test_gpt2.cu

#define TESTING
#ifdef BUILD_AMD
#include "train_gpt2.hip"
#else
#include "train_gpt2.cu"
#endif

// 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 (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]); }
    }

    // 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;

    // 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_zero_grad(&model);
        gpt2_backward_and_reduce(&model, x, y, 1, true);
        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] = {
        5.270009,
        4.060681,
        3.320085,
        2.717550,
        2.181066,
        1.653923,
        1.168050,
        0.736873,
        0.401021,
        0.187493
    };

    // 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_zero_grad(&model);
        gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, true);
        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;
    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_zero_grad(&model);
        gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, true);
        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;
}