wehub-resource-sync
196 lines
7.8 KiB
C
196 lines
7.8 KiB
C
#define TESTING
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#include "train_gpt2.c"
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// poor man's tensor checker
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int check_tensor(float *a, float *b, int n, const char* label) {
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int print_upto = 5;
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int ok = 1;
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float maxdiff = 0.0f;
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float tol = 2e-2f;
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printf("%s\n", label);
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for (int i = 0; i < n; i++) {
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// look at the diffence at position i of these two tensors
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float diff = fabsf(a[i] - b[i]);
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// keep track of the overall error
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ok = ok && (diff <= tol);
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if (diff > maxdiff) { maxdiff = diff; }
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// for the first few elements of each tensor, pretty print
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// the actual numbers, so we can do a visual, qualitative proof/assessment
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if (i < print_upto) {
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if (diff <= tol) {
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if (i < print_upto) { printf("OK "); }
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} else {
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if (i < print_upto) { printf("NOT OK "); }
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}
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printf("%f %f\n", a[i], b[i]);
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}
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}
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// print the final result for this tensor
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if (ok) {
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printf("TENSOR OK, maxdiff = %e\n", maxdiff);
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} else {
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printf("TENSOR NOT OK, maxdiff = %e\n", maxdiff);
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}
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return ok;
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}
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int main(int argc, char *argv[]) {
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// build the GPT-2 model from a checkpoint
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GPT2 model;
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gpt2_build_from_checkpoint(&model, "gpt2_124M.bin");
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int C = model.config.channels;
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int V = model.config.vocab_size;
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int Vp = model.config.padded_vocab_size;
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int maxT = model.config.max_seq_len;
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int L = model.config.num_layers;
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// load additional information that we will use for debugging and error checking
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FILE *state_file = fopen("gpt2_124M_debug_state.bin", "rb");
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if (state_file == NULL) { printf("Error opening state file\n"); return 1; }
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int state_header[256];
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freadCheck(state_header, sizeof(int), 256, state_file);
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if (state_header[0] != 20240327) { printf("Bad magic state file\n"); return 1; }
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if (state_header[1] != 2) {
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printf("Bad version in state file\n");
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printf("---> HINT: try to re-run `python train_gpt2.py`\n");
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return 1;
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}
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int B = state_header[2]; // batch size, e.g. 4
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int T = state_header[3]; // time / sequence length (e.g. 64, up to maxT)
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printf("[State]\n");
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printf("batch_size: %d\n", B);
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printf("seq_len: %d\n", T);
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ParameterTensors expected_grads;
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float* expected_grads_memory = malloc_and_point_parameters(&expected_grads, model.param_sizes);
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// inputs and expected outputs, only used for error checking
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int* x = (int*) malloc(B * T * sizeof(int));
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int* y = (int*) malloc(B * T * sizeof(int));
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float* expected_logits = (float*) malloc(B * T * V * sizeof(float));
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float* expected_loss = (float*) malloc(1 * sizeof(float));
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// read reference information from Python
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freadCheck(x, sizeof(int), B*T, state_file);
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freadCheck(y, sizeof(int), B*T, state_file);
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freadCheck(expected_logits, sizeof(float), B*T*V, state_file);
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freadCheck(expected_loss, sizeof(float), 1, state_file);
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freadCheck(expected_grads_memory, sizeof(float), model.num_parameters, state_file);
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fcloseCheck(state_file);
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// overall OK signal for the test
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int allok = 1;
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// let's do 10 training iterations, following the pytorch code
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float expected_losses[10] = {
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5.270007133483887f,
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4.059706687927246f,
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3.3751230239868164f,
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2.8007826805114746f,
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2.315382242202759f,
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1.8490285873413086f,
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1.3946564197540283f,
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0.9991465210914612f,
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0.6240804195404053f,
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0.37651097774505615f
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};
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for (int step = 0; step < 10; step++) {
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struct timespec start, end;
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clock_gettime(CLOCK_MONOTONIC, &start);
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gpt2_forward(&model, x, y, B, T);
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gpt2_zero_grad(&model);
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gpt2_backward(&model);
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clock_gettime(CLOCK_MONOTONIC, &end);
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double time_elapsed_s = (end.tv_sec - start.tv_sec) + (end.tv_nsec - start.tv_nsec) / 1e9;
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if (step == 0) {
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// error checking at step 0 for reference activations/gradients
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// at this point, target should be equal to expected_logits, let's compare
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int logits_ok = 1;
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float* calculated_logits = model.acts.logits;
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float max_diff = 0.0f;
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for (int bt = 0; bt < B*T; bt++) {
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for (int v = 0; v < V; v++) { // note we only loop to V (ignoring padding)
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int i = bt * Vp + v; // linearized index, using Vp
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if (i < 10) {
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printf("%f, %f\n", expected_logits[i], calculated_logits[i]);
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}
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float diff = fabsf(expected_logits[bt*V + v] - calculated_logits[i]);
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max_diff = fmaxf(max_diff, diff);
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if (diff >= 1e-2f) {
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printf("MISMATCH AT INDEX %d,%d: ", bt, v);
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printf("%f %f\n", expected_logits[bt*V + v], calculated_logits[i]);
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logits_ok = 0;
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bt = B*T; // to break out of both loops
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break;
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}
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}
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}
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if(!logits_ok) { printf("NOT "); }
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printf("OK (LOGITS), max_diff = %e\n", max_diff);
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allok = allok && logits_ok;
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// compare the achieved loss
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if (fabsf(model.mean_loss - *expected_loss) >= 1e-2) {
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printf("LOSS MISMATCH: %f %f\n", model.mean_loss, *expected_loss);
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allok = 0;
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} else {
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printf("LOSS OK: %f %f\n", model.mean_loss, *expected_loss);
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}
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// finally check all the gradients
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int gradoks[16];
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ParameterTensors grads = model.grads;
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gradoks[0] = check_tensor(grads.wte, expected_grads.wte, V*C, "dwte");
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gradoks[1] = check_tensor(grads.wpe, expected_grads.wpe, maxT*C, "dwpe");
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gradoks[2] = check_tensor(grads.ln1w, expected_grads.ln1w, L*C, "dln1w");
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gradoks[3] = check_tensor(grads.ln1b, expected_grads.ln1b, L*C, "dln1b");
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gradoks[4] = check_tensor(grads.qkvw, expected_grads.qkvw, L*3*C*C, "dqkvw");
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gradoks[5] = check_tensor(grads.qkvb, expected_grads.qkvb, L*3*C, "dqkvb");
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gradoks[6] = check_tensor(grads.attprojw, expected_grads.attprojw, L*C*C, "dattprojw");
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gradoks[7] = check_tensor(grads.attprojb, expected_grads.attprojb, L*C, "dattprojb");
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gradoks[8] = check_tensor(grads.ln2w, expected_grads.ln2w, L*C, "dln2w");
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gradoks[9] = check_tensor(grads.ln2b, expected_grads.ln2b, L*C, "dln2b");
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gradoks[10] = check_tensor(grads.fcw, expected_grads.fcw, L*4*C*C, "dfcw");
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gradoks[11] = check_tensor(grads.fcb, expected_grads.fcb, L*4*C, "dfcb");
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gradoks[12] = check_tensor(grads.fcprojw, expected_grads.fcprojw, L*C*4*C, "dfcprojw");
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gradoks[13] = check_tensor(grads.fcprojb, expected_grads.fcprojb, L*C, "dfcprojb");
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gradoks[14] = check_tensor(grads.lnfw, expected_grads.lnfw, C, "dlnfw");
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gradoks[15] = check_tensor(grads.lnfb, expected_grads.lnfb, C, "dlnfb");
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for (int i = 0; i < 16; i++) {
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allok = allok && gradoks[i];
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}
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}
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gpt2_update(&model, 1e-4f, 0.9f, 0.999f, 1e-8f, 0.01f, step+1);
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// compare the losses
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float expected_loss = expected_losses[step];
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float actual_loss = model.mean_loss;
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int step_loss_ok = fabsf(expected_loss - actual_loss) < 1e-2;
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allok = allok && step_loss_ok;
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// print the timing information at the end
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printf("step %d: loss %f (took %f ms) OK = %d\n", step, model.mean_loss, time_elapsed_s * 1000, step_loss_ok);
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}
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// final judgement
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printf("overall okay: %d\n", allok);
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// free everything
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free(x);
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free(y);
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free(expected_logits);
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free(expected_loss);
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free(expected_grads_memory);
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gpt2_free(&model);
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return 0;
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}
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