, e.g. 12 + int num_heads; // number of heads in attention, e.g. 12 + int channels; // number of channels, e.g. 768 +} GPT2Config; + +typedef struct { + GPT2Config config; + // the weights (parameters) of the model, and their sizes + ParameterTensors params; + size_t param_sizes[NUM_PARAMETER_TENSORS]; + float* params_memory; + int num_parameters; + // gradients of the weights + ParameterTensors grads; + float* grads_memory; + // buffers for the AdamW optimizer + float* m_memory; + float* v_memory; + // the activations of the model, and their sizes + ActivationTensors acts; + size_t act_sizes[NUM_ACTIVATION_TENSORS]; + float* acts_memory; + int num_activations; + // gradients of the activations + ActivationTensors grads_acts; + float* grads_acts_memory; + // other run state configuration + int batch_size; // the batch size (B) of current forward pass + int seq_len; // the sequence length (T) of current forward pass + int* inputs; // the input tokens for the current forward pass + int* targets; // the target tokens for the current forward pass + float mean_loss; // after a forward pass with targets, will be populated with the mean loss +} GPT2; + +void gpt2_build_from_checkpoint(GPT2 *model, char* checkpoint_path) { + + // read in model from a checkpoint file + FILE *model_file = fopen(checkpoint_path, "rb"); + if (model_file == NULL) { printf("Error opening model file\n"); exit(1); } + int model_header[256]; + fread(model_header, sizeof(int), 256, model_file); + if (model_header[0] != 20240326) { printf("Bad magic model file"); exit(1); } + if (model_header[1] != 1) { printf("Bad version in model file"); exit(1); } + + // read in hyperparameters + int maxT, V, L, NH, C; + model->config.max_seq_len = maxT = model_header[2]; + model->config.vocab_size = V = model_header[3]; + model->config.num_layers = L = model_header[4]; + model->config.num_heads = NH = model_header[5]; + model->config.channels = C = model_header[6]; + + // allocate space for all the parameters and read them in + model->param_sizes[0] = V * C; // wte + model->param_sizes[1] = maxT * C; // wpe + model->param_sizes[2] = L * C; // ln1w + model->param_sizes[3] = L * C; // ln1b + model->param_sizes[4] = L * (3 * C) * C; // qkvw + model->param_sizes[5] = L * (3 * C); // qkvb + model->param_sizes[6] = L * C * C; // attprojw + model->param_sizes[7] = L * C; // attprojb + model->param_sizes[8] = L * C; // ln2w + model->param_sizes[9] = L * C; // ln2b + model->param_sizes[10] = L * (4 * C) * C; // fcw + model->param_sizes[11] = L * (4 * C); // fcb + model->param_sizes[12] = L * C * (4 * C); // fcprojw + model->param_sizes[13] = L * C; // fcprojb + model->param_sizes[14] = C; // lnfw + model->param_sizes[15] = C; // lnfb + + // cound the number of paramaters + size_t num_parameters = 0; + for (size_t i = 0; i < NUM_PARAMETER_TENSORS; i++) { + num_parameters += model->param_sizes[i]; + } + model->num_parameters = num_parameters; + + // read in all the parameters from file + model->params_memory = malloc_and_point_parameters(&model->params, model->param_sizes); + fread(model->params_memory, sizeof(float), num_parameters, model_file); + fclose(model_file); + + // other inits + model->acts_memory = NULL; + model->grads_memory = NULL; + model->m_memory = NULL; + model->v_memory = NULL; + model->grads_acts_memory = NULL; + model->inputs = NULL; + model->targets = NULL; + model->batch_size = 0; + model->seq_len = 0; + model->mean_loss = -1.0f; // -1.0f will designate no loss +} + +void gpt2_forward(GPT2 *model, int* inputs, int B, int T) { + // convenience parameters + int V = model->config.vocab_size; + int L = model->config.num_layers; + int NH = model->config.num_heads; + int C = model->config.channels; + + // record the current B,T as well + model->batch_size = B; + model->seq_len = T; + // and now allocate the space + model->act_sizes[0] = B * T * C; // encoded + model->act_sizes[1] = L * B * T * C; // ln1 + model->act_sizes[2] = L * B * T; // ln1_mean + model->act_sizes[3] = L * B * T; // ln1_rstd + model->act_sizes[4] = L * B * T * 3*C; // qkv + model->act_sizes[5] = L * B * T * C; // atty + model->act_sizes[6] = L * B * NH * T * T; // preatt + model->act_sizes[7] = L * B * NH * T * T; // att + model->act_sizes[8] = L * B * T * C; // attproj + model->act_sizes[9] = L * B * T * C; // residual2 + model->act_sizes[10] = L * B * T * C; // ln2 + model->act_sizes[11] = L * B * T; // ln2_mean + model->act_sizes[12] = L * B * T; // ln2_rstd + model->act_sizes[13] = L * B * T * 4*C; // fch + model->act_sizes[14] = L * B * T * 4*C; // fch_gelu + model->act_sizes[15] = L * B * T * C; // fcproj + model->act_sizes[16] = L * B * T * C; // residual3 + model->act_sizes[17] = B * T * C; // lnf + model->act_sizes[18] = B * T; // lnf_mean + model->act_sizes[19] = B * T; // lnf_rstd + model->act_sizes[20] = B * T * V; // logits + model->act_sizes[21] = B * T * V; // probs + model->act_sizes[22] = B * T; // losses + size_t num_activations = 0; + for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) { + num_activations += model->act_sizes[i]; + } + model->num_activations = num_activations; + + if (model->acts_memory) { + free(model->acts_memory); + model->acts_memory = NULL; + } + model->acts_memory = malloc_and_point_activations(&model->acts, model->act_sizes); + + // also create memory for caching inputs and targets + if (model->inputs) { + free(model->inputs); + } + model->inputs = (int*)malloc(B * T * sizeof(int)); + + // cache the inputs/targets + memcpy(model->inputs, inputs, B * T * sizeof(int)); + + // forward pass + ParameterTensors params = model->params; // for brevity + ActivationTensors acts = model->acts; + float* residual; + encoder_forward(acts.encoded, inputs, params.wte, params.wpe, B, T, C); // encoding goes into residual[0] + for (int l = 0; l < L; l++) { + + residual = l == 0 ? acts.encoded : acts.residual3 + (l-1) * B * T * C; + + // get the pointers of the weights for this layer + float* l_ln1w = params.ln1w + l * C; + float* l_ln1b = params.ln1b + l * C; + float* l_qkvw = params.qkvw + l * 3*C * C; + float* l_qkvb = params.qkvb + l * 3*C; + float* l_attprojw = params.attprojw + l * C * C; + float* l_attprojb = params.attprojb + l * C; + float* l_ln2w = params.ln2w + l * C; + float* l_ln2b = params.ln2b + l * C; + float* l_fcw = params.fcw + l * 4*C * C; + float* l_fcb = params.fcb + l * 4*C; + float* l_fcprojw = params.fcprojw + l * C * 4*C; + float* l_fcprojb = params.fcprojb + l * C; + + // get the pointers of the activations for this layer + float* l_ln1 = acts.ln1 + l * B * T * C; + float* l_ln1_mean = acts.ln1_mean + l * B * T; + float* l_ln1_rstd = acts.ln1_rstd + l * B * T; + float* l_qkv = acts.qkv + l * B * T * 3*C; + float* l_atty = acts.atty + l * B * T * C; + float* l_preatt = acts.preatt + l * B * NH * T * T; + float* l_att = acts.att + l * B * NH * T * T; + float* l_attproj = acts.attproj + l * B * T * C; + float* l_residual2 = acts.residual2 + l * B * T * C; + float* l_ln2 = acts.ln2 + l * B * T * C; + float* l_ln2_mean = acts.ln2_mean + l * B * T; + float* l_ln2_rstd = acts.ln2_rstd + l * B * T; + float* l_fch = acts.fch + l * B * T * 4*C; + float* l_fch_gelu = acts.fch_gelu + l * B * T * 4*C; + float* l_fcproj = acts.fcproj + l * B * T * C; + float* l_residual3 = acts.residual3 + l * B * T * C; + + // now do the forward pass + layernorm_forward(l_ln1, l_ln1_mean, l_ln1_rstd, residual, l_ln1w, l_ln1b, B, T, C); + matmul_forward(l_qkv, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C); + attention_forward(l_atty, l_preatt, l_att, l_qkv, B, T, C, NH); + matmul_forward(l_attproj, l_atty, l_attprojw, l_attprojb, B, T, C, C); + residual_forward(l_residual2, residual, l_attproj, B*T*C); + layernorm_forward(l_ln2, l_ln2_mean, l_ln2_rstd, l_residual2, l_ln2w, l_ln2b, B, T, C); + matmul_forward(l_fch, l_ln2, l_fcw, l_fcb, B, T, C, 4*C); + gelu_forward(l_fch_gelu, l_fch, B*T*4*C); + matmul_forward(l_fcproj, l_fch_gelu, l_fcprojw, l_fcprojb, B, T, 4*C, C); + residual_forward(l_residual3, l_residual2, l_fcproj, B*T*C); + } + residual = acts.residual3 + (L-1) * B * T * C; // last residual is in residual3 + layernorm_forward(acts.lnf, acts.lnf_mean, acts.lnf_rstd, residual, params.lnfw, params.lnfb, B, T, C); + matmul_forward(acts.logits, acts.lnf, params.wte, NULL, B, T, C, V); + softmax_forward(acts.probs, acts.logits, B, T, V); +} + +void gpt2_zero_grad(GPT2 *model) { + if(model->grads_memory != NULL) { memset(model->grads_memory, 0, model->num_parameters * sizeof(float)); } + if(model->grads_acts_memory != NULL) { memset(model->grads_acts_memory, 0, model->num_activations * sizeof(float)); } +} + +void gpt2_free(GPT2 *model) { + free(model->params_memory); + free(model->grads_memory); + free(model->m_memory); + free(model->v_memory); + free(model->acts_memory); + free(model->grads_acts_memory); + free(model->inputs); + free(model->targets); +} + +int sample_mult(float* probabilities, int n) { + // sample index from probabilities (they must sum to 1!) + // coin can be a random number in [0, 1), usually from random_f32() + float cdf = 0.0f, coin = 0.5f; + for (int i = 0; i < n; i++) { + cdf += probabilities[i]; + if (coin < cdf) { + return i; + } + } + return n - 1; // in case of rounding errors +} + +// the GPT-2 end-of-text token id +#define GPT2_EOT 50256 + +int main(int argc, char* argv[]) { + GPT2 model; + gpt2_build_from_checkpoint(&model, "gpt2_124M.bin"); + + if (argc < 3) { + printf("Provide completion length and at least one token.\n"); + exit(1); + } + + const int input_offset = 2; + int completion_length = atoi(argv[1]); + int input_length = argc - input_offset; + if (input_length > completion_length) { + printf("Tow many tokens.\n"); + exit(1); + } + + int tokens[completion_length]; + + for (int i = 0; i < completion_length; i++) { + if (i < input_length) { + tokens[i] = atoi(argv[input_offset + i]); + } else { + tokens[i] = GPT2_EOT; + } + } + + for (int t = input_length; t < completion_length; t++) { + gpt2_forward(&model, tokens, 1, t); + float* probs = model.acts.probs + (t-1) * model.config.vocab_size; + int next_token = sample_mult(probs, model.config.vocab_size); + tokens[t] = next_token; + + printf("%d\n", tokens[t]); + fflush(stdout); + } + + gpt2_free(&model); +} diff --git a/show.gif b/show.gif new file mode 100644 index 0000000..ff800f3 Binary files /dev/null and b/show.gif differ