# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PanGu predict run """ import json import os import time import mindspore import mindspore.common.dtype as mstype import mindspore.communication.management as D import moxing as mox import numpy as np from mindspore import context, Tensor from mindspore import export from mindspore.context import ParallelMode from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.parallel.nn.transformer import TransformerOpParallelConfig from mindspore.train.model import Model from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.code_tokenizer import CodeTokenizer from src.pangu_alpha import EvalNet, PanguAlphaModel from src.pangu_alpha_config import set_parse, PanguAlphaConfig from src.utils import get_args def load_model(args_opt): r""" The main function for load model """ # Set execution mode context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target) context.set_context(variable_memory_max_size="30GB") # Set parallel context if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False, pipeline_stages=args_opt.stage_num) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() else: rank = 0 device_num = 1 context.reset_auto_parallel_context() context.set_auto_parallel_context( strategy_ckpt_load_file=args_opt.strategy_load_ckpt_path) context.set_context( save_graphs=False, save_graphs_path="/cache/graphs_of_device_id_" + str(rank), ) use_past = (args_opt.use_past == "true") print('local_rank:{}, start to run...'.format(rank), flush=True) if args_opt.export: use_past = True # Set model property model_parallel_num = args_opt.op_level_model_parallel_num data_parallel_num = int(device_num / model_parallel_num) parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, optimizer_shard=False, vocab_emb_dp=bool(args_opt.word_emb_dp), recompute=True) per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num config = PanguAlphaConfig( batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, hidden_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, post_layernorm_residual=False, dropout_rate=0.0, ffn_hidden_size=args_opt.embedding_size * 4, use_past=use_past, eod_token=args_opt.eod_id, eod_reset=False, parallel_config=parallel_config, load_ckpt_path=args_opt.load_ckpt_path, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, ) print("===config is: ", config, flush=True) print("=====args_opt is: ", args_opt, flush=True) ckpt_name = args_opt.load_ckpt_name # Define network pangu_alpha = PanguAlphaModel(config) eval_net = EvalNet(pangu_alpha, pad_token=50256) eval_net.set_train(False) model_predict = Model(eval_net) # Compile network and obtain tensor layout for loading ckpt inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) current_index = Tensor(np.array([0 for _ in range(batch_size)]), mstype.int32) if args_opt.distribute == "false": predict_layout = None elif config.use_past: batch_valid_length = Tensor(np.array([0 for _ in range(batch_size)]), mstype.int32) init_true = Tensor([True], mstype.bool_) print("Input shape:", inputs_np.shape, flush=True) inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32) model_predict.predict_network.add_flags_recursive(is_first_iteration=True) print("is_first_iteration=True", flush=True) predict_layout = model_predict.infer_predict_layout(inputs_np, current_index, init_true, batch_valid_length) model_predict.predict_network.add_flags_recursive(is_first_iteration=False) print("is_first_iteration=False", flush=True) init_false = Tensor([False], mstype.bool_) _ = model_predict.infer_predict_layout(inputs_np_1, current_index, init_false, batch_valid_length) else: predict_layout = model_predict.infer_predict_layout(inputs_np, current_index) if context.get_context("save_graphs"): print("==============save_graph", flush=True) jobid = os.environ["BATCH_JOB_ID"] rank_id = rank mox.file.make_dirs("s3://wudao-1/yyf/graphs_" + jobid) mox.file.copy_parallel(src_url="/cache/graphs_of_device_id_" + str(rank_id), dst_url="s3://wudao-1/yyf/graphs_" + jobid + "/" + str(rank_id)) print("======start load_distributed checkpoint", flush=True) if args_opt.load_ckpt_epoch > 0: time.sleep(rank * 0.1) os.mkdir(os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}")) ckpt_name = f"code-13B{rank}-{args_opt.load_ckpt_epoch}.ckpt" if not mox.file.exists(os.path.join(args_opt.load_ckpt_path, f"rank_{rank}", ckpt_name)): print(f"Checkpoint from rank {rank} doesn't exist!") mox.file.copy(os.path.join(args_opt.load_ckpt_path, f"rank_{rank}", ckpt_name), os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}", ckpt_name)) param_dict = load_checkpoint(os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}", ckpt_name)) if param_dict.get("epoch_num") and param_dict.get("step_num"): args_opt.has_trained_epoches = int(param_dict["epoch_num"].data.asnumpy()) args_opt.has_trained_steps = int(param_dict["step_num"].data.asnumpy()) os.mkdir(f'/home/work/sfs/cache/{os.environ["BATCH_JOB_ID"]}/1/rank_{rank}') while True: num = len(os.listdir(f'/home/work/sfs/cache/{os.environ["BATCH_JOB_ID"]}/1')) if num == device_num: break if rank % 8 == 0: print("Loaded ckpt in step 1: ", num) time.sleep(1) net_not_load = load_param_into_net(pangu_alpha, param_dict) print("====== load_distributed checkpoint done, net_not_load: ", net_not_load, flush=True) return model_predict, config, rank def export_mindir(model_predict, config): """Export mindir model""" inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) batch_valid_length = Tensor(np.array([0]), mstype.int32) init_true = Tensor([True], mstype.bool_) inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32) model_predict.predict_network.add_flags_recursive(is_first_iteration=True) export(model_predict.predict_network, inputs_np, current_index, init_true, batch_valid_length, file_name='pangu_alpha_1024', file_format='MINDIR') model_predict.predict_network.add_flags_recursive(is_first_iteration=False) export(model_predict.predict_network, inputs_np_1, current_index, init_true, batch_valid_length, file_name='pangu_alpha_1', file_format='MINDIR') print("Export finished and now exit.") def run_predict(model_predict, config, args_opt, rank): """run predict""" from src.generate_humaneval import generate_increment # Define tokenizer tokenizer = CodeTokenizer(mode='6b') # Tokenize input sentence to ids humaneval_path = '/home/work/sfs/xx/human_eval_x/data/humaneval_cpp.jsonl' # TODO: set as current humaneval path humaneval = open(humaneval_path, 'r').readlines() humaneval = [json.loads(task) for task in humaneval if len(task) != 0] samples = [task['prompt'] for task in humaneval] generations = [] batch_size = config.batch_size verbose = (rank % 8 == 0) part = int(args_opt.part) gen_times = 12 # TODO: set as generation times of current task print(f"gen times: {gen_times}, part: {part}") save_path = f'/home/work/sfs/xx/pangu_alpha_code/generation_humanevalx/cpp/temp_{args_opt.temperature}/samples_{args_opt.load_ckpt_epoch}_part_{part}.jsonl' # TODO: set as current save path if rank == 0 and not os.path.exists(save_path): os.makedirs(os.path.split(save_path)[0], exist_ok=True) f = open(save_path, 'w') f.close() os.system(f'sudo chmod 777 {save_path}') for i, sample in enumerate(samples): tag = "// language: C++\n" sample = tag + sample if rank % 8 == 0: print(f"=================== prompt {i} ====================") print(sample, flush=True) for j in range((gen_times + batch_size - 1) // batch_size): tokenized_token = tokenizer.encode_code(sample) input_ids = np.array(tokenized_token).reshape(1, -1).repeat(batch_size, axis=0) # Call inference mindspore.set_seed(j + 8 * part) generate_func = generate_increment t0 = time.perf_counter() output_ids = generate_func(model_predict, input_ids, args_opt, tokenizer, verbose) t1 = time.perf_counter() if rank % 8 == 0: print(f"=== Batch time: {t1 - t0}s") for k, out in enumerate(output_ids): print(f"=================== generation {j * batch_size + k} ====================") print(out, flush=True) generations.append(json.dumps({'task_id': humaneval[i]['task_id'], 'completion': out})) if rank == 0: f = open(save_path, 'a') f.write(generations[-1] + '\n') f.close() def main(): """Main process for predict or export model""" print("===Enter main!") opt = get_args(True) set_parse(opt) model_predict, config, rank = load_model(opt) if opt.export: export_mindir(model_predict, config) else: run_predict(model_predict, config, opt, rank) if __name__ == "__main__": main()