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llama3 starting point is at gpt-2 exact copy paste for both train/tes…
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karpathy committed Sep 13, 2024
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395 changes: 395 additions & 0 deletions test_llama3.cu
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#define TESTING
#include "train_gpt2.cu"

// poor man's tensor checker
int check_tensor(float *a, float *b, int n, const char* label, float threshold=1e-0) {
// a is the calculated tensor, b is the reference tensor
int print_upto = 10;
int ok = 1;
float max_diff = 0.0f;
float max_rel_error = 0.0f;
float max_to_threshold = 0.f;
float max_a = 0.0f;
float max_b = 0.0f;
float epsilon = 0.079; // BF16 epsilon value
printf("---\n");
printf("checking tensor: %s\n", label);
for (int i = 0; i < n; i++) {
float t_eff = threshold + fabs(b[i]) * epsilon;
float diff = fabsf(a[i] - b[i]);
max_to_threshold = max(max_to_threshold, diff / t_eff);
if (diff > max_diff) {
max_diff = diff;
float denom = fabsf(b[i]);
max_rel_error = (denom == 0.0f) ? 0.0f : diff / denom;
max_a = a[i];
max_b = b[i];
}
if (diff > t_eff) {
ok = 0;
}
// print the first few elements so we can visually assess the "proof" of the comparison
if (i < print_upto) {
printf(diff <= t_eff ? "OK " : "NOT OK ");
printf("%f %f\n", a[i], b[i]);
}
}
// print the final result
if (ok) {
printf("TENSOR OK, max diff: %.3e, with rel error: %.3e (calculated=%10f, ref=%10f), %.2f%% of maximum error\n",
max_diff, max_rel_error, max_a, max_b, max_to_threshold*100);
} else {
printf("TENSOR NOT OK, max diff: %.3e, with rel error: %.3e (calculated=%10f, ref=%10f), %.2f%% of maximum error\n",
max_diff, max_rel_error, max_a, max_b, max_to_threshold*100);
}
return ok;
}

// the same tensors as in the train file, but in float, which are used as reference
typedef struct {
float* wte; // (Vp, 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)
} FloatParameterTensors;
static_assert(sizeof(FloatParameterTensors) == NUM_PARAMETER_TENSORS * sizeof(void*), "Inconsistent sizes!");

// malloc_and_point, but in float and on CPU, because we use this data to check correctness on CPU
float* float_cpu_malloc_and_point_parameters(FloatParameterTensors* params, size_t* param_sizes) {
// calculate the total number of parameters
size_t num_parameters = 0;
for (int i = 0; i < NUM_PARAMETER_TENSORS; i++) {
num_parameters += param_sizes[i];
}
// everything is float so number of bytes to allocate is a simple multiplication
float* params_memory = (float*)mallocCheck(num_parameters * sizeof(float));
float** ptrs[] = {
&params->wte, &params->wpe, &params->ln1w, &params->ln1b, &params->qkvw, &params->qkvb,
&params->attprojw, &params->attprojb, &params->ln2w, &params->ln2b, &params->fcw, &params->fcb,
&params->fcprojw, &params->fcprojb, &params->lnfw, &params->lnfb
};
float* params_memory_iterator = params_memory;
for (int i = 0; i < NUM_PARAMETER_TENSORS; i++) {
*(ptrs[i]) = params_memory_iterator;
params_memory_iterator += param_sizes[i];
}
return params_memory;
}

int main(int argc, char *argv[]) {
char nccl_init_method[256] = "mpi"; // "tcp" or "fs" or "mpi"
int num_processes = -1; // doesn't matter when using MPI
int process_rank = -1; // doesn't matter when using MPI
int gpus_per_node = -1; // doesn't matter when using MPI
char server_ip[256] = ""; // doesn't matter when using MPI
char fs_path[256] = ""; // doesn't matter when using MPI
multi_gpu_config = multi_gpu_config_init(num_processes, process_rank, gpus_per_node, server_ip, fs_path, nccl_init_method);
common_start(false, true);

// set the right paths
#if defined(ENABLE_BF16)
const char* load_filename = "gpt2_124M_bf16.bin";
#else
const char* load_filename = "gpt2_124M.bin";
#endif

// build the GPT-2 model from a checkpoint
GPT2 model;
gpt2_init_common(&model);

gpt2_build_from_checkpoint(&model, load_filename);
size_t V = model.config.vocab_size;
size_t Vp = model.config.padded_vocab_size;
size_t maxT = model.config.max_seq_len;
size_t L = model.config.num_layers;
size_t C = model.config.channels;

for (int i = 1; i < argc; i+=2) {
if (i + 1 >= argc) { exit(EXIT_FAILURE); } // must have arg after flag
if (!(strlen(argv[i]) == 2 || strlen(argv[i]) == 3)) { exit(EXIT_FAILURE); } // must be -x[y] (one dash, one or two letters)
if (argv[i][0] != '-') { exit(EXIT_FAILURE); } // must start with dash
if (argv[i][1] == 'w') { model.use_master_weights = atoi(argv[i+1]); }
else if (argv[i][1] == 'r') { model.recompute = atoi(argv[i+1]); }
else if (argv[i][1] == 'g' && argv[i][2] == 'e') { model.gelu_fusion = atoi(argv[i+1]); }
}

// load additional information that we will use for debugging and error checking
FILE *state_file = fopenCheck("gpt2_124M_debug_state.bin", "rb");
int state_header[256];
freadCheck(state_header, sizeof(int), 256, state_file);
if (state_header[0] != 20240327) { fprintf(stderr, "Bad magic state file\n"); exit(EXIT_FAILURE); }
if (state_header[1] != 2) {
fprintf(stderr, "Bad version in state file\n");
fprintf(stderr, "---> HINT: try to re-run `python train_gpt2.py`\n");
exit(EXIT_FAILURE);
}
int B = state_header[2]; // batch size, e.g. 4
int T = state_header[3]; // time / sequence length (e.g. 64, up to maxT)
assert(0 <= T && T <= maxT);
printf("[State]\n");
printf("batch_size: %d\n", B);
printf("seq_len: %d\n", T);

set_zero_configs(&multi_gpu_config, 0, model.num_parameters);

// read reference information from the file saved from Python/PyTorch side
// 1) input x and y
int* x = (int*)mallocCheck(B * T * sizeof(int));
int* y = (int*)mallocCheck(B * T * sizeof(int));
freadCheck(x, sizeof(int), B*T, state_file);
freadCheck(y, sizeof(int), B*T, state_file);
// 2) results of forward pass (logits and loss)
float* expected_logits = (float*) mallocCheck(B * T * V * sizeof(float));
float* expected_loss = (float*) mallocCheck(1 * sizeof(float));
freadCheck(expected_logits, sizeof(float), B*T*V, state_file);
freadCheck(expected_loss, sizeof(float), 1, state_file);
// 3) results of backward pass (parameter gradients)
FloatParameterTensors expected_grads; // will be read from file. right now: all in fp32
float* expected_grads_memory = float_cpu_malloc_and_point_parameters(&expected_grads, model.param_elements);
freadCheck(expected_grads_memory, sizeof(float), model.num_parameters, state_file);
fcloseCheck(state_file);

// this memory will be used to do one single copy of all (mixed precision) GPU grads to CPU grads
void* grads_memory_cpu = mallocCheck(model.num_parameters_bytes);
float* grads_memory_cpu_float = (float*)mallocCheck(model.num_parameters * sizeof(float));

// overall OK signal for the test
int allok = 1;

gpt2_allocate_state(&model, B, T);

// First, do target-free forward pass to validate logits
gpt2_forward(&model, x, B, T);
// at this point, target should be equal to expected_logits, let's compare
// copy logits to CPU so we can compare them
floatX* logits_cpu_raw = (floatX*)mallocCheck(B * T * Vp * sizeof(floatX));
float* logits_cpu = (float*)mallocCheck(B * T * Vp * sizeof(float));
cudaCheck(cudaMemcpy(logits_cpu_raw, model.acts.output, B * T * Vp * sizeof(floatX), cudaMemcpyDeviceToHost));
for (int i = 0; i < B * T * Vp; i++) {
logits_cpu[i] = (float)logits_cpu_raw[i];
}

float logit_accuracy_threshold = 1e-3f;
float loss_diff_threshold = 1e-5f;
// FP16 and lower require very high tolerances unfortunately. TODO look into more
#if defined(ENABLE_BF16) || defined(ENABLE_F16)
logit_accuracy_threshold = 25.0f; // 15.0f was too low even without cuDNN?! :(
loss_diff_threshold = 0.05f;
#endif

// compare the output logits from the forward pass
// also careful that we don't access and compare the padded columns of logits
int logits_ok = 1;
float max_diff = 0.0f;
for (int bt = 0; bt < B*T; bt++) {
for (int v = 0; v < V; v++) {
int i = bt * Vp + v; // linearized index
if (i < 10) {
printf("%f, %f\n", expected_logits[i], logits_cpu[i]);
}
float diff = fabsf(expected_logits[bt*V + v] - logits_cpu[i]);
max_diff = fmaxf(max_diff, diff);
if (diff >= logit_accuracy_threshold) {
printf("MISMATCH AT INDEX %d,%d: ", bt, v);
printf("%f %f\n", expected_logits[bt*V + v], logits_cpu[i]);
logits_ok = 0;
bt = B*T; // to break out of both loops
break;
}
}
}
allok = allok && logits_ok;
if(!logits_ok) { printf("NOT "); }
printf("OK (LOGITS)\n");
printf("logit max diff: %f\n", max_diff);

// let's do 10 training iterations, following the pytorch code
float losses[10];
for (int step = 0; step < 10; step++) {
struct timespec start, end;
clock_gettime(CLOCK_MONOTONIC, &start);
gpt2_forward(&model, x, B, T);
gpt2_backward_and_reduce(&model, x, y, 1, 0);
clock_gettime(CLOCK_MONOTONIC, &end);
double time_elapsed_s = (end.tv_sec - start.tv_sec) + (end.tv_nsec - start.tv_nsec) / 1e9;

if (step == 0) {
// error checking at step 0 for reference activations

// move the (mixed precision) grads from GPU to CPU
cudaCheck(cudaMemcpy(grads_memory_cpu, model.grads_memory, model.num_parameters_bytes, cudaMemcpyDeviceToHost));

// convert all gradients to float on the CPU
char* src_iterator = (char*)grads_memory_cpu; // can be lower precision, so we use char*
float* dst_iterator = (float*)grads_memory_cpu_float; // float*
float* exp_iterator = expected_grads_memory; // float* of expected gradients from Python
float* tensors1[NUM_PARAMETER_TENSORS];
float* tensors2[NUM_PARAMETER_TENSORS];
for (int i = 0; i < NUM_PARAMETER_TENSORS; i++) {
if (model.param_sizeof[i] == sizeof(float)) {
// float tensor => copy over directly
memcpy(dst_iterator, src_iterator, model.param_elements[i] * sizeof(float));
} else {
// low-precision tensor => convert to float
assert(model.param_sizeof[i] == sizeof(floatX)); // floatX is the single non-float supported atm
for (size_t j = 0; j < model.param_elements[i]; j++) {
dst_iterator[j] = ((floatX*)src_iterator)[j]; // convert to float
}
}
// for convenience record the position of comparison for reality vs. expectation
tensors1[i] = dst_iterator; // reality
tensors2[i] = exp_iterator; // expectation
// advance the iterators
src_iterator += model.param_elements[i] * model.param_sizeof[i];
dst_iterator += model.param_elements[i];
exp_iterator += model.param_elements[i];
}

// compare the gradients on the parameters all at once, in fp32
// I set the tolerances manually by inspecting the gradient differences for
// a few elements of each tensor. bf16 looks ok but not amazing here.
// It's possible we have bugs lurking, or maybe it is bf16. Not 100% sure.
// Also, if code changes and some of these get tripped, it could be ok if it's not by too much,
// because our use of stochastic rounding is adding some non-determinism "pepper noise".
// In that case it's ok to extend the tolerance by a bit, after a manual review.
// Also, different GPUs may use different matrix multiplication algorithms, so the
// actual errors can be hardware specific.

float grad_thresholds[NUM_PARAMETER_TENSORS] = {5e-1f, 4e-3f, 1e-1f, 3.5e-2f, 2e-2f, 3e-2f, 5e-2f, 5e-2f, 5e-2f, 1.5e-2f, 5e-4f, 8e-3f, 1.5e-3f, 2.5e-3f, 1e-1f, 2e-2f};
#if defined(ENABLE_FP32)
for (int i = 0; i < NUM_PARAMETER_TENSORS; i++) {
grad_thresholds[i] = 1e-6f; // we can be much more precise in FP32
}
#endif

allok = allok & check_tensor(tensors1[0], tensors2[0], V * C, "wte", grad_thresholds[0]);
allok = allok & check_tensor(tensors1[1], tensors2[1], maxT * C, "wpe", grad_thresholds[1]);
allok = allok & check_tensor(tensors1[2], tensors2[2], L * 3*C * C, "qkvw", grad_thresholds[2]);
allok = allok & check_tensor(tensors1[3], tensors2[3], L * 3*C, "qkvb", grad_thresholds[3]);
allok = allok & check_tensor(tensors1[4], tensors2[4], L * C * C, "attprojw", grad_thresholds[4]);
allok = allok & check_tensor(tensors1[5], tensors2[5], L * C, "attprojb", grad_thresholds[5]);
allok = allok & check_tensor(tensors1[6], tensors2[6], L * 4*C * C, "fcw", grad_thresholds[6]);
allok = allok & check_tensor(tensors1[7], tensors2[7], L * 4*C, "fcb", grad_thresholds[7]);
allok = allok & check_tensor(tensors1[8], tensors2[8], L * C * 4*C, "fcprojw", grad_thresholds[8]);
allok = allok & check_tensor(tensors1[9], tensors2[9], L * C, "fcprojb", grad_thresholds[9]);
allok = allok & check_tensor(tensors1[10], tensors2[10], L * C, "ln1w", grad_thresholds[10]);
allok = allok & check_tensor(tensors1[11], tensors2[11], L * C, "ln1b", grad_thresholds[11]);
allok = allok & check_tensor(tensors1[12], tensors2[12], L * C, "ln2w", grad_thresholds[12]);
allok = allok & check_tensor(tensors1[13], tensors2[13], L * C, "ln2b", grad_thresholds[13]);
allok = allok & check_tensor(tensors1[14], tensors2[14], C, "lnfw", grad_thresholds[14]);
allok = allok & check_tensor(tensors1[15], tensors2[15], C, "lnfb", grad_thresholds[15]);
}

float grad_norm = gpt2_calculate_grad_norm(&model, &multi_gpu_config);
float grad_scale = (grad_norm > 1.0f) ? 1.0f / grad_norm : 1.0f;
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, grad_scale, step+1, &multi_gpu_config);

// print the timing information at the end
printf("step %d: loss %f (took %f ms)\n", step+1, model.mean_loss, time_elapsed_s * 1000);
// the expected losses from PyTorch were copied over after the print formatting rounded
// them to 6 decimal places, so we do the same here
float rounded_loss = roundf(model.mean_loss * 1000000) / 1000000;
losses[step] = rounded_loss;
}

// expected losses are as follows, from Python
float expected_losses[10] = {
5.270009f,
4.060681f,
3.320085f,
2.717550f,
2.181066f,
1.653923f,
1.168050f,
0.736873f,
0.401021f,
0.187493f
};

// compare
for (int i = 0; i < 10; i++) {
if (fabsf(losses[i] - expected_losses[i]) >= loss_diff_threshold) {
printf("LOSS MISMATCH AT STEP %d: %f %f\n", i+1, losses[i], expected_losses[i]);
allok = 0;
} else {
printf("loss ok at step %d: %f %f\n", i+1, losses[i], expected_losses[i]);
}
}

// Finally, let's check determinism
gpt2_write_to_checkpoint(&model, "test_gpt2cu_model.ckpt");

DataLoader loader;
dataloader_init(&loader, "dev/data/tinyshakespeare/tiny_shakespeare_val.bin", B, T, multi_gpu_config.process_rank, multi_gpu_config.num_processes, 1);
save_state("test_gpt2cu_state.ckpt", 10, &model, &loader);
int tokens[10];
for (int step = 0; step < 10; step++) {
dataloader_next_batch(&loader);
gpt2_forward(&model, loader.inputs, B, T);
gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, 0);
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, 1.0f, step+11, &multi_gpu_config);
losses[step] = model.mean_loss;
tokens[step] = loader.inputs[0];
}

// reload
gpt2_free(&model);
gpt2_build_from_checkpoint(&model, "test_gpt2cu_model.ckpt");
int ld_step;
gpt2_allocate_state(&model, B, T);
load_state(&ld_step, &model, &loader, "test_gpt2cu_state.ckpt");
for (int step = 0; step < 10; step++) {
dataloader_next_batch(&loader);
gpt2_forward(&model, loader.inputs, B, T);
gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, 0);
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, 1.0f, step+11, &multi_gpu_config);

if(loader.inputs[0] != tokens[step]) {
printf("Nondeterminism! Token mismatch at step %d: %d vs %d\n", step, tokens[step], loader.inputs[0]);
allok = false;
break;
}

if(losses[step] != model.mean_loss) {
printf("Nondeterminism! Loss mismatch at step %d: %.15f vs %.15f\n", step, losses[step], model.mean_loss);
allok = false;
break;
} else {
printf("loss ok at step %d: %f %f\n", step, losses[step], model.mean_loss);
}
}

// final approval
printf("overall okay: %d\n", allok);

// delete intermediate test files
remove("test_gpt2cu_model.ckpt");
remove("test_gpt2cu_state.ckpt");

// free everything
dataloader_free(&loader);
gpt2_free(&model);
common_free(model);
free(x);
free(y);
free(logits_cpu_raw);
free(logits_cpu);
free(expected_logits);
free(expected_loss);
free(expected_grads_memory);
free(grads_memory_cpu);
free(grads_memory_cpu_float);
return allok ? EXIT_SUCCESS : EXIT_FAILURE;
}
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